TY - JOUR A1 - Ghasemi, Marziye A1 - Latifi, Hooman A1 - Pourhashemi, Mehdi T1 - A novel method for detecting and delineating coppice trees in UAV images to monitor tree decline JF - Remote Sensing N2 - Monitoring tree decline in arid and semi-arid zones requires methods that can provide up-to-date and accurate information on the health status of the trees at single-tree and sample plot levels. Unmanned Aerial Vehicles (UAVs) are considered as cost-effective and efficient tools to study tree structure and health at small scale, on which detecting and delineating tree crowns is the first step to extracting varied subsequent information. However, one of the major challenges in broadleaved tree cover is still detecting and delineating tree crowns in images. The frequent dominance of coppice structure in degraded semi-arid vegetation exacerbates this problem. Here, we present a new method based on edge detection for delineating tree crowns based on the features of oak trees in semi-arid coppice structures. The decline severity in individual stands can be analyzed by extracting relevant information such as texture from the crown area. Although the method presented in this study is not fully automated, it returned high performances including an F-score = 0.91. Associating the texture indices calculated in the canopy area with the phenotypic decline index suggested higher correlations of the GLCM texture indices with tree decline at the tree level and hence a high potential to be used for subsequent remote-sensing-assisted tree decline studies. KW - UAV KW - crown delineation KW - coppice KW - Zagros oak forests KW - edge detection KW - decline KW - texture analysis Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-297258 SN - 2072-4292 VL - 14 IS - 23 ER - TY - JOUR A1 - Ouedraogo, Valentin A1 - Hackman, Kwame Oppong A1 - Thiel, Michael A1 - Dukiya, Jaiye T1 - Intensity analysis for urban Land Use/Land Cover dynamics characterization of Ouagadougou and Bobo-Dioulasso in Burkina Faso JF - Land N2 - Ouagadougou and Bobo-Dioulasso remain the two major urban centers in Burkina Faso with an increasing trend in human footprint. The research aimed at analyzing the Land Use/Land Cover (LULC) dynamics in the two cities between 2003 and 2021 using intensity analysis, which decomposes LULC changes into interval, category and transition levels. The satellite data used for this research were composed of surface reflectance imagery from Landsat 5, Landsat 7 and Landsat 8 acquired from the Google Earth Engine Data Catalogue. The Random Forest, Support Vector Machine and Gradient Tree Boost algorithms were employed to run supervised image classifications for four selected years including 2003, 2009, 2015 and 2021. The results showed that the landscape is changing in both cities due to rapid urbanization. Ouagadougou experienced more rapid changes than Bobo-Dioulasso, with a maximum annual change intensity of 3.61% recorded between 2015 and 2021 against 2.22% in Bobo-Dioulasso for the period 2009–2015. The transition of change was mainly towards built-up areas, which gain targeted bare and agricultural lands in both cities. This situation has led to a 78.12% increase of built-up surfaces in Ouagadougou, while 42.24% of agricultural land area was lost. However, in Bobo-Dioulasso, the built class has increased far more by 140.67%, and the agricultural land areas experienced a gain of 1.38% compared with the 2003 baseline. The study demonstrates that the human footprint is increasing in both cities making the inhabitants vulnerable to environmental threats such as flooding and the effect of an Urban Heat Island, which is information that could serve as guide for sustainable urban land use planning. KW - Land Use/Land Cover KW - urbanization KW - intensity analysis KW - Google Earth Engine Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-319397 SN - 2073-445X VL - 12 IS - 5 ER - TY - JOUR A1 - Klein, Igor A1 - Cocco, Arturo A1 - Uereyen, Soner A1 - Mannu, Roberto A1 - Floris, Ignazio A1 - Oppelt, Natascha A1 - Kuenzer, Claudia T1 - Outbreak of Moroccan locust in Sardinia (Italy): a remote sensing perspective JF - Remote Sensing N2 - The Moroccan locust has been considered one of the most dangerous agricultural pests in the Mediterranean region. The economic importance of its outbreaks diminished during the second half of the 20th century due to a high degree of agricultural industrialization and other human-caused transformations of its habitat. Nevertheless, in Sardinia (Italy) from 2019 on, a growing invasion of this locust species is ongoing, being the worst in over three decades. Locust swarms destroyed crops and pasture lands of approximately 60,000 ha in 2022. Drought, in combination with increasing uncultivated land, contributed to forming the perfect conditions for a Moroccan locust population upsurge. The specific aim of this paper is the quantification of land cover land use (LCLU) influence with regard to the recent locust outbreak in Sardinia using remote sensing data. In particular, the role of untilled, fallow, or abandoned land in the locust population upsurge is the focus of this case study. To address this objective, LCLU was derived from Sentinel-2A/B Multispectral Instrument (MSI) data between 2017 and 2021 using time-series composites and a random forest (RF) classification model. Coordinates of infested locations, altitude, and locust development stages were collected during field observation campaigns between March and July 2022 and used in this study to assess actual and previous land cover situation of these locations. Findings show that 43% of detected locust locations were found on untilled, fallow, or uncultivated land and another 23% within a radius of 100 m to such areas. Furthermore, oviposition and breeding sites are mostly found in sparse vegetation (97%). This study demonstrates that up-to-date remote sensing data and target-oriented analyses can provide valuable information to contribute to early warning systems and decision support and thus to minimize the risk concerning this agricultural pest. This is of particular interest for all agricultural pests that are strictly related to changing human activities within transformed habitats. KW - agricultural pests KW - food security KW - remote sensing KW - locust outbreak KW - abandoned land KW - Sentinel-2 KW - Dociostaurus maroccanus Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-297232 SN - 2072-4292 VL - 14 IS - 23 ER - TY - THES A1 - Dhillon, Maninder Singh T1 - Potential of Remote Sensing in Modeling Long-Term Crop Yields T1 - Potenzial der Fernerkundung für die Modellierung Langfristiger Ernteerträge N2 - Accurate crop monitoring in response to climate change at a regional or field scale plays a significant role in developing agricultural policies, improving food security, forecasting, and analysing global trade trends. Climate change is expected to significantly impact agriculture, with shifts in temperature, precipitation patterns, and extreme weather events negatively affecting crop yields, soil fertility, water availability, biodiversity, and crop growing conditions. Remote sensing (RS) can provide valuable information combined with crop growth models (CGMs) for yield assessment by monitoring crop development, detecting crop changes, and assessing the impact of climate change on crop yields. This dissertation aims to investigate the potential of RS data on modelling long-term crop yields of winter wheat (WW) and oil seed rape (OSR) for the Free State of Bavaria (70,550 km2 ), Germany. The first chapter of the dissertation describes the reasons favouring the importance of accurate crop yield predictions for achieving sustainability in agriculture. Chapter second explores the accuracy assessment of the synthetic RS data by fusing NDVIs of two high spatial resolution data (high pair) (Landsat (30 m, 16-days; L) and Sentinel-2 (10 m, 5–6 days; S), with four low spatial resolution data (low pair) (MOD13Q1 (250 m, 16-days), MCD43A4 (500 m, one day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, 8-days)) using the spatial and temporal adaptive reflectance fusion model (STARFM), which fills regions' cloud or shadow gaps without losing spatial information. The chapter finds that both L-MOD13Q1 (R2 = 0.62, RMSE = 0.11) and S-MOD13Q1 (R2 = 0.68, RMSE = 0.13) are more suitable for agricultural monitoring than the other synthetic products fused. Chapter third explores the ability of the synthetic spatiotemporal datasets (obtained in chapter 2) to accurately map and monitor crop yields of WW and OSR at a regional scale. The chapter investigates and discusses the optimal spatial (10 m, 30 m, or 250 m), temporal (8 or 16-day) and CGMs (World Food Studies (WOFOST), and the semi-empiric light use efficiency approach (LUE)) for accurate crop yield estimations of both crop types. Chapter third observes that the observations of high temporal resolution (8-day) products of both S-MOD13Q1 and L-MOD13Q1 play a significant role in accurately measuring the yield of WW and OSR. The chapter investigates that the simple light use efficiency (LUE) model (R2 = 0.77 and relative RMSE (RRMSE) = 8.17%) that required fewer input parameters to simulate crop yield is highly accurate, reliable, and more precise than the complex WOFOST model (R2 = 0.66 and RRMSE = 11.35%) with higher input parameters. Chapter four researches the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for WW and OSR using the LUE model for Bavaria from 2001 to 2019. The chapter states the high positive correlation coefficient (R) = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and modelled yield accuracy for WW and OSR from 2001 to 2019, respectively. The chapter analyses the impact of climate variables on crop yield predictions by observing an increase in R2 (0.79 (WW)/0.86 (OSR)) and a decrease in RMSE (4.51/2.57 dt/ha) when the climate effect is included in the model. The fifth chapter suggests that the coupling of the LUE model to the random forest (RF) model can further reduce the relative root mean square error (RRMSE) from -8% (WW) and -1.6% (OSR) and increase the R2 by 14.3% (for both WW and OSR), compared to results just relying on LUE. The same chapter concludes that satellite-based crop biomass, solar radiation, and temperature are the most influential variables in the yield prediction of both crop types. Chapter six attempts to discuss both pros and cons of RS technology while analysing the impact of land use diversity on crop-modelled biomass of WW and OSR. The chapter finds that the modelled biomass of both crops is positively impacted by land use diversity to the radius of 450 (Shannon Diversity Index ~0.75) and 1050 m (~0.75), respectively. The chapter also discusses the future implications by stating that including some dependent factors (such as the management practices used, soil health, pest management, and pollinators) could improve the relationship of RS-modelled crop yields with biodiversity. Lastly, chapter seven discusses testing the scope of new sensors such as unmanned aerial vehicles, hyperspectral sensors, or Sentinel-1 SAR in RS for achieving accurate crop yield predictions for precision farming. In addition, the chapter highlights the significance of artificial intelligence (AI) or deep learning (DL) in obtaining higher crop yield accuracies. N2 - Die genaue Überwachung von Nutzpflanzen als Reaktion auf den Klimawandel auf regionaler oder feldbezogener Ebene spielt eine wichtige Rolle bei der Entwicklung von Agrarpolitiken, der Verbesserung der Ernährungssicherheit, der Erstellung von Prognosen und der Analyse von Trends im Welthandel. Es wird erwartet, dass sich der Klimawandel erheblich auf die Landwirtschaft auswirken wird, da sich Verschiebungen bei den Temperaturen, Niederschlagsmustern und extremen Wetterereignissen negativ auf die Ernteerträge, die Bodenfruchtbarkeit, die Wasserverfügbarkeit, die Artenvielfalt und die Anbaubedingungen auswirken werden. Die Fernerkundung (RS) kann in Kombination mit Wachstumsmodellen (CGM) wertvolle Informationen für die Ertragsbewertung liefern, indem sie die Entwicklung von Pflanzen überwacht, Veränderungen bei den Pflanzen erkennt und die Auswirkungen des Klimawandels auf die Ernteerträge bewertet. Ziel dieser Dissertation ist es, das Potenzial von RS-Daten für die Modellierung langfristiger Ernteerträge von Winterweizen (WW) und Ölraps (OSR) für den Freistaat Bayern (70.550 km2 ), Deutschland, zu untersuchen. Das erste Kapitel der Dissertation beschreibt die Gründe, die für die Bedeutung genauer Ernteertragsvorhersagen für die Nachhaltigkeit in der Landwirtschaft sprechen. Das zweite Kapitel befasst sich mit der Bewertung der Genauigkeit der synthetischen RS Daten durch die Fusion der NDVIs von zwei Daten mit hoher räumlicher Auflösung (hohes Paar) (Landsat (30 m, 16 Tage; L) und Sentinel-2 (10 m, 5-6 Tage; S) mit vier Daten mit geringer räumlicher Auflösung (niedriges Paar) (MOD13Q1 (250 m, 16 Tage), MCD43A4 (500 m, ein Tag), MOD09GQ (250 m, ein Tag) und MOD09Q1 (250 m, 8 Tage)) unter Verwendung des räumlich und zeitlich adaptiven Reflexionsfusionsmodells (STARFM), das Wolken- oder Schattenlücken in Regionen füllt, ohne räumliche Informationen zu verlieren. In diesem Kapitel wird festgestellt, dass sowohl L-MOD13Q1 (R2 = 0,62, RMSE = 0,11) als auch S-MOD13Q1 (R2 = 0,68, RMSE = 0,13) für die Überwachung der Landwirtschaft besser geeignet sind als die anderen fusionierten synthetischen Produkte. Im dritten Kapitel wird untersucht, inwieweit die (in Kapitel 2 gewonnenen) synthetischen raum-zeitlichen Datensätze geeignet sind, die Ernteerträge von WW und OSR auf regionaler Ebene genau zu kartieren und zu überwachen. Das Kapitel untersucht und diskutiert die optimalen räumlichen (10 m, 30 m oder 250 m),zeitlichen (8 oder 16 Tage) und CGMs (World Food Studies (WOFOST) und den semi-empirischen Ansatz der Lichtnutzungseffizienz (LUE)) für genaue Ertragsschätzungen beider Kulturarten. Im dritten Kapitel wird festgestellt, dass die Beobachtung von Produkten mit hoher zeitlicher Auflösung (8 Tage) sowohl des S-MOD13Q1 als auch des L-MOD13Q1 eine wichtige Rolle bei der genauen Messung des Ertrags von WW und OSR spielt. In diesem Kapitel wird untersucht, dass das einfache Modell der Lichtnutzungseffizienz (LUE) (R2 = 0,77 und relativer RMSE (RRMSE) = 8,17 %), das weniger Eingabeparameter zur Simulation des Ernteertrags benötigt, sehr genau, zuverlässig und präziser ist als das komplexe WOFOST-Modell (R2 = 0,66 und RRMSE = 11,35 %) mit höheren Eingabeparametern. In Kapitel vier wird der Zusammenhang zwischen der raum-zeitlichen Fusionsmodellierung mit STRAFM und der Ertragsvorhersage für WW und OSR mit dem LUE-Modell für Bayern von 2001 bis 2019 untersucht. Das Kapitel stellt den hohen positiven Korrelationskoeffizienten (R) = 0,81 und R = 0,77 zwischen dem jährlichen R2 der synthetischen Genauigkeit und der modellierten Ertragsgenauigkeit für WW bzw. OSR von 2001 bis 2019 fest. In diesem Kapitel werden die Auswirkungen der Klimavariablen auf die Ertragsvorhersagen analysiert, wobei ein Anstieg des R2 (0,79 (WW)/0,86 (OSR)) und eine Verringerung des RMSE (4,51/2,57 dt/ha) festgestellt werden, wenn der Klimaeffekt in das Modell einbezogen wird. Das fünfte Kapitel deutet darauf hin, dass die Kopplung des LUE-Modells mit dem Random-Forest-Modell (RF) den relativen mittleren quadratischen Fehler (RRMSE) von -8 % (WW) und -1,6 % (OSR) weiter reduzieren und das R2 um 14,3 % (sowohl für WW als auch für OSR) erhöhen kann, verglichen mit Ergebnissen, die nur auf LUE beruhen. Das gleiche Kapitel kommt zu dem Schluss, dass die satellitengestützte Pflanzenbiomasse, die Sonneneinstrahlung und die Temperatur die einflussreichsten Variablen bei der Ertragsvorhersage für beide Kulturarten sind. In Kapitel sechs wird versucht, sowohl die Vor- als auch die Nachteile der RS-Technologie zu erörtern, indem die Auswirkungen der unterschiedlichen Landnutzung auf die modellierte Biomasse von WW und OSR analysiert werden. In diesem Kapitel wird festgestellt, dass die modellierte Biomasse beider Kulturen durch die Landnutzungsvielfalt bis zu einem Radius von 450 (Shannon Diversity Index ~0,75) bzw. 1050 m (~0,75) positiv beeinflusst wird. In diesem Kapitel werden auch künftige Auswirkungen erörtert, indem festgestellt wird, dass die Einbeziehung einiger abhängiger Faktoren (wie die angewandten Bewirtschaftungsmethoden, die Bodengesundheit, die Schädlingsbekämpfung und die Bestäuber) die Beziehung zwischen den mit RS modellierten Ernteerträgen und der biologischen Vielfalt verbessern könnte. Im siebten Kapitel schließlich wird die Erprobung neuer Sensoren wie unbemannte Luftfahrzeuge, hyperspektrale Sensoren oder Sentinel-1 SAR in der RS erörtert, um genaue Ertragsvorhersagen für die Präzisionslandwirtschaft zu erreichen. Darüber hinaus wird in diesem Kapitel die Bedeutung der künstlichen Intelligenz (KI) oder des Deep Learning (DL) für die Erzielung einer höheren Genauigkeit der Ernteerträge hervorgehoben. KW - Satellite Remote Sensing KW - Crop YIelds KW - Ernteertrag KW - Datenfusion KW - Landwirtschaft / Nachhaltigkeit KW - Winterweizen KW - Data Fusion KW - Sustainable Agriculture KW - Crop Growth Models KW - Winter wheat Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-322581 N1 - eine "revised edition" der Arbeit finden Sie hier: https://doi.org/10.25972/OPUS-33052 ER - TY - THES A1 - Dhillon, Maninder Singh T1 - Potential of Remote Sensing in Modeling Long-Term Crop Yields T1 - Potenzial der Fernerkundung für die Modellierung Langfristiger Ernteerträge N2 - Accurate crop monitoring in response to climate change at a regional or field scale plays a significant role in developing agricultural policies, improving food security, forecasting, and analysing global trade trends. Climate change is expected to significantly impact agriculture, with shifts in temperature, precipitation patterns, and extreme weather events negatively affecting crop yields, soil fertility, water availability, biodiversity, and crop growing conditions. Remote sensing (RS) can provide valuable information combined with crop growth models (CGMs) for yield assessment by monitoring crop development, detecting crop changes, and assessing the impact of climate change on crop yields. This dissertation aims to investigate the potential of RS data on modelling long-term crop yields of winter wheat (WW) and oil seed rape (OSR) for the Free State of Bavaria (70,550 km2), Germany. The first chapter of the dissertation describes the reasons favouring the importance of accurate crop yield predictions for achieving sustainability in agriculture. Chapter second explores the accuracy assessment of the synthetic RS data by fusing NDVIs of two high spatial resolution data (high pair) (Landsat (30 m, 16-days; L) and Sentinel-2 (10 m, 5–6 days; S), with four low spatial resolution data (low pair) (MOD13Q1 (250 m, 16-days), MCD43A4 (500 m, one day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, 8-days)) using the spatial and temporal adaptive reflectance fusion model (STARFM), which fills regions' cloud or shadow gaps without losing spatial information. The chapter finds that both L-MOD13Q1 (R2 = 0.62, RMSE = 0.11) and S-MOD13Q1 (R2 = 0.68, RMSE = 0.13) are more suitable for agricultural monitoring than the other synthetic products fused. Chapter third explores the ability of the synthetic spatiotemporal datasets (obtained in chapter 2) to accurately map and monitor crop yields of WW and OSR at a regional scale. The chapter investigates and discusses the optimal spatial (10 m, 30 m, or 250 m), temporal (8 or 16-day) and CGMs (World Food Studies (WOFOST), and the semi-empiric light use efficiency approach (LUE)) for accurate crop yield estimations of both crop types. Chapter third observes that the observations of high temporal resolution (8-day) products of both S-MOD13Q1 and L-MOD13Q1 play a significant role in accurately measuring the yield of WW and OSR. The chapter investigates that the simple light use efficiency (LUE) model (R2 = 0.77 and relative RMSE (RRMSE) = 8.17%) that required fewer input parameters to simulate crop yield is highly accurate, reliable, and more precise than the complex WOFOST model (R2 = 0.66 and RRMSE = 11.35%) with higher input parameters. Chapter four researches the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for WW and OSR using the LUE model for Bavaria from 2001 to 2019. The chapter states the high positive correlation coefficient (R) = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and modelled yield accuracy for WW and OSR from 2001 to 2019, respectively. The chapter analyses the impact of climate variables on crop yield predictions by observing an increase in R2 (0.79 (WW)/0.86 (OSR)) and a decrease in RMSE (4.51/2.57 dt/ha) when the climate effect is included in the model. The fifth chapter suggests that the coupling of the LUE model to the random forest (RF) model can further reduce the relative root mean square error (RRMSE) from -8% (WW) and -1.6% (OSR) and increase the R2 by 14.3% (for both WW and OSR), compared to results just relying on LUE. The same chapter concludes that satellite-based crop biomass, solar radiation, and temperature are the most influential variables in the yield prediction of both crop types. Chapter six attempts to discuss both pros and cons of RS technology while analysing the impact of land use diversity on crop-modelled biomass of WW and OSR. The chapter finds that the modelled biomass of both crops is positively impacted by land use diversity to the radius of 450 (Shannon Diversity Index ~0.75) and 1050 m (~0.75), respectively. The chapter also discusses the future implications by stating that including some dependent factors (such as the management practices used, soil health, pest management, and pollinators) could improve the relationship of RS-modelled crop yields with biodiversity. Lastly, chapter seven discusses testing the scope of new sensors such as unmanned aerial vehicles, hyperspectral sensors, or Sentinel-1 SAR in RS for achieving accurate crop yield predictions for precision farming. In addition, the chapter highlights the significance of artificial intelligence (AI) or deep learning (DL) in obtaining higher crop yield accuracies. N2 - Die genaue Überwachung von Nutzpflanzen als Reaktion auf den Klimawandel auf regionaler oder feldbezogener Ebene spielt eine wichtige Rolle bei der Entwicklung von Agrarpolitiken, der Verbesserung der Ernährungssicherheit, der Erstellung von Prognosen und der Analyse von Trends im Welthandel. Es wird erwartet, dass sich der Klimawandel erheblich auf die Landwirtschaft auswirken wird, da sich Verschiebungen bei den Temperaturen, Niederschlagsmustern und extremen Wetterereignissen negativ auf die Ernteerträge, die Bodenfruchtbarkeit, die Wasserverfügbarkeit, die Artenvielfalt und die Anbaubedingungen auswirken werden. Die Fernerkundung (RS) kann in Kombination mit Wachstumsmodellen (CGM) wertvolle Informationen für die Ertragsbewertung liefern, indem sie die Entwicklung von Pflanzen überwacht, Veränderungen bei den Pflanzen erkennt und die Auswirkungen des Klimawandels auf die Ernteerträge bewertet. Ziel dieser Dissertation ist es, das Potenzial von RS-Daten für die Modellierung langfristiger Ernteerträge von Winterweizen (WW) und Ölraps (OSR) für den Freistaat Bayern (70.550 km2 ), Deutschland, zu untersuchen. Das erste Kapitel der Dissertation beschreibt die Gründe, die für die Bedeutung genauer Ernteertragsvorhersagen für die Nachhaltigkeit in der Landwirtschaft sprechen. Das zweite Kapitel befasst sich mit der Bewertung der Genauigkeit der synthetischen RS Daten durch die Fusion der NDVIs von zwei Daten mit hoher räumlicher Auflösung (hohes Paar) (Landsat (30 m, 16 Tage; L) und Sentinel-2 (10 m, 5-6 Tage; S) mit vier Daten mit geringer räumlicher Auflösung (niedriges Paar) (MOD13Q1 (250 m, 16 Tage), MCD43A4 (500 m, ein Tag), MOD09GQ (250 m, ein Tag) und MOD09Q1 (250 m, 8 Tage)) unter Verwendung des räumlich und zeitlich adaptiven Reflexionsfusionsmodells (STARFM), das Wolken- oder Schattenlücken in Regionen füllt, ohne räumliche Informationen zu verlieren. In diesem Kapitel wird festgestellt, dass sowohl L-MOD13Q1 (R2 = 0,62, RMSE = 0,11) als auch S-MOD13Q1 (R2 = 0,68, RMSE = 0,13) für die Überwachung der Landwirtschaft besser geeignet sind als die anderen fusionierten synthetischen Produkte. Im dritten Kapitel wird untersucht, inwieweit die (in Kapitel 2 gewonnenen) synthetischen raum-zeitlichen Datensätze geeignet sind, die Ernteerträge von WW und OSR auf regionaler Ebene genau zu kartieren und zu überwachen. Das Kapitel untersucht und diskutiert die optimalen räumlichen (10 m, 30 m oder 250 m),zeitlichen (8 oder 16 Tage) und CGMs (World Food Studies (WOFOST) und den semi-empirischen Ansatz der Lichtnutzungseffizienz (LUE)) für genaue Ertragsschätzungen beider Kulturarten. Im dritten Kapitel wird festgestellt, dass die Beobachtung von Produkten mit hoher zeitlicher Auflösung (8 Tage) sowohl des S-MOD13Q1 als auch des L-MOD13Q1 eine wichtige Rolle bei der genauen Messung des Ertrags von WW und OSR spielt. In diesem Kapitel wird untersucht, dass das einfache Modell der Lichtnutzungseffizienz (LUE) (R2 = 0,77 und relativer RMSE (RRMSE) = 8,17 %), das weniger Eingabeparameter zur Simulation des Ernteertrags benötigt, sehr genau, zuverlässig und präziser ist als das komplexe WOFOST-Modell (R2 = 0,66 und RRMSE = 11,35 %) mit höheren Eingabeparametern. In Kapitel vier wird der Zusammenhang zwischen der raum-zeitlichen Fusionsmodellierung mit STRAFM und der Ertragsvorhersage für WW und OSR mit dem LUE-Modell für Bayern von 2001 bis 2019 untersucht. Das Kapitel stellt den hohen positiven Korrelationskoeffizienten (R) = 0,81 und R = 0,77 zwischen dem jährlichen R2 der synthetischen Genauigkeit und der modellierten Ertragsgenauigkeit für WW bzw. OSR von 2001 bis 2019 fest. In diesem Kapitel werden die Auswirkungen der Klimavariablen auf die Ertragsvorhersagen analysiert, wobei ein Anstieg des R2 (0,79 (WW)/0,86 (OSR)) und eine Verringerung des RMSE (4,51/2,57 dt/ha) festgestellt werden, wenn der Klimaeffekt in das Modell einbezogen wird. Das fünfte Kapitel deutet darauf hin, dass die Kopplung des LUE-Modells mit dem Random-Forest-Modell (RF) den relativen mittleren quadratischen Fehler (RRMSE) von -8 % (WW) und -1,6 % (OSR) weiter reduzieren und das R2 um 14,3 % (sowohl für WW als auch für OSR) erhöhen kann, verglichen mit Ergebnissen, die nur auf LUE beruhen. Das gleiche Kapitel kommt zu dem Schluss, dass die satellitengestützte Pflanzenbiomasse, die Sonneneinstrahlung und die Temperatur die einflussreichsten Variablen bei der Ertragsvorhersage für beide Kulturarten sind. In Kapitel sechs wird versucht, sowohl die Vor- als auch die Nachteile der RS-Technologie zu erörtern, indem die Auswirkungen der unterschiedlichen Landnutzung auf die modellierte Biomasse von WW und OSR analysiert werden. In diesem Kapitel wird festgestellt, dass die modellierte Biomasse beider Kulturen durch die Landnutzungsvielfalt bis zu einem Radius von 450 (Shannon Diversity Index ~0,75) bzw. 1050 m (~0,75) positiv beeinflusst wird. In diesem Kapitel werden auch künftige Auswirkungen erörtert, indem festgestellt wird, dass die Einbeziehung einiger abhängiger Faktoren (wie die angewandten Bewirtschaftungsmethoden, die Bodengesundheit, die Schädlingsbekämpfung und die Bestäuber) die Beziehung zwischen den mit RS modellierten Ernteerträgen und der biologischen Vielfalt verbessern könnte. Im siebten Kapitel schließlich wird die Erprobung neuer Sensoren wie unbemannte Luftfahrzeuge, hyperspektrale Sensoren oder Sentinel-1 SAR in der RS erörtert, um genaue Ertragsvorhersagen für die Präzisionslandwirtschaft zu erreichen. Darüber hinaus wird in diesem Kapitel die Bedeutung der künstlichen Intelligenz (KI) oder des Deep Learning (DL) für die Erzielung einer höheren Genauigkeit der Ernteerträge hervorgehoben. KW - Accurate crop monitoring KW - Ernteertrag KW - Datenfusion KW - Landwirtschaft / Nachhaltigkeit KW - Winterweizen KW - Climate change KW - Remote sensing (RS) KW - Crop growth models (CGMs) KW - Synthetic RS data KW - Spatiotemporal fusion KW - Crop yield estimations KW - Light use efficiency (LUE) model KW - Random forest (RF) model KW - Land use diversity Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-330529 N1 - die originale ursprüngliche Dissertation finden Sie hier: https://doi.org/10.25972/OPUS-32258 ER - TY - JOUR A1 - Ansah, Christabel Edena A1 - Abu, Itohan-Osa A1 - Kleemann, Janina A1 - Mahmoud, Mahmoud Ibrahim A1 - Thiel, Michael T1 - Environmental contamination of a biodiversity hotspot — action needed for nature conservation in the Niger Delta, Nigeria JF - Sustainability N2 - The Niger Delta belongs to the largest swamp and mangrove forests in the world hosting many endemic and endangered species. Therefore, its conservation should be of highest priority. However, the Niger Delta is confronted with overexploitation, deforestation and pollution to a large extent. In particular, oil spills threaten the biodiversity, ecosystem services, and local people. Remote sensing can support the detection of spills and their potential impact when accessibility on site is difficult. We tested different vegetation indices to assess the impact of oil spills on the land cover as well as to detect accumulations (hotspots) of oil spills. We further identified which species, land cover types, and protected areas could be threatened in the Niger Delta due to oil spills. The results showed that the Enhanced Vegetation Index, the Normalized Difference Vegetation Index, and the Soil Adjusted Vegetation Index were more sensitive to the effects of oil spills on different vegetation cover than other tested vegetation indices. Forest cover was the most affected land-cover type and oil spills also occurred in protected areas. Threatened species are inhabiting the Niger Delta Swamp Forest and the Central African Mangroves that were mainly affected by oil spills and, therefore, strong conservation measures are needed even though security issues hamper the monitoring and control. KW - nature conservation KW - NDVI KW - pollution KW - remote sensing KW - species KW - vegetation indices KW - oil spill Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-297214 SN - 2071-1050 VL - 14 IS - 21 ER - TY - THES A1 - Weigand, Matthias Johann T1 - Fernerkundung und maschinelles Lernen zur Erfassung von urbanem Grün - Eine Analyse am Beispiel der Verteilungsgerechtigkeit in Deutschland T1 - Remote Sensing and Machine Learning to Capture Urban Green – An Analysis Using the Example of Distributive Justice in Germany N2 - Grünflächen stellen einen der wichtigsten Umwelteinflüsse in der Wohnumwelt der Menschen dar. Einerseits wirken sie sich positiv auf die physische und mentale Gesundheit der Menschen aus, andererseits können Grünflächen auch negative Wirkungen anderer Faktoren abmildern, wie beispielsweise die im Laufe des Klimawandels zunehmenden Hitzeereignisse. Dennoch sind Grünflächen nicht für die gesamte Bevölkerung gleichermaßen zugänglich. Bestehende Forschung im Kontext der Umweltgerechtigkeit (UG) konnte bereits aufzeigen, dass unterschiedliche sozio-ökonomische und demographische Gruppen der deutschen Bevölkerung unterschiedlichen Zugriff auf Grünflächen haben. An bestehenden Analysen von Umwelteinflüssen im Kontext der UG wird kritisiert, dass die Auswertung geographischer Daten häufig auf zu stark aggregiertem Level geschieht, wodurch lokal spezifische Expositionen nicht mehr genau abgebildet werden. Dies trifft insbesondere für großflächig angelegte Studien zu. So werden wichtige räumliche Informationen verloren. Doch moderne Erdbeobachtungs- und Geodaten sind so detailliert wie nie und Methoden des maschinellen Lernens ermöglichen die effiziente Verarbeitung zur Ableitung höherwertiger Informationen. Das übergeordnete Ziel dieser Arbeit besteht darin, am Beispiel von Grünflächen in Deutschland methodische Schritte der systematischen Umwandlung umfassender Geodaten in relevante Geoinformationen für die großflächige und hochaufgelöste Analyse von Umwelteigenschaften aufzuzeigen und durchzuführen. An der Schnittstelle der Disziplinen Fernerkundung, Geoinformatik, Sozialgeographie und Umweltgerechtigkeitsforschung sollen Potenziale moderner Methoden für die Verbesserung der räumlichen und semantischen Auflösung von Geoinformationen erforscht werden. Hierfür werden Methoden des maschinellen Lernens eingesetzt, um Landbedeckung und -nutzung auf nationaler Ebene zu erfassen. Diese Entwicklungen sollen dazu beitragen bestehende Datenlücken zu schließen und Aufschluss über die Verteilungsgerechtigkeit von Grünflächen zu bieten. Diese Dissertation gliedert sich in drei konzeptionelle Teilschritte. Im ersten Studienteil werden Erdbeobachtungsdaten der Sentinel-2 Satelliten zur deutschlandweiten Klassifikation von Landbedeckungsinformationen verwendet. In Kombination mit punktuellen Referenzdaten der europaweiten Erfassung für Landbedeckungs- und Landnutzungsinformationen des Land Use and Coverage Area Frame Survey (LUCAS) wird ein maschinelles Lernverfahren trainiert. In diesem Kontext werden verschiedene Vorverarbeitungsschritte der LUCAS-Daten und deren Einfluss auf die Klassifikationsgenauigkeit beleuchtet. Das Klassifikationsverfahren ist in der Lage Landbedeckungsinformationen auch in komplexen urbanen Gebieten mit hoher Genauigkeit abzuleiten. Ein Ergebnis des Studienteils ist eine deutschlandweite Landbedeckungsklassifikation mit einer Gesamtgenauigkeit von 93,07 %, welche im weiteren Verlauf der Arbeit genutzt wird, um grüne Landbedeckung (GLC) räumlich zu quantifizieren. Im zweiten konzeptionellen Teil der Arbeit steht die differenzierte Betrachtung von Grünflächen anhand des Beispiels öffentlicher Grünflächen (PGS), die häufig Gegenstand der UG-Forschung ist, im Vordergrund. Doch eine häufig verwendete Quelle für räumliche Daten zu öffentlichen Grünflächen, der European Urban Atlas (EUA), wird bisher nicht flächendeckend für Deutschland erhoben. Dieser Studienteil verfolgt einen datengetriebenen Ansatz, die Verfügbarkeit von öffentlichem Grün auf der räumlichen Ebene von Nachbarschaften für ganz Deutschland zu ermitteln. Hierfür dienen bereits vom EUA erfasste Gebiete als Referenz. Mithilfe einer Kombination von Erdbeobachtungsdaten und Informationen aus dem OpenStreetMap-Projekt wird ein Deep Learning -basiertes Fusionsnetzwerk erstellt, welche die verfügbare Fläche von öffentlichem Grün quantifiziert. Das Ergebnis dieses Schrittes ist ein Modell, welches genutzt wird, um die Menge öffentlicher Grünflächen in der Nachbarschaft zu schätzen (𝑅 2 = 0.952). Der dritte Studienteil greift die Ergebnisse der ersten beiden Studienteile auf und betrachtet die Verteilung von Grünflächen in Deutschland unter Hinzunahme von georeferenzierten Bevölkerungsdaten. Diese exemplarische Analyse unterscheidet dabei Grünflächen nach zwei Typen: GLC und PGS. Zunächst wird mithilfe deskriptiver Statistiken die generelle Grünflächenverteilung in der Bevölkerung Deutschlands beleuchtet. Daraufhin wird die Verteilungsgerechtigkeit anhand gängiger Gerechtigkeitsmetriken bestimmt. Abschließend werden die Zusammenhänge zwischen der demographischen Komposition der Nachbarschaft und der verfügbaren Menge von Grünflächen anhand dreier exemplarischer soziodemographischer Gesellschaftsgruppen untersucht. Die Analyse zeigt starke Unterschiede der Verfügbarkeit von PGS zwischen städtischen und ländlichen Gebieten. Ein höherer Prozentsatz der Stadtbevölkerung hat Zugriff das Mindestmaß von PGS gemessen an der Vorgabe der Weltgesundheitsorganisation. Die Ergebnisse zeigen auch einen deutlichen Unterschied bezüglich der Verteilungsgerechtigkeit zwischen GLC und PGS und verdeutlichen die Relevanz der Unterscheidung von Grünflächentypen für derartige Untersuchungen. Die abschließende Betrachtung verschiedener Bevölkerungsgruppen arbeitet Unterschiede auf soziodemographischer Ebene auf. In der Zusammenschau demonstriert diese Arbeit wie moderne Geodaten und Methoden des maschinellen Lernens genutzt werden können bisherige Limitierungen räumlicher Datensätze zu überwinden. Am Beispiel von Grünflächen in der Wohnumgebung der Bevölkerung Deutschlands wird gezeigt, dass landesweite Analysen zur Umweltgerechtigkeit durch hochaufgelöste und lokal feingliedrige geographische Informationen bereichert werden können. Diese Arbeit verdeutlicht, wie die Methoden der Erdbeobachtung und Geoinformatik einen wichtigen Beitrag leisten können, die Ungleichheit der Wohnumwelt der Menschen zu identifizieren und schlussendlich den nachhaltigen Siedlungsbau in Form von objektiven Informationen zu unterstützen und überwachen. N2 - Green spaces are one of the most important environmental factors for humans in the living environment. On the one hand they provide benefits to people’s physical and mental health, on the other hand they allow for the mitigation of negative impacts of environmental stressors like heat waves which are increasing as a result of climate change. Yet, green spaces are not equally accessible to all people. Existing literature in the context of Environmental Justice (EJ) research has shown that the access to green space varies among different socio-economic and demographic groups in Germany. However, previous studies in the context of EJ were criticized for using strongly spatially aggregated data for their analyses resulting in a loss of spatial detail on local environmental exposure metrics. This is especially true for large-scale studies where important spatial information often get lost. In this context, modern earth observation and geospatial data are more detailed than ever, and machine learning methods enable efficient processing to derive higher value information for diverse applications. The overall objective of this work is to demonstrate and implement methodological steps that allow for the transformation of vast geodata into relevant geoinformation for the large-scale and high-resolution analysis of environmental characteristics using the example of green spaces in Germany. By bridging the disciplines remote sensing, geoinformatics, social geography and environmental justice research, potentials of modern methods for the improvement of spatial and semantic resolution of geoinformation are explored. For this purpose, machine learning methods are used to map land cover and land use on a national scale. These developments will help to close existing data gaps and provide information on the distributional equity of green spaces. This dissertation comprises three conceptual steps. In the first part of the study, earth observation data from the Sentinel-2 satellites are used to derive land cover information across Germany. In combination with point reference data on land cover and land use from the paneuropean Land Use and Coverage Area Frame Survey (LUCAS) a machine learning model is trained. Therein, different preprocessing steps of the LUCAS data and their influence on the classification accuracy are highlighted. The classification model derives land cover information with high accuracy even in complex urban areas. One result of the study is a Germany-wide land cover classification with an overall accuracy of 93.07 % which is used in the further course of the dissertation to spatially quantify green land cover (GLC). The second conceptual part of this study focuses on the semantic differentiation of green spaces using the example of public green spaces (PGS), which is often the subject of EJ research. A frequently used source of spatial data on public green spaces, the European Urban Atlas (EUA),however, is not available for all of Germany. This part of the study takes a data-driven approach to determine the availability of public green space at the spatial level of neighborhoods for all of Germany. For this purpose, areas already covered by the EUA serve as a reference. Using a combination of earth observation data and information from the OpenStreetMap project, a Deep Learning -based fusion network is created that quantifies the available area of public green space. The result of this step is a model that is utilized to estimate the amount of public green space in the neighborhood (𝑅 2 = 0.952). The third part of this dissertation builds upon the results of the first two parts and integrates georeferenced population data to study the socio-spatial distribution of green spaces in Germany. This exemplary analysis distinguishes green spaces according to two types: GLC and PGS. In this,first, descriptive statistics are used to examine the overall distribution of green spaces available to the German population. Then, the distributional equality is determined using established equality metrics. Finally, the relationships between the demographic composition of the neighborhood and the available amount of green space are examined using three exemplary sociodemographic groups. The analysis reveals strong differences in PGS availability between urban and rural areas. Compared to the rural population, a higher percentage of the urban population has access to the minimum level of PGS defined as a target by the World Health Organization (WHO). The results also show a clear deviation in terms of distributive equality between GLC and PGS, highlighting the relevance of distinguishing green space types for such studies. The final analysis of certain population groups addresses differences at the sociodemographic level. In summary, this dissertation demonstrates how previous limitations of spatial datasets can be overcome through a combination of modern geospatial data and machine learning methods. Using the example of green spaces in the residential environment of the population in Germany,it is shown that nationwide analyses of environmental justice can be enriched by high-resolution and locally fine-grained geographic information. This study illustrates how earth observation and methods of geoinformatics can make an important contribution to identifying inequalities in people’s living environment. Such objective information can ultimately be deployed to support and monitor sustainable urban development. KW - Geografie KW - Fernerkundung KW - Maschinelles Lernen KW - Deep learning KW - Urbanes Grün KW - urban green KW - machine learning KW - distributive justice KW - environmental justice KW - Deutschland KW - Germany KW - Verteilungsgerechtigkeit KW - Umweltgerechtigkeit Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-349610 ER - TY - JOUR A1 - Weismann, Dirk A1 - Möckel, Martin A1 - Paeth, Heiko A1 - Slagman, Anna T1 - Modelling variations of emergency attendances using data on community mobility, climate and air pollution JF - Scientific Reports N2 - Air pollution is associated with morbidity and mortality worldwide. We investigated the impact of improved air quality during the economic lockdown during the SARS-Cov2 pandemic on emergency room (ER) admissions in Germany. Weekly aggregated clinical data from 33 hospitals were collected in 2019 and 2020. Hourly concentrations of nitrogen and sulfur dioxide (NO2, SO2), carbon and nitrogen monoxide (CO, NO), ozone (O3) and particulate matter (PM10, PM2.5) measured by ground stations and meteorological data (ERA5) were selected from a 30 km radius around the corresponding ED. Mobility was assessed using aggregated cell phone data. A linear stepwise multiple regression model was used to predict ER admissions. The average weekly emergency numbers vary from 200 to over 1600 cases (total n = 2,216,217). The mean maximum decrease in caseload was 5 standard deviations. With the enforcement of the shutdown in March, the mobility index dropped by almost 40%. Of all air pollutants, NO2 has the strongest correlation with ER visits when averaged across all departments. Using a linear stepwise multiple regression model, 63% of the variation in ER visits is explained by the mobility index, but still 6% of the variation is explained by air quality and climate change. KW - cardiovascular diseases KW - environmental health KW - environmental impact KW - preclinical research KW - preventive medicine KW - reproductive disorders KW - respiratory signs and symptoms Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-357578 VL - 13 ER - TY - JOUR A1 - Bell, Alexandra A1 - Klein, Doris A1 - Rieser, Jakob A1 - Kraus, Tanja A1 - Thiel, Michael A1 - Dech, Stefan T1 - Scientific evidence from space — a review of spaceborne remote sensing applications at the science–policy interface JF - Remote Sensing N2 - On a daily basis, political decisions are made, often with their full extent of impact being unclear. Not seldom, the decisions and policy measures implemented result in direct or indirect unintended negative impacts, such as on the natural environment, which can vary in time, space, nature, and severity. To achieve a more sustainable world with equitable societies requires fundamental rethinking of our policymaking. It calls for informed decision making and a monitoring of political impact for which evidence-based knowledge is necessary. The most powerful tool to derive objective and systematic spatial information and, thus, add to transparent decisions is remote sensing (RS). This review analyses how spaceborne RS is used by the scientific community to provide evidence for the policymaking process. We reviewed 194 scientific publications from 2015 to 2020 and analysed them based on general insights (e.g., study area) and RS application-related information (e.g., RS data and products). Further, we classified the studies according to their degree of science–policy integration by determining their engagement with the political field and their potential contribution towards four stages of the policy cycle: problem identification/knowledge building, policy formulation, policy implementation, and policy monitoring and evaluation. Except for four studies, we found that studies had not directly involved or informed the policy field or policymaking process. Most studies contributed to the stage problem identification/knowledge building, followed by ex post policy impact assessment. To strengthen the use of RS for policy-relevant studies, the concept of the policy cycle is used to showcase opportunities of RS application for the policymaking process. Topics gaining importance and future requirements of RS at the science–policy interface are identified. If tackled, RS can be a powerful complement to provide policy-relevant evidence to shed light on the impact of political decisions and thus help promote sustainable development from the core. KW - earth observation KW - evidence-based policy KW - policy cycle KW - decision-making KW - sustainable development KW - science–policy interface Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-303925 SN - 2072-4292 VL - 15 IS - 4 ER - TY - JOUR A1 - Dhillon, Maninder Singh A1 - Dahms, Thorsten A1 - Kübert-Flock, Carina A1 - Liepa, Adomas A1 - Rummler, Thomas A1 - Arnault, Joel A1 - Steffan-Dewenter, Ingolf A1 - Ullmann, Tobias T1 - Impact of STARFM on crop yield predictions: fusing MODIS with Landsat 5, 7, and 8 NDVIs in Bavaria Germany JF - Remote Sensing N2 - Rapid and accurate yield estimates at both field and regional levels remain the goal of sustainable agriculture and food security. Hereby, the identification of consistent and reliable methodologies providing accurate yield predictions is one of the hot topics in agricultural research. This study investigated the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for winter wheat (WW) and oil-seed rape (OSR) using a semi-empirical light use efficiency (LUE) model for the Free State of Bavaria (70,550 km\(^2\)), Germany, from 2001 to 2019. A synthetic normalised difference vegetation index (NDVI) time series was generated and validated by fusing the high spatial resolution (30 m, 16 days) Landsat 5 Thematic Mapper (TM) (2001 to 2012), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (2012), and Landsat 8 Operational Land Imager (OLI) (2013 to 2019) with the coarse resolution of MOD13Q1 (250 m, 16 days) from 2001 to 2019. Except for some temporal periods (i.e., 2001, 2002, and 2012), the study obtained an R\(^2\) of more than 0.65 and a RMSE of less than 0.11, which proves that the Landsat 8 OLI fused products are of higher accuracy than the Landsat 5 TM products. Moreover, the accuracies of the NDVI fusion data have been found to correlate with the total number of available Landsat scenes every year (N), with a correlation coefficient (R) of +0.83 (between R\(^2\) of yearly synthetic NDVIs and N) and −0.84 (between RMSEs and N). For crop yield prediction, the synthetic NDVI time series and climate elements (such as minimum temperature, maximum temperature, relative humidity, evaporation, transpiration, and solar radiation) are inputted to the LUE model, resulting in an average R\(^2\) of 0.75 (WW) and 0.73 (OSR), and RMSEs of 4.33 dt/ha and 2.19 dt/ha. The yield prediction results prove the consistency and stability of the LUE model for yield estimation. Using the LUE model, accurate crop yield predictions were obtained for WW (R\(^2\) = 0.88) and OSR (R\(^2\) = 0.74). Lastly, the study observed a high positive correlation of R = 0.81 and R = 0.77 between the yearly R\(^2\) of synthetic accuracy and modelled yield accuracy for WW and OSR, respectively. KW - MOD13Q1 KW - precision agriculture KW - fusion KW - sustainable agriculture KW - decision making KW - winter wheat KW - oil-seed rape KW - crop models Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-311092 SN - 2072-4292 VL - 15 IS - 6 ER - TY - JOUR A1 - Kunz, Julius A1 - Ullmann, T. A1 - Kneisel, C. A1 - Baumhauer, R. T1 - Three-dimensional subsurface architecture and its influence on the spatiotemporal development of a retrogressive thaw slump in the Richardson Mountains, Northwest Territories, Canada JF - Arctic, Antarctic, and Alpine Research N2 - The development of retrogressive thaw slumps (RTS) is known to be strongly influenced by relief-related parameters, permafrost characteristics, and climatic triggers. To deepen the understanding of RTS, this study examines the subsurface characteristics in the vicinity of an active thaw slump, located in the Richardson Mountains (Western Canadian Arctic). The investigations aim to identify relationships between the spatiotemporal slump development and the influence of subsurface structures. Information on these were gained by means of electrical resistivity tomography (ERT) and ground-penetrating radar (GPR). The spatiotemporal development of the slump was revealed by high-resolution satellite imagery and unmanned aerial vehicle–based digital elevation models (DEMs). The analysis indicated an acceleration of slump expansion, especially since 2018. The comparison of the DEMs enabled the detailed balancing of erosion and accumulation within the slump area between August 2018 and August 2019. In addition, manual frost probing and GPR revealed a strong relationship between the active layer thickness, surface morphology, and hydrology. Detected furrows in permafrost table topography seem to affect the active layer hydrology and cause a canalization of runoff toward the slump. The three-dimensional ERT data revealed a partly unfrozen layer underlying a heterogeneous permafrost body. This may influence the local hydrology and affect the development of the RTS. The results highlight the complex relationships between slump development, subsurface structure, and hydrology and indicate a distinct research need for other RTSs. KW - retrogressive thaw slump KW - permafrost KW - spatiotemporal slump development KW - near-surface geophysics KW - remote sensing Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-350147 SN - 1523-0430 VL - 55 IS - 1 ER - TY - JOUR A1 - Meister, Julia A1 - von Suchodoletz, Hans A1 - Zeeden, Christian T1 - Preface: Quaternary research from and inspired by the first virtual DEUQUA conference JF - E&G Quaternary Science Journal N2 - No abstract available. KW - DEUQUA KW - vDEUQUA2021 KW - preface Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-350157 VL - 72 IS - 2 ER - TY - JOUR A1 - Reinermann, Sophie A1 - Asam, Sarah A1 - Gessner, Ursula A1 - Ullmann, Tobias A1 - Kuenzer, Claudia T1 - Multi-annual grassland mowing dynamics in Germany BT - spatio-temporal patterns and the influence of climate, topographic and socio-political conditions JF - Frontiers in Environmental Science N2 - Introduction: Grasslands cover one third of the agricultural area in Germany and are mainly used for fodder production. However, grasslands fulfill many other ecosystem functions, like carbon storage, water filtration and the provision of habitats. In Germany, grasslands are mown and/or grazed multiple times during the year. The type and timing of management activities and the use intensity vary strongly, however co-determine grassland functions. Large-scale spatial information on grassland activities and use intensity in Germany is limited and not openly provided. In addition, the cause for patterns of varying mowing intensity are usually not known on a spatial scale as data on the incentives of farmers behind grassland management decisions is not available. Methods: We applied an algorithm based on a thresholding approach utilizing Sentinel-2 time series to detect grassland mowing events to investigate mowing dynamics in Germany in 2018–2021. The detected mowing events were validated with an independent dataset based on the examination of public webcam images. We analyzed spatial and temporal patterns of the mowing dynamics and relationships to climatic, topographic, soil or socio-political conditions. Results: We found that most intensively used grasslands can be found in southern/south-eastern Germany, followed by areas in northern Germany. This pattern stays the same among the investigated years, but we found variations on smaller scales. The mowing event detection shows higher accuracies in 2019 and 2020 (F1 = 0.64 and 0.63) compared to 2018 and 2021 (F1 = 0.52 and 0.50). We found a significant but weak (R2 of 0–0.13) relationship for a spatial correlation of mowing frequency and climate as well as topographic variables for the grassland areas in Germany. Further results indicate a clear value range of topographic and climatic conditions, characteristic for intensive grassland use. Extensive grassland use takes place everywhere in Germany and on the entire spectrum of topographic and climatic conditions in Germany. Natura 2000 grasslands are used less intensive but this pattern is not consistent among all sites. Discussion: Our findings on mowing dynamics and relationships to abiotic and socio-political conditions in Germany reveal important aspects of grassland management, including incentives of farmers. KW - remote sensing KW - Sentinel-2 KW - time series KW - cutting KW - management KW - pasture KW - meadow KW - Earth observation Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-320700 SN - 2296-665X VL - 11 ER - TY - JOUR A1 - Redlich, Sarah A1 - Zhang, Jie A1 - Benjamin, Caryl A1 - Dhillon, Maninder Singh A1 - Englmeier, Jana A1 - Ewald, Jörg A1 - Fricke, Ute A1 - Ganuza, Cristina A1 - Haensel, Maria A1 - Hovestadt, Thomas A1 - Kollmann, Johannes A1 - Koellner, Thomas A1 - Kübert‐Flock, Carina A1 - Kunstmann, Harald A1 - Menzel, Annette A1 - Moning, Christoph A1 - Peters, Wibke A1 - Riebl, Rebekka A1 - Rummler, Thomas A1 - Rojas‐Botero, Sandra A1 - Tobisch, Cynthia A1 - Uhler, Johannes A1 - Uphus, Lars A1 - Müller, Jörg A1 - Steffan‐Dewenter, Ingolf T1 - Disentangling effects of climate and land use on biodiversity and ecosystem services—A multi‐scale experimental design JF - Methods in Ecology and Evolution N2 - Climate and land-use change are key drivers of environmental degradation in the Anthropocene, but too little is known about their interactive effects on biodiversity and ecosystem services. Long-term data on biodiversity trends are currently lacking. Furthermore, previous ecological studies have rarely considered climate and land use in a joint design, did not achieve variable independence or lost statistical power by not covering the full range of environmental gradients. Here, we introduce a multi-scale space-for-time study design to disentangle effects of climate and land use on biodiversity and ecosystem services. The site selection approach coupled extensive GIS-based exploration (i.e. using a Geographic information system) and correlation heatmaps with a crossed and nested design covering regional, landscape and local scales. Its implementation in Bavaria (Germany) resulted in a set of study plots that maximise the potential range and independence of environmental variables at different spatial scales. Stratifying the state of Bavaria into five climate zones (reference period 1981–2010) and three prevailing land-use types, that is, near-natural, agriculture and urban, resulted in 60 study regions (5.8 × 5.8 km quadrants) covering a mean annual temperature gradient of 5.6–9.8°C and a spatial extent of ~310 × 310 km. Within these regions, we nested 180 study plots located in contrasting local land-use types, that is, forests, grasslands, arable land or settlement (local climate gradient 4.5–10°C). This approach achieved low correlations between climate and land use (proportional cover) at the regional and landscape scale with |r ≤ 0.33| and |r ≤ 0.29| respectively. Furthermore, using correlation heatmaps for local plot selection reduced potentially confounding relationships between landscape composition and configuration for plots located in forests, arable land and settlements. The suggested design expands upon previous research in covering a significant range of environmental gradients and including a diversity of dominant land-use types at different scales within different climatic contexts. It allows independent assessment of the relative contribution of multi-scale climate and land use on biodiversity and ecosystem services. Understanding potential interdependencies among global change drivers is essential to develop effective restoration and mitigation strategies against biodiversity decline, especially in expectation of future climatic changes. Importantly, this study also provides a baseline for long-term ecological monitoring programs. KW - study design KW - biodiversity KW - climate change KW - ecosystem functioning KW - insect monitoring KW - land use KW - space-for-time approach KW - spatial scales Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-258270 VL - 13 IS - 2 ER - TY - JOUR A1 - Libanda, Brigadier A1 - Paeth, Heiko T1 - Modelling wind speed across Zambia: Implications for wind energy JF - International Journal of Climatology N2 - Wind energy is a key option in global dialogues about climate change mitigation. Here, we combined observations from surface wind stations, reanalysis datasets, and state‐of‐the‐art regional climate models from the Coordinated Regional Climate Downscaling Experiment (CORDEX Africa) to study the current and future wind energy potential in Zambia. We found that winds are dominated by southeasterlies and are rarely strong with an average speed of 2.8 m·s\(^{−1}\). When we converted the observed surface wind speed to a turbine hub height of 100 m, we found a ~38% increase in mean wind speed for the period 1981–2000. Further, both simulated and observed wind speed data show statistically significant increments across much of the country. The only areas that divert from this upward trend of wind speeds are the low land terrains of the Eastern Province bordering Malawi. Examining projections of wind power density (WPD), we found that although wind speed is increasing, it is still generally too weak to support large‐scale wind power generation. We found a meagre projected annual average WPD of 46.6 W·m\(^{−2}\). The highest WPDs of ~80 W·m\(^{−2}\) are projected in the northern and central parts of the country while the lowest are to be expected along the Luangwa valley in agreement with wind speed simulations. On average, Zambia is expected to experience minor WPD increments of 0.004 W·m\(^{−2}\) per year from 2031 to 2050. We conclude that small‐scale wind turbines that accommodate cut‐in wind speeds of 3.8 m·s\(^{−1}\) are the most suitable for power generation in Zambia. Further, given the limitations of small wind turbines, they are best suited for rural and suburban areas of the country where obstructions are few, thus making them ideal for complementing the government of the Republic of Zambia's rural electrification efforts. KW - CORDEX Africa KW - renewable energy KW - wind speed KW - Zambia Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-312134 VL - 43 IS - 2 SP - 772 EP - 786 ER - TY - JOUR A1 - Stangl, Stephanie A1 - Rauch, Sebastian A1 - Rauh, Jürgen A1 - Meyer, Martin A1 - Müller‐Nordhorn, Jacqueline A1 - Wildner, Manfred A1 - Wöckel, Achim A1 - Heuschmann, Peter U. T1 - Disparities in Accessibility to Evidence-Based Breast Cancer Care Facilities by Rural and Urban Areas in Bavaria, Germany JF - Cancer N2 - Background Breast cancer (BC), which is most common in elderly women, requires a multidisciplinary and continuous approach to care. With demographic changes, the number of patients with chronic diseases such as BC will increase. This trend will especially hit rural areas, where the majority of the elderly live, in terms of comprehensive health care. Methods Accessibility to several cancer facilities in Bavaria, Germany, was analyzed with a geographic information system. Facilities were identified from the national BC guideline and from 31 participants in a proof‐of‐concept study from the Breast Cancer Care for Patients With Metastatic Disease registry. The timeframe for accessibility was defined as 30 or 60 minutes for all population points. The collection of address information was performed with different sources (eg, a physician registry). Routine data from the German Census 2011 and the population‐based Cancer Registry of Bavaria were linked at the district level. Results Females from urban areas (n = 2,938,991 [ie, total of females living in urban areas]) had a higher chance for predefined accessibility to the majority of analyzed facilities in comparison with females from rural areas (n = 3,385,813 [ie, total number of females living in rural areas]) with an odds ratio (OR) of 9.0 for cancer information counselling, an OR of 17.2 for a university hospital, and an OR of 7.2 for a psycho‐oncologist. For (inpatient) rehabilitation centers (OR, 0.2) and genetic counselling (OR, 0.3), women from urban areas had lower odds of accessibility within 30 or 60 minutes. Conclusions Disparities in accessibility between rural and urban areas exist in Bavaria. The identification of underserved areas can help to inform policymakers about disparities in comprehensive health care. Future strategies are needed to deliver high‐quality health care to all inhabitants, regardless of residence. KW - accessibility KW - breast cancer KW - evidence‐based medicine KW - geographic information science KW - health care service research Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-239854 VL - 127 IS - 13 SP - 2319 EP - 2332 ER - TY - JOUR A1 - Ibebuchi, Chibuike Chiedozie A1 - Paeth, Heiko T1 - The Imprint of the Southern Annular Mode on Black Carbon AOD in the Western Cape Province JF - Atmosphere N2 - This study examines the relationship between variations of the Southern Annular Mode (SAM) and black carbon (BC) at 550 nm aerosol optical depth (AOD) in the Western Cape province (WC). Variations of the positive (negative) phase of the SAM are found to be related to regional circulation types (CTs) in southern Africa, associated with suppressed (enhanced) westerly wind over the WC through the southward (northward) migration of Southern Hemisphere mid-latitude cyclones. The CTs related to positive (negative) SAM anomalies induce stable (unstable) atmospheric conditions over the southwestern regions of the WC, especially during the austral winter and autumn seasons. Through the control of CTs, positive (negative) SAM phases tend to contribute to the build-up (dispersion and dilution) of BC in the study region because they imply dry (wet) conditions which favor the build-up (washing out) of pollutant particles in the atmosphere. Indeed, recent years with an above-average frequency of CTs related to positive (negative) SAM anomalies are associated with a high (low) BC AOD over southwesternmost Africa. KW - black carbon AOD KW - Western Cape KW - southern annular mode KW - circulation type KW - air quality Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-248387 SN - 2073-4433 VL - 12 IS - 10 ER - TY - BOOK A1 - Guth, Denis T1 - Zur Sicherstellung der ‚Verträglichkeit‘ innerstädtischer Einkaufszentren - Raumbezogene Diskurs- und Kalkulationsordnungen am Beispiel der Mainzer Innenstadt N2 - Der Begriff der ‚Verträglichkeit‘ spielt eine zentrale Rolle für die politisch-planerische Steuerung von Einzelhandels- und Stadtentwicklung. Besonders kontrovers wird v.a. seit Mitte der 1990er Jahre die Frage der ‚Verträglichkeit‘ innerstädtischer Einkaufszentren diskutiert. Die vorliegende Studie untersucht anhand ehemaliger Shopping-Center-Planungen für die Mainzer Innenstadt, wie der Verträglichkeitsbegriff in der Praxis gefüllt wird und welche planerischen Steuerungslogiken hieraus hervorgehen. Die Arbeit setzt sich kritisch mit der Frage auseinander, auf welche normativen Wissensordnungen über den innerstädtischen Raum sich die politisch-planerische Bearbeitung der Verträglichkeitsproblematik stützt und welche Machtwirkungen hiermit einhergehen. Ausgehend von einer poststrukturalistisch inspirierten, diskurstheoretischen Perspektive verschiebt die Studie damit den geographischen Blick auf die Verträglichkeitsfrage: Was ‚Verträglichkeit‘ für die politisch-planerische Praxis konkret bedeutet, ob ein geplantes Einkaufszentrum als ‚(innenstadt)verträglich‘ gelten kann bzw. welche konkreten Interventionen dies erfordert, hängt demzufolge weniger von objektiven ökonomischen, räumlichen oder städtebaulichen Gegebenheiten ab – vielmehr zeigt die Studie, dass eine ganzen Reihe von Techniken raumbezogener Wissensproduktion mobilisiert werden müssen, damit die Verträglichkeitsfrage überhaupt als eine objektivierbare Frage erscheinen kann. T3 - Geographische Handelsforschung - 29 KW - Einkaufszentrum KW - Diskurs KW - Diskurs KW - Einkaufszentrum KW - Mainz KW - Innenstadt Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-192670 SN - 978-3-95826-130-3 SN - 978-3-95826-131-0 N1 - Parallel erschienen als Druckausgabe in Würzburg University Press, 978-3-95826-130-3, 29,80 Euro. PB - Würzburg University Press CY - Würzburg ER - TY - RPRT A1 - Meyer, Constantin A1 - Job, Hubert A1 - Laner, Peter A1 - Omizzolo, Andrea A1 - Kollmann, Nadia A1 - Clare, Jasmin A1 - Vesely, Philipp A1 - Riedler, Walter A1 - Plassmann, Guido A1 - Coronado, Oriana A1 - Praper Gulič, Sergeja A1 - Gulič, Andrej A1 - Koblar, Simon A1 - Teofili, Corrado A1 - Rohringer, Verena A1 - Schoßleitner, Richard A1 - Ainz, Gerhard T1 - OpenSpaceAlps - Manuale di Pianificazione: Prospettive per la salvaguardia coerente degli Spazi Aperti nella regione alpina N2 - Nella regione alpina, si può osservare il continuo consumo di spazi aperti a causa dell’aumento di aree di insediamento e di infrastrutture tecniche e la conseguente impermeabilizzazione del suolo. Questo fenomeno porta principalmente alla perdita di suolo agricolo. A seconda dell'estensione dello sviluppo, si riscontra anche una maggiore frammentazione del paesaggio, che è associata all'isolamento degli habitat naturali e alla perdita della connettività ecologica, così come ad altre conseguenze negative. Il progetto OpenSpaceAlps ha affrontato questo problema e, sulla base di procedure cooperative partecipate attuate in diverse regioni pilota alpine, ha sviluppato approcci e strategie di soluzione per la salvaguardia sostenibile degli spazi aperti. Questo manuale supporta le attività e il processo decisionale di vari stakeholder, in primo luogo i pianificatori delle autorità pubbliche di pianificazione. Sulla base di un'analisi delle sfide e delle condizioni generali nella regione alpina, il manuale presenta e confronta i "principi" centrali della pianificazione degli spazi aperti. Inoltre, vengono discusse strategie di pianificazione integrata per diverse categorie spaziali. KW - Raumordnung KW - Alpen KW - OpenSpaceAlps KW - Alpi KW - pianificazione territoriale KW - spazi aperti KW - collaborazione transnazionale Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-277042 N1 - English version available at: https://doi.org/10.25972/OPUS-27040. German version available at: https://doi.org/10.25972/OPUS-27307. Slovenian version available at: https://doi.org/10.25972/OPUS-28651. N1 - The OpenSpaceAlps project is co-financed by the European Regional Development Fund through the Interreg Alpine Space programme. ER - TY - JOUR A1 - Reinermann, Sophie A1 - Asam, Sarah A1 - Kuenzer, Claudia T1 - Remote Sensing of Grassland Production and Management - A Review JF - Remote Sensing N2 - Grasslands cover one third of the earth’s terrestrial surface and are mainly used for livestock production. The usage type, use intensity and condition of grasslands are often unclear. Remote sensing enables the analysis of grassland production and management on large spatial scales and with high temporal resolution. Despite growing numbers of studies in the field, remote sensing applications in grassland biomes are underrepresented in literature and less streamlined compared to other vegetation types. By reviewing articles within research on satellite-based remote sensing of grassland production traits and management, we describe and evaluate methods and results and reveal spatial and temporal patterns of existing work. In addition, we highlight research gaps and suggest research opportunities. The focus is on managed grasslands and pastures and special emphasize is given to the assessment of studies on grazing intensity and mowing detection based on earth observation data. Grazing and mowing highly influence the production and ecology of grassland and are major grassland management types. In total, 253 research articles were reviewed. The majority of these studies focused on grassland production traits and only 80 articles were about grassland management and use intensity. While the remote sensing-based analysis of grassland production heavily relied on empirical relationships between ground-truth and satellite data or radiation transfer models, the used methods to detect and investigate grassland management differed. In addition, this review identified that studies on grassland production traits with satellite data often lacked including spatial management information into the analyses. Studies focusing on grassland management and use intensity mostly investigated rather small study areas with homogeneous intensity levels among the grassland parcels. Combining grassland production estimations with management information, while accounting for the variability among grasslands, is recommended to facilitate the development of large-scale continuous monitoring and remote sensing grassland products, which have been rare thus far. KW - pasture KW - use intensity KW - grazing KW - mowing KW - productivity KW - biomass KW - yield KW - satellite data KW - optical KW - SAR Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-207799 SN - 2072-4292 VL - 12 IS - 12 ER - TY - JOUR A1 - Rieser, Jakob A1 - Veste, Maik A1 - Thiel, Michael A1 - Schönbrodt-Stitt, Sarah T1 - Coverage and Rainfall Response of Biological Soil Crusts Using Multi-Temporal Sentinel-2 Data in a Central European Temperate Dry Acid Grassland JF - Remote Sensing N2 - Biological soil crusts (BSCs) are thin microbiological vegetation layers that naturally develop in unfavorable higher plant conditions (i.e., low precipitation rates and high temperatures) in global drylands. They consist of poikilohydric organisms capable of adjusting their metabolic activities depending on the water availability. However, they, and with them, their ecosystem functions, are endangered by climate change and land-use intensification. Remote sensing (RS)-based studies estimated the BSC cover in global drylands through various multispectral indices, and few of them correlated the BSCs’ activity response to rainfall. However, the allocation of BSCs is not limited to drylands only as there are areas beyond where smaller patches have developed under intense human impact and frequent disturbance. Yet, those areas were not addressed in RS-based studies, raising the question of whether the methods developed in extensive drylands can be transferred easily. Our temperate climate study area, the ‘Lieberoser Heide’ in northeastern Germany, is home to the country’s largest BSC-covered area. We applied a Random Forest (RF) classification model incorporating multispectral Sentinel-2 (S2) data, indices derived from them, and topographic information to spatiotemporally map the BSC cover for the first time in Central Europe. We further monitored the BSC response to rainfall events over a period of around five years (June 2015 to end of December 2020). Therefore, we combined datasets of gridded NDVI as a measure of photosynthetic activity with daily precipitation data and conducted a change detection analysis. With an overall accuracy of 98.9%, our classification proved satisfactory. Detected changes in BSC activity between dry and wet conditions were found to be significant. Our study emphasizes a high transferability of established methods from extensive drylands to BSC-covered areas in the temperate climate. Therefore, we consider our study to provide essential impulses so that RS-based biocrust mapping in the future will be applied beyond the global drylands. KW - biocrusts activity KW - random forest classification KW - rainfall response KW - Sentinel-2 multispectral indices KW - change detection KW - Lieberoser Heide Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-245006 SN - 2072-4292 VL - 13 IS - 16 ER - TY - JOUR A1 - Dhillon, Maninder Singh A1 - Dahms, Thorsten A1 - Kuebert-Flock, Carina A1 - Borg, Erik A1 - Conrad, Christopher A1 - Ullmann, Tobias T1 - Modelling Crop Biomass from Synthetic Remote Sensing Time Series: Example for the DEMMIN Test Site, Germany JF - Remote Sensing N2 - This study compares the performance of the five widely used crop growth models (CGMs): World Food Studies (WOFOST), Coalition for Environmentally Responsible Economies (CERES)-Wheat, AquaCrop, cropping systems simulation model (CropSyst), and the semi-empiric light use efficiency approach (LUE) for the prediction of winter wheat biomass on the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site, Germany. The study focuses on the use of remote sensing (RS) data, acquired in 2015, in CGMs, as they offer spatial information on the actual conditions of the vegetation. Along with this, the study investigates the data fusion of Landsat (30 m) and Moderate Resolution Imaging Spectroradiometer (MODIS) (500 m) data using the spatial and temporal reflectance adaptive reflectance fusion model (STARFM) fusion algorithm. These synthetic RS data offer a 30-m spatial and one-day temporal resolution. The dataset therefore provides the necessary information to run CGMs and it is possible to examine the fine-scale spatial and temporal changes in crop phenology for specific fields, or sub sections of them, and to monitor crop growth daily, considering the impact of daily climate variability. The analysis includes a detailed comparison of the simulated and measured crop biomass. The modelled crop biomass using synthetic RS data is compared to the model outputs using the original MODIS time series as well. On comparison with the MODIS product, the study finds the performance of CGMs more reliable, precise, and significant with synthetic time series. Using synthetic RS data, the models AquaCrop and LUE, in contrast to other models, simulate the winter wheat biomass best, with an output of high R2 (>0.82), low RMSE (<600 g/m\(^2\)) and significant p-value (<0.05) during the study period. However, inputting MODIS data makes the models underperform, with low R2 (<0.68) and high RMSE (>600 g/m\(^2\)). The study shows that the models requiring fewer input parameters (AquaCrop and LUE) to simulate crop biomass are highly applicable and precise. At the same time, they are easier to implement than models, which need more input parameters (WOFOST and CERES-Wheat). KW - crop growth models KW - Landsat KW - MODIS KW - data fusion KW - STARFM KW - climate parameters KW - winter wheat Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-207845 SN - 2072-4292 VL - 12 IS - 11 ER - TY - JOUR A1 - Fakhri, Seyed Arvin A1 - Latifi, Hooman T1 - A consumer grade UAV-based framework to estimate structural attributes of coppice and high oak forest stands in semi-arid regions JF - Remote Sensing N2 - Semi-arid tree covers, in both high and coppice growth forms, play an essential role in protecting water and soil resources and provides multiple ecosystem services across fragile ecosystems. Thus, they require continuous inventories. Quantification of forest structure in these tree covers provides important measures for their management and biodiversity conservation. We present a framework, based on consumer-grade UAV photogrammetry, to separately estimate primary variables of tree height (H) and crown area (A) across diverse coppice and high stands dominated by Quercus brantii Lindl. along the latitudinal gradient of Zagros mountains of western Iran. Then, multivariate linear regressions were parametrized with H and A to estimate the diameter at breast height (DBH) of high trees because of its importance to accelerate the existing practical DBH inventories across Zagros Forests. The estimated variables were finally applied to a model tree aboveground biomass (AGB) for both vegetative growth forms by local allometric equations and Random Forest models. In each step, the estimated variables were evaluated against the field reference values, indicating practically high accuracies reaching root mean square error (RMSE) of 0.68 m and 4.74 cm for H and DBH, as well as relative RMSE < 10% for AGB estimates. The results generally suggest an effective framework for single tree-based attribute estimation over mountainous, semi-arid coppice, and high stands. KW - tree structure KW - biomass KW - DBH KW - Zagros Forests KW - Q. brantii Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-248469 SN - 2072-4292 VL - 13 IS - 21 ER - TY - JOUR A1 - Reinermann, Sophie A1 - Gessner, Ursula A1 - Asam, Sarah A1 - Ullmann, Tobias A1 - Schucknecht, Anne A1 - Kuenzer, Claudia T1 - Detection of grassland mowing events for Germany by combining Sentinel-1 and Sentinel-2 time series JF - Remote Sensing N2 - Grasslands cover one-third of the agricultural area in Germany and play an important economic role by providing fodder for livestock. In addition, they fulfill important ecosystem services, such as carbon storage, water purification, and the provision of habitats. These ecosystem services usually depend on the grassland management. In central Europe, grasslands are grazed and/or mown, whereby the management type and intensity vary in space and time. Spatial information on the mowing timing and frequency on larger scales are usually not available but would be required in order to assess the ecosystem services, species composition, and grassland yields. Time series of high-resolution satellite remote sensing data can be used to analyze the temporal and spatial dynamics of grasslands. Within this study, we aim to overcome the drawbacks identified by previous studies, such as optical data availability and the lack of comprehensive reference data, by testing the time series of various Sentinel-2 (S2) and Sentinal-1 (S1) parameters and combinations of them in order to detect mowing events in Germany in 2019. We developed a threshold-based algorithm by using information from a comprehensive reference dataset of heterogeneously managed grassland parcels in Germany, obtained by RGB cameras. The developed approach using the enhanced vegetation index (EVI) derived from S2 led to a successful mowing event detection in Germany (60.3% of mowing events detected, F1-Score = 0.64). However, events shortly before, during, or shortly after cloud gaps were missed and in regions with lower S2 orbit coverage fewer mowing events were detected. Therefore, S1-based backscatter, InSAR, and PolSAR features were investigated during S2 data gaps. From these, the PolSAR entropy detected mowing events most reliably. For a focus region, we tested an integrated approach by combining S2 and S1 parameters. This approach detected additional mowing events, but also led to many false positive events, resulting in a reduction in the F1-Score (from 0.65 of S2 to 0.61 of S2 + S1 for the focus region). According to our analysis, a majority of grasslands in Germany are only mown zero to two times (around 84%) and are probably additionally used for grazing. A small proportion is mown more often than four times (3%). Regions with a generally higher grassland mowing frequency are located in southern, south-eastern, and northern Germany. KW - earth observation KW - remote sensing KW - harvests KW - cutting events KW - grazing KW - pasture KW - meadow KW - optical KW - SAR KW - PolSAR KW - InSAR Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-267164 SN - 2072-4292 VL - 14 IS - 7 ER - TY - JOUR A1 - Mayr, Stefan A1 - Klein, Igor A1 - Rutzinger, Martin A1 - Kuenzer, Claudia T1 - Determining temporal uncertainty of a global inland surface water time series JF - Remote Sensing N2 - Earth observation time series are well suited to monitor global surface dynamics. However, data products that are aimed at assessing large-area dynamics with a high temporal resolution often face various error sources (e.g., retrieval errors, sampling errors) in their acquisition chain. Addressing uncertainties in a spatiotemporal consistent manner is challenging, as extensive high-quality validation data is typically scarce. Here we propose a new method that utilizes time series inherent information to assess the temporal interpolation uncertainty of time series datasets. For this, we utilized data from the DLR-DFD Global WaterPack (GWP), which provides daily information on global inland surface water. As the time series is primarily based on optical MODIS (Moderate Resolution Imaging Spectroradiometer) images, the requirement of data gap interpolation due to clouds constitutes the main uncertainty source of the product. With a focus on different temporal and spatial characteristics of surface water dynamics, seven auxiliary layers were derived. Each layer provides probability and reliability estimates regarding water observations at pixel-level. This enables the quantification of uncertainty corresponding to the full spatiotemporal range of the product. Furthermore, the ability of temporal layers to approximate unknown pixel states was evaluated for stratified artificial gaps, which were introduced into the original time series of four climatologic diverse test regions. Results show that uncertainty is quantified accurately (>90%), consequently enhancing the product's quality with respect to its use for modeling and the geoscientific community. KW - Earth observation KW - interpolation KW - MODIS KW - optical remote sensing KW - probability KW - reliability KW - validation KW - variability Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-245234 SN - 2072-4292 VL - 13 IS - 17 ER - TY - JOUR A1 - Wei, Chunzhu A1 - Blaschke, Thomas T1 - Pixel-wise vs. object-based impervious surface analysis from remote sensing: correlations with land surface temperature and population density JF - Urban Science N2 - Impervious surface areas (ISA) are heavily influenced by urban structure and related structural features. We examined the effects of object-based impervious surface spatial pattern analysis on land surface temperature and population density in Guangzhou, China, in comparison to classic per-pixel analyses. An object-based support vector machine (SVM) and a linear spectral mixture analysis (LSMA) were integrated to estimate ISA fraction using images from the Chinese HJ-1B satellite for 2009 to 2011. The results revealed that the integrated object-based SVM-LSMA algorithm outperformed the traditional pixel-wise LSMA algorithm in classifying ISA fraction. More specifically, the object-based ISA spatial patterns extracted were more suitable than pixel-wise patterns for urban heat island (UHI) studies, in which the UHI areas (landscape surface temperature >37 °C) generally feature high ISA fraction values (ISA fraction >50%). In addition, the object-based spatial patterns enable us to quantify the relationship of ISA with population density (correlation coefficient >0.2 in general), with global human settlement density (correlation coefficient >0.2), and with night-time light map (correlation coefficient >0.4), and, whereas pixel-wise ISA did not yield significant correlations. These results indicate that object-based spatial patterns have a high potential for UHI detection and urbanization monitoring. Planning measures that aim to reduce the urbanization impacts and UHI intensities can be better supported. KW - impervious surface areas KW - object-based image analysis KW - land surface temperature KW - population density Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-197829 SN - 2413-8851 VL - 2 IS - 1 ER - TY - JOUR A1 - Uereyen, Soner A1 - Kuenzer, Claudia T1 - A review of earth observation-based analyses for major river basins JF - Remote Sensing N2 - Regardless of political boundaries, river basins are a functional unit of the Earth’s land surface and provide an abundance of resources for the environment and humans. They supply livelihoods supported by the typical characteristics of large river basins, such as the provision of freshwater, irrigation water, and transport opportunities. At the same time, they are impacted i.e., by human-induced environmental changes, boundary conflicts, and upstream–downstream inequalities. In the framework of water resource management, monitoring of river basins is therefore of high importance, in particular for researchers, stake-holders and decision-makers. However, land surface and surface water properties of many major river basins remain largely unmonitored at basin scale. Several inventories exist, yet consistent spatial databases describing the status of major river basins at global scale are lacking. Here, Earth observation (EO) is a potential source of spatial information providing large-scale data on the status of land surface properties. This review provides a comprehensive overview of existing research articles analyzing major river basins primarily using EO. Furthermore, this review proposes to exploit EO data together with relevant open global-scale geodata to establish a database and to enable consistent spatial analyses and evaluate past and current states of major river basins. KW - major river basins KW - catchment KW - watershed KW - Earth observation KW - remote sensing KW - spatial analyses KW - land surface KW - surface water Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193849 SN - 2072-4292 VL - 11 IS - 24 ER - TY - JOUR A1 - Nill, Leon A1 - Ullmann, Tobias A1 - Kneisel, Christof A1 - Sobiech-Wolf, Jennifer A1 - Baumhauer, Roland T1 - Assessing Spatiotemporal Variations of Landsat Land Surface Temperature and Multispectral Indices in the Arctic Mackenzie Delta Region between 1985 and 2018 JF - Remote Sensing N2 - Air temperatures in the Arctic have increased substantially over the last decades, which has extensively altered the properties of the land surface. Capturing the state and dynamics of Land Surface Temperatures (LSTs) at high spatial detail is of high interest as LST is dependent on a variety of surficial properties and characterizes the land–atmosphere exchange of energy. Accordingly, this study analyses the influence of different physical surface properties on the long-term mean of the summer LST in the Arctic Mackenzie Delta Region (MDR) using Landsat 30 m-resolution imagery between 1985 and 2018 by taking advantage of the cloud computing capabilities of the Google Earth Engine. Multispectral indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Tasseled Cap greenness (TCG), brightness (TCB), and wetness (TCW) as well as topographic features derived from the TanDEM-X digital elevation model are used in correlation and multiple linear regression analyses to reveal their influence on the LST. Furthermore, surface alteration trends of the LST, NDVI, and NDWI are revealed using the Theil-Sen (T-S) regression method. The results indicate that the mean summer LST appears to be mostly influenced by the topographic exposition as well as the prevalent moisture regime where higher evapotranspiration rates increase the latent heat flux and cause a cooling of the surface, as the variance is best explained by the TCW and northness of the terrain. However, fairly diverse model outcomes for different regions of the MDR (R2 from 0.31 to 0.74 and RMSE from 0.51 °C to 1.73 °C) highlight the heterogeneity of the landscape in terms of influential factors and suggests accounting for a broad spectrum of different factors when modeling mean LSTs. The T-S analysis revealed large-scale wetting and greening trends with a mean decadal increase of the NDVI/NDWI of approximately +0.03 between 1985 and 2018, which was mostly accompanied by a cooling of the land surface given the inverse relationship between mean LSTs and vegetation and moisture conditions. Disturbance through wildfires intensifies the surface alterations locally and lead to significantly cooler LSTs in the long-term compared to the undisturbed surroundings. KW - LST KW - thermal remote sensing KW - Landsat time series KW - arctic greening KW - Google Earth Engine Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193301 SN - 2072-4292 VL - 11 IS - 19 ER - TY - JOUR A1 - Asare-Kyei, Daniel A1 - Forkuor, Gerald A1 - Venus, Valentijn T1 - Modeling Flood Hazard Zones at the Sub-District Level with the Rational Model Integrated with GIS and Remote Sensing Approaches JF - Water N2 - Robust risk assessment requires accurate flood intensity area mapping to allow for the identification of populations and elements at risk. However, available flood maps in West Africa lack spatial variability while global datasets have resolutions too coarse to be relevant for local scale risk assessment. Consequently, local disaster managers are forced to use traditional methods such as watermarks on buildings and media reports to identify flood hazard areas. In this study, remote sensing and Geographic Information System (GIS) techniques were combined with hydrological and statistical models to delineate the spatial limits of flood hazard zones in selected communities in Ghana, Burkina Faso and Benin. The approach involves estimating peak runoff concentrations at different elevations and then applying statistical methods to develop a Flood Hazard Index (FHI). Results show that about half of the study areas fall into high intensity flood zones. Empirical validation using statistical confusion matrix and the principles of Participatory GIS show that flood hazard areas could be mapped at an accuracy ranging from 77% to 81%. This was supported with local expert knowledge which accurately classified 79% of communities deemed to be highly susceptible to flood hazard. The results will assist disaster managers to reduce the risk to flood disasters at the community level where risk outcomes are first materialized. KW - climate change KW - rational model KW - community KW - flood hazard index KW - West Africa KW - GIS KW - vulnerability KW - performance KW - impact KW - risk KW - mapping KW - runoff Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-151581 VL - 7 SP - 3531 EP - 3564 ER - TY - JOUR A1 - Ullmann, Tobias A1 - Sauerbrey, Julia A1 - Hoffmeister, Dirk A1 - May, Simon Matthias A1 - Baumhauer, Roland A1 - Bubenzer, Olaf T1 - Assessing Spatiotemporal Variations of Sentinel-1 InSAR Coherence at Different Time Scales over the Atacama Desert (Chile) between 2015 and 2018 JF - Remote Sensing N2 - This study investigates synthetic aperture radar (SAR) time series of the Sentinel-1 mission acquired over the Atacama Desert, Chile, between March 2015 and December 2018. The contribution analyzes temporal and spatial variations of Sentinel-1 interferometric SAR (InSAR) coherence and exemplarily illustrates factors that are responsible for observed signal differences. The analyses are based on long temporal baselines (365–1090 days) and temporally dense time series constructed with short temporal baselines (12–24 days). Results are compared to multispectral data of Sentinel-2, morphometric features of the digital elevation model (DEM) TanDEM-X WorldDEM™, and to a detailed governmental geographic information system (GIS) dataset of the local hydrography. Sentinel-1 datasets are suited for generating extensive, nearly seamless InSAR coherence mosaics covering the entire Atacama Desert (>450 × 1100 km) at a spatial resolution of 20 × 20 meter per pixel. Temporal baselines over several years lead only to very minor decorrelation, indicating a very high signal stability of C-Band in this region, especially in the hyperarid uplands between the Coastal Cordillera and the Central Depression. Signal decorrelation was associated with certain types of surface cover (e.g., water or aeolian deposits) or with actual surface dynamics (e.g., anthropogenic disturbance (mining) or fluvial activity and overland flow). Strong rainfall events and fluvial activity in the periods 2015 to 2016 and 2017 to 2018 caused spatial patterns with significant signal decorrelation; observed linear coherence anomalies matched the reference channel network and indicated actual episodic and sporadic discharge events. In the period 2015–2016, area-wide loss of coherence appeared as strip-like patterns of more than 80 km length that matched the prevailing wind direction. These anomalies, and others observed in that period and in the period 2017–2018, were interpreted to be caused by overland flow of high magnitude, as their spatial location matched well with documented heavy rainfall events that showed cumulative precipitation amounts of more than 20 mm. KW - Chile KW - Atacama KW - Sentinel-1 KW - InSAR KW - coherence KW - geomorphology Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193836 SN - 2072-4292 VL - 11 IS - 24 ER - TY - JOUR A1 - Baumhoer, Celia A. A1 - Dietz, Andreas J. A1 - Kneisel, C. A1 - Kuenzer, C. T1 - Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning JF - Remote Sensing N2 - Sea level rise contribution from the Antarctic ice sheet is influenced by changes in glacier and ice shelf front position. Still, little is known about seasonal glacier and ice shelf front fluctuations as the manual delineation of calving fronts from remote sensing imagery is very time-consuming. The major challenge of automatic calving front extraction is the low contrast between floating glacier and ice shelf fronts and the surrounding sea ice. Additionally, in previous decades, remote sensing imagery over the often cloud-covered Antarctic coastline was limited. Nowadays, an abundance of Sentinel-1 imagery over the Antarctic coastline exists and could be used for tracking glacier and ice shelf front movement. To exploit the available Sentinel-1 data, we developed a processing chain allowing automatic extraction of the Antarctic coastline from Seninel-1 imagery and the creation of dense time series to assess calving front change. The core of the proposed workflow is a modified version of the deep learning architecture U-Net. This convolutional neural network (CNN) performs a semantic segmentation on dual-pol Sentinel-1 data and the Antarctic TanDEM-X digital elevation model (DEM). The proposed method is tested for four training and test areas along the Antarctic coastline. The automatically extracted fronts deviate on average 78 m in training and 108 m test areas. Spatial and temporal transferability is demonstrated on an automatically extracted 15-month time series along the Getz Ice Shelf. Between May 2017 and July 2018, the fronts along the Getz Ice Shelf show mostly an advancing tendency with the fastest moving front of DeVicq Glacier with 726 ± 20 m/yr. KW - Antarctica KW - coastline KW - deep learning KW - semantic segmentation KW - Getz Ice Shelf KW - calving front KW - glacier front KW - U-Net KW - convolutional neural network KW - glacier terminus Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193150 SN - 2072-4292 VL - 11 IS - 21 ER - TY - JOUR A1 - Philipp, Marius B. A1 - Levick, Shaun R. T1 - Exploring the potential of C-Band SAR in contributing to burn severity mapping in tropical savanna JF - Remote Sensing N2 - The ability to map burn severity and to understand how it varies as a function of time of year and return frequency is an important tool for landscape management and carbon accounting in tropical savannas. Different indices based on optical satellite imagery are typically used for mapping fire scars and for estimating burn severity. However, cloud cover is a major limitation for analyses using optical data over tropical landscapes. To address this pitfall, we explored the suitability of C-band Synthetic Aperture Radar (SAR) data for detecting vegetation response to fire, using experimental fires in northern Australia. Pre- and post-fire results from Sentinel-1 C-band backscatter intensity data were compared to those of optical satellite imagery and were corroborated against structural changes on the ground that we documented through terrestrial laser scanning (TLS). Sentinel-1 C-band backscatter (VH) proved sensitive to the structural changes imparted by fire and was correlated with the Normalised Burn Ratio (NBR) derived from Sentinel-2 optical data. Our results suggest that C-band SAR holds potential to inform the mapping of burn severity in savannas, but further research is required over larger spatial scales and across a broader spectrum of fire regime conditions before automated products can be developed. Combining both Sentinel-1 SAR and Sentinel-2 multi-spectral data will likely yield the best results for mapping burn severity under a range of weather conditions. KW - burn severity KW - Sentinel-1 KW - Sentinel-2 KW - terrestrial LiDAR Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193789 SN - 2072-4292 VL - 12 IS - 1 ER - TY - JOUR A1 - Sogno, Patrick A1 - Klein, Igor A1 - Kuenzer, Claudia T1 - Remote sensing of surface water dynamics in the context of global change — a review JF - Remote Sensing N2 - Inland surface water is often the most accessible freshwater source. As opposed to groundwater, surface water is replenished in a comparatively quick cycle, which makes this vital resource — if not overexploited — sustainable. From a global perspective, freshwater is plentiful. Still, depending on the region, surface water availability is severely limited. Additionally, climate change and human interventions act as large-scale drivers and cause dramatic changes in established surface water dynamics. Actions have to be taken to secure sustainable water availability and usage. This requires informed decision making based on reliable environmental data. Monitoring inland surface water dynamics is therefore more important than ever. Remote sensing is able to delineate surface water in a number of ways by using optical as well as active and passive microwave sensors. In this review, we look at the proceedings within this discipline by reviewing 233 scientific works. We provide an extensive overview of used sensors, the spatial and temporal resolution of studies, their thematic foci, and their spatial distribution. We observe that a wide array of available sensors and datasets, along with increasing computing capacities, have shaped the field over the last years. Multiple global analysis-ready products are available for investigating surface water area dynamics, but so far none offer high spatial and temporal resolution. KW - remote sensing KW - surface water KW - dynamics KW - global change KW - earth observation KW - hydrology KW - biosphere KW - anthroposphere KW - review Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-275274 SN - 2072-4292 VL - 14 IS - 10 ER - TY - JOUR A1 - Lappe, Ronja A1 - Ullmann, Tobias A1 - Bachofer, Felix T1 - State of the Vietnamese coast — assessing three decades (1986 to 2021) of coastline dynamics using the Landsat archive JF - Remote Sensing N2 - Vietnam's 3260 km coastline is densely populated, experiences rapid urban and economic growth, and faces at the same time a high risk of coastal hazards. Satellite archives provide a free and powerful opportunity for long-term area-wide monitoring of the coastal zone. This paper presents an automated analysis of coastline dynamics from 1986 to 2021 for Vietnam's entire coastal zone using the Landsat archive. The proposed method is implemented within the cloud-computing platform Google Earth Engine to only involve publicly and globally available datasets and tools. We generated annual coastline composites representing the mean-high water level and extracted sub-pixel coastlines. We further quantified coastline change rates along shore-perpendicular transects, revealing that half of Vietnam's coast did not experience significant change, while the remaining half is classified as erosional (27.7%) and accretional (27.1%). A hotspot analysis shows that coastal segments with the highest change rates are concentrated in the low-lying deltas of the Mekong River in the south and the Red River in the north. Hotspots with the highest accretion rates of up to +47 m/year are mainly associated with the construction of artificial coastlines, while hotspots with the highest erosion rates of −28 m/year may be related to natural sediment redistribution and human activity. KW - coastline dynamics KW - Landsat archive KW - sub-pixel coastline extraction KW - time series KW - hotspot analysis KW - Google Earth Engine Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-275281 SN - 2072-4292 VL - 14 IS - 10 ER - TY - JOUR A1 - Halbgewachs, Magdalena A1 - Wegmann, Martin A1 - da Ponte, Emmanuel T1 - A spectral mixture analysis and landscape metrics based framework for monitoring spatiotemporal forest cover changes: a case study in Mato Grosso, Brazil JF - Remote Sensing N2 - An increasing amount of Brazilian rainforest is being lost or degraded for various reasons, both anthropogenic and natural, leading to a loss of biodiversity and further global consequences. Especially in the Brazilian state of Mato Grosso, soy production and large-scale cattle farms led to extensive losses of rainforest in recent years. We used a spectral mixture approach followed by a decision tree classification based on more than 30 years of Landsat data to quantify these losses. Research has shown that current methods for assessing forest degradation are lacking accuracy. Therefore, we generated classifications to determine land cover changes for each year, focusing on both cleared and degraded forest land. The analyses showed a decrease in forest area in Mato Grosso by 28.8% between 1986 and 2020. In order to measure changed forest structures for the selected period, fragmentation analyses based on diverse landscape metrics were carried out for the municipality of Colniza in Mato Grosso. It was found that forest areas experienced also a high degree of fragmentation over the study period, with an increase of 83.3% of the number of patches and a decrease of the mean patch area of 86.1% for the selected time period, resulting in altered habitats for flora and fauna. KW - Landsat KW - Google Earth Engine KW - spectral mixture analysis KW - deforestation KW - forest degradation KW - landscape metrics KW - forest fragmentaion KW - Mato Grosso Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-270644 SN - 2072-4292 VL - 14 IS - 8 ER - TY - JOUR A1 - Yang, Xuting A1 - Yao, Wanqiang A1 - Li, Pengfei A1 - Hu, Jinfei A1 - Latifi, Hooman A1 - Kang, Li A1 - Wang, Ningjing A1 - Zhang, Dingming T1 - Changes of SOC content in China's Shendong coal mining area during 1990–2020 investigated using remote sensing techniques JF - Sustainability N2 - Coal mining, an important human activity, disturbs soil organic carbon (SOC) accumulation and decomposition, eventually affecting terrestrial carbon cycling and the sustainability of human society. However, changes of SOC content and their relation with influential factors in coal mining areas remained unclear. In the study, predictive models of SOC content were developed based on field sampling and Landsat images for different land-use types (grassland, forest, farmland, and bare land) of the largest coal mining area in China (i.e., Shendong). The established models were employed to estimate SOC content across the Shendong mining area during 1990–2020, followed by an investigation into the impacts of climate change and human disturbance on SOC content by a Geo-detector. Results showed that the models produced satisfactory results (R\(^2\) > 0.69, p < 0.05), demonstrating that SOC content over a large coal mining area can be effectively assessed using remote sensing techniques. Results revealed that average SOC content in the study area rose from 5.67 gC·kg\(^{−1}\) in 1990 to 9.23 gC·kg\(^{−1}\) in 2010 and then declined to 5.31 gC·Kg\(^{−1}\) in 2020. This could be attributed to the interaction between the disturbance of soil caused by coal mining and the improvement of eco-environment by land reclamation. Spatially, the SOC content of farmland was the highest, followed by grassland, and that of bare land was the lowest. SOC accumulation was inhibited by coal mining activities, with the effect of high-intensity mining being lower than that of moderate- and low-intensity mining activities. Land use was found to be the strongest individual influencing factor for SOC content changes, while the interaction between vegetation coverage and precipitation exerted the most significant influence on the variability of SOC content. Furthermore, the influence of mining intensity combined with precipitation was 10 times higher than that of mining intensity alone. KW - loess plateau KW - coal mining area KW - SOC content prediction KW - human disturbance KW - vegetation restoration KW - climate change Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-278939 SN - 2071-1050 VL - 14 IS - 12 ER - TY - JOUR A1 - Dhillon, Maninder Singh A1 - Dahms, Thorsten A1 - Kuebert-Flock, Carina A1 - Rummler, Thomas A1 - Arnault, Joel A1 - Steffan-Dewenter, Ingolf A1 - Ullmann, Tobias T1 - Integrating random forest and crop modeling improves the crop yield prediction of winter wheat and oil seed rape JF - Frontiers in Remote Sensing N2 - The fast and accurate yield estimates with the increasing availability and variety of global satellite products and the rapid development of new algorithms remain a goal for precision agriculture and food security. However, the consistency and reliability of suitable methodologies that provide accurate crop yield outcomes still need to be explored. The study investigates the coupling of crop modeling and machine learning (ML) to improve the yield prediction of winter wheat (WW) and oil seed rape (OSR) and provides examples for the Free State of Bavaria (70,550 km2), Germany, in 2019. The main objectives are to find whether a coupling approach [Light Use Efficiency (LUE) + Random Forest (RF)] would result in better and more accurate yield predictions compared to results provided with other models not using the LUE. Four different RF models [RF1 (input: Normalized Difference Vegetation Index (NDVI)), RF2 (input: climate variables), RF3 (input: NDVI + climate variables), RF4 (input: LUE generated biomass + climate variables)], and one semi-empiric LUE model were designed with different input requirements to find the best predictors of crop monitoring. The results indicate that the individual use of the NDVI (in RF1) and the climate variables (in RF2) could not be the most accurate, reliable, and precise solution for crop monitoring; however, their combined use (in RF3) resulted in higher accuracies. Notably, the study suggested the coupling of the LUE model variables to the RF4 model can reduce the relative root mean square error (RRMSE) from −8% (WW) and −1.6% (OSR) and increase the R 2 by 14.3% (for both WW and OSR), compared to results just relying on LUE. Moreover, the research compares models yield outputs by inputting three different spatial inputs: Sentinel-2(S)-MOD13Q1 (10 m), Landsat (L)-MOD13Q1 (30 m), and MOD13Q1 (MODIS) (250 m). The S-MOD13Q1 data has relatively improved the performance of models with higher mean R 2 [0.80 (WW), 0.69 (OSR)], and lower RRMSE (%) (9.18, 10.21) compared to L-MOD13Q1 (30 m) and MOD13Q1 (250 m). Satellite-based crop biomass, solar radiation, and temperature are found to be the most influential variables in the yield prediction of both crops. KW - crop modeling KW - random forest KW - machine learning KW - NDVI KW - satellite KW - landsat KW - sentinel-2 KW - winter wheat Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-301462 SN - 2673-6187 VL - 3 ER - TY - JOUR A1 - Emmert, Adrian A1 - Kneisel, Christof T1 - Internal structure and palsa development at Orravatnsrústir Palsa Site (Central Iceland), investigated by means of integrated resistivity and ground‐penetrating radar methods JF - Permafrost and Periglacial Processes N2 - The natural cyclical development of palsas makes it difficult to use visible signs of decay as reference points for environmental change. Thus, to determine the actual development stage of a palsa, investigations of the internal structure are crucial. Our study presents 2‐D and 3‐D electrical resistivity imaging (ERI) and 2‐D ground‐penetrating radar (GPR) results, measurements of surface and subsurface temperatures, and of the soil matric potential from Orravatnsrústir Palsa Site in Central Iceland. By a joint interpretation of the results, we deduce the internal structure (i.e., thickness of thaw zone and permafrost, ice/water content) of five palsas of different size and shape. The results differentiate between initial and mature development stages and show that palsas of different development stages can exist in close proximity. While internal characteristics indicate undisturbed development of four palsas, one palsa shows indications of environmental change. Our study shows the value of the multimethod geophysical approach and introduces measurements of the soil matric potential as a promising method to assess the current state of the subsurface. KW - 3‐D electrical resistivity imaging KW - ground‐penetrating radar KW - palsa development KW - soil matric potential Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-238933 VL - 32 IS - 3 SP - 503 EP - 519 ER - TY - JOUR A1 - Ibebuchi, Chibuike Chiedozie T1 - On the representation of atmospheric circulation modes in regional climate models over Western Europe JF - International Journal of Climatology N2 - Atmospheric circulation is a key driver of climate variability, and the representation of atmospheric circulation modes in regional climate models (RCMs) can enhance the credibility of regional climate projections. This study examines the representation of large‐scale atmospheric circulation modes in Coupled Model Inter‐comparison Project phase 5 RCMs once driven by ERA‐Interim, and by two general circulation models (GCMs). The study region is Western Europe and the circulation modes are classified using the Promax rotated T‐mode principal component analysis. The results indicate that the RCMs can replicate the classified atmospheric modes as obtained from ERA5 reanalysis, though with biases dependent on the data providing the lateral boundary condition and the choice of RCM. When the boundary condition is provided by ERA‐Interim that is more consistent with observations, the simulated map types and the associating time series match well with their counterparts from ERA5. Further, on average, the multi‐model ensemble mean of the analysed RCMs, driven by ERA‐Interim, indicated a slight improvement in the representation of the modes obtained from ERA5. Conversely, when the RCMs are driven by the GCMs that are models without assimilation of observational data, the representation of the atmospheric modes, as obtained from ERA5, is relatively less accurate compared to when the RCMs are driven by ERA‐Interim. This suggests that the biases stem from the GCMs. On average, the representation of the modes was not improved in the multi‐model ensemble mean of the five analysed RCMs driven by either of the GCMs. However, when the best‐performed RCMs were selected on average the ensemble mean indicated a slight improvement. Moreover, the presence of the North Atlantic Oscillation (NAO) in the simulated modes depends also on the lateral boundary conditions. The relationship between the modes and the NAO was replicated only when the RCMs were driven by reanalysis. The results indicate that the forcing model is the main factor in reproducing the atmospheric circulation. KW - general circulation model KW - large‐scale atmospheric circulation modes KW - multi‐model ensemble KW - regional climate model KW - Western Europe Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-312424 VL - 43 IS - 1 SP - 668 EP - 682 ER - TY - JOUR A1 - Geyer, Gerd A1 - Landing, Ed T1 - The Souss lagerstatte of the Anti-Atlas, Morocco: discovery of the first Cambrian fossil lagerstatte from Africa JF - Scientific Reports N2 - Episodic low oxygenated conditions on the sea-floor are likely responsible for exceptional preservation of animal remains in the upper Amouslek Formation (lower Cambrian, Stage 3) on the northern slope of the western Anti-Atlas, Morocco. This stratigraphic interval has yielded trilobite, brachiopod, and hyolith fossils with preserved soft parts, including some of the oldest known trilobite guts. The "Souss fossil lagerstatte" (newly proposed designation) represents the first Cambrian fossil lagerstatte in Cambrian strata known from Africa and is one of the oldest trilobite-bearing fossil lagerstatten on Earth. Inter-regional correlation of the Souss fossil lagerstatte in West Gondwana suggests its development during an interval of high eustatic levels recorded by dark shales that occur in informal upper Cambrian Series 2 in Siberia, South China, and East Gondwana. KW - biodiversity KW - palaeontology KW - sedimentology Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-259236 VL - 11 IS - 1 ER - TY - THES A1 - Engelbauer, Manuel T1 - Global assessment of recent UNESCO Biosphere Reserve quality enhancement strategies and interlinkages with other UNESCO labels T1 - Globale Bewertung der jüngsten Strategien zur Qualitätssteigerung von UNESCO-Biosphärenreservaten und deren Verknüpfungen mit anderen UNESCO-Labeln N2 - In 1995, the Second International Biosphere Reserve Congress in Seville resulted in a set of new regulations that spurred a significant paradigm shift in the UNESCO Man and Bio-sphere (MAB) Programme, reconceptualizing the research programme as a modern instrument for the dual mandate of nature conservation and sustainable development. But almost 20 years later, a large proportion of biosphere reserves designated before 1996 still did not comply with the new regulations. In 2013, the International Coordination Council of the MAB Programme announced the ‘Exit Strategy’ to assess, monitor and improve the quality of the World Network of Biosphere Reserves. However, the strategy also meant that 266 biosphere reserves in 76 member states were faced with the possibility of exclusion from the world network. This study presents a global assessment of the challenges that result from the Exit Strategy and the Process of Excellence and Enhancement that follows. Specifically, it investigates the differences in quality management strategies and the periodic review processes of various biosphere reserves, the effects of those quality management strategies on the MAB Programme and on the 76 directly affected member states, and the interlinkages between the MAB Programme and other UNESCO designations for nature conservation: the natural World Heritage Sites and the Global Geoparks. Semi-structured expert interviews were conducted with 31 participants in 21 different countries, representing all UN regions. To showcase the diversity of the World Network of Bio-sphere Reserves, 20 country-specific case studies are presented, highlighting the challenges of implementing the biosphere reserve concept and, more specifically, the periodic review process. Information gleaned from the experts was transcribed and evaluated using a qualitative content analysis method. The results of this study demonstrate major differences worldwide in the implementation biosphere reserves, especially in the case of the national affiliation of the MAB Programme, the legal recognition of biosphere reserves in national legislation, the usage of the term ‘bio-sphere reserve’ and the governance structures of the biosphere reserves. Of those represented by the case studies, the four countries with the highest number of voluntary biosphere reserves withdrawals after 2013, Australia, Austria, Bulgaria and the United States of America, show that the Exit Strategy contributed to the streamlining and quality enhancement of the world network. The biosphere reserves in those countries were strictly nature conservation areas without human settlements and were designated as such in the 1970s and 1980s. Only post-Seville biosphere reserves remain in those countries. Some experts have pointed out that there appears to be competition for political attention and funding between the three UNESCO labels for nature conservation. While a combination of the designation of biosphere reserves and World Heritage Sites in one place is favoured by experts, Global Geoparks and Biosphere Reserves are seen as being in competition with each other. This study concludes that quality enhancement strategies were fundamental to improving the credibility and coherence of the MAB Programme. Most pre-Seville biosphere reserves were adapted or the member states were encouraged to withdraw them voluntarily. Challenges in implementing the Exit Strategy were not unique to individual countries but applied equally to all member states with pre-Seville sites. Over the course of the quality enhancement process, many UNESCO member states have become more involved with the MAB Programme, which has led to rejuvenation of the national biosphere reserves network in many countries. N2 - Im Jahr 1995 führte der zweite internationale Kongress für Biosphärenreservate in Sevilla zu einer Reihe neuer Richtlinien, die einen bedeutenden Paradigmenwechsel im UNESCO-Programm „Der Mensch und die Biosphäre“ (MAB) einleiteten und das bestehende For-schungsprogramm in ein modernes Instrument für das doppelte Mandat des Naturschutzes und der nachhaltigen Entwicklung entwickelte. Doch fast 20 Jahre später entsprach ein gro-ßer Teil der vor 1996 ausgewiesenen Biosphärenreservate immer noch nicht den neuen Vorschriften. Im Jahr 2013 verkündete der Internationale Koordinierungsrat des MAB-Programms die „Exit-Strategie“ zur Evaluierung, Monitoring und Qualitätsverbesserung des Weltnetzes der Biosphärenreservate. Die Exit-Strategie bedeutete jedoch auch, dass 266 Biosphärenreservate in 76 Mitgliedsstaaten mit der Möglichkeit des Ausschlusses aus dem Weltnetz konfrontiert wurden. Diese Studie präsentiert eine globale Bewertung der Herausforderungen, die sich aus der Exit-Strategie und dem darauffolgenden Prozess der Exzellenz und Aufwertung ergeben. Es werden insbesondere die Unterschiede in den Qualitätsmanagementstrategien und den pe-riodischen Überprüfungsprozessen der verschiedenen Biosphärenreservate, die Auswir-kungen dieser Qualitätsmanagementstrategien auf das MAB-Programm und auf die 76 di-rekt betroffenen Mitgliedsstaaten sowie die Verflechtungen zwischen dem MAB-Programm und anderen UNESCO-Naturschutzsiegeln untersucht: die Weltnaturerbestätten und die Globalen Geoparks. Es wurden halbstrukturierte Experteninterviews mit 31 Teilnehmern aus 21 verschiede-nen Ländern geführt, die alle UN-Regionen repräsentieren. Um die Vielfalt des Weltnetzes der Biosphärenreservate zu veranschaulichen, werden 20 länderspezifische Fallstudien vor-gestellt, in denen die Herausforderungen bei der Umsetzung des Biosphärenreservatskon-zepts und insbesondere des periodischen Überprüfungsprozesses beleuchtet werden. Die von den Experten gesammelten Informationen wurden transkribiert und mit Hilfe einer qualitativen Inhaltsanalyse ausgewertet. Die Ergebnisse dieser Studie zeigen, dass es weltweit große Unterschiede bei der Imple-mentierung von Biosphärenreservaten gibt, insbesondere was die nationale Zuständigkeit für das MAB-Programm, die rechtliche Verankerung von Biosphärenreservaten in der na-tionalen Gesetzgebung, die Verwendung des Begriffs „Biosphärenreservat“ und die Gover-nancestrukturen der Biosphärenreservate betrifft. Von den Fallbeispielländern dieser Ar-beit zeigen die vier Nationen mit den meisten freiwilligen Rücknahmen von Biosphä-renreservaten aus dem Weltnetzwerk nach 2013, nämlich Australien, Österreich, Bulgarien und die Vereinigten Staaten von Amerika, dass die Exit-Strategie zur Vereinheitlichung und Qualitätsverbesserung des Weltnetzes beigetragen hat. Die Biosphärenreservate in diesen Ländern waren reine Naturschutzgebiete ohne menschliche Besiedlung und wurden in den 1970er und 1980er Jahren als solche ausgewiesen. In diesen Ländern gibt es nur noch Bio-sphärenreservate, die den Qualitätsstandards nach der Konferenz von Sevilla im Jahr 1995 entsprechen. Einige Experten haben darauf hingewiesen, dass es zwischen den drei UNE-SCO-Naturschutzsiegeln einen Wettbewerb um politische Aufmerksamkeit und Finanzie-rung gibt. Während eine Kombination von Biosphärenreservaten und Weltnaturerbe-stätten an einem Ort von Experten favorisiert wird, werden Globale Geoparks und Biosphä-renreservate als miteinander konkurrierend angesehen. Diese Arbeit kommt zu dem Schluss, dass die eingeführten Strategien zur Qualitätsver-besserung von grundlegender Bedeutung waren, um die Glaubwürdigkeit und Kohärenz des MAB-Programms zu verbessern. Die meisten Biosphärenreservate aus der ersten Gene-ration vor der Sevilla-Konferenz wurden angepasst oder die Mitgliedsstaaten wurden ermu-tigt, diese freiwillig aus dem Weltnetzwerk zurückzuziehen. Die Herausforderungen bei der Umsetzung der Exit-Strategie waren nicht auf einzelne Länder beschränkt, sondern betra-fen alle Mitgliedstaaten mit Biosphärenreservaten aus der Zeit vor Sevilla gleichermaßen. Im Zuge der Qualitätssteigerung haben sich viele UNESCO-Mitgliedstaaten stärker im MAB-Programm engagiert, was in vielen Ländern zu einer Belebung der nationalen Bio-sphärenreservatsnetzwerke geführt hat. N2 - The Seville Strategy spurred a signifi cant paradigm shift in UNESCO’s MAB Programme, re-conceptualising the research programme as a modern tool for the dual mandate of nature conservation and sustainable development. However, many biosphere reserves failed to comply with the new regulations and in 2013 the ‘Exit Strategy’ was announced to improve the quality of the global network. This study presents a global assessment of the implementation of the quality enhancement strategies, highlighting signifi cant differences worldwide through 20 country-specifi c case studies. It concludes that the strategies have been fundamental in improving the credibility and coherence of the MAB Programme. Challenges in the implementation were not unique to individual countries but were common to all Member States with pre-Seville sites, and in many states the process has led to a rejuvenation of national biosphere reserve networks. KW - Naturschutz KW - Nature Conservation KW - Quality Management KW - Biosphere Reserves KW - UNESCO designations KW - Sustainable Development KW - Qualitätsmanagement KW - Biosphärenreservat KW - Nachhaltigkeit Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-286538 SN - 978-3-95826-196-9 SN - 978-3-95826-197-6 N1 - Parallel erschienen als Druckausgabe bei Würzburg University Press, ISBN 978-3-95826-196-9, 31,80 Euro. PB - Würzburg University Press CY - Würzburg ER - TY - JOUR A1 - Richard, Kyalo A1 - Abdel-Rahman, Elfatih M. A1 - Subramanian, Sevgan A1 - Nyasani, Johnson O. A1 - Thiel, Michael A1 - Jozani, Hosein A1 - Borgemeister, Christian A1 - Landmann, Tobias T1 - Maize cropping systems mapping using RapidEye observations in agro-ecological landscapes in Kenya JF - Sensors N2 - Cropping systems information on explicit scales is an important but rarely available variable in many crops modeling routines and of utmost importance for understanding pests and disease propagation mechanisms in agro-ecological landscapes. In this study, high spatial and temporal resolution RapidEye bio-temporal data were utilized within a novel 2-step hierarchical random forest (RF) classification approach to map areas of mono- and mixed maize cropping systems. A small-scale maize farming site in Machakos County, Kenya was used as a study site. Within the study site, field data was collected during the satellite acquisition period on general land use/land cover (LULC) and the two cropping systems. Firstly, non-cropland areas were masked out from other land use/land cover using the LULC mapping result. Subsequently an optimized RF model was applied to the cropland layer to map the two cropping systems (2nd classification step). An overall accuracy of 93% was attained for the LULC classification, while the class accuracies (PA: producer’s accuracy and UA: user’s accuracy) for the two cropping systems were consistently above 85%. We concluded that explicit mapping of different cropping systems is feasible in complex and highly fragmented agro-ecological landscapes if high resolution and multi-temporal satellite data such as 5 m RapidEye data is employed. Further research is needed on the feasibility of using freely available 10–20 m Sentinel-2 data for wide-area assessment of cropping systems as an important variable in numerous crop productivity models. KW - remote sensing KW - RapidEye KW - bi-temporal KW - cropping systems KW - random forest KW - Kenya Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-173285 VL - 17 IS - 11 ER - TY - BOOK A1 - Wiedemann, Cathrin T1 - Picken, Packen, Radeln? Betriebsformen, Standorte, Arbeitsprozesse und deren Auswirkungen auf Beschäftigte im Lebensmittelonlinehandel in Deutschland T1 - Picking, packing, cycling? Operational forms, locations, work processes and their effects on employees in E-Food in Germany N2 - Der Lebensmittelonlinehandel in Deutschland gewann, verstärkt durch die Covid-19-Pandemie, an Umsatzanteilen im Lebensmitteleinzelhandel. Hierdurch wurden neue Anforderungen an Arbeit und Beschäftigung in Deutschland geschaffen. Insbesondere in urbanen Räumen hat die Lebensmittelzustellung durch neu entstandene Betriebsformen zugenommen. So entstehen durch das Versprechen der Betriebe, Lebensmittel in kurzen Zeiträumen zu liefern, verschiedene Logistikstandorte und u.a. urbane Fahrradlieferdienste. Während Medien und Gewerkschaften bereits vor der Entstehung prekärer Arbeitsbedingungen warnen, sind die genauen Auswirkungen des Lebensmittelonlinehandels auf die Entwicklung neuer Arbeitsstandorte und die dort stattfindende Beschäftigung nur unzureichend bekannt. Diese Arbeit untersucht den Lebensmittelonlinehandel anhand seiner Betriebsformen, Standorte und Arbeitsprozesse sowie deren Auswirkungen auf Beschäftigte in Deutschland. Den konzeptionellen Hintergrund bilden Arbeiten der geographischen Handelsforschung sowie Debatten zu Arbeitsplatzqualität und Beschäftigung. Für die Analyse sind Primärdaten und Sekundärdaten erhoben worden. Es zeigt sich, dass teilweise komplexe Betriebsformen entstehen, bei denen sich die Arbeit und Arbeitsorte verändern. Zudem entstehen neue Herausforderungen für die Beschäftigten (u.a. physische und psychische Belastung), welche in dieser Arbeit identifiziert werden. N2 - In Germany, E-Food has gained sales shares in food retailing, boosted by the Covid-19 pandemic. This has created new demands on labour and employment, especially in urban areas. E-Food delivery has increased due to newly emerged types of operations. For example, the promise of firms to deliver groceries in short periods of time has given rise to various logistics locations and, among other things, urban cycling delivery services. Whilst the media and unions are already warning of the emergence of precarious working conditions, the precise impact of E-Food on the development of new work locations and corresponding employment is poorly understood. This thesis examines E-Food in terms of its operational forms, locations, and work processes, as well as its impact on employees in Germany. The conceptual background is provided by work in geographic retail research and debates on employment and job quality. Primary and secondary data were collected for the analysis. It is shown that in some cases, complex forms of operation are emerging in which work and work locations are changing. In addition, new challenges arise for employees (including physical and psychological stress), which are identified in this work. T3 - Geographische Handelsforschung - 34 KW - Arbeitsprozess KW - Standort KW - Beschäftigung KW - Einzelhandel KW - E-Food KW - prekäre Arbeit KW - Logistikzentren KW - urbane Lebensmittellieferant:innen KW - Lebensmittelhandel KW - räumliche Verteilung KW - Arbeitsbedingungen KW - Onlinehandel KW - Deutschland Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-298886 SN - 978-3-95826-208-9 SN - 978-3-95826-209-6 SN - 2196-5811 SN - 2626-8906 N1 - Dissertation, Geographisches Institut, Universität zu Köln, 2022 N1 - Parallel erschienen als Druckausgabe in Würzburg University Press, ISBN 978-3-95826-208-9, 29,80 EUR PB - Würzburg University Press CY - Würzburg ET - 1. Auflage ER - TY - JOUR A1 - Aich, Valentin A1 - Akhundzadah, Noor Ahmad A1 - Knuerr, Alec A1 - Khoshbeen, Ahmad Jamshed A1 - Hattermann, Fred A1 - Paeth, Heiko A1 - Scanlon, Andrew A1 - Paton, Eva Nora T1 - Climate change in Afghanistan deduced from reanalysis and coordinated regional climate downscaling experiment (CORDEX)—South Asia Simulations JF - Climate N2 - Past and the projected future climate change in Afghanistan has been analyzed systematically and differentiated with respect to its different climate regions to gain some first quantitative insights into Afghanistan’s vulnerability to ongoing and future climate changes. For this purpose, temperature, precipitation and five additional climate indices for extremes and agriculture assessments (heavy precipitation; spring precipitation; growing season length (GSL), the Heat Wave Magnitude Index (HWMI); and the Standardized Precipitation Evapotranspiration Index (SPEI)) from the reanalysis data were examined for their consistency to identify changes in the past (data since 1950). For future changes (up to the year 2100), the same parameters were extracted from an ensemble of 12 downscaled regional climate models (RCM) of the Coordinated Regional Climate Downscaling Experiment (CORDEX)-South Asia simulations for low and high emission scenarios (Representative Concentration Pathways 4.5 and 8.5). In the past, the climatic changes were mainly characterized by a mean temperature increase above global level of 1.8 °C from 1950 to 2010; uncertainty with regard to reanalyzed rainfall data limited a thorough analysis of past changes. Climate models projected the temperature trend to accelerate in the future, depending strongly on the global carbon emissions (2006–2050 Representative Concentration Pathways 4.5/8.5: 1.7/2.3 °C; 2006–2099: 2.7/6.4 °C, respectively). Despite the high uncertainty with regard to precipitation projections, it became apparent that the increasing evapotranspiration is likely to exacerbate Afghanistan’s already existing water stress, including a very strong increase of frequency and magnitude of heat waves. Overall, the results show that in addition to the already extensive deficiency in adaptation to current climate conditions, the situation will be aggravated in the future, particularly in regard to water management and agriculture. Thus, the results of this study underline the importance of adequate adaptation to climate change in Afghanistan. This is even truer taking into account that GSL is projected to increase substantially by around 20 days on average until 2050, which might open the opportunity for extended agricultural husbandry or even additional harvests when water resources are properly managed. KW - climate change KW - Afghanistan KW - Coordinated Regional Climate Downscaling Experiment (CORDEX)-South Asia KW - trend analysis KW - Heat Wave Magnitude Index (HWMI) KW - Standardized Precipitation Evapotranspiration Index (SPEI) KW - growing season length (GSL) Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-198024 SN - 2225-1154 VL - 5 IS - 2 ER - TY - THES A1 - Nwaila, Tsundukani Glen T1 - Geochemistry of Palaeoarchaean to Palaeoproterozoic Kaapvaal Craton marine shales: Implications for sediment provenance and siderophile elements endowment T1 - Geochemie paläoarchaischer bis paläoproterozoischer mariner Tonschiefer des Kaapvaal Kratons: Hinweise auf Sediment Provenienz und Anreicherung an siderophilen Elementen N2 - The Kaapvaal Craton hosts a number of large gold deposits (e.g. Witwatersrand Supergroup) which mining companies have exploited at certain stratigraphic positions. It also hosts the largest platinum group element (PGE) deposits (e.g. Bushveld Igneous Complex) which mining companies have exploited in different mineralised layered magmatic zones. In spite of the extensive exploration history in the Kaapvaal Craton, the origin of the Witwatersrand gold deposits and Bushveld Igneous Complex PGE deposits has remained one of the most debated topics in economic geology. The goal of this study was to identify the geochemical characteristics of marine shales in the Barberton, Witwatersrand, and Transvaal supergroups in South Africa in order to make inferences on their sediment provenance and siderophile element endowments. Understanding why some of the Archaean and Proterozoic hinterlands are heavily mineralised, compared to others with similar geological characteristics, will aid in the development of more efficient exploration models. Fresh, unmineralised marine shales from the Barberton (Fig Tree and Moodies groups), Witwatersrand (West Rand and Central Rand groups), and Transvaal (Black Reef Formation and Pretoria Group) supergroups were sampled from drill core and underground mining exposures. Analytical methods, such as X-ray powder diffraction (XRD), optical microscopy, X-ray fluorescence (XRF), inductively coupled plasma optical emission spectroscopy (ICP-OES), inductively coupled plasma mass spectrometry (ICP-MS), and electron microprobe analysis (EMPA) were applied to comprehensively characterise the shales. All of the Au and PGE assays examined the newly collected shale samples. The Barberton Supergroup shales consist mainly of quartz, illite, chlorite, and albite, with diverse heavy minerals, including sulfides and oxides, representing the minor constituents. The regionally persistent Witwatersrand Supergroup shales consist mainly of quartz, muscovite, and chlorite, and also contain minor constituents of sulfides and oxides. The Transvaal Supergroup shales comprise quartz, chlorite, and carbonaceous material. Major, trace (including rare-earth element) concentrations were determined for shales from the above supergroups to constrain their source and post-depositional evolution. Chemical variations were observed in all the studied marine shales. Results obtained from this study revealed that post-depositional modification of shale chemistry was significant only near contacts with over- and underlying coarser-grained siliciclastic rocks and along cross-cutting faults, veins, and dykes. Away from such zones, the shale composition remained largely unaltered and can be used to draw inferences concerning sediment provenance and palaeoweathering in the source region and/or on intrabasinal erosion surfaces. Evaluation of weathering profiles through sections of the studied supergroups revealed that the shales therein are characterised by high chemical index of alteration (CIA), chemical index of weathering (CIW), and index of compositional variability (ICV), suggesting that the source area was lithologically complex and subject to intense chemical weathering. A progressive change in the chemical composition was identified, from a dominant ultramafic–mafic source for the Fig Tree Group to a progressively felsic–plutonic provenance for the Moodies Group. The West Rand Group of the Witwatersrand Supergroup shows a dominance of tonalite–trondhjemite–granodiorite and calcalkaline granite sources. Compositional profiles through the only major marine shale unit within the Central Rand Group indicate the progressive unroofing of a granitic source in an otherwise greenstone-dominated hinterland during the course of sedimentation. No plausible likely tectonic setting was obtained through geochemical modelling. However, the combination of the systematic shale chemistry, geochronology, and sedimentology in the Witwatersrand Supergroup supports the hypothesised passive margin setting for the >2.98 to 2.91 Ga West Rand Group, and an active continental margin source for the overlying >2.90 to 2.78 Ga Central Rand Group, along with a foreland basin setting for the latter. Ultra-low detection limit analyses of gold and PGE concentrations revealed a variable degree of gold accumulation within pristine unmineralised shales. All the studied shales contain elevated gold and PGE contents relative to the upper continental crust, with marine shales from the Central Rand Group showing the highest Au (±9.85 ppb) enrichment. Based on this variation in the provenance of contemporaneous sediments in different parts of the Kaapvaal Craton, one can infer that the siderophile elements were sourced from a fertile hinterland, but concentrated into the marine shales by a combination of different processes. It is proposed that accumulation of siderophile elements in the studied marine shales was mainly controlled by mechanical coagulation and aggregation. These processes involved suspended sediments, fine gold particles, and other trace elements being trapped in marine environments. Mechanical coagulation and aggregation resulted in gold enrichments by 2–3 orders of magnitude, whereas some of the gold in these marine shales can be reconciled by seawater adsorption into sedimentary pyrite. For the source of gold and PGEs in the studied marine shales in the Kaapvaal Craton, a genetic model is proposed that involves the following: (1) A highly siderophile elements enriched upper mantle domain, herein referred to as “geochemically anomalous mantle domain”, from which the Kaapvaal crust was sourced. This mantle domain enriched in highly siderophile elements was formed either by inhomogeneous mixing with cosmic material that was added during intense meteorite bombardment of the Hadaean to Palaeoarchaean Earth or by plume-like ascent of relics from the core–mantle boundary. In both cases, elevated siderophile elements concentrations would be expected. The geochemically anomalous mantle domain is likely the ultimate source of the Witwatersrand modified palaeoplacer gold deposits and was tapped again ca. 2.054 Ga during the emplacement of the Bushveld Igneous Complex. Therefore, I propose that there is a genetic link (i.e. common geochemically anomalous mantle source) between the Witwatersrand gold deposits and the younger Bushveld Igneous Complex PGE deposits. (2) Scavenging of crustal gold by various surface processes such as trapping of gold from Archaean/Palaeoproterozoic river water on the surface of local photosynthesizing cyanobacterial or microbial mats, and reworking of these mats into erosion channels during flooding events. The above two models complement each other, with model (1) providing a common geological source for the Witwatersrand gold and Bushveld Igneous Complex PGE deposits, and model (2) explaining the processes responsible for Witwatersrand-type gold pre-concentration processes. In sequences such as the Transvaal Supergroup, a less fertile hinterland and/or less reworking of older sediments led to a correspondingly lower gold endowment. These findings indicate temporal distribution of siderophile elements in the upper crust (e.g. marine shales). The overall implications of these findings are that background concentrations of gold and PGEs can be used to target potential exploration areas in other cratons of similar age. This increases the likelihood of finding other Witwatersrand-type gold or Bushveld Igneous Complex-type PGE deposits in other cratons. N2 - Der Kaapvaal Kraton beherbergt eine Vielzahl großer Goldlagerstätten (vor allem in der Witwatersrand Hauptgruppe), die von Bergbaugesellschaften in ihrer jeweiligen stratigraphischen Position abgebaut werden. Im diesem Kraton liegen auch die größten Lagerstätten für Platingruppenelemente (vornehmlich im Bushveld Komplex), die aus diversen magmatischen Intrusionskörpern gewonnen werden. Trotz der intensiven und langen Explorationsgeschichte im Bereich des Kaapvaal Kratons ist die Herkunft des Goldes in den Witwatersrand Lagerstätten und die der Platingruppenelemente in den Lagerstätten des Bushveld-Komplex noch ungeklärt und Gegenstand aktueller Diskussionen. Ziel der Arbeit war die geochemische Charakterisierung von Tonschiefern in den Barberton-, Witwatersrand und Transvaal-Hauptgruppen, um Aussagen über deren Provenienz zu treffen und die Gehalte an siderophilen Elementen darin zu ermitteln. Ein verbessertes Verständnis, warum manche archaischen und proterozoischen Einheiten stark mineralisiert sind und andere nicht, sollte bei der Planung zukünftiger Explorationsprojekte dienlich sein. Um dieses Ziel zu erreichen, wurden unalterierte und nicht mineralisierte Proben mariner Tonschiefer aus der Barberton Hauptgruppe (Fig Tree und Moodies Gruppen), der Witwatersrand Hauptgruppe (West Rand und Central Rand Gruppen) und der Transvaal Hauptgruppe (Black Reef Formation und Pretoria Gruppe) aus Untertage Bergbau-Bereichen sowie aus Bohrkernen genommen. Zur Charakterisierung der Tonschiefer kamen verschiedene Methoden zum Einsatz, darunter die Pulverdiffraktometrie (XRD), Durchlichtmikroskopie, Röntgenfluoreszenz (XRF), Optische Emissionsspektroskopie (ICP-OES), Laserablationsmassenspektrometrie (ICP-MS) und Elektronenstrahlmikrosonde (EMPA), sowie Bestimmung der Gold und Platingruppen-Elementkonzentrationen mittels Graphitrohr-AAS nach Voranreicherung mit der Nickelsulfid-Dokimasie. Die untersuchten Tonschiefer verhielten sich seit ihrer Ablagerung als größtenteils geschlossene Systeme. Nur entlang der Kontakte mit unter- und überlagernden grobkörnigeren Metasedimentgesteinen sowie entlang durchkreuzender Störungen, Quarzadern und Gängen konnte lokal nennenswerte Alteration festgestellt werden. Solche Zonen wurden explizit von der Provenienz-Analyse ausgenommen. Systematische Unterschiede in der primären chemischen Zusammensetzung einzelner Tonschiefer-Abfolgen belegen unterschiedliche Sedimentquellen. So wurde in der Barberton Hauptgruppe der Sedimenteintrag der Fig Tree-Gruppe von einer ultramafisch-mafischen Quelle dominiert, während in der Moodies-Gruppe felsische Quellen eine zunehmende Rolle spielten. In der Witwatersrand Hauptgruppe wurde eine Dominanz von Tonalit-Trondhjemit-Granodiorit sowie kalkalkaline Granite im Liefergebiet der West Rand Gruppe festgestellt, während in der Central Rand Gruppe anfänglich mafisch-ultramafische Gesteine im Sedimentliefergebiet vorherrschten, im Lauf der Zeit aber granitische Gesteine mehr und mehr durch Erosion im Hinterland freigelegt worden waren. Die Geochemie der Witwatersrand Tonschiefer unterstützt die Hypothese, dass die Sedimente der West Rand Gruppe an einem passiven Kontinentalrand abgelagert wurden, jene der Central Rand Gruppe in einem Vorlandbecken. Alle untersuchten archaischen Tonschiefer zeigen, verglichen mit dem Durchschnitt der oberen Erdkruste, deutlich erhöhte Gehalte an Gold und Platingruppenelementen, wobei die marinen Tonschiefer aus der Central Rand Gruppe mit durchschnittlich 9,85 ppm Au die höchsten Konzentrationen aufweisen. Die Gehalte an siderophilen Elementen in der palaeoproterozoischen Transvaal Hauptgruppe nähern sich hingegen typischen kontinentalen Krustenwerten an. Der verstärkte Eintrag von Au und PGE in die archaischen marinen tonigen Sedimente wird durch mechanische Koagulation und Aggregation erklärte, wobei feinstkörnige Goldpartikel im suspendierten Sediment weit ins Meer transportiert worden sind. Adsorption von Au aus Meerwasser an syn-sedimentärem Pyrit spielte auch eine Rolle, aber keine ausschlaggebende. Für die Quelle des Goldes und der Platingruppenelemente in den untersuchten Tonschiefern wurde folgendes genetisches Modell entwickelt. (1) Es wird angenommen, dass sich die Kaapvaal-Kruste aus einem Mantelreservoir differenzierte, welches an siderophilen Elementen angereichert war. Diese Anreicherung könnte entweder das Produkt eines nicht vollständig homogenisierten Eintrags kosmischen Materials sein, welches im Hadaikum oder im Paläoarchaikum durch intensives Meteoritenbombardement eingebracht wurde, oder durch den Aufstieg eines Manteldiapirs aus dem Bereich der Kern-Mantel-Grenze. (2) Tiefgründige Verwitterung unter anoxischen Bedingungen ermögliche die Freisetzung großer Mengen von Au, welches in gelöster Form über Oberflächenwässer in den archaischen Ozean transportiert wurde. Hinweise auf solch intensive Verwitterung liefern die geochemischen Daten der hier untersuchten Tonschiefern, insbesondere hohe chemische Alterationsindizes. Fixierung dieses Goldes durch verschiedene Oberflächenprozesse, wie Filterung aus archaischen/paläoproterozoischen Flüssen durch Photosynthese-betreibende Bakterienrasen führte vor allem im Mesoarchaikum in Zeiten der Sedimentation der Central Rand Gruppe zu lokal extremen Goldanreicherungen, die in der Folge durch Erosion und mechanischen Transport großteils weiter umgelagert wurden. Punkt 1 könnte eventuell die räumliche Nähe der weltweit größten bekannten Goldanomalie im Witwatersrand Becken und der größten PGE-Anomalie im Bushveld Komplex erklären. In wie weit die erhöhten Hintergrundkonzentrationen von Gold und Platingruppenelementen im Kaapvaal Kraton einzigartig sind, gilt es in zukünftigen Studien dieser Art auch an marinen Tonschiefern aus dem Archaikum in anderen Kratonen zu testen. KW - Gold KW - Gold KW - Platinmetalle KW - Tonschiefer KW - Kaapvaal Kraton KW - PGE KW - Shale KW - Kaapvaal Craton KW - Witwatersrand Supergroup KW - Barberton Supergroup KW - Transvaal Supergroup KW - Archean KW - Archaikum KW - Platingruppenmetalle Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-155326 ER - TY - THES A1 - Ziegler, Katrin T1 - Implementierung von verbesserten Landoberflächenparametern und -prozessen in das hochaufgelöste Klimamodell REMO T1 - Implementation of improved land surface parameters and processes for the high-resolution climate model REMO N2 - Das Ziel dieser Arbeit war neue Eingangsdaten für die Landoberflächenbeschreibung des regionalen Klimamodells REMO zu finden und ins Modell zu integrieren, um die Vorhersagequalität des Modells zu verbessern. Die neuen Daten wurden so in das Modell eingebaut, dass die bisherigen Daten weiterhin als Option verfügbar sind. Dadurch kann überprüft werden, ob und in welchem Umfang sich die von jedem Klimamodell benötigten Rahmendaten auf Modellergebnisse auswirken. Im Zuge der Arbeit wurden viele unterschiedliche Daten und Methoden zur Generierung neuer Parameter miteinander verglichen, denn neben dem Ersetzen der konstanten Eingangswerte für verschiedene Oberflächenparameter und den damit verbundenen Änderungen wurden als zusätzliche Verbesserung auch Veränderungen an der Parametrisierung des Bodens speziell in Hinblick auf die Bodentemperaturen in REMO vorgenommen. Im Rahmen dieser Arbeit wurden die durch die verschiedenen Änderungen ausgelösten Auswirkungen für das CORDEX-Gebiet EUR-44 mit einer Auflösung von ca. 50km und für das in dem darin eingebetteten neu definierten Deutschlandgebiet GER-11 mit einer Auflösung von ca. 12km getestet sowie alle Änderungen anhand von verschiedenen Beobachtungsdatensätzen validiert. Die vorgenommenen Arbeiten gliederten sich in drei Hauptteile. Der erste Teil bestand in dem vom eigentlichen Klimamodell unabhängigen Vergleich der verschiedenen Eingangsdaten auf unterschiedlichen Auflösungen und deren Performanz in allen Teilen der Erde, wobei ein besonderer Fokus auf der Qualität in den späteren Modellgebieten lag. Unter Berücksichtigung der Faktoren, wie einer globalen Verfügbarkeit der Daten, einer verbesserten räumlichen Auflösung und einer kostenlosen Nutzung der Daten sowie verschiedener Validationsergebnissen von anderen Studien, wurden in dieser Arbeit vier neue Topographiedatensätze (SRTM, ALOS, TANDEM und ASTER) und drei neue Bodendatensätze (FAOn, Soilgrid und HWSD) für die Verwendung im Präprozess von REMO aufbereitet und miteinander sowie mit den bisher in REMO verwendeten Daten verglichen. Auf Grundlage dieser Vergleichsstudien schieden bei den Topographiedaten die verwendeten Datensatz-Versionen von SRTM, ALOS und TANDEM für die in dieser Arbeit durchgeführten REMO-Läufe aus. Bei den neuen Bodendatensätzen wurde ausgenutzt, dass diese verschiedenen Bodeneigenschaften für unterschiedliche Tiefen als Karten zur Verfügung stellen. In REMO wurden bisher alle benötigten Bodenparameter abhängig von fünf verschiedenen Bodentexturklassen und einer zusätzlichen Torfklasse ausgewiesen und als konstant über die gesamte Modellbodensäule (bis ca. 10m) angenommen. Im zweiten Teil wurden auf Basis der im ersten Teil ausgewählten neuen Datensätze und den neu verfügbaren Bodenvariablen verschiedene Sensitivitätsstudien über das Beispieljahr 2000 durchgeführt. Dabei wurden verschiedene neue Parametrisierungen für die bisher aus der Textur abgeleiteten Bodenvariablen und die Parametrisierung von weiteren hydrologischen und thermalen Bodeneigenschaften verglichen. Ferner wurde aufgrund der neuen nicht über die Tiefe konstanten Bodeneigenschaften eine neue numerische Methode zur Berechnung der Bodentemperaturen der fünf Schichten in REMO getestet, welche wiederum andere Anpassungen erforderte. Der Test und die Auswahl der verschiedenen Datensatz- und Parametrisierungsversionen auf die Modellperformanz wurde in drei Experimentpläne unterteilt. Im ersten Plan wurden die Auswirkungen der ausgewählten Topographie- und Bodendatensätze überprüft. Der zweite Plan behandelte die Unterschiede der verschiedenen Parametrisierungsarten der Bodenvariablen hinsichtlich der verwendeten Variablen zur Berechnung der Bodeneigenschaften, der über die Tiefe variablen oder konstanten Eigenschaften und der verwendeten Berechnungsmethode der Bodentemperaturänderungen. Durch die Erkenntnisse aus diesen beiden Experimentplänen, die für beide Untersuchungsgebiete durchgeführt wurden, ergaben sich im dritten Plan weitere Parametrisierungsänderungen. Alle Änderungen dieses dritten Experimentplans wurden sukzessiv getestet, sodass der paarweise Vergleich von zwei aufeinanderfolgenden Modellläufen die Auswirkungen der Neuerung im jeweils zweiten Lauf widerspiegelt. Der letzte Teil der Arbeit bestand aus der Analyse von fünf längeren Modellläufen (2000-2018), die zur Überprüfung der Ergebnisse aus den Sensitivitätsstudien sowie zur Einschätzung der Performanz in weiteren teilweise extremen atmosphärischen Bedingungen durchgeführt wurden. Hierfür wurden die bisherige Modellversion von REMO (id01) für die beiden Untersuchungsgebiete EUR-44 und GER-11 als Referenzläufe, zwei aufgrund der Vergleichsergebnisse von Experimentplan 3 selektierte Modellversionen (id06 und id15a für GER-11) sowie die finale Version (id18a für GER-11), die alle vorgenommenen Änderungen dieser Arbeit enthält, ausgewählt. Es stellte sich heraus, dass sowohl die neuen Topographiedaten als auch die neuen Bodendaten große Differenzen zu den bisherigen Daten in REMO haben. Zudem änderten sich die von diesen konstanten Eingangsdaten abgeleiteten Hilfsvariablen je nach verwendeter Parametrisierung sehr deutlich. Dies war besonders gut anhand der Bodenparameter zu erkennen. Sowohl die räumliche Verteilung als auch der Wertebereich der verschiedenen Modellversionen unterschieden sich stark. Eine Einschätzung der Qualität der resultierenden Parameter wurde jedoch dadurch erschwert, dass auch die verschiedenen zur Validierung herangezogenen Bodendatensätze für diese Parameter deutlich voneinander abweichen. Die finale Modellversion id18a ähnelte trotz der umfassenden Änderungen in den meisten Variablen den Ergebnissen der bisherigen REMO-Version. Je nach zeitlicher und räumlicher Aggregation sowie unterschiedlichen Regionen und Jahreszeiten wurden leichte Verbesserungen, aber auch leichte Verschlechterungen im Vergleich zu den klimatologischen Validationsdaten festgestellt. Größere Veränderungen im Vergleich zur bisherigen Modellversion konnten in den tieferen Bodenschichten aufgezeigt werden, welche allerdings aufgrund von fehlenden Validationsdaten nicht beurteilt werden konnten. Für alle 2m-Temperaturen konnte eine tendenzielle leichte Erwärmung im Vergleich zum bisherigen Modelllauf beobachtet werden, was sich einerseits negativ auf die ohnehin durchschnittlich zu hohe Minimumtemperatur, aber andererseits positiv auf die bisher zu niedrige Maximumtemperatur des Modells in den betrachteten Gebieten auswirkte. Im Niederschlagssignal und in den 10m-Windvariablen konnten keine signifikanten Änderungen nachgewiesen werden, obwohl die neue Topographie an manchen Stellen im Modellgebiet deutlich von der bisherigen abweicht. Des Weiteren variierte das Ranking der verschiedenen Modellversionen jeweils nach dem angewendeten Qualitätsindex. Um diese Ergebnisse besser einordnen zu können, muss berücksichtigt werden, dass die neuen Daten für Modellgebiete mit 50 bzw. 12km räumlicher Auflösung und der damit verbundenen hydrostatischen Modellversion getestet wurden. Zudem sind vor allem in Fall der Topographie die bisher enthaltenen GTOPO-Daten (1km Auflösung) für die Aggregation auf diese gröbere Modellauflösung geeignet. Die bisherigen Bodendaten stoßen jedoch mit 50km Auflösung bereits an ihre Grenzen. Zusätzlich ist zu beachten, dass nicht nur die Mittelwerte dieser Daten, sondern auch deren Subgrid-Variabilität als Variablen im Modell für verschiedene Parametrisierungen verwendet werden. Daher ist es essentiell, dass die Eingangsdaten eine deutlich höhere Auflösung bereitstellen als die zur Modellierung definierte Auflösung. Für lokale Klimasimulationen mit Auflösungen im niedrigen Kilometerbereich spielen auch die Vertikalbewegungen (nicht-hydrostatische Modellversion) eine wichtige Rolle, die stark von der Topographie sowie deren horizontaler und vertikaler Änderungsrate beeinflusst werden, was die in dieser Arbeit eingebauten wesentlich höher aufgelösten Daten für die zukünftige Weiterentwicklung von REMO wertvoll machen kann. N2 - The main aim of this work was to find new input data sets for the land surface description of the regional climate model REMO and to integrate them into the model in order to improve the predictive quality of the model. The new data sets have been incorporated into the model in such a way that the previous data are still available as an option for the model run. This allows to check whether and to what extent the boundary data required by each climate model have an impact on the model results. In this study comparisons of many different data sets and methods for generating new parameters are included. In addition to replacing the constant input values for different surface parameters and the associated changes, changes were also made for the parameterization of the soil, especially with regard to the soil temperatures in REMO. The effects of different changes which were made in this study were analysed for the CORDEX region EUR-44 with a resolution of 50km and for a newly defined German area GER-11 with a resolution of 12km. All changes were validated with different observational data sets. The work process was divided into three main parts. The first part was independent of the actual climate model and included the comparison of different input data sets at different resolutions and their performance in all parts of the world. Taking into account factors such as global availability of the data, improved spatial resolution and free use of the data, as well as various validation results from other studies, four new topography data sets (SRTM, ALOS, TANDEM and ASTER) and three new soil data sets (FAOn, Soilgrid and HWSD) were processed for the usage by REMO and compared with each other and with the data sets previously used in REMO. Based on these comparative studies of the topographical data sets the SRTM, ALOS and TANDEM data set versions were excluded from the further usage in REMO in this study. For the new soil data sets the fact that they provide different soil properties for different depths as maps has been taken advantage of. In the previous REMO versions, all required soil parameters so far have been determined depending on five different soil texture classes with an additional peat class and assumed to be constant over the entire model soil column (up to approximately 10m). In the second part, several sensitivity studies were tested for the year 2000 based on the new data sets selected in the first part of the analysis and on the new available soil variables. Different new parameterizations for soil variables previously derived from the soil texture now based on the sand, clay and organic content of the soil as well as new parameterizations of further hydrological and thermal properties of soil were compared. In addition, due to the new non-constant soil properties, a new numerical method for calculating the soil temperatures of the five layers in the model was tested, which in turn necessitated further adjustments. The testing and selection of the different data sets and parameterization versions for the model according to performance was divided into three experimental plans. In the first plan, the effects of the selected topography and soil data sets were examined. The second plan dealt with the differences between the different types of parameterization of the soil variables in terms of the variables used to calculate the properties, the properties variable or constant over depth, and the method used to calculate the changes in soil temperature. The findings of these two experimental plans, which were carried out for both study areas, led to further parameterization changes in the third plan. All changes in this third experimental plan were tested successively, so the pairwise comparison of two consecutive model runs reflects the impact of the innovation in the second run. The final part of the analysis consists of five longer model runs (2000-2018), which were carried out to review the results of the sensitivity studies and to assess the performance under other, sometimes extreme, atmospheric conditions. For this purpose, the previous model version of REMO (id01) for the two study areas (EUR-44 and GER-11) served as reference runs. Two new model versions (GER-11 of id06 and id15a) were selected on the basis of the comparison results of the third experimental plan and the final version (GER-11 of id18a) which contains all changes made in this work was also chosen for a detailed analysis. Taken together the results show that both the new topography data and the new soil data differ crucially from the previous data sets in REMO. In addition, the auxiliary variables derived from these constant input data change significantly depending on the parameterization used, especially for the soil parameters. Both the spatial distribution and the range of values of the different model versions differ greatly. However, a quality assessment of the parameterization is difficult because different soil data sets used for the validation of the parameters also differ significantly. The final model version (id18a) is similar to the results of the previous REMO version in most variables, despite the extensive changes of the input data and parametrizations. Depending on temporal and spatial aggregation as well as different regions and seasons, slight improvements have been observed, but also slight deterioration compared to the climatological validation data. In the deeper soil layers larger changes could be identified compared to the previous model version, which could not be assessed due to a lack of validation data. Overall, there was also a slight warming of all 2m temperatures compared to the previous model run, which on the one hand has a negative effect on the already too high minimum temperature, but on the other hand has a positive effect on the previously too low maximum temperature of the model in the study areas. No significant changes could be detected in the precipitation signal and in the 10m wind variables, although the new topography differs significantly from the previous topography at some points in the test area. Furthermore, the ranking of the different model versions varied according to the quality index applied. To evaluate the results it has to be considered that the new data were tested for model regions with 50 and 12km spatial resolution and the associated hydrostatic model version. The so far already included data are suitable for aggregation to this coarser model resolution, especially in the case of topography (GTOPO with 1km resolution). However, the previous soil data already reach their limits with 50km resolution. In addition, it should be noted that not only the mean values of these data, but also their subgrid variability are used as variables in the model for different parameterizations. Therefore, it is essential that the input data provide a significantly higher resolution than the resolution defined for modeling. Vertical fluxes (non-hydrostatic model version) play an important role in local climate simulations with resolutions in the low kilometre range, which are strongly influenced by the topography and its horizontal and vertical change rate, which may make the much higher resolution data incorporated in this work valuable for the future development of REMO. KW - Klimamodell KW - Datenanalyse KW - Modellierung KW - Topographie KW - Klimamodellierung KW - REMO KW - Vergleich verschiedener Modellparameterisierungen KW - Bodenparameter KW - Topographiedaten KW - parametrizations Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-261285 ER - TY - JOUR A1 - Qamar, Muhammad Uzair A1 - Azmat, Muhammad A1 - Abbas, Azhar A1 - Usman, Muhammad A1 - Shahid, Muhammad Adnan A1 - Khan, Zahid Mahmood T1 - Water Pricing and Implementation Strategies for the Sustainability of an Irrigation System: A Case Study within the Command Area of the Rakh Branch Canal JF - Water N2 - The command area of the Rakh branch canal grows wheat, sugarcane, and rice crops in abundance. The canal water, which is trivial for irrigating these crops, is conveyed to the farms through the network of canals and distributaries. For the maintenance of this vast infrastructure; the end users are charged on a seasonal basis. The present water charges are severely criticized for not being adequate to properly manage the entire infrastructure. We use the residual value to determine the value of the irrigation water and then based on the quantity of irrigation water supplied to farm land coupled with the infrastructure maintenance cost, full cost recovery figures are executed for the study area, and policy recommendations are made for the implementation of the full cost recovery system. The approach is unique in the sense that the pricings are based on the actual quantity of water conveyed to the field for irrigating crops. The results of our analysis showed that the canal water is severely under charged in the culturable command area of selected distributaries, thus negating the plan of having a self-sustainable irrigation system. KW - irrigation pricing KW - value of water KW - distributary KW - culturable command area KW - sustainable irrigation system Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-224711 VL - 10 IS - 4, 509 ER - TY - JOUR A1 - Reinermann, Sophie A1 - Gessner, Ursula A1 - Asam, Sarah A1 - Kuenzer, Claudia A1 - Dech, Stefan T1 - The Effect of Droughts on Vegetation Condition in Germany: An Analysis Based on Two Decades of Satellite Earth Observation Time Series and Crop Yield Statistics JF - Remote Sensing N2 - Central Europe experienced several droughts in the recent past, such as in the year 2018, which was characterized by extremely low rainfall rates and high temperatures, resulting in substantial agricultural yield losses. Time series of satellite earth observation data enable the characterization of past drought events over large temporal and spatial scales. Within this study, Moderate Resolution Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) (MOD13Q1) 250 m time series were investigated for the vegetation periods of 2000 to 2018. The spatial and temporal development of vegetation in 2018 was compared to other dry and hot years in Europe, like the drought year 2003. Temporal and spatial inter- and intra-annual patterns of EVI anomalies were analyzed for all of Germany and for its cropland, forest, and grassland areas individually. While vegetation development in spring 2018 was above average, the summer months of 2018 showed negative anomalies in a similar magnitude as in 2003, which was particularly apparent within grassland and cropland areas in Germany. In contrast, the year 2003 showed negative anomalies during the entire growing season. The spatial pattern of vegetation status in 2018 showed high regional variation, with north-eastern Germany mainly affected in June, north-western parts in July, and western Germany in August. The temporal pattern of satellite-derived EVI deviances within the study period 2000-2018 were in good agreement with crop yield statistics for Germany. The study shows that the EVI deviation of the summer months of 2018 were among the most extreme in the study period compared to other years. The spatial pattern and temporal development of vegetation condition between the drought years differ. KW - drought KW - time series KW - heat wave KW - agriculture KW - climate extremes KW - climate change KW - crop statistics KW - MODIS KW - Germany Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-225165 VL - 11 IS - 15 ER - TY - JOUR A1 - Khare, Suyash A1 - Latifi, Hooman A1 - Khare, Siddhartha T1 - Vegetation growth analysis of UNESCO World Heritage Hyrcanian forests using multi-sensor optical remote sensing data JF - Remote Sensing N2 - Freely available satellite data at Google Earth Engine (GEE) cloud platform enables vegetation phenology analysis across different scales very efficiently. We evaluated seasonal and annual phenology of the old-growth Hyrcanian forests (HF) of northern Iran covering an area of ca. 1.9 million ha, and also focused on 15 UNESCO World Heritage Sites. We extracted bi-weekly MODIS-NDVI between 2017 and 2020 in GEE, which was used to identify the range of NDVI between two temporal stages. Then, changes in phenology and growth were analyzed by Sentinel 2-derived Temporal Normalized Phenology Index. We modelled between seasonal phenology and growth by additionally considering elevation, surface temperature, and monthly precipitation. Results indicated considerable difference in onset of forests along the longitudinal gradient of the HF. Faster growth was observed in low- and uplands of the western zone, whereas it was lower in both the mid-elevations and the western outskirts. Longitudinal range was a major driver of vegetation growth, to which environmental factors also differently but significantly contributed (p < 0.0001) along the west-east gradient. Our study developed at GEE provides a benchmark to examine the effects of environmental parameters on the vegetation growth of HF, which cover mountainous areas with partly no or limited accessibility. KW - Hyrcanian forest KW - NDVI KW - phenology KW - Sentinel-2 KW - TNPI KW - World Heritage Sites KW - Google Earth Engine Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-248398 SN - 2072-4292 VL - 13 IS - 19 ER - TY - JOUR A1 - Thonfeld, Frank A1 - Steinbach, Stefanie A1 - Muro, Javier A1 - Kirimi, Fridah T1 - Long-term land use/land cover change assessment of the Kilombero catchment in Tanzania using random forest classification and robust change vector analysis JF - Remote Sensing N2 - Information about land use/land cover (LULC) and their changes is useful for different stakeholders to assess future pathways of sustainable land use for food production as well as for nature conservation. In this study, we assess LULC changes in the Kilombero catchment in Tanzania, an important area of recent development in East Africa. LULC change is assessed in two ways: first, post-classification comparison (PCC) which allows us to directly assess changes from one LULC class to another, and second, spectral change detection. We perform LULC classification by applying random forests (RF) on sets of multitemporal metrics that account for seasonal within-class dynamics. For the spectral change detection, we make use of the robust change vector analysis (RCVA) and determine those changes that do not necessarily lead to another class. The combination of the two approaches enables us to distinguish areas that show (a) only PCC changes, (b) only spectral changes that do not affect the classification of a pixel, (c) both types of change, or (d) no changes at all. Our results reveal that only one-quarter of the catchment has not experienced any change. One-third shows both, spectral changes and LULC conversion. Changes detected with both methods predominantly occur in two major regions, one in the West of the catchment, one in the Kilombero floodplain. Both regions are important areas of food production and economic development in Tanzania. The Kilombero floodplain is a Ramsar protected area, half of which was converted to agricultural land in the past decades. Therefore, LULC monitoring is required to support sustainable land management. Relatively poor classification performances revealed several challenges during the classification process. The combined approach of PCC and RCVA allows us to detect spatial patterns of LULC change at distinct dimensions and intensities. With the assessment of additional classifier output, namely class-specific per-pixel classification probabilities and derived parameters, we account for classification uncertainty across space. We overlay the LULC change results and the spatial assessment of classification reliability to provide a thorough picture of the LULC changes taking place in the Kilombero catchment. KW - land-use/land-cover change KW - robust change vector analysis KW - Kilombero KW - wetland KW - food production KW - random forest KW - multitemporal metrics KW - Landsat KW - post-classification comparison Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-203513 SN - 2072-4292 VL - 12 IS - 7 ER - TY - JOUR A1 - Akhundzadah, Noor Ahmad A1 - Soltani, Salim A1 - Aich, Valentin T1 - Impacts of climate change on the water resources of the Kunduz River Basin, Afghanistan JF - Climate N2 - The Kunduz River is one of the main tributaries of the Amu Darya Basin in North Afghanistan. Many communities live in the Kunduz River Basin (KRB), and its water resources have been the basis of their livelihoods for many generations. This study investigates climate change impacts on the KRB catchment. Rare station data are, for the first time, used to analyze systematic trends in temperature, precipitation, and river discharge over the past few decades, while using Mann–Kendall and Theil–Sen trend statistics. The trends show that the hydrology of the basin changed significantly over the last decades. A comparison of landcover data of the river basin from 1992 and 2019 shows significant changes that have additional impact on the basin hydrology, which are used to interpret the trend analysis. There is considerable uncertainty due to the data scarcity and gaps in the data, but all results indicate a strong tendency towards drier conditions. An extreme warming trend, partly above 2 °C since the 1960s in combination with a dramatic precipitation decrease by more than −30% lead to a strong decrease in river discharge. The increasing glacier melt compensates the decreases and leads to an increase in runoff only in the highland parts of the upper catchment. The reduction of water availability and the additional stress on the land leads to a strong increase of barren land and a reduction of vegetation cover. The detected trends and changes in the basin hydrology demand an active management of the already scarce water resources in order to sustain water supply for agriculture and ecosystems in the KRB. KW - climate change KW - Kunduz River Basin KW - trend analysis KW - river discharge KW - landcover changes Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-213199 SN - 2225-1154 VL - 8 IS - 10 ER - TY - JOUR A1 - Weigand, Matthias A1 - Wurm, Michael A1 - Dech, Stefan A1 - Taubenböck, Hannes T1 - Remote sensing in environmental justice research—a review JF - ISPRS International Journal of Geo-Information N2 - Human health is known to be affected by the physical environment. Various environmental influences have been identified to benefit or challenge people's physical condition. Their heterogeneous distribution in space results in unequal burdens depending on the place of living. In addition, since societal groups tend to also show patterns of segregation, this leads to unequal exposures depending on social status. In this context, environmental justice research examines how certain social groups are more affected by such exposures. Yet, analyses of this per se spatial phenomenon are oftentimes criticized for using “essentially aspatial” data or methods which neglect local spatial patterns by aggregating environmental conditions over large areas. Recent technological and methodological developments in satellite remote sensing have proven to provide highly detailed information on environmental conditions. This narrative review therefore discusses known influences of the urban environment on human health and presents spatial data and applications for analyzing these influences. Furthermore, it is discussed how geographic data are used in general and in the interdisciplinary research field of environmental justice in particular. These considerations include the modifiable areal unit problem and ecological fallacy. In this review we argue that modern earth observation data can represent an important data source for research on environmental justice and health. Especially due to their high level of spatial detail and the provided large-area coverage, they allow for spatially continuous description of environmental characteristics. As a future perspective, ongoing earth observation missions, as well as processing architectures, ensure data availability and applicability of ’big earth data’ for future environmental justice analyses. KW - satellite remote sensing KW - review KW - environmental justice KW - big earth data KW - urban environments Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-196950 SN - 2220-9964 VL - 8 IS - 1 ER - TY - JOUR A1 - Khare, Siddhartha A1 - Latifi, Hooman A1 - Rossi, Sergio A1 - Ghosh, Sanjay Kumar T1 - Fractional cover mapping of invasive plant species by combining very high-resolution stereo and multi-sensor multispectral imageries JF - Forests N2 - Invasive plant species are major threats to biodiversity. They can be identified and monitored by means of high spatial resolution remote sensing imagery. This study aimed to test the potential of multiple very high-resolution (VHR) optical multispectral and stereo imageries (VHRSI) at spatial resolutions of 1.5 and 5 m to quantify the presence of the invasive lantana (Lantana camara L.) and predict its distribution at large spatial scale using medium-resolution fractional cover analysis. We created initial training data for fractional cover analysis by classifying smaller extent VHR data (SPOT-6 and RapidEye) along with three dimensional (3D) VHRSI derived digital surface model (DSM) datasets. We modelled the statistical relationship between fractional cover and spectral reflectance for a VHR subset of the study area located in the Himalayan region of India, and finally predicted the fractional cover of lantana based on the spectral reflectance of Landsat-8 imagery of a larger spatial extent. We classified SPOT-6 and RapidEye data and used the outputs as training data to create continuous field layers of Landsat-8 imagery. The area outside the overlapping region was predicted by fractional cover analysis due to the larger extent of Landsat-8 imagery compared with VHR datasets. Results showed clear discrimination of understory lantana from upperstory vegetation with 87.38% (for SPOT-6), and 85.27% (for RapidEye) overall accuracy due to the presence of additional VHRSI derived DSM information. Independent validation for lantana fractional cover estimated root-mean-square errors (RMSE) of 11.8% (for RapidEye) and 7.22% (for SPOT-6), and R\(^2\) values of 0.85 and 0.92 for RapidEye (5 m) and SPOT-6 (1.5 m), respectively. Results suggested an increase in predictive accuracy of lantana within forest areas along with increase in the spatial resolution for the same Landsat-8 imagery. The variance explained at 1.5 m spatial resolution to predict lantana was 64.37%, whereas it decreased by up to 37.96% in the case of 5 m spatial resolution data. This study revealed the high potential of combining small extent VHR and VHRSI- derived 3D optical data with larger extent, freely available satellite data for identification and mapping of invasive species in mountainous forests and remote regions. KW - Lantana camara KW - SPOT-6 KW - RapidEye KW - 3D KW - DSM KW - Fractional cover analysis Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-197250 SN - 1999-4907 VL - 10 IS - 7 ER - TY - JOUR A1 - Ulloa-Torrealba, Yrneh A1 - Stahlmann, Reinhold A1 - Wegmann, Martin A1 - Koellner, Thomas T1 - Over 150 years of change: object-oriented analysis of historical land cover in the Main river catchment, Bavaria/Germany JF - Remote Sensing N2 - The monitoring of land cover and land use change is critical for assessing the provision of ecosystem services. One of the sources for long-term land cover change quantification is through the classification of historical and/or current maps. Little research has been done on historical maps using Object-Based Image Analysis (OBIA). This study applied an object-based classification using eCognition tool for analyzing the land cover based on historical maps in the Main river catchment, Upper Franconia, Germany. This allowed land use change analysis between the 1850s and 2015, a time span which covers the phase of industrialization of landscapes in central Europe. The results show a strong increase in urban area by 2600%, a severe loss of cropland (−24%), a moderate reduction in meadows (−4%), and a small gain in forests (+4%). The method proved useful for the application on historical maps due to the ability of the software to create semantic objects. The confusion matrix shows an overall accuracy of 82% for the automatic classification compared to manual reclassification considering all 17 sample tiles. The minimum overall accuracy was 65% for historical maps of poor quality and the maximum was 91% for very high-quality ones. Although accuracy is between high and moderate, coarse land cover patterns in the past and trends in land cover change can be analyzed. We conclude that such long-term analysis of land cover is a prerequisite for quantifying long-term changes in ecosystem services. KW - historical KW - land cover change KW - object-based classification KW - eCognition Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-220029 SN - 2072-4292 VL - 12 IS - 24 ER - TY - JOUR A1 - Walz, Yvonne A1 - Wegmann, Martin A1 - Dech, Stefan A1 - Raso, Giovanna A1 - Utzinger, Jürg T1 - Risk profiling of schistosomiasis using remote sensing: approaches, challenges and outlook JF - Parasites & Vectors N2 - Background: Schistosomiasis is a water-based disease that affects an estimated 250 million people, mainly in sub-Saharan Africa. The transmission of schistosomiasis is spatially and temporally restricted to freshwater bodies that contain schistosome cercariae released from specific snails that act as intermediate hosts. Our objective was to assess the contribution of remote sensing applications and to identify remaining challenges in its optimal application for schistosomiasis risk profiling in order to support public health authorities to better target control interventions. Methods: We reviewed the literature (i) to deepen our understanding of the ecology and the epidemiology of schistosomiasis, placing particular emphasis on remote sensing; and (ii) to fill an identified gap, namely interdisciplinary research that bridges different strands of scientific inquiry to enhance spatially explicit risk profiling. As a first step, we reviewed key factors that govern schistosomiasis risk. Secondly, we examined remote sensing data and variables that have been used for risk profiling of schistosomiasis. Thirdly, the linkage between the ecological consequence of environmental conditions and the respective measure of remote sensing data were synthesised. Results: We found that the potential of remote sensing data for spatial risk profiling of schistosomiasis is - in principle - far greater than explored thus far. Importantly though, the application of remote sensing data requires a tailored approach that must be optimised by selecting specific remote sensing variables, considering the appropriate scale of observation and modelling within ecozones. Interestingly, prior studies that linked prevalence of Schistosoma infection to remotely sensed data did not reflect that there is a spatial gap between the parasite and intermediate host snail habitats where disease transmission occurs, and the location (community or school) where prevalence measures are usually derived from. Conclusions: Our findings imply that the potential of remote sensing data for risk profiling of schistosomiasis and other neglected tropical diseases has yet to be fully exploited. KW - ecology KW - scale KW - remote sensing KW - risk profiling KW - spatial modelling KW - schistosomiasis KW - geographical information system KW - intermediate host snail KW - epidemology Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-148778 VL - 8 IS - 163 ER - TY - BOOK A1 - Sonntag, Christian T1 - Wie kommen Obst und Gemüse in Supermärkte im Globalen Süden? Supermarktexpansion und Liefersysteme/Intermediäre für Frischeprodukte in Kenia und Tansania N2 - In den letzten drei Jahrzehnten expandierten Supermarktketten aus dem Globalen Norden in Länder des Globalen Südens. Insbesondere Länder mit einem raschen wirtschaftlichen Wachstum und damit neuen Marktpotentialen waren dabei Expansionsziele. Zugleich zeigt sich innerhalb der Länder des Globalen Südens eine Ausbreitung von regionalen Supermarktketten. Mittlerweile gehört frisches Obst und Gemüse fast immer zum Sortiment dieser Einzelhandelsunternehmen. Bisher untersuchte eine Reihe von Studien die Auswirkungen der Kooperation mit den Einzelhändlern auf die landwirtschaftlichen Produzierenden. Weniger ist dagegen bekannt, welche Liefersysteme und Intermediäre für die Verbindung zwischen landwirtschaftlichen Produzierenden und Supermarktketten in Ländern des Globalen Südens bestehen und sich entwickeln. Insbesondere für leicht verderbliche Frischeprodukte (Obst und Gemüse) ist die Herausbildung dieser Intermediäre eine große Herausforderung. Die vorliegende Studie betrachtet den Zusammenhang zwischen der räumlichen und zeitlichen Ausbreitung von Supermärkten und der Etablierung von Liefersystemen sowie Intermediären am Beispiel von Kenia und Tansania. T3 - Geographische Handelsforschung - 30 KW - Wertschöpfungskette KW - Ostafrika KW - Wertschöpfungsketten KW - Supermärkte KW - Frischeprodukte KW - Intermediäre KW - Ostafrika KW - Tansania KW - Kenia KW - Wertschöpfungsnetzwerk KW - Obst KW - Gemüse KW - Supermarkt Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-244320 SN - 978-3-95826-170-9 SN - 978-3-95826-171-6 SN - 2196-5811 SN - 2626-8906 N1 - Parallel erschienen als Druckausgabe in Würzburg University Press, 978-3-95826-170-9, 32,90 Euro. N1 - Eingereicht unter dem Titel: Wie kommen Obst und Gemüse in Supermärkte im Globalen Süden? Der Zusammenhang zwischen der Expansion von Supermarktketten und der Etablierung von Liefersystemen / Intermediären für Frischeprodukte ER - TY - JOUR A1 - Knauer, Kim A1 - Gessner, Ursula A1 - Fensholt, Rasmus A1 - Forkuor, Gerald A1 - Kuenzer, Claudia T1 - Monitoring agricultural expansion in Burkina Faso over 14 years with 30 m resolution time series: the role of population growth and implications for the environment JF - Remote Sensing N2 - Burkina Faso ranges amongst the fastest growing countries in the world with an annual population growth rate of more than three percent. This trend has consequences for food security since agricultural productivity is still on a comparatively low level in Burkina Faso. In order to compensate for the low productivity, the agricultural areas are expanding quickly. The mapping and monitoring of this expansion is difficult, even on the basis of remote sensing imagery, since the extensive farming practices and frequent cloud coverage in the area make the delineation of cultivated land from other land cover and land use types a challenging task. However, as the rapidly increasing population could have considerable effects on the natural resources and on the regional development of the country, methods for improved mapping of LULCC (land use and land cover change) are needed. For this study, we applied the newly developed ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) framework to generate high temporal (8-day) and high spatial (30 m) resolution NDVI time series for all of Burkina Faso for the years 2001, 2007, and 2014. For this purpose, more than 500 Landsat scenes and 3000 MODIS scenes were processed with this automated framework. The generated ESTARFM NDVI time series enabled extraction of per-pixel phenological features that all together served as input for the delineation of agricultural areas via random forest classification at 30 m spatial resolution for entire Burkina Faso and the three years. For training and validation, a randomly sampled reference dataset was generated from Google Earth images and based on expert knowledge. The overall accuracies of 92% (2001), 91% (2007), and 91% (2014) indicate the well-functioning of the applied methodology. The results show an expansion of agricultural area of 91% between 2001 and 2014 to a total of 116,900 km\(^2\). While rainfed agricultural areas account for the major part of this trend, irrigated areas and plantations also increased considerably, primarily promoted by specific development projects. This expansion goes in line with the rapid population growth in most provinces of Burkina Faso where land was still available for an expansion of agricultural area. The analysis of agricultural encroachment into protected areas and their surroundings highlights the increased human pressure on these areas and the challenges of environmental protection for the future. KW - remote sensing KW - Africa KW - agriculture KW - Burkina Faso KW - data fusion KW - ESTARFM framework KW - irrigation KW - land surface phenology KW - Landsat KW - MODIS KW - plantation KW - protected areas KW - TIMESAT Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-171905 VL - 9 IS - 2 ER - TY - THES A1 - Büdel, Christian T1 - Quaternary alluvial fan morphodynamics and basin sedimentation in North Iran N2 - Several hundred meter thick alluvial fans are dominating the landscape of semiarid plains of north-eastern Iranian mountain-talus. These fans developed since Miocene times and are still prograding today. They are storing and transporting major amount of weathered debris from the mountain ranges and sediments are directed to the depressions of the endorheic basins. In this course, the debris gets increasingly weathered and abraded to sand silt and clay fractions, which finally constitute the fine loamy layers of the typical central playa lakes and playas. The study focused on the detailed investigation of a characteristic section of this prominent sediment cascades. The sediments were planned be comprehensively documented by using a fully analytical geomorphological mapping approach, also suitable for the classification of the sediment’s geomorphological system contexts. Therefore, a geodatabase structure was developed, which is capable of managing and analyzing geomorphological data. The corresponding data was acquired using remote sensing imagery, digital elevation models and field mapping campaigns. Additionally, mapped sediments were selected and analyzed to reveal representative stratigraphic and sedimentologic characteristics. The fieldwork was conducted in Damghan Basin on sections in geomorphologically subdivided alluvial apron sediments. The corresponding, more regularly layered and partly laminated sediments from the central playa, were also investigated and comparably described. Attending to the preparation of profile sections and the percussion core probing, samples from both origins were taken. Detailed lithostratigraphical and geochemical analyses were carried out in the laboratory, in order to develop sound comparable sediment and sediment alteration indices. In addition, OSL sampling was done on distinct alluvial fan surfaces, and the samples were measured and dated in the laboratory in order to develop a functional chronostratigraphy. The results of the geomorphological and stratigraphical investigations reveal five classes of surface ages on the alluvial apron. Parallel to that observation, the ramming core samples exhibit four main phases of sediment development in the playa, each with internal differentiation. This brings up opportunities for a valid correlation of continuously deposited playa sediments, with widespread alluvial surfaces. By considering the OSL ages of selected terrestrial and lacustrine layers, the stratigraphy and relative chronological order of the studied sediments, are aligned to Pleistocene and mid to late Holocene periods. This is also confirmed by proxy ages from the chronological framework of the landscape development, within the basin. Geophysical and geochemical data reveals roughly cyclic sedimentation and sediment alteration that can also be observed in genetically differing geoarchives from alluvial fan surfaces to the playa sediments. These parallel developments are confirmed by the sequence-stratigraphic order of the archives which is derived from geomorphological mapping. We postulate a time span covered by the playa sediments from today to late Pleistocene times. Drying and Lake level retreat at the end of Pleistocene is documented in parallel geoarchives of terrestrial and lacustrine origin. Thus, geomorphologic landform-succession and alluvial fan morphogenesis, as well as geochemical and geophysical fingerprinting data of playa sediments characterize this development. Therefore, it is present in the two youngest generations of alluvial fans, which deposited at the toe of the alluvial apron and which incorporate reworked still water deposits of a former lake level terrace. The geochemical parameters, like pH-value and Ca/S-ratio, in both, sediments of a former lake level and those from the recent playa extent exhibit a similar development. In addition, Ti/Al-ratio shows the potential for quantifying lake level retreat, as it seems to be tightly depending on lake level and increasing transportation distances along the sediment cascade. Major Findings - Analytical geomorphological mapping is a key tool for understanding the sedimentation history of the Iranian Quaternary and for describing Quaternary stratigraphic units of North Iran. - The younger playa deposits in Damghan Basin cover a time span from today to late Pleistocene. They are 10 meters thick and reveal very low organic matter contents throughout the core. - Drying and Lake level retreat is documented in parallel geoarchives (1) in geomorphologic landform-succession and alluvial fan morphogenesis and (2) in geochemical and geophysical fingerprinting data of playa sediments - Sequences of torrential sedimentation are detectable signals and can be correlated in both: playa sediments and landform morphology N2 - Die nordostiranische Gebirgsabdachung wird maßgeblich von mehrere hundert Meter mächtigen Schwemmfächern gebildet. Die Aufschüttung der Schwemmfächer setze im Miozän ein und bis heute progradieren die Fächer ins Beckeninnere. Dabei transportieren und speichern sie den Großteil des Verwitterungsschutts der Gebirgszone und die Sedimente werden von hier weiter in die zentralen Depressionen der endorheischen Beckenlandschaften geleitet. Auf ihrem Weg wird der Schutt weiter zerkleinert und verwittert, und bildet schließlich in feinen Sand-, Schluff- und Tongemengelagen die Lehmschichten der typischen zentralen Playaseen und Playas. Mit der vorliegenden Studie wurde ein charakteristischer Abschnitt dieser bedeutenden Sedimentkaskade detailliert untersucht. Durch einen vollanalytischen geomorphologischen Kartieransatz sollten die Sedimente umfangreich erfasst und in ihren geomorphologischen Systemzusammenhang gebracht werden. Zu diesem Zweck wurde eine Geodatenbankstruktur entwickelt, die die Verwaltung und Analyse geomorphologischer Daten ermöglicht. Die entsprechenden Dateneinträge wurden aus spektralen Fernerkundungsdaten, digitalen Geländemodellen und den Feldkartierungen mehrerer Expeditionen extrahiert. Für die kartierten Sedimente wurden an repräsentativen Standorten zusätzlich die stratigraphischen und sedimentologischen Charakteristika ermittelt und mit den Kartenelementen verknüpft. Für die Feldarbeiten wurden die Sedimente einer typischen Schwemmfächerschürze des Damghan Beckens geomorphologisch untergegliedert und ergraben. Entsprechend wurden die, das Ende der Sedimentkaskade bildenden, regelmäßiger geschichteten und zum Teil laminierten Sedimente der Playa sondiert und vergleichbar beschrieben. Die Landformen wurden als Kartenelemente im Vektorformat gespeichert und nach standardisierten geomorphologischen Konzepten in Systemelemente aus Form, Material und Prozess klassifiziert. Spezifiziert wurden die Landformen und Landformelemente dann in den Klassen Moprhometrie und Morphographie (Form), Morphostruktur (Material), Morphodynamik und Morphogenese (Prozess), sowie Geomorphologie (System Synthese). Begleitend zu den Kartierungen und der Bearbeitung der Profilschnitte und Rammkernsondagen, wurden die Schichten beider Sedimentationsräume detailliert beprobt. Weitere Untersuchungen zu Lithostratigraphie, Geochronologie und Geochemie der Sedimente wurden im Labor ausgeführt und sollten aussagekräftige Indizes zu Sedimenteigenschaften und Art und Alter der Sedimentvariation erbringen. Neben pH- und Leitfähigkeitsmessungen, sowie der Ionenchromatographie leicht löslicher Salze, wurde für alle Proben die Granulometrie mittels Laserdiffraktometrie ermittelt. Die mineralogische Zusammensetzung wurde röntgendiffraktometrisch bestimmt. Haupt- und Nebenelemente wurden im Wesentlichen mit einer Kombination aus Röntgenfluoreszenzmessungen (XRF) und optischer Emissionsspektrometrie von Königswasseraufschlüssen mittels induktiv gekoppeltem Plasma (ICP-OES) analysiert. Altersbestimmungen konnten lediglich an ausgewählten Proben der terrestrischen Sedimente durchgeführt werden; dabei wurden im Labor Dosimetrie und Optisch Stimulierte Lumineszenz gemessen (OSL) und daraus die Alter modelliert. Die Ergebnisse der geomorphologischen und stratigraphischen Untersuchungen zeigen fünf Hauptphasen vergangener Schwemmfächeroberflächenaktivität. Parallel dazu konnten in den Rammkernen der zentralen Playa vier in sich gegliederte Hauptsedimentationsphasen nachgewiesen werden. Die kontinuierlichen Akkumulationsphasen der Playasedimente bieten aussagekräftige Korrelationen mit den Aktivitätsphasen auf den ausgedehnten Flächen der Schwemmfächerschürze. Über die OSL-Alter der ausgewählten terrestrischen und lakustrinen Sedimentschichten können Stratigraphie und relative Chronologie der untersuchten Sedimente Pleistozänen und mittel- bis spätholozänen Perioden zugeordnet und in die allgemeine Chronostratigraphy des Beckens eingefügt werden. Die geophysikalischen und geochemischen Messungen lassen auf zyklische Sedimentationsphasen schließen, die sich über Geoarchive verschiedenen Ursprungs von der Schwemmfächerschürze bis in die zentrale Playa koppeln lassen. Diese stratigraphische Entwicklung wurde über die historisch-genetische Systemrekonstruktion im Zuge der geomorphologischen Kartierung in ihren sequenzstratigraphischen Zusammenhang gebracht und bestätigt so die parallele Entwicklung der verschiedenen Geoarchive. Die untersuchten Playasedimente belegen somit eine Zeitspanne vom Spätpleistozän bis zur aktuell anhaltenden Phase. Die Entwicklung der verschiedenen Sedimentkörper zeigt auch den Wechsel zu trockeneren Bedingungen und die Austrocknung eines früher ausgedehnten (Playa-)Sees in spätpleistozäner Zeit an. Sowohl die geomorphologische Landformensukzession und Schwemmfächermorphogenese, wie auch das Elementsignal und die veränderten geophysikalischen und geochemischen Eigenschaften der Playasedimente charakterisieren diese Entwicklung. So ist diese präsent in den beiden jüngsten Schwemmfächergenerationen, die am Fuß der Schwemmfächerschürze liegen und erodierte Sedimente einer ehemaligen Seeterrasse beinhalten. Auch geochemische Parameter, hier die pH-Werte und das Ca/S-Verhältnis, der Schichten in Position der ehemaligen Seeterrasse, und der Schichtung innerhalb der rezenten Playaausdehnung weisen auf die zunehmende Salinität des ehemaligen Sees im Zuge der Austrocknung hin. Ergänzend hat das Ti/Al-Verhältnis Potential den Seespiegelrückzug zu quantifizieren, indem es als Indikator für zunehmende Transportdistanzen entlang der Sedimentkaskade genutzt wird. Der beobachtete Seespiegelrückgang und die Phasen der Schwemmfächeraktivität können weiterhin mit Humiditätsphasen korreliert werden, die aus verschiedenen See- und Sumpfsedimenten des Nordiran bekannt sind. So beginnt in Damghan mit der Jüngeren Dyas eine intensive Trockenphase mit veränderten klimatischen Bedingungen. Nach einer folgenden Humiditätsphase mit neuerlichem Seehochstand können zwei weitere Trockenphasen im Frühholozän ausgemacht werden. Die letzte Trockenphase setzt somit nach einer mittelholozänen Phase relativer Stabilität ein. Wesentliche Erkenntnisse - Die analytisch geomorphologische Kartierung bildet ein essentielles Werkzeug zur Untersuchung der Sedimentation im iranischen Quartär und zur Beschreibung der quartären stratigraphischen Einheiten des Nordiran - Die jüngeren Playasedimente des Damghan Beckens repräsentieren einen Zeitabschnitt der von heute bis in das Spätpleistozän reicht. Sie weisen eine Mächtigkeit von 10 Meter auf und enthalten durchweg sehr geringe Anteile an organischer Substanz. - Austrocknung und Seespiegelrückgang sind in Damghan parallel in zwei Geoarchivtypen dokumentiert. Sie zeigen sich (1) in der geomorphologischen Landformenfolge und Schwemmfächerentwicklung und (2) im Wandel der geochemischen und geophysikalischen Charakteristika der Playasedimente. - Sequenzen mit torrentieller Sedimentation konnten ebenfalls in beiden Sedimentationsräumen erkannt und korreliert werden. N2 - Availability of water and desiccation of important water reservoirs is a vital challenge in semi-arid to arid climates with growing economy and population. Low quantities of precipitation and high evaporation rates leave the water supply vulnerable to human activity and climatic variations. Endorheic basins of Northern Iran were hydrologically landlocked within geological timescales and thus bear evidence of past variations of water resources in generations of water related landforms, like abandoned lake level shorelines, alluvial fans and stream terraces. Understanding the development of these landforms reveals crucial information about past water reservoirs and landscape history. This study offers a comprehensive approach on understanding the geomorphological development of the landscape throughout Late Pleistocene and Holocene times. It integrates remote sensing and geographic information system analysis, with geomorphological and stratigraphical mapping fieldwork and detailed sedimentological investigations. The work shows the importance of analytical geomorphological mapping for delineating stratigraphic units of the Iranian Quaternary. Thus, several phases of drying and lake level retreat were identified in parallel geoarchives and could be dated to a time span from today to Late Pleistocene. The findings link the fate of the citizens of the ancient city of "Tepe Hissar" to their access to water and to the power of geomorphological processes, which started changing their environment. T2 - Quartäre Schwemmfächerdynamik und Beckensedimentation im Nordiran T3 - Würzburger Geographische Arbeiten - 123 KW - Quartärgeomorphologie KW - Quartär KW - Trockengebiet KW - Schwemmfächer KW - Playa KW - Sedimentarchiv KW - Quaternary KW - drylands KW - alluvial fan KW - basin geomorphology Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-184508 SN - 978-3-95826-114-3 SN - 978-3-95826-115-0 SN - 0510-9833 SN - 2194-3656 N1 - Parallel erschienen als Druckausgabe in Würzburg University Press, 978-3-95826-114-3, 29,80 EUR PB - Würzburg University Press CY - Würzburg ET - 1. Auflage ER - TY - JOUR A1 - Höhn, Stefan A1 - Frimmel, Hartwig E. A1 - Prince, Westley T1 - Syn-metamorphic sulfidation of the Gamsberg zinc deposit, South Africa JF - Mineralogy and Petrology N2 - The Mesoproterozoic Aggeneys-Gamsberg ore district, South Africa, is one of the world´s largest sulfidic base metal concentrations and well-known as a prime example of Broken Hill-type base metal deposits, traditionally interpreted as metamorphosed SEDEX deposits. Within this district, the Gamsberg deposit stands out for its huge size and strongly Zn-dominated ore ( >14 Mt contained Zn). New electron microprobe analyses and element abundance maps of sulfides and silicates point to fluid-driven sulfidation during retrograde metamorphism. Differences in the chemistry of sulfide inclusions within zoned garnet grains reflect different degrees of interaction of sulfides with high metal/sulfur-ratio with a sulfur-rich metamorphic fluid. Independent evidence of sulfidation during retrograde metamorphism comes from graphic-textured sulfide aggregates that previously have been interpreted as quenched sulfidic melts, replacement of pyrrhotite by pyrite along micro-fractures, and sulfides in phyllic alteration zones. Limited availability of fluid under retrograde conditions caused locally different degrees of segregation of Fe-rich sphalerite into Zn-rich sphalerite and pyrite, and thus considerable heterogeneity in sphalerite chemistry. The invoked sulfur-rich metamorphic fluids would have been able to sulfidize base metal-rich zones in the whole deposit and thus camouflage a potential pre-metamorphic oxidation. These findings support the recently established hypothesis of a pre-Klondikean weathering-induced oxidation event and challenge the traditional explanation of Broken Hill-type deposits as merely metamorphosed SEDEX deposits. Instead, we suggest that the massive sulfide deposits experienced a complex history, starting with initial SEDEX-type mineralization, followed by near-surface oxidation with spatial metal separation, and then sulfidation of this oxidized ore during medium- to high-grade metamorphism. KW - Gamsberg KW - metamorphic sulfidation KW - sulfide inclusions KW - base metal deposit KW - Aggeneys Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-268574 SN - 1438-1168 VL - 115 IS - 6 ER - TY - JOUR A1 - Ibebuchi, Chibuike Chiedozie T1 - Circulation pattern controls of wet days and dry days in Free State, South Africa JF - Meteorology and Atmospheric Physics N2 - Atmospheric circulation is a vital process in the transport of heat, moisture, and pollutants around the globe. The variability of rainfall depends to some extent on the atmospheric circulation. This paper investigates synoptic situations in southern Africa that can be associated with wet days and dry days in Free State, South Africa, in addition to the underlying dynamics. Principal component analysis was applied to the T-mode matrix (variable is time series and observation is grid points at which the field was observed) of daily mean sea level pressure field from 1979 to 2018 in classifying the circulation patterns in southern Africa. 18 circulation types (CTs) were classified in the study region. From the linkage of the CTs to the observed rainfall data, from 11 stations in Free State, it was found that dominant austral winter and late austral autumn CTs have a higher probability of being associated with dry days in Free State. Dominant austral summer and late austral spring CTs were found to have a higher probability of being associated with wet days in Free State. Cyclonic/anti-cyclonic activity over the southwest Indian Ocean, explained to a good extent, the inter-seasonal variability of rainfall in Free State. The synoptic state associated with a stronger anti-cyclonic circulation at the western branch of the South Indian Ocean high-pressure, during austral summer, leading to enhanced low-level moisture transport by southeast winds was found to have the highest probability of being associated with above-average rainfall in most regions in Free State. On the other hand, the synoptic state associated with enhanced transport of cold dry air, by the extratropical westerlies, was found to have the highest probability of being associated with (winter) dryness in Free State. KW - South Africa KW - atmospheric circulation KW - circulation patterns Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-268552 SN - 1436-5065 VL - 133 IS - 5 ER - TY - THES A1 - Majewski, Lisa T1 - Input-Output-Analyse zur Ermittlung der regionalökonomischen Effekte des Tourismus in Schutzgebieten : Eine Adaption der Methodik an internationale Standards am Fallbeispiel Biosphärengebiet Schwarzwald T1 - Input-output analysis to estimate the regional economic effects of tourism in protected areas. An adaptation of the methodology towards international standards using the case of Black Forest Biosphere Reserve N2 - Schutzgebiete gelten laut der Convention on Biological Diversity als Flächeninstrument zum Schutz der Biodiversität. Menschen profitieren davon unter anderem durch die Nutzung als touristische Attraktion. Schätzungen zufolge werden weltweit etwa acht Mrd. Besuche zur Wahrnehmung des Naturerlebnisangebots der Schutzgebiete erreicht, woraus direkte Besucherausgaben in Höhe von 600 Mrd. US-$ resultieren. Schutzgebiete sind damit auch bedeutende Wirtschaftsmotoren der regionalen Ökonomien. Deutschlands Nationalparks zählen jährlich etwa 53 Mio. Besuchstage, deren tägliche Ausgaben vor Ort einen Bruttoumsatz in Höhe von 2,78 Mrd. € generieren. Die touristische Wertschöpfung beträgt 1,45 Mrd. €. Die weiteren 65 Mio. Besuchstage in deutschen Biosphärenreservaten erwirtschaften einen Bruttoumsatz in Höhe von 2,94 Mrd. €. Das Einkommen von 172.000 Personen ist vom Tourismus in deutschen Nationalparken und Biosphärenreservaten abhängig. Im Rahmen einer ersten Studie zu den regionalökonomischen Effekten des Nationalparks Berchtesgaden im Jahr 2002 wurde die touristische Wertschöpfungsanalyse als Standardmethode der deutschen Schutzgebietsforschung etabliert. Im Laufe der Jahre wurde sie dahingehend modifiziert, ein vergleichbares, weil standardisiertes Vorgehen anwenden zu können. Die internationale Forschung manifestiert mit der Herausgabe eines Leitfadens einen anderen Standard zur regionalökonomischen Wirkungsanalyse des Tourismus in Schutzgebieten: die Input-Output-Analyse. Schutzgebietsverwaltungen in den USA, Kanada, Brasilien, Namibia, Südafrika und Finnland führen für ihr Besuchermonitoring Input-Output-Analysen durch. Diese sind im Vergleich zur Wertschöpfungsanalyse als der validiere Ansatz einzustufen, weil damit ein Rechenwerk gegeben ist, womit indirekte Vorleistungs- und induzierte Konsumwirkungen zuverlässig quantifiziert werden können. Die Wertschöpfungsanalyse arbeitet hingegen mit pauschalen Wertschöpfungsquoten für alle touristischen Wirtschaftszweige und auf jeder Maßstabsebene. Aufgrund der fehlenden Datenverfügbarkeit konnte die Input-Output-Analyse in Deutschland bisher nicht angewandt werden. Eine potenzielle Datenquelle eröffnete sich durch das US-amerikanische Modellierungsunternehmen IMPLAN, welches Input-Output-Tabellen für die regionale Ebene der EU anbot. IMPLAN-Daten werden auch vom US-amerikanischen National Park Service verwendet. Die vorliegende Arbeit versteht sich als methodische Weiterentwicklung regionalökonomischer Wirkungsanalysen in Deutschlands Schutzgebieten zur Adaption an internationale Standards. Dazu erfolgt die Applikation der Input-Output-Analyse für das Fallbeispiel des Biosphärengebiets Schwarzwald, dessen Regionalökonomie einen überschaubaren Analyserahmen bietet. Der Nationalpark Schwarzwald wurde als Vergleichsregion zur Validierung der Ergebnisse untersucht. Für eine erweiterte Einordnung der touristischen Multiplikatorwirkung in der Schwarzwaldregion wurde eine multiregionale Input-Output-Analyse durchgeführt, die sich auf die Gebietsabgrenzung der beiden Naturparke Schwarzwald Mitte/Nord und Südschwarzwald bezieht. Zur Quantifizierung der direkten Wirkungsebene wurden touristische Kenngrößen der amtlichen Statistik entnommen. Die Berechnung von direkten Wertschöpfungsquoten erfolgte gemäß ihrer Definition als die in der Region verbleibende Wertschöpfung am touristischen Produktionswert. Mittels der Input-Output-Analyse wurden die indirekten und induzierten Effekte des Tourismus im Biosphärengebiet Schwarzwald ermittelt. Aus den regionalen Input-Output-Tabellen wurden inverse Koeffizienten abgeleitet, welche die regionalökonomischen Multiplikatoren anzeigen. Zwei Multiplikatortypen wurden für die touristischen Kenngrößen Output, Wertschöpfung und Beschäftigung berechnet: Typ I-Multiplikatoren bemessen die indirekten Vorleistungseffekte touristischer Ausgaben. Typ II-Multiplikatoren inkludieren auch die induzierten Konsumeffekte. In einem mehrstufigen Prozess der Analyse von weiteren Fallbeispielen können regionalökonomische Multiplikatoren für verschiedene Gebietseinheiten validiert und so ganzheitlich abgestimmt für das deutsche Schutzgebietssystem adaptiert werden. Dadurch könnte die Input-Output-Analyse als neue Standardmethode für ein dauerhaftes regionalökonomisches Monitoring in deutschen Schutzgebieten etabliert werden. N2 - According to the Convention on Biological Diversity, protected areas are a spatial instrument for the protection of biodiversity. People benefit from protected areas, among other things, by using it as a tourist attraction. An estimated eight billion visits per year benefit from the nature experience offered by protected areas, resulting in a direct spending of US-$ 600 billion worldwide. Protected areas are thus important economic drivers of regional economies. In Germany, annually 53 million visitor days are registered in the countries national parks. Their daily expenditures generate an estimated gross sales of € 2.78 billion. The tourism value added amounts to € 1.45 billion. Another 65 million visitor days to German biosphere reserves generate a gross sales of € 2.94 billion. The income of 172,000 people depends on tourism in German national parks and biosphere reserves. In a first study on the regional economic effects of the Berchtesgaden National Park in 2002, the tourism value added analysis was established as a standard method in German protected area research. Over the years, it was modified to be able to apply a comparable, standardized procedure. International research manifests another standard for regional economic impact analysis of tourism in protected areas using economic input-output analysis. Protected area administrations in the USA, Canada, Brazil, Namibia, South Africa, and Finland conduct input-output analyses for their visitor monitoring. Compared to the value added analysis, the input-output approach can be considered the more valid approach because it provides a calculation framework with which indirect, intermediate, and induced consumption effects can be reliably quantified. The value added analysis, on the other hand, works with value added ratios that are generalized across all tourism economic sectors and for every geographic scale. Due to the lack of available data, the input-output approach has not been applied in Germany so far. A potential data source was opened by the US modelling company IMPLAN, which offered input-output tables for the regional level of the EU. IMPLAN data is also used by the US National Park Service. The present study is intended to further develop Germany’s protected areas regional economic impact analysis methodologies for adaptation to international standards. For this purpose, the input-output analysis is applied to the case study of the Black Forest Biosphere Reserve, whose regional economy offers a manageable analytical framework. The Black Forest National Park was examined as a comparative region to validate the results. For an extended classification of the tourism multiplier effect in the Black Forest region, a multi-regional input-output analysis was carried out, which refers to the area delineations of the two Nature Parks Black Forest Central/North and Southern Black Forest. To quantify the direct effects, tourism measures were taken from official statistics. Direct value added ratios were calculated according to their definition as spending remaining in the region as value added. The indirect and induced effects of tourism in the Black Forest Biosphere Reserve region were determined by the input-output analysis. Inverse coefficients were derived from the regional input-output tables, which indicate the regional economic multipliers. Two types of multipliers (Type I and Type II) were derived for the tourism parameters output, value added and employment: Type I multipliers measure the indirect effects of tourism expenditure; Type II multipliers also include the induced effects. In a multi-stage process of applying the input-output method to further case studies, it is possible to validate the multipliers for different spatial levels and thus establish it as a new standard method for the permanent regional economic monitoring in German protected areas. N2 - Die einzigartigen Natur- und Kulturlandschaften von Schutzgebieten sind weltweit bedeutende Destinationen für Tages- und Übernachtungsgäste. Die Ausgaben von Besuchern erzeugen ökonomische Effekte und sichern so regionale Wertschöpfung und Beschäftigung. Zur Analyse dieser regionalökonomischen Effekte des Tourismus in Schutzgebieten stehen heute verschiedene Methoden zur Verfügung. International ist die Input-Output-Analyse das etablierte Standardverfahren in mehreren Monitoringsystemen. Die Schutzgebietsforschung in Deutschland hat sich hingegen auf die Wertschöpfungsanalyse spezialisiert und geht dabei von generellen Annahmen der touristischen Multiplikatorwirkung aus. Vor dem Hintergrund einer Adaption an internationale Standards wird erstmals eine Input-Output-Analyse der regionalökonomischen Effekte des Tourismus in deutschen Schutzgebieten durchgeführt. Berechnungen auf Grundlage eines Input-Output-Modells liefern für das Fallbeispiel Biosphärengebiet Schwarzwald regionale und branchenspezifsche Multiplikatoren. Die Ergebnisse werden zum einen mit einer Input-Output-Analyse des Nationalparks Schwarzwald und zum anderen mit einer klassischen Wertschöpfungsanalyse verglichen. Darüber hinaus ermöglicht die Anwendung eines multiregionalen Ansatzes die Analyse der touristischen Multiplikatorwirkung in der gesamten Naturparkregion Schwarzwald Mitte/Nord und Südschwarzwald. T3 - Würzburger Geographische Arbeiten - 126 KW - Schutzgebiete KW - Tourismus KW - Regionalökonomie KW - Input-Output-Analyse KW - Wirkungsanalyse Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-316545 SN - 978-3-95826-216-4 SN - 978-3-95826-217-1 SN - 0510-9833 SN - 2194-3656 N1 - Parallel erschienen als Druckausgabe bei Würzburg University Press, ISBN 978-3-95826-216-4, 37,80 Euro PB - Würzburg University Press CY - Würzburg ER - TY - THES A1 - Reinermann, Sophie T1 - Earth Observation Time Series for Grassland Management Analyses – Development and large-scale Application of a Framework to detect Grassland Mowing Events in Germany T1 - Erdbeobachtungszeitserien zur Analyse der Grünlandbewirtschaftung – Entwicklung und großflächige Anwendung einer Prozessierungsarchitektur zur automatisierten Detektion von Grünlandmahden N2 - Grasslands shape many landscapes of the earth as they cover about one-third of its surface. They are home and provide livelihood for billions of people and are mainly used as source of forage for animals. However, grasslands fulfill many additional ecosystem functions next to fodder production, such as storage of carbon, water filtration, provision of habitats and cultural values. They play a role in climate change (mitigation) and in preserving biodiversity and ecosystem functions on a global scale. The degree to what these ecosystem functions are present within grassland ecosystems is largely determined by the management. Individual management practices and the use intensity influence the species composition as well as functions, like carbon storage, while higher use intensities (e.g. high mowing frequencies) usually show a negative impact. Especially in Central European countries, like in Germany, the determining influence of grassland management on its physiognomy and ecosystem functions leads to a large variability and small-scale alternations of grassland parcels. Large-scale information on the management and use intensity of grasslands is not available. Consequently, estimations of grassland ecosystem functions are challenging which, however, would be required for large-scale assessments of the status of grassland ecosystems and optimized management plans for the future. The topic of this thesis tackles this gap by investigating the major grassland management practice in Germany, which is mowing, for multiple years, in high spatial resolution and on a national scale. Earth Observation (EO) has the advantage of providing information of the earth’s surface on multi-temporal time steps. An extensive literature review on the use of EO for grassland management and production analyses, which was part of this thesis, showed that in particular research on grasslands consisting of small parcels with a large variety of management and use intensity, like common in Central Europe, is underrepresented. Especially the launch of the Sentinel satellites in the recent past now enables the analyses of such grasslands due to their high spatial and temporal resolution. The literature review specifically on the investigation of grassland mowing events revealed that most previous studies focused on small study areas, were exploratory, only used one sensor type and/or lacked a reference data set with a complete range of management options. Within this thesis a novel framework to detect grassland mowing events over large areas is presented which was applied and validated for the entire area of Germany for multiple years (2018–2021). The potential of both sensor types, optical (Sentinel-2) and Synthetic Aperture Radar (SAR) (Sentinel-1) was investigated regarding grassland mowing event detection. Eight EO parameters were investigated, namely the Enhanced Vegetation Index (EVI), the backscatter intensity and the interferometric (InSAR) temporal coherence for both available polarization modes (VV and VH), and the polarimetric (PolSAR) decomposition parameters Entropy, K0 and K1. An extensive reference data set was generated based on daily images of webcams distributed in Germany which resulted in mowing information for grasslands with the entire possible range of mowing frequencies – from one to six in Germany – and in 1475 reference mowing events for the four years of interest. For the first time a observation-driven mowing detection approach including data from Sentinel-2 and Sentinel-1 and combining the two was developed, applied and validated on large scale. Based on a subset of the reference data (13 grassland parcels with 44 mowing events) from 2019 the EO parameters were investigated and the detection algorithm developed and parameterized. This analysis showed that a threshold-based change detection approach based on EVI captured grassland mowing events best, which only failed during periods of clouds. All SAR-based parameters showed a less consistent behavior to mowing events, with PolSAR Entropy and InSAR Coherence VH, however, revealing the highest potential among them. A second, combined approach based on EVI and a SARbased parameter was developed and tested for PolSAR Entropy and InSAR VH. To avoid additional false positive detections during periods in which mowing events are anyhow reliably detected using optical data, the SAR-based mowing detection was only initiated during long gaps within the optical time series (< 25 days). Application and validation of these approaches in a focus region revealed that only using EVI leads to the highest accuracies (F1-Score = 0.65) as combining this approach with SAR-based detection led to a strong increase in falsely detected mowing events resulting in a decrease of accuracies (EVI + PolSAR ENT F1-Score = 0.61; EVI + InSAR COH F1-Score = 0.61). The mowing detection algorithm based on EVI was applied for the entire area of Germany for the years 2018-2021. It was revealed that the largest share of grasslands with high mowing frequencies (at least four mowing events) can be found in southern/south-eastern Germany. Extensively used grassland (mown up to two times) is distributed within the entire country with larger shares in the center and north-eastern parts of Germany. These patterns stay constant in general, but small fluctuations between the years are visible. Early mown grasslands can be found in southern/south-eastern Germany – in line with high mowing frequency areas – but also in central-western parts. The years 2019 and 2020 revealed higher accuracies based on the 1475 mowing events of the multi-annual validation data set (F1-Scores of 0.64 and 0.63), 2018 and 2021 lower ones (F1-Score of 0.52 and 0.50). Based on this new, unprecedented data set, potential influencing factors on the mowing dynamics were investigated. Therefore, climate, topography, soil data and information on conservation schemes were related to mowing dynamics for the year 2020, which showed a high number of valid observations and detection accuracy. It was revealed that there are no strong linear relationships between the mowing frequency or the timing of the first mowing event and the investigated variables. However, it was found that for intensive grassland usage certain climatic and topographic conditions have to be fulfilled, while extensive grasslands appear on the entire spectrum of these variables. Further, higher mowing frequencies occur on soils with influence of ground water and lower mowing frequencies in protected areas. These results show the complex interplay between grassland mowing dynamics and external influences and highlight the challenges of policies aiming to protect grassland ecosystem functions and their need to be adapted to regional circumstances. N2 - Grünland prägt viele Landschaften der Erde, da es etwa ein Drittel der Erdoberfläche bedeckt. Es ist Heimat und Lebensgrundlage für Milliarden von Menschen und wird hauptsächlich als Futterquelle für die Viehhaltung genutzt. Neben der Futterproduktion erfüllen Grünlandflächen jedoch viele weitere Ökosystemfunktionen, wie die Speicherung von Kohlenstoff, die Wasserfilterung, die Bereitstellung von Lebensräumen, als auch kulturelle Werte. Sie spielen eine Rolle bei der Abschwächung des Klimawandels und bei der Erhaltung der biologischen Vielfalt und der Ökosystemfunktionen auf globaler Ebene. Das Ausmaß, in dem diese Ökosystemfunktionen in Grünlandökosystemen vorhanden sind, wird weitgehend durch die Bewirtschaftung bestimmt. Einzelne Bewirtschaftungspraktiken und die Nutzungsintensität beeinflussen sowohl die Artenzusammensetzung als auch Funktionen wie die Kohlenstoffspeicherung, wobei höhere Nutzungsintensitäten (z. B. hohe Mähfrequenzen) in der Regel einen negativen Einfluss haben. Insbesondere in mitteleuropäischen Ländern wie Deutschland, führt der bestimmende Einfluss der Grünlandbewirtschaftung auf die Physiognomie und die Ökosystemfunktionen zu einer großen Variabilität und kleinräumigen Differenziertheit einzelner Grünlandflächen. Großräumige Informationen über die Bewirtschaftungs- und Nutzungsintensität von Grünland sind nicht verfügbar. Folglich sind Schätzungen der Ökosystemfunktionen von Grünland eine Herausforderung, die jedoch für großräumige Bewertungen des Zustands von Grünlandökosystemen und optimierte Bewirtschaftungspläne für die Zukunft erforderlich wären. Das Thema dieser Arbeit greift diese Lücke auf, indem es die wichtigste Grünlandbewirtschaftungsmethode in Deutschland, die Mahd, über mehrere Jahre, mit hoher räumlicher Auflösung und auf nationaler Ebene untersucht. Die Erdbeobachtung hat den Vorteil, Informationen über die Erdoberfläche in multitemporalen Zeitschritten zu liefern. Eine umfangreiche Literaturrecherche zur Nutzung von Erdbeobachtung für Grünlandmanagement und Produktion, welche Teil dieser Arbeit war, hat gezeigt, dass insbesondere die Forschung zu kleinparzelligem Grünland mit einer großen Vielfalt an Bewirtschaftungs- und Nutzungsintensitäten, wie in Mitteleuropa gängig, unterrepräsentiert ist. Insbesondere die vor wenigen Jahren erfolgte Start der Sentinel-Satellitenmissionen ermöglicht nun auch die Analyse solcher Grünlandflächen aufgrund der hohen räumlichen und zeitlichen Auflösung ihrer Aufnahmen. Die Literaturrecherche speziell zur Untersuchung von Mähereignissen auf Grünland ergab, dass die meisten bisherigen Studien sich auf kleine Untersuchungsgebiete konzentrierten, explorativ waren, nur einen Sensortyp verwendeten und/oder keinen Referenzdatensatz mit einer vollständigen Palette von Managementoptionen enthielten. Im Rahmen dieser Arbeit wird eine neuartige Methodik zur Erkennung von Grünlandmahdereignissen vorgestellt, welches über mehrere Jahre (2018-2021) flächendeckend in Deutschland angewendet und validiert wurde. Beide Sensortypen – optisch (Sentinel-2) und SAR (Sentinel-1) – wurden hinsichtlich ihres Potentials zur Detektion von Grünlandmahdereignissen ausgewertet. Acht EO-Parameter wurden untersucht, nämlich der Enhanced Vegetation Index (EVI), die Rückstreuintensität und die interferometrische zeitliche Kohärenz (InSAR) für beide verfügbaren Polarimetrien (VV und VH), sowie die polarimetrischen (PolSAR) Zerlegungsparameter Entropie, K0 und K1. Ein umfangreicher Referenzdatensatz wurde auf der Basis täglicher Bilder von Webcams generiert, welche über Deutschland verteilt sind. Dieser enthält Mahdinformationen für Grünland mit dem gesamten möglichen Spektrum an Mähfrequenzen – von eins bis sechs Mahden – und 1475 Referenz-Mähereignisse für die Untersuchungsjahre. Zum ersten Mal wurde ein Ansatz basierend auf tatsächlichen Beobachtungen zur Erkennung der Mahd entwickelt, angewandt und großflächig validiert, der Daten von Sentinel - 2 und Sentinel - 1 verwendet und beide miteinander kombiniert. Anhand eines Subset der Referenzdaten (13 Grünlandparzellen) wurden die EO-Parameter untersucht und der Algorithmus zur Mahddetektion entwickelt und parametrisiert. Die Analyse hat gezeigt, dass ein schwellenwertbasierter Ansatz zur Erkennung von Veränderungen auf der Grundlage des EVI die Ereignisse der Grünlandmahd am besten erfasst, und nur während Bewölkungsperioden Mahden nicht erfolgreich detektiert. Alle SAR-basierten Parameter zeigten ein inkonsistenteres Verhalten gegenüber Mähaktivitäten als EVI, wobei PolSAR Entropie und InSAR Kohärenz VH noch das höchste Potenzial aufwiesen. Ein zweiter, kombinierter Ansatz, der auf EVI und einem SAR Parameter basiert, wurde entwickelt und für PolSAR Entropie und InSAR VH getestet. Aufgrund vieler zusätzlicher Veränderungen, die in den Zeitreihen erkennbar sind, wurde die SAR-basierte Mahddetektion nur während langer Lücken in den optischen Zeitreihen (< 25 Tage) initiiert. Die Anwendung und Validierung dieser Ansätze in einer Fokusregion ergab, dass die Verwendung des EVI-Ansatzes zu den höchsten Genauigkeiten führt (F1-Score = 0.65), da die Kombination dieses Ansatzes mit der SAR-basierten Detektion zu einem starken Anstieg der falsch erkannten Mähereignisse und damit zu einer Abnahme der Genauigkeiten führte (EVI + PolSAR ENT F1-Score=0.61; EVI + InSAR COH F1-Score = 0.61). Der auf EVI basierende Mahddetektionsalgorithmus wurde für die gesamte Fläche Deutschlands für die Jahre 2018–2021 angewendet. Es zeigte sich, dass der größte Anteil an Grünland mit hoher Mähfrequenz (mindestens vier Mähereignisse) im Süden/Südosten Deutschlands zu finden ist. Extensiv genutztes Grünland (bis zu zweimal gemäht) ist über das gesamte Bundesgebiet verteilt, mit größeren Anteilen in der Mitte und im Nordosten Deutschlands. Diese Muster bleiben im Allgemeinen konstant, aber es sind kleine Schwankungen zwischen den Jahren erkennbar. Früh gemähtes Grünland findet sich in Süd-/Südostdeutschland - entsprechend den Gebieten mit hoher Mähfrequenz -, aber auch in Mittel- und Westdeutschland. Die Jahre 2019 und 2020 zeigen höhere Genauigkeiten (F1- Scores von 0.64 und 0.63), 2018 und 2021 niedrigere (F1-Score von 0.52 und 0.50). Darüber hinaus wurden mögliche Einflussfaktoren auf die Mahddynamik untersucht. So wurden Klima, Topografie, Bodendaten und Informationen über Schutzmaßnahmen mit der Mahddynamik für das Jahr 2020 in Verbindung gebracht, für welches eine hohe Anzahl gültiger Beobachtungen und eine hohe Erfassungsgenauigkeit erzielt werden konnten. Es zeigte sich, dass es keine starken linearen Beziehungen zwischen der Mahdhäufigkeit oder dem Zeitpunkt der ersten Mahd und den untersuchten Variablen gibt. Es wurde jedoch festgestellt, dass für eine intensive Grünlandnutzung bestimmte klimatische und topografische Bedingungen erfüllt sein müssen, wohingegen extensive Grünlandflächen im gesamten Spektrum dieser Variablen auftreten. Außerdem treten auf Böden mit Grundwassereinfluss höhere und in Schutzgebieten niedrigere Mahdhäufigkeiten auf. Diese Ergebnisse zeigen das komplexe Zusammenspiel zwischen der Dynamik der Grünlandmahd und äußeren Einflüssen und verdeutlichen die Herausforderungen in der gezielten Erstellung von Maßnahmen zum Schutz von Grünland-Ökosystemfunktionen und die Notwendigkeit diese regional anzupassen. KW - Grünland KW - Erdbeobachtung KW - Fernerkundung KW - Mähen KW - Grünlandnutzung KW - Zeitreihe KW - Erde KW - Sentinel-1 KW - Sentinel-2 KW - Enhanced Vegetation Index KW - PolSAR KW - InSAR Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-322737 ER - TY - JOUR A1 - Ha, Tuyen V. A1 - Huth, Juliane A1 - Bachofer, Felix A1 - Kuenzer, Claudia T1 - A review of Earth observation-based drought studies in Southeast Asia JF - Remote Sensing N2 - Drought is a recurring natural climatic hazard event over terrestrial land; it poses devastating threats to human health, the economy, and the environment. Given the increasing climate crisis, it is likely that extreme drought phenomena will become more frequent, and their impacts will probably be more devastating. Drought observations from space, therefore, play a key role in dissimilating timely and accurate information to support early warning drought management and mitigation planning, particularly in sparse in-situ data regions. In this paper, we reviewed drought-related studies based on Earth observation (EO) products in Southeast Asia between 2000 and 2021. The results of this review indicated that drought publications in the region are on the increase, with a majority (70%) of the studies being undertaken in Vietnam, Thailand, Malaysia and Indonesia. These countries also accounted for nearly 97% of the economic losses due to drought extremes. Vegetation indices from multispectral optical remote sensing sensors remained a primary source of data for drought monitoring in the region. Many studies (~21%) did not provide accuracy assessment on drought mapping products, while precipitation was the main data source for validation. We observed a positive association between spatial extent and spatial resolution, suggesting that nearly 81% of the articles focused on the local and national scales. Although there was an increase in drought research interest in the region, challenges remain regarding large-area and long time-series drought measurements, the combined drought approach, machine learning-based drought prediction, and the integration of multi-sensor remote sensing products (e.g., Landsat and Sentinel-2). Satellite EO data could be a substantial part of the future efforts that are necessary for mitigating drought-related challenges, ensuring food security, establishing a more sustainable economy, and the preservation of the natural environment in the region. KW - drought KW - drought impact KW - agricultural drought KW - hydrological drought KW - meteorological drought KW - earth observation KW - remote sensing KW - Southeast Asia KW - Mekong Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-286258 SN - 2072-4292 VL - 14 IS - 15 ER - TY - JOUR A1 - Koehler, Jonas A1 - Bauer, André A1 - Dietz, Andreas J. A1 - Kuenzer, Claudia T1 - Towards forecasting future snow cover dynamics in the European Alps — the potential of long optical remote-sensing time series JF - Remote Sensing N2 - Snow is a vital environmental parameter and dynamically responsive to climate change, particularly in mountainous regions. Snow cover can be monitored at variable spatial scales using Earth Observation (EO) data. Long-lasting remote sensing missions enable the generation of multi-decadal time series and thus the detection of long-term trends. However, there have been few attempts to use these to model future snow cover dynamics. In this study, we, therefore, explore the potential of such time series to forecast the Snow Line Elevation (SLE) in the European Alps. We generate monthly SLE time series from the entire Landsat archive (1985–2021) in 43 Alpine catchments. Positive long-term SLE change rates are detected, with the highest rates (5–8 m/y) in the Western and Central Alps. We utilize this SLE dataset to implement and evaluate seven uni-variate time series modeling and forecasting approaches. The best results were achieved by Random Forests, with a Nash–Sutcliffe efficiency (NSE) of 0.79 and a Mean Absolute Error (MAE) of 258 m, Telescope (0.76, 268 m), and seasonal ARIMA (0.75, 270 m). Since the model performance varies strongly with the input data, we developed a combined forecast based on the best-performing methods in each catchment. This approach was then used to forecast the SLE for the years 2022–2029. In the majority of the catchments, the shift of the forecast median SLE level retained the sign of the long-term trend. In cases where a deviating SLE dynamic is forecast, a discussion based on the unique properties of the catchment and past SLE dynamics is required. In the future, we expect major improvements in our SLE forecasting efforts by including external predictor variables in a multi-variate modeling approach. KW - forecast KW - Earth Observation KW - time series KW - Snow Line Elevation KW - Alps KW - mountains KW - environmental modeling KW - machine learning Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-288338 SN - 2072-4292 VL - 14 IS - 18 ER - TY - JOUR A1 - Job, Hubert A1 - Meyer, Constantin A1 - Coronado, Oriana A1 - Koblar, Simon A1 - Laner, Peter A1 - Omizzolo, Andrea A1 - Plassmann, Guido A1 - Riedler, Walter A1 - Vesely, Philipp A1 - Schindelegger, Arthur T1 - Open spaces in the European Alps — GIS-based analysis and implications for spatial planning from a transnational perspective JF - Land N2 - This article presents an open space concept of areas that are kept permanently free from buildings, technical infrastructure, and soil sealing. In the European Alps, space is scarce because of the topography; conflicts often arise between competing land uses such as permanent settlements and commercial activity. However, the presence of open spaces is important for carbon sequestration and the prevention of natural hazards, especially given climate change. A GIS-based analysis was conducted to identify an alpine-wide inventory of large-scale near-natural areas, or simply stated, open spaces. The method used identified the degree of infrastructure development for natural landscape units. Within the Alpine Convention perimeter, near-natural areas (with a degree of infrastructural development of up to 20%) account for a share of 51.5%. Only 14.5% of those areas are highly protected and are mostly located in high altitudes of over 1500 m or 2000 m above sea level. We advocate that the remaining Alpine open spaces must be preserved through the delimitation of more effective protection mechanisms, and green corridors should be safeguarded through spatial planning. To enhance the ecological connectivity of open spaces, there is the need for tailored spatial and sectoral planning strategies to prevent further landscape fragmentation and to coordinate new forms of land use for renewable energy production. KW - Alps KW - conservation KW - connectivity KW - fragmentation KW - GIS-analysis KW - land use KW - open spaces KW - protected areas KW - sectoral planning KW - spatial planning Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-288207 SN - 2073-445X VL - 11 IS - 9 ER - TY - THES A1 - Kraff, Nicolas Johannes T1 - Analyse raumzeitlicher Veränderungen und ontologische Kategorisierung morphologischer Armutserscheinungen - Eine globale Betrachtung mithilfe von Satellitenbildern und manueller Bildinterpretation T1 - Analysis of spatiotemporal changes and ontological categorization of morphological manifestations of poverty - A global view using satellite imagery and manual image interpretation N2 - Die städtische Umwelt ist in steter Veränderung, vor allem durch den Bau, aber auch durch die Zerstörung von städtischen Elementen. Die formelle Entwicklung ist ein Prozess mit langen Planungszeiträumen und die bebaute Landschaft wirkt daher statisch. Dagegen unterliegen informelle oder spontane Siedlungen aufgrund ihrer stets unvollendeten städtischen Form einer hohen Dynamik – so wird in der Literatur berichtet. Allerdings sind Dynamik und die morphologischen Merkmale der physischen Transformation in solchen Siedlungen, die städtische Armut morphologisch repräsentieren, auf globaler Ebene bisher kaum mit einer konsistenten Datengrundlage empirisch untersucht worden. Hier setzt die vorliegende Arbeit an. Unter der Annahme, dass die erforschte zeitliche Dynamik in Europa geringer ausfällt, stellt sich die generelle Frage nach einer katalogisierten Erfassung physischer Wohnformen von Armut speziell in Europa. Denn Wohnformen der Armut werden oft ausschließlich mit dem ‚Globalen Süden‘ assoziiert, insbesondere durch die Darstellung von Slums. Tatsächlich ist Europa sogar die Wiege der Begriffe ‚Slum‘ und ‚Ghetto‘, die vor Jahrhunderten zur Beschreibung von Missständen und Unterdrückung auftauchten. Bis heute weist dieser facettenreiche Kontinent eine enorme Vielfalt an physischen Wohnformen der Armut auf, die ihre Wurzeln in unterschiedlichen Politiken, Kulturen, Geschichten und Lebensstilen haben. Um über diese genannten Aspekte Aufschluss zu erlangen, bedarf es u.a. der Bildanalyse durch Satellitenbilder. Diese Arbeit wird daher mittels Fernerkundung bzw. Erdbeobachtung (EO) sowie zusätzlicher Literaturrecherchen und einer empirischen Erhebung erstellt. Um Unsicherheiten konzeptionell und in der Erfassung offenzulegen, ist die Methode der manuellen Bildinterpretation von Armutsgebieten kritisch zu hinterfragen. Das übergeordnete Ziel dieser Arbeit ist eine bessere Wissensbasis über Armut zu schaffen, um Maßnahmen zur Reduzierung von Armut entwickeln zu können. Die Arbeit dient dabei als eine Antwort auf die Nachhaltigkeitsziele der Vereinten Nationen. Es wird Grundlagenforschung betrieben, indem Wissenslücken in der Erdbeobachtung zu physisch-baulichen bzw. morphologischen Erscheinungen von Armut auf Gebäude-Ebene explorativ analysiert werden. Die Arbeit wird in drei Forschungsthemen bzw. Studienteile untergliedert: Ziel des ersten Studienteils ist die globale raumzeitliche Erfassung von Dynamiken durch Anknüpfung an bisherige Kategorisierungen von Armutsgebieten. Die bisherige Wissenslücke soll gefüllt werden, indem über einen Zeitraum von etwa sieben Jahren in 16 dokumentierten Manifestationen städtischer Armut anhand von Erdbeobachtungsdaten eine zeitliche Analyse der bebauten Umwelt durchgeführt wird. Neben einer global verteilten Gebietsauswahl wird die visuelle Bildinterpretation (MVII) unter Verwendung von hochauflösenden optischen Satellitendaten genutzt. Dies geschieht in Kombination mit in-situ- und Google Street View-Bildern zur Ableitung von 3D-Stadtmodellen. Es werden physische Raumstrukturen anhand von sechs räumlichen morphologischen Variablen gemessen: Anzahl, Größe, Höhe, Ausrichtung und Dichte der Gebäude sowie Heterogenität der Bebauung. Diese ‚temporale Analyse‘ zeigt zunächst sowohl inter- als auch intra-urbane Unterschiede. Es lassen sich unterschiedliche, aber generell hohe morphologische Dynamiken zwischen den Untersuchungsgebieten finden. Dies drückt sich in vielfältiger Weise aus: von abgerissenen und rekonstruierten Gebieten bis hin zu solchen, wo Veränderungen innerhalb der gegebenen Strukturen auftreten. Geographisch gesehen resultiert in der Stichprobe eine fortgeschrittene Dynamik, insbesondere in Gebieten des Globalen Südens. Gleichzeitig lässt sich eine hohe räumliche Variabilität der morphologischen Transformationen innerhalb der untersuchten Gebiete beobachten. Trotz dieser teilweise hohen morphologischen Dynamik sind die räumlichen Muster von Gebäudefluchten, Straßen und Freiflächen überwiegend konstant. Diese ersten Ergebnisse deuten auf einen geringen Wandel in Europa hin, weshalb diese europäischen Armutsgebiete im folgenden Studienteil von Grund auf erhoben und kategorisiert werden. Ziel des zweiten Studienteils ist die Erschaffung einer neuen Kategorisierung, speziell für das in der Wissenschaft unterrepräsentierte Europa. Die verschiedenen Formen nicht indizierter Wohnungsmorphologien werden erforscht und kategorisiert, um das bisherige globale wissenschaftliche ontologische Portfolio für Europa zu erweitern. Hinsichtlich dieses zweiten Studienteils bietet eine Literaturrecherche mit mehr als 1.000 gesichteten Artikeln die weitere Grundlage für den folgenden Fokus auf Europa. Auf der Recherche basierend werden mittels der manuellen visuellen Bildinterpretation (engl.: MVII) erneut Satellitendaten zur Erfassung der physischen Morphologien von Wohnformen genutzt. Weiterhin kommen selbst definierte geographische Indikatoren zu Lage, Struktur und formellem Status zum Einsatz. Darüber hinaus werden gesellschaftliche Hintergründe, die durch Begriffe wie ‚Ghetto‘, ‚Wohnwagenpark‘, ‚ethnische Enklave‘ oder ‚Flüchtlingslager‘ beschrieben werden, recherchiert und implementiert. Sie sollen als Erklärungsansatz für Armutsviertel in Europa dienen. Die Stichprobe der europäischen, insgesamt aber unbekannten Grundgesamtheit verdeutlicht eine große Vielfalt an physischen Formen: Es wird für Europa eine neue Kategorisierung von sechs Hauptklassen entwickelt, die von ‚einfachsten Wohnstätten‘ (z. B. Zelten) über ‚behelfsmäßige Unterkünfte ‘ (z. B. Baracken, Container) bis hin zu ‚mehrstöckigen Bauten‘ - als allgemeine Taxonomie der Wohnungsnot in Europa - reicht. Die Untersuchung zeigt verschiedene Wohnformen wie z. B. unterirdische oder mobile Typen, verfallene Wohnungen oder große Wohnsiedlungen, die die Armut im Europa des 21. Jahrhunderts widerspiegeln. Über die Wohnungsmorphologie hinaus werden diese Klassen durch die Struktur und ihren rechtlichen Status beschrieben - entweder als geplante oder als organisch-gewachsene bzw. weiterhin als formelle, informelle oder hybride (halblegale) Formen. Geographisch lassen sich diese ärmlichen Wohnformen sowohl in städtischen als auch in ländlichen Gebieten finden, mit einer Konzentration in Südeuropa. Der Hintergrund bei der Mehrheit der Morphologien betrifft Flüchtlinge, ethnische Minderheiten und sozioökonomisch benachteiligte Menschen - die ‚Unterprivilegierten‘. Ziel des dritten Studienteils ist eine kritische Analyse der Methode. Zur Erfassung all dieser Siedlungen werden heutzutage Satellitenbilder aufgrund der Fortschritte bei den Bildklassifizierungsmethoden meist automatisch ausgewertet. Dennoch spielt die MVII noch immer eine wichtige Rolle, z.B. um Trainingsdaten für Machine-Learning-Algorithmen zu generieren oder für Validierungszwecke. In bestimmten städtischen Umgebungen jedoch, z.B. solchen mit höchster Dichte und struktureller Komplexität, fordern spektrale und textur-basierte Verflechtungen von überlappenden Dachstrukturen den menschlichen Interpreten immer noch heraus, wenn es darum geht einzelne Gebäudestrukturen zu erfassen. Die kognitive Wahrnehmung und die Erfahrung aus der realen Welt sind nach wie vor unumgänglich. Vor diesem Hintergrund zielt die Arbeit methodisch darauf ab, Unsicherheiten speziell bei der Kartierung zu quantifizieren und zu interpretieren. Kartiert werden Dachflächen als ‚Fußabdrücke‘ solcher Gebiete. Der Fokus liegt dabei auf der Übereinstimmung zwischen mehreren Bildinterpreten und welche Aspekte der Wahrnehmung und Elemente der Bildinterpretation die Kartierung beeinflussen. Um letztlich die Methode der MVII als drittes Ziel selbstkritisch zu reflektieren, werden Experimente als sogenannte ‚Unsicherheitsanalyse‘ geschaffen. Dabei digitalisieren zehn Testpersonen bzw. Probanden/Interpreten sechs komplexe Gebiete. Hierdurch werden quantitative Informationen über räumliche Variablen von Gebäuden erzielt, um systematisch die Konsistenz und Kongruenz der Ergebnisse zu überprüfen. Ein zusätzlicher Fragebogen liefert subjektive qualitative Informationen über weitere Schwierigkeiten. Da die Grundlage der hierfür bisher genutzten Kategorisierungen auf der subjektiven Bildinterpretation durch den Menschen beruht, müssen etwaige Unsicherheiten und damit Fehleranfälligkeiten offengelegt werden. Die Experimente zu dieser Unsicherheitsanalyse erfolgen quantifiziert und qualifiziert. Es lassen sich generell große Unterschiede zwischen den Kartierungsergebnissen der Probanden, aber eine hohe Konsistenz der Ergebnisse bei ein und demselben Probanden feststellen. Steigende Abweichungen korrelieren mit einer steigenden baustrukturellen (morphologischen) Komplexität. Ein hoher Grad an Individualität bei den Probanden äußert sich in Aspekten wie z.B. Zeitaufwand beim Kartieren, in-situ Vorkenntnissen oder Vorkenntnissen beim Umgang mit Geographischen Informationssystemen (GIS). Nennenswert ist hierbei, dass die jeweilige Datenquelle das Kartierungsverfahren meist beeinflusst. Mit dieser Studie soll also auch an der Stelle der angewandten Methodik eine weitere Wissenslücke gefüllt werden. Die bisherige Forschung komplexer urbaner Areale unter Nutzung der manuellen Bildinterpretation implementiert oftmals keine Unsicherheitsanalyse oder Quantifizierung von Kartierungsfehlern. Fernerkundungsstudien sollten künftig zur Validierung nicht nur zweifelsfrei auf MVII zurückgreifen können, sondern vielmehr sind Daten und Methoden notwendig, um Unsicherheiten auszuschließen. Zusammenfassend trägt diese Arbeit zur bisher wenig erforschten morphologischen Dynamik von Armutsgebieten bei. Es werden inter- wie auch intra-urbane Unterschiede auf globaler Ebene präsentiert. Dabei sind allgemein hohe morphologische Transformationen zwischen den selektierten Gebieten festzustellen. Die Ergebnisse deuten auf einen grundlegenden Kenntnismangel in Europa hin, weshalb an dieser Stelle angeknüpft wird. Eine über Europa verteilte Stichprobe erlaubt eine neue morphologische Kategorisierung der großen Vielfalt an gefundenen physischen Formen. Die Menge an Gebieten erschließt sich in einer unbekannten Grundgesamtheit. Zur Datenaufbereitung bisheriger Analysen müssen Satellitenbilder manuell interpretiert werden. Das Verfahren birgt Unsicherheiten. Als kritische Selbstreflexion zeigt eine Reihe von Experimenten signifikante Unterschiede zwischen den Ergebnissen der Probanden auf, verdeutlicht jedoch bei ein und derselben Person Beständigkeit. N2 - Through construction as well as destruction of urban elements, the morphological manifestation of cities is in constant change. As reported in literature, there is a difference between formal and informal development: Whereas formal planning periods lead to a built landscape that appears static, unfinished informal urban forms reflect high dynamics leading to informal or spontaneous settlements. With respect to data base and scale, these kinds of settlements, which morphologically represent urban poverty, have hardly been subject to empirical studies that analyze their dynamics and morphological characteristics of physical transformation consistently. This is where the present work begins. Assuming that the temporal dynamics explored are less pronounced in Europe, the general question of indexing physical housing forms of poverty arises specifically in Europe. This is because housing forms of poverty are often exclusively associated with the 'Global South', especially through the representation of slums. In fact, Europe is even the cradle of the terms 'slum' and 'ghetto', which emerged centuries ago to describe grievances and oppression. To this day, this multifaceted continent exhibits a tremendous variety of physical housing forms of poverty that have their roots in different histories, cultures, policies and lifestyles. To gain insight into these aforementioned aspects requires, among other things, image analysis through satellite imagery. Therefore, this work is done through remote sensing or Earth Observation (EO) as well as additional literature review and an empirical survey. In order to reveal uncertainties conceptually and in the coverage, the method of manual image interpretation of poverty areas has to be critically questioned. The overall goal of this work is to create a better knowledge base about poverty in order to be able to develop measures to reduce poverty. The work serves as a response to the United Nations Sustainable Development Goals. Basic research is carried out by exploratively analyzing knowledge gaps in Earth observation on physical-structural or morphological manifestations of poverty at the building level. The work is divided into three research themes or study parts: The aim of the first part of the study is to capture global spatiotemporal dynamics by linking to established categorizations of poverty areas. The knowledge gap will be filled by conducting a temporal analysis of the built environment over a period of seven years, in 16 documented manifestations of urban poverty using earth observation data. In addition to a globally distributed area selection, visual image interpretation (MVII) and very high-resolution optical satellite data are used. In order to derive 3D city models, MVII is applied combining in-situ and Google Street View imagery. Six spatial morphological variables are applied: number, size, height, orientation and density of buildings as well as heterogeneity of the built-up pattern. In this way, physical spatial structures are measured. Inter-urban and intra-urban differences are demonstrated in the temporal analysis. Findings show different, yet generally high morphological dynamics across the study areas. The variety comprises demolished and reconstructed areas as well as such, where changes occur within the given structures. Results demonstrate increased dynamics, especially in areas of the Global South. At the intra-urban scale, morphological transformations show a high spatial variability simultaneously. However, in spite of these findings of high dynamics, the spatial patterns are mostly constant, including building alignments, streets and open spaces. These initial results indicate little change in Europe, which is why these European poverty areas are surveyed and categorized from scratch in the following part of the study. The aim of the second part of the study is to create a new categorization, specifically for Europe, which is underrepresented in science. In order to expand the existing global scientific ontological inventory for Europe, different forms of non-indexed residential morphologies are detected and categorized. Regarding this second part of the study, a literature search with more than 1,000 articles reviewed provides the further basis for the following focus on Europe. Based on the research, satellite data are again used by means of manual visual image interpretation (MVII) to obtain the physical morphologies of housing types. Furthermore, self-defined geographical indicators of location, structure and formal status are used. Additionally, social backgrounds described by terms like 'ghetto', 'trailer park', 'ethnic enclave' or 'refugee camp' are researched and implemented. They are intended to serve as an explanatory approach to poverty neighborhoods in Europe. The sample for Europe, however is an overall unknown basic population and illustrates a wide variety of physical forms: A new categorization of six main classes is developed for Europe, ranging from 'simplest dwellings' (e.g., tents) to 'makeshift shelters ' (e.g., shacks, containers) to 'multi-story structures' - as a general taxonomy of housing deprivation in Europe. The study discloses different housing types such as underground or mobile types, dilapidated dwellings or large housing estates that reflect poverty in 21st century Europe. Next to housing morphology, these classes are described by structural settlement patterns and their legal status - either as planned or organic-grown, or further as formal, informal or hybrid (semi-legal) forms. From a geographic point of view, a concentration of these poor housing forms can be found in Southern Europe and all across Europe in urban and rural areas. The societal background of the most morphologies concern the 'underprivileged' who are represented, by refugees, ethnic minorities and socioeconomically disadvantaged people. The aim of the third part of the study is a critical analysis of the method. To capture all these settlements and due to the advances in image classification methods, satellite images are typically analyzed automatically nowadays. Still, MVII is important, e.g., for the purpose of validation or to generate training data for machine learning algorithms. Thus, cognitive perception and real-world experience are still unavoidable. Nevertheless, such urban environments with highest density and structural complexity challenge the human interpreter, when it comes to detecting individual building structures because spectral and texture-based restrictions of overlapping roof structures encounter building delineation. Considering that, the aim of this work is to quantify and interpret uncertainties methodologically specifically in mapping. Roof areas are mapped as 'footprints' of such areas. One focus is the agreement between multiple image interpreters. The other focus explores influences by interpreter perception and different elements of image interpretation. Finally, to reflect self-critically on the method of MVII as a third goal, experiments are created as a so-called 'uncertainty analysis'. In these experiments, ten test persons respectively interpreters map six complex areas and produce quantitative data of spatial variables of buildings. This data allows to assess the consistency and congruence of the results in a systematical way. Additionally, a questionnaire provides subjective qualitative information about further difficulties. Since the basis of the categorizations used for this purpose so far is based on subjective image interpretation by humans, any uncertainties and thus error-proneness have to be revealed. The experiments for this uncertainty analysis are quantified and qualified. On the one hand results show remarkable differences between the mapping results of the interpreters. On the other hand, the results for one and the same interpreter reveal high consistency. Another finding demonstrates a correlation between increasing deviations among interpreters and increasing structural (morphological) complexity of the selected areas. Considering the qualitative responses, aspects such as time spent for mapping, prior in-situ knowledge, or prior knowledge of using Geographic Information Systems (GIS) reveal a high degree of individuality among the interpreters. It is noteworthy that particularly ‘data source’ usually influences the mapping procedure. Thus, this study also aims to fill another knowledge gap at the point of applied methodology. Uncertainty analyses often are neither part of research studies of complex urban areas using MVII, nor quantification of mapping errors. In future, remote sensing studies should not only be able to rely on MVII without doubt for validation, but rather data and methods are needed to rule out uncertainty. In summary, this work contributes to the hitherto little researched morphological dynamics of poverty areas. Inter- as well as intra-urban differences on a global scale are presented. Generally, high morphological transformations between the selected areas can be observed. The results indicate a fundamental lack of knowledge in Europe, which is why this work continues at this point. A sample distributed all across Europe allows a new morphological categorization of the large variety of physical forms found. The number of areas opens up in an unknown basic population. For data preparation of previous analyses, satellite images have to be interpreted manually. The procedure involves uncertainties. As a critical self-reflection, a series of experiments reveal significant differences between interpreters’ results, but illustrates consistency in the same subject. KW - Slum KW - Armutsviertel KW - Fernerkundung KW - Stadtgeographie KW - Bildinterpretation KW - physische Morphologie KW - urbane Strukturanalyse KW - raumzeitliche Dynamik KW - Manuelle visuelle Bildinterpretation KW - Wohnformen der Armut KW - Europa KW - Bildbetrachtung Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-320264 ER - TY - JOUR A1 - Dong, Ruirui A1 - Wurm, Michael A1 - Taubenböck, Hannes T1 - Seasonal and diurnal variation of land surface temperature distribution and its relation to land use/land cover patterns JF - International Journal of Environmental Research and Public Health N2 - The surface urban heat island (SUHI) affects the quality of urban life. Because varying urban structures have varying impacts on SUHI, it is crucial to understand the impact of land use/land cover characteristics for improving the quality of life in cities and urban health. Satellite-based data on land surface temperatures (LST) and derived land use/cover pattern (LUCP) indicators provide an efficient opportunity to derive the required data at a large scale. This study explores the seasonal and diurnal variation of spatial associations from LUCP and LST employing Pearson correlation and ordinary least squares regression analysis. Specifically, Landsat-8 images were utilized to derive LSTs in four seasons, taking Berlin as a case study. The results indicate that: (1) in terms of land cover, hot spots are mainly distributed over transportation, commercial and industrial land in the daytime, while wetlands were identified as hot spots during nighttime; (2) from the land composition indicators, the normalized difference built-up index (NDBI) showed the strongest influence in summer, while the normalized difference vegetation index (NDVI) exhibited the biggest impact in winter; (3) from urban morphological parameters, the building density showed an especially significant positive association with LST and the strongest effect during daytime. KW - surface urban heat island (SUHI) KW - land use/cover pattern (LUCP) KW - land surface temperature (LST) KW - seasonal KW - diurnal Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-290393 SN - 1660-4601 VL - 19 IS - 19 ER - TY - JOUR A1 - Buchelt, Sebastian A1 - Blöthe, Jan Henrik A1 - Kuenzer, Claudia A1 - Schmitt, Andreas A1 - Ullmann, Tobias A1 - Philipp, Marius A1 - Kneisel, Christof T1 - Deciphering small-scale seasonal surface dynamics of rock glaciers in the Central European Alps using DInSAR time series JF - Remote Sensing N2 - The Essential Climate Variable (ECV) Permafrost is currently undergoing strong changes due to rising ground and air temperatures. Surface movement, forming characteristic landforms such as rock glaciers, is one key indicator for mountain permafrost. Monitoring this movement can indicate ongoing changes in permafrost; therefore, rock glacier velocity (RGV) has recently been added as an ECV product. Despite the increased understanding of rock glacier dynamics in recent years, most observations are either limited in terms of the spatial coverage or temporal resolution. According to recent studies, Sentinel-1 (C-band) Differential SAR Interferometry (DInSAR) has potential for monitoring RGVs at high spatial and temporal resolutions. However, the suitability of DInSAR for the detection of heterogeneous small-scale spatial patterns of rock glacier velocities was never at the center of these studies. We address this shortcoming by generating and analyzing Sentinel-1 DInSAR time series over five years to detect small-scale displacement patterns of five high alpine permafrost environments located in the Central European Alps on a weekly basis at a range of a few millimeters. Our approach is based on a semi-automated procedure using open-source programs (SNAP, pyrate) and provides East-West displacement and elevation change with a ground sampling distance of 5 m. Comparison with annual movement derived from orthophotos and unpiloted aerial vehicle (UAV) data shows that DInSAR covers about one third of the total movement, which represents the proportion of the year suited for DInSAR, and shows good spatial agreement (Pearson R: 0.42–0.74, RMSE: 4.7–11.6 cm/a) except for areas with phase unwrapping errors. Moreover, the DInSAR time series unveils spatio-temporal variations and distinct seasonal movement dynamics related to different drivers and processes as well as internal structures. Combining our approach with in situ observations could help to achieve a more holistic understanding of rock glacier dynamics and to assess the future evolution of permafrost under changing climatic conditions. KW - Sentinel-1 KW - DInSAR KW - rock glaciers KW - seasonal dynamics KW - periglacial KW - feature tracking Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-362939 SN - 2072-4292 VL - 15 IS - 12 ER - TY - JOUR A1 - Fleuchaus, Paul A1 - Blum, Philipp A1 - Wilde, Martina A1 - Terhorst, Birgit A1 - Butscher, Christoph T1 - Retrospective evaluation of landslide susceptibility maps and review of validation practice JF - Environmental Earth Sciences N2 - Despite the widespread application of landslide susceptibility analyses, there is hardly any information about whether or not the occurrence of recent landslide events was correctly predicted by the relevant susceptibility maps. Hence, the objective of this study is to evaluate four landslide susceptibility maps retrospectively in a landslide-prone area of the Swabian Alb (Germany). The predictive performance of each susceptibility map is evaluated based on a landslide event triggered by heavy rainfalls in the year 2013. The retrospective evaluation revealed significant variations in the predictive accuracy of the analyzed studies. Both completely erroneous as well as very precise predictions were observed. These differences are less attributed to the applied statistical method and more to the quality and comprehensiveness of the used input data. Furthermore, a literature review of 50 peer-reviewed articles showed that most landslide susceptibility analyses achieve very high validation scores. 73% of the analyzed studies achieved an area under curve (AUC) value of at least 80%. These high validation scores, however, do not reflect the high uncertainty in statistical susceptibility analysis. Thus, the quality assessment of landslide susceptibility maps should not only comprise an index-based, quantitative validation, but also an additional qualitative plausibility check considering local geomorphological characteristics and local landslide mechanisms. Finally, the proposed retrospective evaluation approach cannot only help to assess the quality of susceptibility maps and demonstrate the reliability of such statistical methods, but also identify issues that will enable the susceptibility maps to be improved in the future. KW - landslides KW - hazard maps KW - predictive performance KW - review KW - Swabian Alb Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-308911 SN - 1866-6280 SN - 1866-6299 VL - 80 ER - TY - JOUR A1 - Schamel, Johannes T1 - A demographic perspective on the spatial behaviour of hikers in mountain areas: the example of Berchtesgaden National Park JF - eco.mont - Journal on Protected Mountain Areas Research and Management N2 - In Germany, as in many Western societies, demographic change will lead to a higher number of senior visitors to natural recreational areas and national parks. Given the high physiological requirements of many outdoor recreation activities, especially in mountain areas, it seems likely that demographic change will affect the spatial behaviour of national park visitors, which may pose a challenge to the management of these areas. With the help of GPS tracking and a standardized questionnaire (n=481), this study empirically investigates the spatial behaviour of demographic age brackets in Berchtesgaden National Park (NP) and the potential effects of demographic change on the use of the area. Cluster analysis revealed four activity types in the study area. More than half of the groups with visitors aged 60 and older belong to the activity type of Walker. KW - geography KW - demographic change KW - spatial behaviour KW - GPS tracking KW - outdoor recreation KW - Berchtesgaden NP Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-172128 SN - 2073-1558 VL - 9 IS - Special issue ER - TY - JOUR A1 - Dirscherl, Mariel A1 - Dietz, Andreas J. A1 - Kneisel, Christof A1 - Kuenzer, Claudia T1 - Automated mapping of Antarctic supraglacial lakes using a Machine Learning approach JF - Remote Sensing N2 - Supraglacial lakes can have considerable impact on ice sheet mass balance and global sea-level-rise through ice shelf fracturing and subsequent glacier speedup. In Antarctica, the distribution and temporal development of supraglacial lakes as well as their potential contribution to increased ice mass loss remains largely unknown, requiring a detailed mapping of the Antarctic surface hydrological network. In this study, we employ a Machine Learning algorithm trained on Sentinel-2 and auxiliary TanDEM-X topographic data for automated mapping of Antarctic supraglacial lakes. To ensure the spatio-temporal transferability of our method, a Random Forest was trained on 14 training regions and applied over eight spatially independent test regions distributed across the whole Antarctic continent. In addition, we employed our workflow for large-scale application over Amery Ice Shelf where we calculated interannual supraglacial lake dynamics between 2017 and 2020 at full ice shelf coverage. To validate our supraglacial lake detection algorithm, we randomly created point samples over our classification results and compared them to Sentinel-2 imagery. The point comparisons were evaluated using a confusion matrix for calculation of selected accuracy metrics. Our analysis revealed wide-spread supraglacial lake occurrence in all three Antarctic regions. For the first time, we identified supraglacial meltwater features on Abbott, Hull and Cosgrove Ice Shelves in West Antarctica as well as for the entire Amery Ice Shelf for years 2017–2020. Over Amery Ice Shelf, maximum lake extent varied strongly between the years with the 2019 melt season characterized by the largest areal coverage of supraglacial lakes (~763 km\(^2\)). The accuracy assessment over the test regions revealed an average Kappa coefficient of 0.86 where the largest value of Kappa reached 0.98 over George VI Ice Shelf. Future developments will involve the generation of circum-Antarctic supraglacial lake mapping products as well as their use for further methodological developments using Sentinel-1 SAR data in order to characterize intraannual supraglacial meltwater dynamics also during polar night and independent of meteorological conditions. In summary, the implementation of the Random Forest classifier enabled the development of the first automated mapping method applied to Sentinel-2 data distributed across all three Antarctic regions. KW - Antarctica KW - Antarctic ice sheet KW - supraglacial lakes KW - surface melt KW - hydrology KW - ice sheet dynamics KW - sentinel-2 KW - remote sensing KW - random forest KW - machine learning Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-203735 SN - 2072-4292 VL - 12 IS - 7 ER - TY - THES A1 - Üreyen, Soner T1 - Multivariate Time Series for the Analysis of Land Surface Dynamics - Evaluating Trends and Drivers of Land Surface Variables for the Indo-Gangetic River Basins T1 - Multivariate Zeitreihen zur Analyse von Landoberflächendynamiken - Auswertung von Trends und Treibern von Landoberflächenvariablen für Flusseinzugsgebiete der Indus-Ganges Ebene N2 - The investigation of the Earth system and interplays between its components is of utmost importance to enhance the understanding of the impacts of global climate change on the Earth's land surface. In this context, Earth observation (EO) provides valuable long-term records covering an abundance of land surface variables and, thus, allowing for large-scale analyses to quantify and analyze land surface dynamics across various Earth system components. In view of this, the geographical entity of river basins was identified as particularly suitable for multivariate time series analyses of the land surface, as they naturally cover diverse spheres of the Earth. Many remote sensing missions with different characteristics are available to monitor and characterize the land surface. Yet, only a few spaceborne remote sensing missions enable the generation of spatio-temporally consistent time series with equidistant observations over large areas, such as the MODIS instrument. In order to summarize available remote sensing-based analyses of land surface dynamics in large river basins, a detailed literature review of 287 studies was performed and several research gaps were identified. In this regard, it was found that studies rarely analyzed an entire river basin, but rather focused on study areas at subbasin or regional scale. In addition, it was found that transboundary river basins remained understudied and that studies largely focused on selected riparian countries. Moreover, the analysis of environmental change was generally conducted using a single EO-based land surface variable, whereas a joint exploration of multivariate land surface variables across spheres was found to be rarely performed. To address these research gaps, a methodological framework enabling (1) the preprocessing and harmonization of multi-source time series as well as (2) the statistical analysis of a multivariate feature space was required. For development and testing of a methodological framework that is transferable in space and time, the transboundary river basins Indus, Ganges, Brahmaputra, and Meghna (IGBM) in South Asia were selected as study area, having a size equivalent to around eight times the size of Germany. These basins largely depend on water resources from monsoon rainfall and High Mountain Asia which holds the largest ice mass outside the polar regions. In total, over 1.1 billion people live in this region and in parts largely depend on these water resources which are indispensable for the world's largest connected irrigated croplands and further domestic needs as well. With highly heterogeneous geographical settings, these river basins allow for a detailed analysis of the interplays between multiple spheres, including the anthroposphere, biosphere, cryosphere, hydrosphere, lithosphere, and atmosphere. In this thesis, land surface dynamics over the last two decades (December 2002 - November 2020) were analyzed using EO time series on vegetation condition, surface water area, and snow cover area being based on MODIS imagery, the DLR Global WaterPack and JRC Global Surface Water Layer, as well as the DLR Global SnowPack, respectively. These data were evaluated in combination with further climatic, hydrological, and anthropogenic variables to estimate their influence on the three EO land surface variables. The preprocessing and harmonization of the time series was conducted using the implemented framework. The resulting harmonized feature space was used to quantify and analyze land surface dynamics by means of several statistical time series analysis techniques which were integrated into the framework. In detail, these methods involved (1) the calculation of trends using the Mann-Kendall test in association with the Theil-Sen slope estimator, (2) the estimation of changes in phenological metrics using the Timesat tool, (3) the evaluation of driving variables using the causal discovery approach Peter and Clark Momentary Conditional Independence (PCMCI), and (4) additional correlation tests to analyze the human influence on vegetation condition and surface water area. These analyses were performed at annual and seasonal temporal scale and for diverse spatial units, including grids, river basins and subbasins, land cover and land use classes, as well as elevation-dependent zones. The trend analyses of vegetation condition mostly revealed significant positive trends. Irrigated and rainfed croplands were found to contribute most to these trends. The trend magnitudes were particularly high in arid and semi-arid regions. Considering surface water area, significant positive trends were obtained at annual scale. At grid scale, regional and seasonal clusters with significant negative trends were found as well. Trends for snow cover area mostly remained stable at annual scale, but significant negative trends were observed in parts of the river basins during distinct seasons. Negative trends were also found for the elevation-dependent zones, particularly at high altitudes. Also, retreats in the seasonal duration of snow cover area were found in parts of the river basins. Furthermore, for the first time, the application of the causal discovery algorithm on a multivariate feature space at seasonal temporal scale revealed direct and indirect links between EO land surface variables and respective drivers. In general, vegetation was constrained by water availability, surface water area was largely influenced by river discharge and indirectly by precipitation, and snow cover area was largely controlled by precipitation and temperature with spatial and temporal variations. Additional analyses pointed towards positive human influences on increasing trends in vegetation greenness. The investigation of trends and interplays across spheres provided new and valuable insights into the past state and the evolution of the land surface as well as on relevant climatic and hydrological driving variables. Besides the investigated river basins in South Asia, these findings are of great value also for other river basins and geographical regions. N2 - Die Untersuchung von Erdsystemkomponenten und deren Wechselwirkungen ist von großer Relevanz, um das Prozessverständnis sowie die Auswirkungen des globalen Klimawandels auf die Landoberfläche zu verbessern. In diesem Zusammenhang liefert die Erdbeobachtung (EO) wertvolle Langzeitaufnahmen zu einer Vielzahl an Landoberflächenvariablen. Diese können als Indikator für die Erdsystemkomponenten genutzt werden und sind essenziell für großflächige Analysen. Flusseinzugsgebiete sind besonders geeignet um Landoberflächendynamiken mit multivariaten Zeitreihen zu analysieren, da diese verschiedene Sphären des Erdsystems umfassen. Zur Charakterisierung der Landoberfläche stehen zahlreiche EO-Missionen mit unterschiedlichen Eigenschaften zur Verfügung. Nur einige wenige Missionen gewährleisten jedoch die Erstellung von räumlich und zeitlich konsistenten Zeitreihen mit äquidistanten Beobachtungen über großräumige Untersuchungsgebiete, wie z.B. die MODIS Sensoren. Um bisherige EO-Analysen zu Landoberflächendynamiken in großen Flusseinzugsgebieten zu untersuchen, wurde eine Literaturrecherche durchgeführt, wobei mehrere Forschungslücken identifiziert wurden. Studien untersuchten nur selten ein ganzes Einzugsgebiet, sondern konzentrierten sich lediglich auf Teilgebietsgebiete oder regionale Untersuchungsgebiete. Darüber hinaus wurden transnationale Einzugsgebiete nur unzureichend analysiert, wobei sich die Studien größtenteils auf ausgewählte Anrainerstaaten beschränkten. Auch wurde die Analyse von Umweltveränderungen meistens anhand einer einzigen EO-Landoberflächenvariable durchgeführt, während eine synergetische Untersuchung von sphärenübergreifenden Landoberflächenvariablen kaum unternommen wurde. Um diese Forschungslücken zu adressieren, ist ein methodischer Ansatz notwendig, der (1) die Vorverarbeitung und Harmonisierung von Zeitreihen aus mehreren Quellen und (2) die statistische Analyse eines multivariaten Merkmalsraums ermöglicht. Für die Entwicklung und Anwendung eines methodischen Frameworks, das raum-zeitlich übertragbar ist, wurden die transnationalen Einzugsgebiete Indus, Ganges, Brahmaputra und Meghna (IGBM) in Südasien, deren Größe etwa der achtfachen Fläche von Deutschland entspricht, ausgewählt. Diese Einzugsgebiete hängen weitgehend von den Wasserressourcen des Monsunregens und des Hochgebirges Asiens ab. Insgesamt leben über 1,1 Milliarden Menschen in dieser Region und sind zum Teil in hohem Maße von diesen Wasserressourcen abhängig, die auch für die größten zusammenhängenden bewässerten Anbauflächen der Welt und auch für weitere inländische Bedarfe unerlässlich sind. Aufgrund ihrer sehr heterogenen geographischen Gegebenheiten ermöglichen diese Einzugsgebiete eine detaillierte sphärenübergreifende Analyse der Wechselwirkungen, einschließlich der Anthroposphäre, Biosphäre, Kryosphäre, Hydrosphäre, Lithosphäre und Atmosphäre. In dieser Dissertation wurden Landoberflächendynamiken der letzten zwei Jahrzehnte anhand von EO-Zeitreihen zum Vegetationszustand, zu Oberflächengewässern und zur Schneebedeckung analysiert. Diese basieren auf MODIS-Aufnahmen, dem DLR Global WaterPack und dem JRC Global Surface Water Layer sowie dem DLR Global SnowPack. Diese Zeitreihen wurden in Kombination mit weiteren klimatischen, hydrologischen und anthropogenen Variablen ausgewertet. Die Harmonisierung des multivariaten Merkmalsraumes ermöglichte die Analyse von Landoberflächendynamiken unter Nutzung von statistischen Methoden. Diese Methoden umfassen (1) die Berechnung von Trends mittels des Mann-Kendall und des Theil-Sen Tests, (2) die Berechnung von phänologischen Metriken anhand des Timesat-Tools, (3) die Bewertung von treibenden Variablen unter Nutzung des PCMCI Algorithmus und (4) zusätzliche Korrelationstests zur Analyse des menschlichen Einflusses auf den Vegetationszustand und die Wasseroberfläche. Diese Analysen wurden auf jährlichen und saisonalen Zeitskalen und für verschiedene räumliche Einheiten durchgeführt. Für den Vegetationszustand wurden weitgehend signifikant positive Trends ermittelt. Analysen haben gezeigt, dass landwirtschaftliche Nutzflächen am meisten zu diesen Trends beitragen haben. Besonders hoch waren die Trends in ariden Regionen. Bei Oberflächengewässern wurden auf jährlicher Ebene signifikant positive Trends festgestellt. Auf Pixelebene wurden jedoch sowohl regional als auch saisonal Cluster mit signifikant negativen Trends identifiziert. Die Trends für die Schneebedeckung blieben auf jährlicher Ebene weitgehend stabil, jedoch wurden in Teilen der Einzugsgebiete zu bestimmten Jahreszeiten signifikant negative Trends beobachtet. Die negativen Trends wurden auch für höhenabhängige Zonen festgestellt, insbesondere in hohen Lagen. Außerdem wurden in Teilen der Einzugsgebiete Rückgänge bei der saisonalen Dauer der Schneebedeckung ermittelt. Darüber hinaus ergab die Untersuchung des multivariaten Merkmalsraums auf kausale Zusammenhänge auf saisonaler Ebene erstmals Aufschluss über direkte und indirekte Relationen zwischen EO-Landoberflächenvariablen und den entsprechenden Einflussfaktoren. Zusammengefasst wurde die Vegetation durch die Wasserverfügbarkeit, die Oberflächengewässer durch den Abfluss und indirekt durch den Niederschlag sowie die Schneebedeckung durch Niederschlag und Temperatur mit räumlichen und saisonalen Unterschieden kontrolliert. Zusätzliche Analysen wiesen auf einen positiven Zusammenhang zwischen dem menschlichen Einfluss und den zunehmenden Trends in der Vegetationsfläche hin. Diese sphärenübergreifenden Untersuchungen zu Trends und Wechselwirkungen liefern neue und wertvolle Einblicke in den vergangenen Zustand von Landoberflächendynamiken sowie in die relevanten klimatischen und hydrologischen Einflussfaktoren. Neben den untersuchten Einzugsgebieten in Südasien sind diese Erkenntnisse auch für weitere Einzugsgebiete und geographische Regionen von großer Bedeutung. KW - Multivariate Analyse KW - Zeitreihe KW - Fernerkundung KW - Geographie KW - Multivariate Time Series KW - River Basins KW - Earth Observation KW - Remote Sensing Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-291941 ER - TY - JOUR A1 - Näschen, Kristian A1 - Diekkrüger, Bernd A1 - Evers, Mariele A1 - Höllermann, Britta A1 - Steinbach, Stefanie A1 - Thonfeld, Frank T1 - The impact of land use/land cover change (LULCC) on water resources in a tropical catchment in Tanzania under different climate change scenarios JF - Sustainability N2 - Many parts of sub-Saharan Africa (SSA) are prone to land use and land cover change (LULCC). In many cases, natural systems are converted into agricultural land to feed the growing population. However, despite climate change being a major focus nowadays, the impacts of these conversions on water resources, which are essential for agricultural production, is still often neglected, jeopardizing the sustainability of the socio-ecological system. This study investigates historic land use/land cover (LULC) patterns as well as potential future LULCC and its effect on water quantities in a complex tropical catchment in Tanzania. It then compares the results using two climate change scenarios. The Land Change Modeler (LCM) is used to analyze and to project LULC patterns until 2030 and the Soil and Water Assessment Tool (SWAT) is utilized to simulate the water balance under various LULC conditions. Results show decreasing low flows by 6–8% for the LULC scenarios, whereas high flows increase by up to 84% for the combined LULC and climate change scenarios. The effect of climate change is stronger compared to the effect of LULCC, but also contains higher uncertainties. The effects of LULCC are more distinct, although crop specific effects show diverging effects on water balance components. This study develops a methodology for quantifying the impact of land use and climate change and therefore contributes to the sustainable management of the investigated catchment, as it shows the impact of environmental change on hydrological extremes (low flow and floods) and determines hot spots, which are critical for environmental development. KW - SWAT model KW - Land Change Modeler KW - Scenario analysis KW - Extreme flows KW - Tanzania KW - Kilombero Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193825 SN - 2071-1050 VL - 11 IS - 24 ER - TY - JOUR A1 - Latifi, Hooman A1 - Valbuena, Ruben T1 - Current trends in forest ecological applications of three-dimensional remote sensing: Transition from experimental to operational solutions? JF - Forests N2 - The alarming increase in the magnitude and spatiotemporal patterns of changes in composition, structure and function of forest ecosystems during recent years calls for enhanced cross-border mitigation and adaption measures, which strongly entail intensified research to understand the underlying processes in the ecosystems as well as their dynamics. Remote sensing data and methods are nowadays the main complementary sources of synoptic, up-to-date and objective information to support field observations in forest ecology. In particular, analysis of three-dimensional (3D) remote sensing data is regarded as an appropriate complement, since they are hypothesized to resemble the 3D character of most forest attributes. Following their use in various small-scale forest structural analyses over the past two decades, these sources of data are now on their way to be integrated in novel applications in fields like citizen science, environmental impact assessment, forest fire analysis, and biodiversity assessment in remote areas. These and a number of other novel applications provide valuable material for the Forests special issue “3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function”, which shows the promising future of these technologies and improves our understanding of the potentials and challenges of 3D remote sensing in practical forest ecology worldwide. KW - 3D remote sensing KW - composition KW - forest ecology KW - function KW - structure Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193282 SN - 1999-4907 VL - 10 IS - 10 ER - TY - JOUR A1 - Ghazaryan, Gohar A1 - Rienow, Andreas A1 - Oldenburg, Carsten A1 - Thonfeld, Frank A1 - Trampnau, Birte A1 - Sticksel, Sarah A1 - Jürgens, Carsten T1 - Monitoring of urban sprawl and densification processes in Western Germany in the light of SDG indicator 11.3.1 based on an automated retrospective classification approach JF - Remote Sensing N2 - By 2050, two-third of the world’s population will live in cities. In this study, we develop a framework for analyzing urban growth-related imperviousness in North Rhine-Westphalia (NRW) from the 1980s to date using Landsat data. For the baseline 2017-time step, official geodata was extracted to generate labelled data for ten classes, including three classes representing low, middle, and high level of imperviousness. We used the output of the 2017 classification and information based on radiometric bi-temporal change detection for retrospective classification. Besides spectral bands, we calculated several indices and various temporal composites, which were used as an input for Random Forest classification. The results provide information on three imperviousness classes with accuracies exceeding 75%. According to our results, the imperviousness areas grew continuously from 1985 to 2017, with a high imperviousness area growth of more than 167,000 ha, comprising around 30% increase. The information on the expansion of urban areas was integrated with population dynamics data to estimate the progress towards SDG 11. With the intensity analysis and the integration of population data, the spatial heterogeneity of urban expansion and population growth was analysed, showing that the urban expansion rates considerably excelled population growth rates in some regions in NRW. The study highlights the applicability of earth observation data for accurately quantifying spatio-temporal urban dynamics for sustainable urbanization and targeted planning. KW - impervious surface KW - Landsat time series KW - change detection KW - SDG 11.3.1 KW - population change Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-236671 SN - 2072-4292 VL - 13 IS - 9 ER - TY - JOUR A1 - Fekri, Erfan A1 - Latifi, Hooman A1 - Amani, Meisam A1 - Zobeidinezhad, Abdolkarim T1 - A training sample migration method for wetland mapping and monitoring using Sentinel data in Google Earth Engine JF - Remote Sensing N2 - Wetlands are one of the most important ecosystems due to their critical services to both humans and the environment. Therefore, wetland mapping and monitoring are essential for their conservation. In this regard, remote sensing offers efficient solutions due to the availability of cost-efficient archived images over different spatial scales. However, a lack of sufficient consistent training samples at different times is a significant limitation of multi-temporal wetland monitoring. In this study, a new training sample migration method was developed to identify unchanged training samples to be used in wetland classification and change analyses over the International Shadegan Wetland (ISW) areas of southwestern Iran. To this end, we first produced the wetland map of a reference year (2020), for which we had training samples, by combining Sentinel-1 and Sentinel-2 images and the Random Forest (RF) classifier in Google Earth Engine (GEE). The Overall Accuracy (OA) and Kappa coefficient (KC) of this reference map were 97.93% and 0.97, respectively. Then, an automatic change detection method was developed to migrate unchanged training samples from the reference year to the target years of 2018, 2019, and 2021. Within the proposed method, three indices of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and the mean Standard Deviation (SD) of the spectral bands, along with two similarity measures of the Euclidean Distance (ED) and Spectral Angle Distance (SAD), were computed for each pair of reference–target years. The optimum threshold for unchanged samples was also derived using a histogram thresholding approach, which led to selecting the samples that were most likely unchanged based on the highest OA and KC for classifying the test dataset. The proposed migration sample method resulted in high OAs of 95.89%, 96.83%, and 97.06% and KCs of 0.95, 0.96, and 0.96 for the target years of 2018, 2019, and 2021, respectively. Finally, the migrated samples were used to generate the wetland map for the target years. Overall, our proposed method showed high potential for wetland mapping and monitoring when no training samples existed for a target year. KW - wetland KW - Google Earth Engine (GEE) KW - training sample migration KW - sentinel Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-248542 SN - 2072-4292 VL - 13 IS - 20 ER - TY - JOUR A1 - Schönbrodt-Stitt, Sarah A1 - Ahmadian, Nima A1 - Kurtenbach, Markus A1 - Conrad, Christopher A1 - Romano, Nunzio A1 - Bogena, Heye R. A1 - Vereecken, Harry A1 - Nasta, Paolo T1 - Statistical Exploration of SENTINEL-1 Data, Terrain Parameters, and in-situ Data for Estimating the Near-Surface Soil Moisture in a Mediterranean Agroecosystem JF - Frontiers in Water N2 - Reliable near-surface soil moisture (θ) information is crucial for supporting risk assessment of future water usage, particularly considering the vulnerability of agroforestry systems of Mediterranean environments to climate change. We propose a simple empirical model by integrating dual-polarimetric Sentinel-1 (S1) Synthetic Aperture Radar (SAR) C-band single-look complex data and topographic information together with in-situ measurements of θ into a random forest (RF) regression approach (10-fold cross-validation). Firstly, we compare two RF models' estimation performances using either 43 SAR parameters (θNov\(^{SAR}\)) or the combination of 43 SAR and 10 terrain parameters (θNov\(^{SAR+Terrain}\)). Secondly, we analyze the essential parameters in estimating and mapping θ for S1 overpasses twice a day (at 5 a.m. and 5 p.m.) in a high spatiotemporal (17 × 17 m; 6 days) resolution. The developed site-specific calibration-dependent model was tested for a short period in November 2018 in a field-scale agroforestry environment belonging to the “Alento” hydrological observatory in southern Italy. Our results show that the combined SAR + terrain model slightly outperforms the SAR-based model (θNov\(^{SAR+Terrain}\) with 0.025 and 0.020 m3 m\(^{−3}\), and 89% compared to θNov\(^{SAR}\) with 0.028 and 0.022 m\(^3\) m\(^{−3}\, and 86% in terms of RMSE, MAE, and R2). The higher explanatory power for θNov\(^{SAR+Terrain}\) is assessed with time-variant SAR phase information-dependent elements of the C2 covariance and Kennaugh matrix (i.e., K1, K6, and K1S) and with local (e.g., altitude above channel network) and compound topographic attributes (e.g., wetness index). Our proposed methodological approach constitutes a simple empirical model aiming at estimating θ for rapid surveys with high accuracy. It emphasizes potentials for further improvement (e.g., higher spatiotemporal coverage of ground-truthing) by identifying differences of SAR measurements between S1 overpasses in the morning and afternoon. KW - near-surface soil moisture KW - Sentinel-1 single-look complex data KW - SAR backscatters KW - terrain parameters KW - Alento hydrological observatory KW - Mediterranean environment Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-259062 VL - 3 ER - TY - JOUR A1 - Hagg, Wilfried A1 - Mayr, Elisabeth A1 - Mannig, Birgit A1 - Reyers, Mark A1 - Schubert, David A1 - Pinto, Joaquim G. A1 - Peters, Juliane A1 - Pieczonka, Tino A1 - Juen, Martin A1 - Bolch, Tobias A1 - Paeth, Heiko A1 - Mayer, Christoph T1 - Future climate change and its impact on runoff generation from the debris-covered Inylchek glaciers, Central Tian Shan, Kyrgyzstan JF - Water N2 - The heavily debris-covered Inylchek glaciers in the central Tian Shan are the largest glacier system in the Tarim catchment. It is assumed that almost 50% of the discharge of Tarim River are provided by glaciers. For this reason, climatic changes, and thus changes in glacier mass balance and glacier discharge are of high impact for the whole region. In this study, a conceptual hydrological model able to incorporate discharge from debris-covered glacier areas is presented. To simulate glacier melt and subsequent runoff in the past (1970/1971–1999/2000) and future (2070/2071–2099/2100), meteorological input data were generated based on ECHAM5/MPI-OM1 global climate model projections. The hydrological model HBV-LMU was calibrated by an automatic calibration algorithm using runoff and snow cover information as objective functions. Manual fine-tuning was performed to avoid unrealistic results for glacier mass balance. The simulations show that annual runoff sums will increase significantly under future climate conditions. A sensitivity analysis revealed that total runoff does not decrease until the glacier area is reduced by 43%. Ice melt is the major runoff source in the recent past, and its contribution will even increase in the coming decades. Seasonal changes reveal a trend towards enhanced melt in spring, but a change from a glacial-nival to a nival-pluvial runoff regime will not be reached until the end of this century. KW - glaciers KW - debris-covered glaciers KW - hydrological modelling KW - climate scenarios KW - Tian Shan Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-197592 SN - 2073-4441 VL - 10 IS - 11 ER - TY - JOUR A1 - Forkuor, Gerald A1 - Hounkpatin, Ozias K.L. A1 - Welp, Gerhard A1 - Thiel, Michael T1 - High resolution mapping of soil properties using remote sensing variables in south-western Burkina Faso: a comparison of machine learning and multiple linear regression models JF - PLOS One N2 - Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties–sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen–in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models–multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)–were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources. KW - Agricultural soil science KW - Forecasting KW - Machine learning KW - Support vector machines KW - Paleopedology KW - Trees KW - Clay mineralogy KW - Remote sensing KW - South-western Burkina Faso Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-180978 VL - 12 IS - 1 ER - TY - JOUR A1 - Philipp, Marius A1 - Dietz, Andreas A1 - Ullmann, Tobias A1 - Kuenzer, Claudia T1 - Automated extraction of annual erosion rates for Arctic permafrost coasts using Sentinel-1, Deep Learning, and Change Vector Analysis JF - Remote Sensing N2 - Arctic permafrost coasts become increasingly vulnerable due to environmental drivers such as the reduced sea-ice extent and duration as well as the thawing of permafrost itself. A continuous quantification of the erosion process on large to circum-Arctic scales is required to fully assess the extent and understand the consequences of eroding permafrost coastlines. This study presents a novel approach to quantify annual Arctic coastal erosion and build-up rates based on Sentinel-1 (S1) Synthetic Aperture RADAR (SAR) backscatter data, in combination with Deep Learning (DL) and Change Vector Analysis (CVA). The methodology includes the generation of a high-quality Arctic coastline product via DL, which acted as a reference for quantifying coastal erosion and build-up rates from annual median and standard deviation (sd) backscatter images via CVA. The analysis was applied on ten test sites distributed across the Arctic and covering about 1038 km of coastline. Results revealed maximum erosion rates of up to 160 m for some areas and an average erosion rate of 4.37 m across all test sites within a three-year temporal window from 2017 to 2020. The observed erosion rates within the framework of this study agree with findings published in the previous literature. The proposed methods and data can be applied on large scales and, prospectively, even for the entire Arctic. The generated products may be used for quantifying the loss of frozen ground, estimating the release of stored organic material, and can act as a basis for further related studies in Arctic coastal environments. KW - permafrost KW - coastal erosion KW - deep learning KW - change vector analysis KW - Google Earth Engine KW - synthetic aperture RADAR Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-281956 SN - 2072-4292 VL - 14 IS - 15 ER - TY - THES A1 - Hoffmann, Susanne T1 - Demographischer Wandel und innerstädtische Einkaufszentren in Deutschland. Entwicklungen in Erlangen, Koblenz und Zwickau T1 - Demographic change and city-centre shopping locations in Germany. Developments in Erlangen, Koblenz and Zwickau. N2 - Der Anteil älterer und alter Menschen an der Gesamtbevölkerung steigt kontinuierlich an. Diese Entwicklung wird sich auch in den kommenden Jahren fortsetzen. So werden 2050 rund 40 % der deutschen Bevölkerung 60 Jahre oder älter sein. Die Alterung der Bevölkerung wirkt sich auf nahezu alle Lebensbereiche aus und stellt damit Planer und Entscheider auf staatlicher wie auf privater Seite vor neue Herausforderungen. Dies betrifft auch die Frage, wie innerstädtische Einkaufsstandorte, und zwar traditionelle innerstädtische Einkaufsstraßen und innerstädtische Shopping Center, gestaltet werden müssen, um den Anforderungen und Bedürfnissen möglichst aller Altersgruppen und damit auch denjenigen der älteren und alten Konsumenten zu entsprechen. Am Beispiel der Städte Erlangen, Koblenz und Zwickau wird in vorliegender Untersuchung der Frage nachgegangen, wie ältere und alte Menschen die verschiedenen innerstädtischen Einkaufsstandorte wahrnehmen und nutzen, welche Unterschiede diesbezüglich zu jüngeren Kundengruppen bestehen und welche Schlussfolgerungen sich daraus für eine zukunftsgerichtete Gestaltung der traditionellen Einkaufsstraßen und der innerstädtischen Shopping Center ableiten lassen. Für die Untersuchung kam ein breites methodisches Instrumentarium aus Zeitungsrecherchen, Kartierungen, qualitativen Beobachtungen, qualitativen Haushaltsbefragungen sowie quantitativen Passantenbefragungen zur Anwendung. N2 - The portion of the elderly and the aged in Germany’s total population is continuously growing. This development will continue also in the coming years. It is assumed that in 2050 about 40 % of Germany’s population is 60 years of age or older. The ageing of the population has effects on almost all aspects of life und poses new challenges for public and private planners and decision makers. This also goes for the question of designing shopping locations in the city centre including traditional city-centre shopping streets as well as city-centre shopping malls in a way that preferably corresponds to the needs and exigencies of all age groups and hence also to those of the elderly and aged consumers. Using the example of the cities of Erlangen, Koblenz and Zwickau the present investigation explores as to how elderly and aged people conceive and make use of the various shopping locations in the city centre, how this differs from younger groups of customers and which conclusions can be drawn from the results in regard of achieving a forward-looking design of traditional shopping streets and shopping malls in the city centre. A wide methodological spectrum ranging from newspaper research, mapping, qualitative observations and qualitative household surveys to quantitative passers-by surveys were brought to bear within the investigation. T3 - Würzburger Geographische Arbeiten - 119 KW - Demographie KW - Einkaufszentrum KW - Innenstadt KW - demographischer Wandel KW - Erlangen KW - Zwickau KW - Koblenz KW - Shopping Center KW - demographic change KW - elderly and aged consumers KW - city-centre shopping locations KW - city-centre shopping malls Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-148218 SN - 978-3-95826-062-7 (print) SN - 978-3-95826-063-4 (online) SN - 0510-9833 SN - 2194-3656 N1 - Parallel erschienen als Druckausgabe in Würzburg University Press, 978-3-95826-062-7, 36,90 EUR. PB - Würzburg University Press CY - Würzburg ER - TY - RPRT A1 - Meyer, Constantin A1 - Job, Hubert A1 - Laner, Peter A1 - Omizzolo, Andrea A1 - Kollmann, Nadia A1 - Clare, Jasmin A1 - Vesely, Philipp A1 - Riedler, Walter A1 - Plassmann, Guido A1 - Coronado, Oriana A1 - Praper Gulič, Sergeja A1 - Gulič, Andrej A1 - Koblar, Simon A1 - Teofili, Corrado A1 - Rohringer, Verena A1 - Schoßleitner, Richard A1 - Ainz, Gerhard T1 - Izhodišča za usklajeno ohranjanje odprtega prostora na območju Alp: Načrtovalski priročnik projekta OpenSpaceAlps N2 - Na območju Alp že dalj časa poteka preobrazba odprtega prostora zaradi gradnje in širjenja naselij ter tehnične infrastrukture. Navedeni procesi povzročajo zlasti izgubo kmetijskih zemljišč, stalno pokritje površine tal z nepropustnimi snovmi in razdrobljenost krajine. Razdrobljenost je odvisna od vrst posegov in stopnje pozidanosti prostora, poglavitna negativna učinka pa sta izolacija naravnih habitatov in slabšanje ekološke povezljivosti. Opisana problematika je bila glavna tema projekta OpenSpaceAlps, v katerem so bili ob sodelovanju z deležniki na več pilotnih območjih razviti pristopi in rešitve, ki omogočajo trajnostno ohranjanje odprtega prostora. Načrtovalski priročnik povzema del rezultatov projekta. Namenjen je različnim deležnikom, zlasti načrtovalcem v javnih službah, kot pripomoček pri izvajanju načrtovalskih nalog in odločanju. V priročniku so predstavljeni analiza izzivov in okvirnih pogojev v Alpah ter opis in primerjava poglavitnih načel načrtovanja odprtega prostora, obravnavane pa so tudi celostne načrtovalske strategije za različne kategorije prostora. KW - Raumordnung KW - Alpen KW - OpenSpaceAlps KW - Alpe KW - Prostorsko načrtovanje KW - Transnacionalno sodelovanje Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-286518 N1 - English version available at: https://doi.org/10.25972/OPUS-27040. German version available at: https://doi.org/10.25972/OPUS-27307. Italian version available at: https://doi.org/10.25972/OPUS-27704 N1 - The OpenSpaceAlps project is co-financed by the European Regional Development Fund through the Interreg Alpine Space programme. ER - TY - JOUR A1 - Mayr, Stefan A1 - Klein, Igor A1 - Rutzinger, Martin A1 - Kuenzer, Claudia T1 - Systematic water fraction estimation for a global and daily surface water time-series JF - Remote Sensing N2 - Fresh water is a vital natural resource. Earth observation time-series are well suited to monitor corresponding surface dynamics. The DLR-DFD Global WaterPack (GWP) provides daily information on globally distributed inland surface water based on MODIS (Moderate Resolution Imaging Spectroradiometer) images at 250 m spatial resolution. Operating on this spatiotemporal level comes with the drawback of moderate spatial resolution; only coarse pixel-based surface water quantification is possible. To enhance the quantitative capabilities of this dataset, we systematically access subpixel information on fractional water coverage. For this, a linear mixture model is employed, using classification probability and pure pixel reference information. Classification probability is derived from relative datapoint (pixel) locations in feature space. Pure water and non-water reference pixels are located by combining spatial and temporal information inherent to the time-series. Subsequently, the model is evaluated for different input sets to determine the optimal configuration for global processing and pixel coverage types. The performance of resulting water fraction estimates is evaluated on the pixel level in 32 regions of interest across the globe, by comparison to higher resolution reference data (Sentinel-2, Landsat 8). Results show that water fraction information is able to improve the product's performance regarding mixed water/non-water pixels by an average of 11.6% (RMSE). With a Nash-Sutcliffe efficiency of 0.61, the model shows good overall performance. The approach enables the systematic provision of water fraction estimates on a global and daily scale, using only the reflectance and temporal information contained in the input time-series. KW - earth observation KW - landsat KW - MODIS KW - remote sensing KW - probability KW - Sentinel-2 KW - subpixel KW - water Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-242586 SN - 2072-4292 VL - 13 IS - 14 ER - TY - JOUR A1 - Stereńczak, Krzysztof A1 - Laurin, Gaia Vaglio A1 - Chirici, Gherardo A1 - Coomes, David A. A1 - Dalponte, Michele A1 - Latifi, Hooman A1 - Puletti, Nicola T1 - Global Airborne Laser Scanning Data Providers Database (GlobALS) — a new tool for monitoring ecosystems and biodiversity JF - Remote Sensing N2 - Protection and recovery of natural resource and biodiversity requires accurate monitoring at multiple scales. Airborne Laser Scanning (ALS) provides high-resolution imagery that is valuable for monitoring structural changes to vegetation, providing a reliable reference for ecological analyses and comparison purposes, especially if used in conjunction with other remote-sensing and field products. However, the potential of ALS data has not been fully exploited, due to limits in data availability and validation. To bridge this gap, the global network for airborne laser scanner data (GlobALS) has been established as a worldwide network of ALS data providers that aims at linking those interested in research and applications related to natural resources and biodiversity monitoring. The network does not collect data itself but collects metadata and facilitates networking and collaborative research amongst the end-users and data providers. This letter describes this facility, with the aim of broadening participation in GlobALS. KW - LiDAR KW - forest KW - database KW - networking KW - GlobALS Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-207819 SN - 2072-4292 VL - 12 IS - 11 ER - TY - JOUR A1 - Huth, Juliane A1 - Gessner, Ursula A1 - Klein, Igor A1 - Yesou, Hervé A1 - Lai, Xijun A1 - Oppelt, Natascha A1 - Kuenzer, Claudia T1 - Analyzing water dynamics based on Sentinel-1 time series — a study for Dongting Lake wetlands in China JF - Remote Sensing N2 - In China, freshwater is an increasingly scarce resource and wetlands are under great pressure. This study focuses on China's second largest freshwater lake in the middle reaches of the Yangtze River — the Dongting Lake — and its surrounding wetlands, which are declared a protected Ramsar site. The Dongting Lake area is also a research region of focus within the Sino-European Dragon Programme, aiming for the international collaboration of Earth Observation researchers. ESA's Copernicus Programme enables comprehensive monitoring with area-wide coverage, which is especially advantageous for large wetlands that are difficult to access during floods. The first year completely covered by Sentinel-1 SAR satellite data was 2016, which is used here to focus on Dongting Lake's wetland dynamics. The well-established, threshold-based approach and the high spatio-temporal resolution of Sentinel-1 imagery enabled the generation of monthly surface water maps and the analysis of the inundation frequency at a 10 m resolution. The maximum extent of the Dongting Lake derived from Sentinel-1 occurred in July 2016, at 2465 km\(^2\), indicating an extreme flood year. The minimum size of the lake was detected in October, at 1331 km\(^2\). Time series analysis reveals detailed inundation patterns and small-scale structures within the lake that were not known from previous studies. Sentinel-1 also proves to be capable of mapping the wetland management practices for Dongting Lake polders and dykes. For validation, the lake extent and inundation duration derived from the Sentinel-1 data were compared with excerpts from the Global WaterPack (frequently derived by the German Aerospace Center, DLR), high-resolution optical data, and in situ water level data, which showed very good agreement for the period studied. The mean monthly extent of the lake in 2016 from Sentinel-1 was 1798 km\(^2\), which is consistent with the Global WaterPack, deviating by only 4%. In summary, the presented analysis of the complete annual time series of the Sentinel-1 data provides information on the monthly behavior of water expansion, which is of interest and relevance to local authorities involved in water resource management tasks in the region, as well as to wetland conservationists concerned with the Ramsar site wetlands of Dongting Lake and to local researchers. KW - Earth observation KW - SAR KW - Sentinel–1 KW - time series KW - Dongting Lake KW - water dynamics KW - floodpath lake KW - Ramsar Convention on Wetlands Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-205977 SN - 2072-4292 VL - 12 IS - 11 ER - TY - JOUR A1 - Forkuor, Gerald A1 - Ullmann, Tobias A1 - Griesbeck, Mario T1 - Mapping and monitoring small-scale mining activities in Ghana using Sentinel-1 time series (2015−2019) JF - Remote Sensing N2 - Illegal small-scale mining (galamsey) in South-Western Ghana has grown tremendously in the last decade and caused significant environmental degradation. Excessive cloud cover in the area has limited the use of optical remote sensing data to map and monitor the extent of these activities. This study investigated the use of annual time-series Sentinel-1 data to map and monitor illegal mining activities along major rivers in South-Western Ghana between 2015 and 2019. A change detection approach, based on three time-series features — minimum, mean, maximum — was used to compute a backscatter threshold value suitable to identify/detect mining-induced land cover changes in the study area. Compared to the mean and maximum, the minimum time-series feature (in both VH and VV polarization) was found to be more sensitive to changes in backscattering within the period of investigation. Our approach permitted the detection of new illegal mining areas on an annual basis. A backscatter threshold value of +1.65 dB was found suitable for detecting illegal mining activities in the study area. Application of this threshold revealed illegal mining area extents of 102 km\(^2\), 60 km\(^2\) and 33 km\(^2\) for periods 2015/2016–2016/2017, 2016/2017–2017/2018 and 2017/2018–2018/2019, respectively. The observed decreasing trend in new illegal mining areas suggests that efforts at stopping illegal mining yielded positive results in the period investigated. Despite the advantages of Synthetic Aperture Radar data in monitoring phenomena in cloud-prone areas, our analysis revealed that about 25% of the Sentinel-1 data, mostly acquired in March and October (beginning and end of rainy season respectively), were unusable due to atmospheric effects from high intensity rainfall events. Further investigation in other geographies and climatic regions is needed to ascertain the susceptibility of Sentinel-1 data to atmospheric conditions. KW - Sentine-1 KW - mining KW - image artifacts KW - time-series features KW - galamsey KW - Ghana Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-203204 SN - 2072-4292 VL - 12 IS - 6 ER - TY - BOOK A1 - Herb, Christopher T1 - Restrukturierung von Wertschöpfungsketten in der Digitalisierung. Eine Analyse der deutschen Schuhbranche vom Hersteller bis zum Konsumenten T1 - Restructuring of value chains in digitalization. An analysis of the German footwear sector from manufacturer to consumer N2 - Globale Wertschöpfungsketten stellen nicht nur hochkomplexe Beziehungsgefüge dar, sondern unterliegen auch einem ständigen Wandlungsprozess. Ein zentraler Treiber dieser Wandlungsprozesse ist der technologische Fortschritt. Moderne Informations- und Kommunikationstechnologien, insbesondere die Phänomene der Digitalisierung und des Online-Handels, sind derzeit von besonderer Bedeutung für Wertschöpfungsketten, da unterschiedliche Fortschritte in der Digitalisierung nicht nur zu wirtschaftlichen Vor- und Nachteilen von Unternehmen führen können, sondern auch zu Up- bzw. Downgradingprozessen innerhalb der Wertschöpfungsketten. In der vorliegenden Studie wird der Fokus auf den handels- bzw. konsumentennahen Teil von Wertschöpfungsketten gelegt, um die Folgen der Digitalisierung für Hersteller, Händler und Konsumenten näher zu betrachten. Als konkretes Forschungsbeispiel dient die deutsche Schuhbranche, da sich diese gegenwärtig – von Industrie bis Handel – in einem umfassenden Strukturwandel befindet. Die Analyse zeigt, dass sich die Komplexität von Wertschöpfungsketten im Zuge der Digitalisierung deutlich erhöht (hat). In der Schuhbranche drängen neue Akteure auf den Markt, bestehende Akteure müssen sich anpassen. Direkte Folgen sind nicht nur eine neue Akteurskonstellation, sondern auch ein sich neu bildendes Machtgefüge. Es kommt somit zur Restrukturierung bisheriger Wertschöpfungsketten. N2 - Global value chains (GVC) not only represent highly complex structures of relationships, but are also subject to a constant process of change. A key driver of these processes of change is technological progress. Modern information and communication technologies, especially the phenomena of digitalization and e-commerce, are currently of particular importance for GVC, as different advances in digitalization can lead not only to economic advantages and disadvantages for companies, but also to up- or downgrading processes within GVC. This study examines on the retail- and consumer-related part of GVC in order to investigate the consequences of digitalization for manufacturers, retailers and consumers. The German footwear sector serves as a specific research example, as it is currently undergoing major structural change – from industry to retail. The analysis shows that the complexity of GVC has increased significantly in the course of digitalization. In the footwear sector, new players are entering the market and existing players have to adapt. Direct consequences are not only a new constellation of players, but also a newly forming power structure. This results in the re-organization of previous value chains. T3 - Geographische Handelsforschung - 32 KW - Wertschöpfungskette KW - Schuh KW - Digitalisierung KW - Einzelhandel KW - Wertschöpfungsketten KW - Schuhbranche KW - Digitalisierung KW - Governance KW - Konsumentenverhalten KW - Global Value Chains KW - Footwear sector KW - Digitalization KW - Governance KW - Consumer behavior Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-269564 SN - 978-3-95826-186-0 SN - 978-3-95826-187-7 SN - 2626-8906 SN - 2196-5811 N1 - Dissertation, RWTH Aachen, 2021 N1 - Parallel erschienen als Druckausgabe in Würzburg University Press, ISBN 978-3-95826-186-0, 34,90 EUR PB - Würzburg University Press CY - Würzburg ER - TY - JOUR A1 - Hoeser, Thorsten A1 - Bachofer, Felix A1 - Kuenzer, Claudia T1 - Object detection and image segmentation with deep learning on Earth Observation data: a review — part II: applications JF - Remote Sensing N2 - In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by investigating aggregated classes. The increase in data with a very high spatial resolution enables investigations on a fine-grained feature level which can help us to better understand the dynamics of land surfaces by taking object dynamics into account. To extract fine-grained features and objects, the most popular deep-learning model for image analysis is commonly used: the convolutional neural network (CNN). In this review, we provide a comprehensive overview of the impact of deep learning on EO applications by reviewing 429 studies on image segmentation and object detection with CNNs. We extensively examine the spatial distribution of study sites, employed sensors, used datasets and CNN architectures, and give a thorough overview of applications in EO which used CNNs. Our main finding is that CNNs are in an advanced transition phase from computer vision to EO. Upon this, we argue that in the near future, investigations which analyze object dynamics with CNNs will have a significant impact on EO research. With a focus on EO applications in this Part II, we complete the methodological review provided in Part I. KW - artificial intelligence KW - AI KW - machine learning KW - deep learning KW - neural networks KW - convolutional neural networks KW - CNN KW - image segmentation KW - object detection KW - earth observation Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-213152 SN - 2072-4292 VL - 12 IS - 18 ER - TY - JOUR A1 - Hoeser, Thorsten A1 - Kuenzer, Claudia T1 - Object detection and image segmentation with deep learning on Earth observation data: a review-part I: evolution and recent trends JF - Remote Sensing N2 - Deep learning (DL) has great influence on large parts of science and increasingly established itself as an adaptive method for new challenges in the field of Earth observation (EO). Nevertheless, the entry barriers for EO researchers are high due to the dense and rapidly developing field mainly driven by advances in computer vision (CV). To lower the barriers for researchers in EO, this review gives an overview of the evolution of DL with a focus on image segmentation and object detection in convolutional neural networks (CNN). The survey starts in 2012, when a CNN set new standards in image recognition, and lasts until late 2019. Thereby, we highlight the connections between the most important CNN architectures and cornerstones coming from CV in order to alleviate the evaluation of modern DL models. Furthermore, we briefly outline the evolution of the most popular DL frameworks and provide a summary of datasets in EO. By discussing well performing DL architectures on these datasets as well as reflecting on advances made in CV and their impact on future research in EO, we narrow the gap between the reviewed, theoretical concepts from CV and practical application in EO. KW - artificial intelligence KW - AI KW - machine learning KW - deep learning KW - neural networks KW - convolutional neural networks KW - CNN KW - image segmentation KW - object detection KW - Earth observation Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-205918 SN - 2072-4292 VL - 12 IS - 10 ER - TY - JOUR A1 - Heinemann, Sascha A1 - Siegmann, Bastian A1 - Thonfeld, Frank A1 - Muro, Javier A1 - Jedmowski, Christoph A1 - Kemna, Andreas A1 - Kraska, Thorsten A1 - Muller, Onno A1 - Schultz, Johannes A1 - Udelhoven, Thomas A1 - Wilke, Norman A1 - Rascher, Uwe T1 - Land surface temperature retrieval for agricultural areas using a novel UAV platform equipped with a thermal infrared and multispectral sensor JF - Remote Sensing N2 - Land surface temperature (LST) is a fundamental parameter within the system of the Earth’s surface and atmosphere, which can be used to describe the inherent physical processes of energy and water exchange. The need for LST has been increasingly recognised in agriculture, as it affects the growth phases of crops and crop yields. However, challenges in overcoming the large discrepancies between the retrieved LST and ground truth data still exist. Precise LST measurement depends mainly on accurately deriving the surface emissivity, which is very dynamic due to changing states of land cover and plant development. In this study, we present an LST retrieval algorithm for the combined use of multispectral optical and thermal UAV images, which has been optimised for operational applications in agriculture to map the heterogeneous and diverse agricultural crop systems of a research campus in Germany (April 2018). We constrain the emissivity using certain NDVI thresholds to distinguish different land surface types. The algorithm includes atmospheric corrections and environmental thermal emissions to minimise the uncertainties. In the analysis, we emphasise that the omission of crucial meteorological parameters and inaccurately determined emissivities can lead to a considerably underestimated LST; however, if the emissivity is underestimated, the LST can be overestimated. The retrieved LST is validated by reference temperatures from nearby ponds and weather stations. The validation of the thermal measurements indicates a mean absolute error of about 0.5 K. The novelty of the dual sensor system is that it simultaneously captures highly spatially resolved optical and thermal images, in order to construct the precise LST ortho-mosaics required to monitor plant diseases and drought stress and validate airborne and satellite data. KW - UAV KW - thermal infrared KW - multispectral VNIR KW - LST KW - emissivity KW - NDVI thresholds KW - atmospheric correction KW - agricultural mapping KW - low-cost applications Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-203557 SN - 2072-4292 VL - 12 IS - 7 ER - TY - JOUR A1 - Latifi, Hooman A1 - Heurich, Marco T1 - Multi-scale remote sensing-assisted forest inventory: a glimpse of the state-of-the-art and future prospects JF - Remote Sensing N2 - Advances in remote inventory and analysis of forest resources during the last decade have reached a level to be now considered as a crucial complement, if not a surrogate, to the long-existing field-based methods. This is mostly reflected in not only the use of multiple-band new active and passive remote sensing data for forest inventory, but also in the methodic and algorithmic developments and/or adoptions that aim at maximizing the predictive or calibration performances, thereby minimizing both random and systematic errors, in particular for multi-scale spatial domains. With this in mind, this editorial note wraps up the recently-published Remote Sensing special issue “Remote Sensing-Based Forest Inventories from Landscape to Global Scale”, which hosted a set of state-of-the-art experiments on remotely sensed inventory of forest resources conducted by a number of prominent researchers worldwide. KW - remote sensing KW - forest resources inventory KW - spatial scale Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-197358 SN - 2072-4292 VL - 11 IS - 11 ER - TY - JOUR A1 - Appel, Alexandra A1 - Hardaker, Sina T1 - Strategies in Times of Pandemic Crisis — Retailers and Regional Resilience in Würzburg, Germany JF - Sustainability N2 - Research on the COVID-19 crisis and its implications on regional resilience is still in its infancy. To understand resilience on its aggregate level it is important to identify (non)resilient actions of individual actors who comprise regions. As the retail sector among others represents an important factor in an urban regions recovery, we focus on the resilience of (textile) retailers within the city of Würzburg in Germany to the COVID-19 pandemic. To address the identified research gap, this paper applies the concept of resilience. Firstly, conducting expert interviews, the individual (textile) retailers’ level and their strategies in coping with the crisis is considered. Secondly, conducting a contextual analysis of the German city of Würzburg, we wish to contribute to the discussion of how the resilience of a region is influenced inter alia by actors. Our study finds three main strategies on the individual level, with retailers: (1) intending to “bounce back” to a pre-crisis state, (2) reorganising existing practices, as well as (3) closing stores and winding up business. As at the time of research, no conclusions regarding long-term impacts and resilience are possible, the results are limited. Nevertheless, detailed analysis of retailers’ strategies contributes to a better understanding of regional resilience. KW - resilience KW - COVID-19 KW - pandemic crisis KW - regional resilience KW - retail KW - owner-operated retailers KW - textile sector KW - Germany Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-233991 SN - 2071-1050 VL - 13 IS - 5 ER - TY - JOUR A1 - Holzwarth, Stefanie A1 - Thonfeld, Frank A1 - Abdullahi, Sahra A1 - Asam, Sarah A1 - Da Ponte Canova, Emmanuel A1 - Gessner, Ursula A1 - Huth, Juliane A1 - Kraus, Tanja A1 - Leutner, Benjamin A1 - Kuenzer, Claudia T1 - Earth Observation based monitoring of forests in Germany: a review JF - Remote Sensing N2 - Forests in Germany cover around 11.4 million hectares and, thus, a share of 32% of Germany's surface area. Therefore, forests shape the character of the country's cultural landscape. Germany's forests fulfil a variety of functions for nature and society, and also play an important role in the context of climate levelling. Climate change, manifested via rising temperatures and current weather extremes, has a negative impact on the health and development of forests. Within the last five years, severe storms, extreme drought, and heat waves, and the subsequent mass reproduction of bark beetles have all seriously affected Germany’s forests. Facing the current dramatic extent of forest damage and the emerging long-term consequences, the effort to preserve forests in Germany, along with their diversity and productivity, is an indispensable task for the government. Several German ministries have and plan to initiate measures supporting forest health. Quantitative data is one means for sound decision-making to ensure the monitoring of the forest and to improve the monitoring of forest damage. In addition to existing forest monitoring systems, such as the federal forest inventory, the national crown condition survey, and the national forest soil inventory, systematic surveys of forest condition and vulnerability at the national scale can be expanded with the help of a satellite-based earth observation. In this review, we analysed and categorized all research studies published in the last 20 years that focus on the remote sensing of forests in Germany. For this study, 166 citation indexed research publications have been thoroughly analysed with respect to publication frequency, location of studies undertaken, spatial and temporal scale, coverage of the studies, satellite sensors employed, thematic foci of the studies, and overall outcomes, allowing us to identify major research and geoinformation product gaps. KW - remote sensing KW - earth observation KW - forest KW - forest monitoring KW - forest disturbances KW - Germany KW - review Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-216334 SN - 2072-4292 VL - 12 IS - 21 ER - TY - JOUR A1 - Usman, Muhammad A1 - Mahmood, Talha A1 - Conrad, Christopher A1 - Bodla, Habib Ullah T1 - Remote Sensing and modelling based framework for valuing irrigation system efficiency and steering indicators of consumptive water use in an irrigated region JF - Sustainability N2 - Water crises are becoming severe in recent times, further fueled by population increase and climate change. They result in complex and unsustainable water management. Spatial estimation of consumptive water use is vital for performance assessment of the irrigation system using Remote Sensing (RS). For this study, its estimation is done using the Soil Energy Balance Algorithm for Land (SEBAL) approach. Performance indicators including equity, adequacy, and reliability were worked out at various spatiotemporal scales. Moreover, optimization and sustainable use of water resources are not possible without knowing the factors mainly influencing consumptive water use of major crops. For that purpose, random forest regression modelling was employed using various sets of factors for site-specific, proximity, and cropping system. The results show that the system is underperforming both for Kharif (i.e., summer) and Rabi (i.e., winter) seasons. Performance indicators highlight poor water distribution in the system, a shortage of water supply, and unreliability. The results are relatively good for Rabi as compared to Kharif, with an overall poor situation for both seasons. Factors importance varies for different crops. Overall, distance from canal, road density, canal density, and farm approachability are the most important factors for explaining consumptive water use. Auditing of consumptive water use shows the potential for resource optimization through on-farm water management by the targeted approach. The results are based on the present situation without considering future changes in canal water supply and consumptive water use under climate change. KW - consumptive water use KW - performance assessment KW - indicator importance assessment KW - water management KW - Pakistan Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-219358 SN - 2071-1050 VL - 12 IS - 22 ER - TY - THES A1 - Bangelesa, Freddy Fefe T1 - Impacts of climate variability and change on Maize (\(Zea\) \(mays\)) production in tropical Africa T1 - Auswirkungen von Klimavariabilität und Veränderungen auf die Mais (\(Zea\) \(mays\)) Produktion im tropischen Afrika N2 - Climate change is undeniable and constitutes one of the major threats of the 21st century. It impacts sectors of our society, usually negatively, and is likely to worsen towards the middle and end of the century. The agricultural sector is of particular concern, for it is the primary source of food and is strongly dependent on the weather. Considerable attention has been given to the impact of climate change on African agriculture because of the continent’s high vulnerability, which is mainly due to its low adaptation capac- ity. Several studies have been implemented to evaluate the impact of climate change on this continent. The results are sometimes controversial since the studies are based on different approaches, climate models and crop yield datasets. This study attempts to contribute substantially to this large topic by suggesting specific types of climate pre- dictors. The study focuses on tropical Africa and its maize yield. Maize is considered to be the most important crop in this region. To estimate the effect of climate change on maize yield, the study began by developing a robust cross-validated multiple linear regression model, which related climate predictors and maize yield. This statistical trans- fer function is reputed to be less prone to overfitting and multicollinearity problems. It is capable of selecting robust predictors, which have a physical meaning. Therefore, the study combined: large-scale predictors, which were derived from the principal component analysis of the monthly precipitation and temperature; traditional local-scale predictors, mainly, the mean precipitation, mean temperature, maximum temperature and minimum temperature; and the Water Requirement Satisfaction Index (WRSI), derived from the specific crop (maize) water balance model. The projected maize-yield change is forced by a regional climate model (RCM) REMO under two emission scenarios: high emission scenario (RCP8.5) and mid-range emission scenario (RCP4.5). The different effects of these groups of predictors in projecting the future maize-yield changes were also assessed. Furthermore, the study analysed the impact of climate change on the global WRSI. The results indicate that almost 27 % of the interannual variability of maize production of the entire region is explained by climate variables. The influence of climate predictors on maize-yield production is more pronounced in West Africa, reaching 55 % in some areas. The model projection indicates that the maize yield in the entire region is expected to decrease by the middle of the century under an RCP8.5 emission scenario, and from the middle of the century to the end of the century, the production will slightly recover but will remain negative (around -10 %). However, in some regions of East Africa, a slight increase in maize yield is expected. The maize-yield projection under RCP4.5 remains relatively unchanged compared to the baseline period (1982-2016). The results further indicate that large-scale predictors are the most critical drivers of the global year-to-year maize-yield variability, and ENSO – which is highly correlated with the most important predictor (PC2) – seems to be the physical process underlying this variability. The effects of local predictors are more pronounced in the eastern parts of the region. The impact of the future climate change on WRSI reveals that the availability of maize water is expected to decrease everywhere, except in some parts of eastern Africa. N2 - Weil die Folgen des Klimawandels die Lebensgrundlagen aller Lebewesen beeinträchtigen, ist der Klimawandel ein sehr relevantes Thema des 21. Jahrhunderts. Seine negativen Effekte betreffen bereits viele Sektoren unserer Gesellschaft und die Prognosen zeigen, dass sich die Auswirkungen des Klimawandels Mitte und Ende dieses Jahrhunderts ver- schärfen werden. Die Landwirtschaft ist besonders betroffen, denn sie ist sehr abhängig vom Klima. Da die Landwirtschaft als Hauptnahrungsquelle der Menschen gilt, ist es erforderlich sich mit den Problemen des Klimawandels rechtzeitig zu beschäftigen, um in der Zukunft die Ernährung der Menschheit gewährleisten zu können. Viele Forscher beschäftigen sich mit den Folgen des Klimawandels in der Landwirtschaft. Besonders in Afrika wurde viel geforscht, weil die Landwirtschaft in Afrika sich technisch schlecht anpassen kann, um die Schwierigkeiten, die mit dem Klimawandel einhergehen, zu über- winden. Mehrere Studien wurden durchgeführt, um die Auswirkungen des Klimawan- dels in Afrika zu bewerten. Aufgrund der unterschiedlichen verwendeten statistischen Methoden, Modellierungen der Umweltprozesse oder Ertragsdaten sind die Ergebnisse teilweise kontrovers. Diese Studie versucht, einen wesentlichen Beitrag zum Einfluss des Klimawandels auf die Landwirtschaft in Westafrika zu leisten, indem sie spezifis- che Methoden vorschlägt, um das Klima der Zukunft projizieren zu können. Diese Studie behandelt Maiserträge in den Tropen Afrikas, da Mais dort die wichtigste Nutzpflanze ist. Um die Auswirkungen des Klimawandels auf den Maisertrag abzuschätzen, wurde ein Regressionsmodell (aus dem Englischen: robust cross-validated multiple) entwickelt, das Klimaprädiktoren und Maiserträge koppelt. Diese entwickelte statistische Übertra- gungsfunktion ist zuverlässiger bei Schwierigkeiten mit der Überanpassung und der Mul- tikollinearität. Außerdem ist sie auch in der Lage robuste Prädiktoren mit physikalischer Bedeutung auszuwählen. Deshalb wurden in der Studie großräumige und lokale Prädik- toren kombiniert. Erstere entstammen der Analyse der Komponenten des monatlichen Niederschlags und der Temperatur, letztere basieren basieren auf den mittleren und Ex- tremtemperaturen sowie dem mittleren Niederschlag. Zusätzlich zu den Prädiktoren wurde ein Index der Wasserbedarfsdeckung (Water Requirement Satisfaction Index, WRSI) verwendet, der auf einem Wasserhaushaltsmodell der Nutzpflanzen basiert. Die erwartete Mais-Ertragsänderung wird mithilfe eines regionalen Klimamodells (RCM) REMO für die Emissionsszenarien RCP8.5 und RCP4.5 simuliert. Die einzelnen Effekte der Prädiktoren- Gruppen bei der Prognose der zukünftigen Mais-Ertragsänderungen wurden ebenfalls bewertet. Darüber hinaus analysierte die Studie die Auswirkungen des Klimawandels auf den WSRI. Durchschnittlich zeigen die Ergebnisse eine jährliche Maisproduktionsän- derung von ca. 27 % in der gesamten Region. Diese Änderung, die in Westafrika mit ca. 55 % stärker ausgeprägt ist, ist eine Folge des Klimawandels. Die Simulationen des Mod- ells anhand von RCP8.5-Emissionsszenario zeigen auch, dass der Maisertrag der gesamten Region voraussichtlich bis Mitte des Jahrhunderts abnehmen wird. Danach findet eine geringe Ertragserhöhung statt, die jedoch um ca. 10 % unter der ursprünglichen Menge liegt. Im Gegensatz zu Westafrika wird in einigen Regionen Ostafrikas wird ein leichter Anstieg des Maisertrags simuliert. Die Mais-Ertragsprognose für die gesamte Region mittels RCP4.5 bleibt relativ unverändert im Vergleich zum ursprünglichen Ertrag. Die Ergebnisse zeigen weiterhin, dass die großräumigen Prädiktoren die wichtigste Rolle bei den globalen jährlichen Maisertragsschwankungen spielen. ENSO ist stark mit dem wichtigsten Prädiktor korreliert, was auf den physikalischen Prozess hinweist, der diese Ertragsänderung erklärt. Die Relevanz der lokalen Prädiktoren ist in den östlichen Re- gionen Afrikas stärker ausgeprägt. Sie beeinflussen den WRSI, sodass der Maisertrag im Verhältnis zur Wasserverfügbarkeit voraussichtlich überall abnehmen wird. Ausgenom- men sind einigen Regionen Ostafrikas. KW - Climate change KW - Food security KW - Modelling Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-259347 ER - TY - THES A1 - Karama, Alphonse T1 - East African Seasonal Rainfall prediction using multiple linear regression and regression with ARIMA errors models T1 - Vorhersage des saisonalen Niederschlags in Ostafrika mit multipler linearer Regression und Regression mit ARIMA-Fehlermodellen N2 - The detrimental impacts of climate variability on water, agriculture, and food resources in East Africa underscore the importance of reliable seasonal climate prediction. To overcome this difficulty RARIMAE method were evolved. Applications RARIMAE in the literature shows that amalgamating different methods can be an efficient and effective way to improve the forecasts of time series under consideration. With these motivations, attempt have been made to develop a multiple linear regression model (MLR) and a RARIMAE models for forecasting seasonal rainfall in east Africa under the following objectives: 1. To develop MLR model for seasonal rainfall prediction in East Africa. 2. To develop a RARIMAE model for seasonal rainfall prediction in East Africa. 3. Comparison of model's efficiency under consideration In order to achieve the above objectives, the monthly precipitation data covering the period from 1949 to 2000 was obtained from Climate Research Unit (CRU). Next to that, the first differenced climate indices were used as predictors. In the first part of this study, the analyses of the rainfall fluctuation in whole Central- East Africa region which span over a longitude of 15 degrees East to 55 degrees East and a latitude of 15 degrees South to 15 degrees North was done by the help of maps. For models’ comparison, the R-squared values for the MLR model are subtracted from the R-squared values of RARIMAE model. The results show positive values which indicates that R-squared is improved by RARIMAE model. On the other side, the root mean square errors (RMSE) values of the RARIMAE model are subtracted from the RMSE values of the MLR model and the results show negative value which indicates that RMSE is reduced by RARIMAE model for training and testing datasets. For the second part of this study, the area which is considered covers a longitude of 31.5 degrees East to 41 degrees East and a latitude of 3.5 degrees South to 0.5 degrees South. This region covers Central-East of the Democratic Republic of Congo (DRC), north of Burundi, south of Uganda, Rwanda, north of Tanzania and south of Kenya. Considering a model constructed based on the average rainfall time series in this region, the long rainfall season counts the nine months lead of the first principal component of Indian sea level pressure (SLP_PC19) and the nine months lead of Dipole Mode Index (DMI_LR9) as selected predictors for both statistical and predictive model. On the other side, the short rainfall season counts the three months lead of the first principal component of Indian sea surface temperature (SST_PC13) and the three months lead of Southern Oscillation Index (SOI_SR3) as predictors for predictive model. For short rainfall season statistical model SAOD current time series (SAOD_SR0) was added on the two predictors in predictive model. By applying a MLR model it is shown that the forecast can explain 27.4% of the total variation and has a RMSE of 74.2mm/season for long rainfall season while for the RARIMAE the forecast explains 53.6% of the total variation and has a RMSE of 59.4mm/season. By applying a MLR model it is shown that the forecast can explain 22.8% of the total variation and has a RMSE of 106.1 mm/season for short rainfall season predictive model while for the RARIMAE the forecast explains 55.1% of the total variation and has a RMSE of 81.1 mm/season. From such comparison, a significant rise in R-squared, a decrease of RMSE values were observed in RARIMAE models for both short rainfall and long rainfall season averaged time series. In terms of reliability, RARIMAE outperformed its MLR counterparts with better efficiency and accuracy. Therefore, whenever the data suffer from autocorrelation, we can go for MLR with ARIMA error, the ARIMA error part is more to correct the autocorrelation thereby improving the variance and productiveness of the model. N2 - Die nachteiligen Auswirkungen der Klimavariabilität auf Wasser, Landwirtschaft und Nahrungsressourcen in Ostafrika unterstreichen die Bedeutung einer zuverlässigen saisonalen Klimavorhersage. Um diese Schwierigkeit zu überwinden, wurden die Regression mit ARIMA-Fehlern (RARIMAE)-Methoden entwickelt. Die Anwendungen RARIMAE in der Literatur zeigen, dass die Zusammenführung verschiedener Methoden ein effizienter und effektiver Weg sein kann, um die Vorhersagen der betrachteten Zeitreihen zu verbessern. Aus dieser Motivation heraus wurde versucht, ein multiples lineares Regressionsmodell (MLR) und ein RARIMAE-Modell zur Vorhersage saisonaler Niederschläge in Ostafrika unter folgenden Zielsetzungen zu entwickeln: 1. Entwicklung eines MLR-Modells für die Vorhersage der saisonalen Regenfälle in Ostafrika. 2. Entwicklung eines RARIMAE-Modells für die saisonale Niederschlagsvorhersage in Ostafrika. 3. Vergleich der betrachteten Modelleffizienz Um die oben genannten Ziele zu erreichen, wurden die monatlichen Niederschlagsdaten für den Zeitraum von 1949 bis 2000 von der Climate Research Unit (CRU) bezogen. Daneben wurden die ersten differenzierten Klimaindizes als Prädiktoren verwendet. Im ersten Teil dieser Studie wurden die Niederschlagsschwankungen in der gesamten Region Zentral-Ostafrika, die sich über einen Längengrad von 15 Grad Ost bis 55 Grad Ost und einen Breitengrad von 15 Grad Süd bis 15 Grad Nord erstrecken, analysiert mit Hilfe von Karten gemacht. Für den Modellvergleich werden die Erklärte Varianz-Werte für das MLR-Modell von den R-Quadrat-Werten des RARIMAE-Modells abgezogen. Die Ergebnisse zeigen positive Werte, was darauf hinweist, die Erklärte Varianz durch das RARIMAE-Modell verbessert wird. Auf der anderen Seite werden die Root-Mean-Square-Error-Werte (RMSE) des RARIMAE-Modells von den RMSE-Werten des MLR-Modells subtrahiert und die Ergebnisse zeigen einen negativen Wert, der darauf hinweist, dass der RMSE durch das RARIMAE-Modell für Trainings- und Testdatensätze reduziert wird. Für den zweiten Teil dieser Studie umfasst das betrachtete Gebiet einen Längengrad von 31,5 Grad Ost bis 41 Grad Ost und einen Breitengrad von 3,5 Grad Süd bis 0,5 Grad Süd. Diese Region umfasst den Zentral-Osten der Demokratischen Republik Kongo (DRC), nördlich von Burundi, südlich von Uganda, Ruanda, nördlich von Tansania und südlich von Kenia. Betrachtet man ein Modell, das auf der Grundlage der durchschnittlichen Niederschlagszeitreihen in dieser Region erstellt wurde, zählt die lange Regensaison den neunmonatigen Vorsprung der ersten Hauptkomponente des indischen Meeresspiegeldrucks (SLP_PC19) und den neunmonatigen Vorsprung des Dipolmodus-Index (DMI_LR9) als ausgewählte Prädiktoren für statistische und prädiktive Modelle. Auf der anderen Seite zählt die kurze Regenzeit den dreimonatigen Vorsprung der ersten Hauptkomponente der indischen Meeresoberflächentemperatur (SST_PC13) und den dreimonatigen Vorsprung des Southern Oscillation Index (SOI_SR3) als Prädiktoren für das Vorhersagemodell. Für das statistische Modell der kurzen Regenzeit wurde die aktuelle SAOD-Zeitreihe (SAOD_SR0) zu den beiden Prädiktoren im Vorhersagemodell hinzugefügt. Durch die Anwendung eines MLR-Modells wird gezeigt, dass die Vorhersage 27,4 % der Gesamtvariation erklären kann und einen RMSE von 74,2 mm/Saison für eine lange Regenzeit hat, während die Vorhersage für RARIMAE 53,6% der Gesamtvariation erklärt und einen RMSE von 59,4 mm/Saison hat. Durch die Anwendung eines MLR-Modells wird gezeigt, dass die Vorhersage 22,8% der Gesamtvariation erklären kann und einen RMSE von 106,1 mm/Saison für das Vorhersagemodell für kurze Regenzeiten hat, während die Vorhersage für RARIMAE 55,1% der Gesamtvariation erklärt und a RMSE von 81,1 mm/Saison. Aus einem solchen Vergleich wurde ein signifikanter Anstieg die Erklärte Varianz und eine Abnahme der RMSE-Werte in RARIMAE-Modellen sowohl für die gemittelten Zeitreihen für kurze Regenfälle als auch für lange Regenzeiten beobachtet. In Bezug auf die Zuverlässigkeit übertraf RARIMAE seine MLR-Pendants mit besserer Effizienz und Genauigkeit. Wenn die Daten unter Autokorrelation leiden, können wir uns daher für MLR mit ARIMA-Fehler entscheiden. Der ARIMA-Fehlerteil dient mehr dazu, die Autokorrelation zu korrigieren, wodurch die Varianz und Produktivität des Modells verbessert wird. KW - Regression KW - Niederschlag KW - Telekonnektion KW - Precipitation KW - ARIMA KW - Teleconnection Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-251831 ER - TY - JOUR A1 - Job, Hubert A1 - Willi, Gero A1 - Mayer, Marius A1 - Pütz, Marco T1 - Open Spaces in Alpine Countries: Analytical Concepts and Preservation Strategies in Spatial Planning JF - Mountain Research and Development N2 - Open spaces in the Alps are becoming noticeably scarcer, and the long-term consequences for humans and the environment are often overlooked. Open spaces preserve ecosystem services but are under pressure in many Alpine valleys due to demographic and economic development as well as corresponding technical and tourism infrastructure. This article conceptualizes and measures open spaces in Alpine environments. In addition to analyzing existing spatial planning instruments and the open spaces resulting from 2 of them-the Bavarian Alpenplan in Germany and the Tyrolean Ruhegebiete in Austria-we identify open spaces in Switzerland using a geographic information system. More generally, we discuss how spatial planning deals with open spaces. Results show that both the Alpenplan and the Ruhegebiete have contributed significantly to the protection of open spaces in the Bavarian and Tyrolean Alps since the 1970s. Indeed, both approaches prevented several development projects. In the Swiss Alps, open spaces cover 41.9% of the Alpine Convention area. A share of 40.3% vegetation-free open spaces shows that they are concentrated in high alpine areas. Of the open spaces identified, 64.6% are covered by protected areas. Hence, about one third of the open spaces still existing in the Swiss Alps need preservation, not only for ecological connectivity reasons but also to preserve them for generations to come. We conclude that different sectoral approaches for the conservation of open spaces for people and natural heritage in the Alps and other high mountain ranges should be better coordinated. In addition, much more intensive crossborder cooperation in spatial development and planning is needed to preserve open spaces throughout the Alpine arc. KW - Alps KW - ecological connectivity KW - open spaces KW - sectoral planning KW - spatial planning; sustainable development; cross-border coordination Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-259338 VL - 40 IS - 3 ER - TY - JOUR A1 - Asam, Sarah A1 - Gessner, Ursula A1 - Almengor González, Roger A1 - Wenzl, Martina A1 - Kriese, Jennifer A1 - Kuenzer, Claudia T1 - Mapping crop types of Germany by combining temporal statistical metrics of Sentinel-1 and Sentinel-2 time series with LPIS data JF - Remote Sensing N2 - Nationwide and consistent information on agricultural land use forms an important basis for sustainable land management maintaining food security, (agro)biodiversity, and soil fertility, especially as German agriculture has shown high vulnerability to climate change. Sentinel-1 and Sentinel-2 satellite data of the Copernicus program offer time series with temporal, spatial, radiometric, and spectral characteristics that have great potential for mapping and monitoring agricultural crops. This paper presents an approach which synergistically uses these multispectral and Synthetic Aperture Radar (SAR) time series for the classification of 17 crop classes at 10 m spatial resolution for Germany in the year 2018. Input data for the Random Forest (RF) classification are monthly statistics of Sentinel-1 and Sentinel-2 time series. This approach reduces the amount of input data and pre-processing steps while retaining phenological information, which is crucial for crop type discrimination. For training and validation, Land Parcel Identification System (LPIS) data were available covering 15 of the 16 German Federal States. An overall map accuracy of 75.5% was achieved, with class-specific F1-scores above 80% for winter wheat, maize, sugar beet, and rapeseed. By combining optical and SAR data, overall accuracies could be increased by 6% and 9%, respectively, compared to single sensor approaches. While no increase in overall accuracy could be achieved by stratifying the classification in natural landscape regions, the class-wise accuracies for all but the cereal classes could be improved, on average, by 7%. In comparison to census data, the crop areas could be approximated well with, on average, only 1% of deviation in class-specific acreages. Using this streamlined approach, similar accuracies for the most widespread crop types as well as for smaller permanent crop classes were reached as in other Germany-wide crop type studies, indicating its potential for repeated nationwide crop type mapping. KW - agriculture KW - random forest classification KW - multispectral data KW - radar data KW - spectral statistics KW - temporal statistics KW - IACS Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-278969 SN - 2072-4292 VL - 14 IS - 13 ER - TY - JOUR A1 - Appel, Alexandra A1 - Hardaker, Sina T1 - Einzelhandel als Katalysator für nachhaltige urbane Radlogistik? – WüLivery, ein Fallbeispiel aus Würzburg JF - Standort N2 - Die Covid-19-Pandemie gilt in vielen gesellschaftlichen Teilbereichen als Beschleuniger für Transformationsprozesse. Auch im Bereich der Organisation urbaner Logistik und Einzelhandelslandschaften etablieren sich neue Akteur*innen und Funktionen. Logistiker*innen integrieren lokale Onlinemarktplätze in ihre Profile und der stationäre Einzelhandel generiert Wettbewerbsfähigkeit gegenüber großen Onlinehändler*innen über die Nutzung lokaler Radlogistiknetzwerke, mittels derer Lieferungen noch am Tag der Bestellung (Same-Day-Delivery) verteilt werden können. Damit leisten die involvierten Akteur*innen potenziell auch einen Beitrag zur Nachhaltigkeitstransformation im Bereich urbaner Logistiksysteme. Im Fokus steht das Fallbeispiel WüLivery, ein Kooperationsprojekt des Stadtmarketingvereins, der Wirtschaftsförderung, Radlogistiker*innen sowie Einzelhändler*innen in Würzburg, welches während des zweiten coronabedingten Lockdowns im November 2020 umgesetzt wurde. Die entstehenden Dynamiken und Organisationsformen werden auf Basis von 11 Expert*inneninterviews dargestellt und analysiert. Es kann gezeigt werden, dass städtische Akteur*innen grundlegende Mediator*innen für Transformationsprozesse darstellen und Einzelhändler*innen und lokale Onlinemarktplätze als Katalysator*innen fungieren können. Das ist auch vor dem Hintergrund planerischer und politischer Kommunikationsprozesse zur Legitimation neuer Verkehrsinfrastrukturen nutzbar, da die einzelnen Akteur*innengruppen in Austausch kommen und ein gesteigertes Bewusstsein für die jeweiligen Bedarfe entsteht. KW - lokaler Onlinemarktplatz KW - urbane Logistik KW - Nachhaltigkeitstransformation KW - letzte Meile KW - Einzelhandel Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-268437 SN - 1432-220X VL - 46 IS - 1 ER - TY - JOUR A1 - Wehner, Helena A1 - Huchler, Katharina A1 - Fritz, Johannes T1 - Quantification of foraging areas for the Northern Bald Ibis (Geronticus eremita) in the northern Alpine foothills: a random forest model fitted with optical and actively sensed earth observation data JF - Remote Sensing N2 - The Northern Bald Ibis (Geronticus eremita, NBI) is an endangered migratory species, which went extinct in Europe in the 17th century. Currently, a translocation project in the frame of the European LIFE program is carried out, to reintroduce a migratory population with breeding colonies in the northern and southern Alpine foothills and a common wintering area in southern Tuscany. The population meanwhile consists of about 200 individuals, with about 90% of them carrying a GPS device on their back. We used biologging data from 2021 to model the habitat suitability for the species in the northern Alpine foothills. To set up a species distribution model, indices describing environmental conditions were calculated from satellite images of Landsat-8, and in addition to the well-proven use of optical remote sensing data, we also included Sentinel-1 actively sensed observation data, as well as climate and urbanization data. A random forest model was fitted on NBI GPS positions, which we used to identify regions with high predicted foraging suitability within the northern Alpine foothills. The model resulted in 84.5% overall accuracy. Elevation and slope had the highest predictive power, followed by grass cover and VV intensity of Sentinel-1 radar data. The map resulting from the model predicts the highest foraging suitability for valley floors, especially of Inn, Rhine, and Salzach-Valley as well as flatlands, like the Swiss Plateau and the agricultural areas surrounding Lake Constance. Areas with a high suitability index largely overlap with known historic breeding sites. This is particularly noteworthy because the model only refers to foraging habitats without considering the availability of suitable breeding cliffs. Detailed analyses identify the transition zone from extensive grassland management to intensive arable farming as the northern range limit. The modeling outcome allows for defining suitable areas for further translocation and management measures in the frame of the European NBI reintroduction program. Although required in the international IUCN translocation guidelines, the use of models in the context of translocation projects is still not common and in the case of the Northern Bald Ibis not considered in the present Single Species Action Plan of the African-Eurasian Migratory Water bird Agreement. Our species distribution model represents a contemporary snapshot, but sustainability is essential for conservation planning, especially in times of climate change. In this regard, a further model could be optimized by investigating sustainable land use, temporal dynamics, and climate change scenarios. KW - Northern Bald Ibis KW - conservation KW - species distribution modeling KW - random forest modeling KW - remote sensing KW - reintroduction Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-262245 SN - 2072-4292 VL - 14 IS - 4 ER -