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 -