@phdthesis{Abel2023,
  author    = {Abel, Daniel Karl-Joseph},
  title     = {Weiterentwicklung der Bodenhydrologie des regionalen Klimamodells REMO},
  doi       = {10.25972/OPUS-31146},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-311468},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2023},
  abstract  = {Die Bodenfeuchte stellt eine essenzielle Variable f{\"u}r den Energie-, Feuchte- und Stoffaustausch zwischen Landoberfl{\"a}che und Atmosph{\"a}re dar. Ihre Auswirkungen auf Temperatur und Niederschlag sind vielf{\"a}ltig und komplex. Die in Klimamodellen verwendeten Schemata zur Simulation der Bodenfeuchte, auch bodenhydrologische Schemata genannt, sind aufgrund des Ursprungs der Klimamodelle aus Wettermodellen jedoch h{\"a}ufig sehr stark vereinfacht dargestellt. Bei Klimamodellen, die Simulationen mit einer groben Aufl{\"o}sung von mehreren Zehner- oder Hunderterkilometern rechnen, k{\"o}nnen viele Prozesse vernachl{\"a}ssigt werden. Da die Aufl{\"o}sung der Klimamodelle jedoch stetig steigt und mittlerweile beim koordinierten Projekt regionaler Klimamodelle CORDEX-CORE standardm{\"a}ßig bei 0.22° Kantenl{\"a}nge liegt, m{\"u}ssen auch h{\"o}her aufgel{\"o}ste Daten und mehr Prozesse simuliert werden. Dies gilt erst recht mit Blick auf konvektionsaufl{\"o}sende Simulationen mit wenigen Kilometern Kantenl{\"a}nge. Mit steigenden Modellaufl{\"o}sungen steigt zugleich die Komplexit{\"a}t und Differenziertheit der Fragestellungen, die mit Hilfe von Klimamodellen beantwortet werden sollen. An diesem Punkt setzt auch das Projekt BigData@Geo an, in dessen Rahmen die vorliegende Arbeit entstand. Ziel dieses Projektes ist es, hochaufgel{\"o}ste Klimainformationen f{\"u}r den bayerischen Regierungsbezirk Unterfranken f{\"u}r Akteure aus der Land- und Forstwirtschaft sowie dem Weinbau zur Verf{\"u}gung zu stellen. Auf diesen angewandten und grundlegenden Anforderungen und Zielsetzungen basierend, bedarf auch das in dieser Arbeit verwendete regionale Klimamodell REMO (Version 2015) der weiteren Entwicklung. So ist das Hauptziel der Arbeit das bestehende einschichtige bodenhydrologische Schema durch ein mehrschichtiges zu ersetzen. Der Vorteil mehrerer simulierter Bodenschichten besteht darin, dass nun die vertikale Bewegung des Wassers in Form von Versickerung und kapillarem Aufstieg simuliert werden kann. Dies geschieht auf der Basis bodenhydrologischer Parameter, deren Wert in Abh{\"a}ngigkeit vom Boden und der Bodenfeuchte {\"u}ber die Wasserr{\"u}ckhaltekurve bestimmt wird. F{\"u}r diese Kurve existieren verschiedene Parametrisierungen, von denen die Ans{\"a}tze von Clapp-Hornberger und van Genuchten verwendet wurden. Außerdem kann die Bodenfeuchte nun bis zu einer Tiefe von circa 10 m beziehungsweise der Tiefe des anstehenden Gesteins simuliert werden. Damit besteht im Gegensatz zum vorherigen Schema, dessen Tiefe auf die Wurzeltiefe beschr{\"a}nkt ist, die M{\"o}glichkeit, dass Wasser auch unterhalb der Wurzeln zur Verf{\"u}gung stehen kann und somit die absolute im Boden verf{\"u}gbare Wassermenge zunimmt. Die Schichtung erlaubt dar{\"u}ber hinaus die Verdunstung aus unbewachsenem Boden lediglich auf Basis des in der obersten Schicht verf{\"u}gbaren Wassers. Ein weiterer Prozess, der dank der Schichtung und der weiter unten erl{\"a}uterten Datens{\"a}tze neu parametrisiert werden kann, ist die Infiltration. F{\"u}r die Verwendung des Schemas sind Informationen {\"u}ber bodenhydrologische Parameter, die Wurzeltiefe und die Tiefe bis zum anstehenden Gestein erforderlich. Entsprechende Datens{\"a}tze m{\"u}ssen hierf{\"u}r aufbereitet und in das Modell eingebaut werden. Bez{\"u}glich der Wurzeltiefe wurden drei sich bez{\"u}glich der Tiefe, der Definition und der verf{\"u}gbaren Aufl{\"o}sung stark voneinander unterscheidende Datens{\"a}tze verglichen. Letztendlich wird die Wurzeltiefe aus dem mit einer anderen REMO-Version gekoppelten Vegetationsmodul iMOVE verwendet, da zuk{\"u}nftig eine Kopplung dieses Moduls mit dem mehrschichtigen Boden geplant ist und die Wurzeltiefen damit konsistent sind. Zudem ist die zugrundeliegende Aufl{\"o}sung der Daten hoch und es werden maximale Wurzeltiefen ber{\"u}cksichtigt, die besonders wichtig f{\"u}r die Simulation von Landoberfl{\"a}che-Atmosph{\"a}re-Interaktionen sind. Diese Vorteile brachten die anderen Datens{\"a}tze nicht mit. In der finalen Modellversion werden f{\"u}r die Tiefe bis zum anstehenden Gestein und die Korngr{\"o}ßenverteilungen die Daten von SoilGrids verwendet. Ein Vergleich mit anderen Bodendatens{\"a}tzen fand in einer parallel laufenden Dissertation statt (Ziegler 2022). Bei SoilGrids ist hervorzuheben, dass die Korngr{\"o}ßenverteilungen in einer hohen r{\"a}umlichen Aufl{\"o}sung (1 km^2 oder h{\"o}her) und mit mehreren vertikalen Schichten vorliegen. Gegen{\"u}ber dem urspr{\"u}nglich in REMO verwendeten Datensatz mit einer Kantenl{\"a}nge von 0.5° und ohne vertikale Differenzierung ist dies eine starke Verbesserung der Eingangsdaten. Dazu kommt, dass die Korngr{\"o}ßenverteilungen die Verwendung kontinuierlicher Pedotransferfunktionen statt f{\"u}nf diskreter Texturklassen, denen f{\"u}r die bodenhydrologischen Parameter fixe Tabellenwerte zugewiesen werden, erm{\"o}glichen. Dies f{\"u}hrt zu einer deutlich besseren Differenzierung des heterogenen Bodens. Im Rahmen der Arbeit wurden insgesamt 19 Simulationen f{\"u}r Europa und ein erweitertes Deutschlandgebiet mit Aufl{\"o}sungen von 0.44° beziehungsweise 0.11° f{\"u}r den Zeitraum 2000 bis 2018 gerechnet. Dabei zeigte sich, dass die Einf{\"u}hrung des mehrschichtigen Bodenschemas gegen{\"u}ber dem einschichtigen Schema zu einer Verringerung der Bodenfeuchte in der Wurzeltiefe f{\"u}hrt. Nichtsdestotrotz nimmt die absolute Wassermenge des Bodens durch die Ber{\"u}cksichtigung des Bodens unterhalb der Wurzelzone zu. Bezogen auf die einzelnen Schichten wird die Bodenfeuchte damit zwar untersch{\"a}tzt, im Laufe der Modellentwicklung kann jedoch eine Verbesserung im Vergleich zu ERA5 erzielt werden. Das neue Schema f{\"u}hrt zu einer Verringerung der Evapotranspiration, die {\"u}ber alle Schritte der Modellentwicklung und besonders w{\"a}hrend der Sommermonate auftritt. Im Vergleich zu Validationsdaten von ERA5 und GLEAM zeigt sich, dass dies eine Verbesserung dieser Gr{\"o}ße bedeutet, die sowohl in der Fl{\"a}che als auch beim Fehler und in der Verteilung auftritt. Gleiches l{\"a}sst sich f{\"u}r den Oberfl{\"a}chenabfluss sagen. Hierf{\"u}r implementierte Schemata (Philip, Green-Ampt), die anders als das standardm{\"a}ßig verwendete Improved-Arno-Schema bodenhydrologische Parameter ber{\"u}cksichtigen, konnten eine weitere Verbesserung im Flachland zeigen. In Gebirgsregionen nahm der Fehler durch die nicht enthaltene Ber{\"u}cksichtigung der Hangneigung jedoch zu, sodass in der finalen Modellversion auf das Improved-Arno-Schema zur{\"u}ckgegriffen wurde. Die Temperatur steigt durch die urspr{\"u}ngliche Version des mehrschichtigen Schemas zun{\"a}chst an, was zu einer {\"U}ber- statt der vorherigen Untersch{\"a}tzung gegen{\"u}ber E-OBS f{\"u}hrt. Die Modellentwicklung resultiert zwar in einer Reduzierung der Temperatur, jedoch f{\"a}llt diese zu stark aus, sodass der Temperaturfehler letztendlich gr{\"o}ßer als in der einschichtigen Modellversion ist. Da die Evapotranspiration jedoch maßgeblich verbessert wurde, kann dieser Fehler eventuell auf ein {\"u}berm{\"a}ßiges Tuning der Temperatur zur{\"u}ckgef{\"u}hrt werden. Die Betrachtung von Hitzeereignissen am Beispiel der Sommer 2003 und 2018 hat gezeigt, dass die Modellentwicklung dazu beitr{\"a}gt, diese Ereignisse besser als das einschichtige Schema zu simulieren. Zwar trifft dies nicht auf das r{\"a}umliche Verhalten der mittleren Temperatur zu, jedoch auf deren zeitlichen Verlauf. Hinzu kommt die bessere Simulation der t{\"a}glichen Extrem- und besonders der Minimaltemperatur, was zu einer Erh{\"o}hung der t{\"a}glichen Temperaturspanne f{\"u}hrt. Diese wird von Klimamodellen in der Regel zu stark untersch{\"a}tzt. Durch die Ber{\"u}cksichtigung der vertikalen Wasserfl{\"u}sse hat sich jedoch auch gezeigt, dass noch enormes Entwicklungspotenzial mit Blick auf (boden)hydrologische Prozesse besteht. Dies gilt in besonderem Maße f{\"u}r zuk{\"u}nftige Simulationen mit konvektionserlaubender Aufl{\"o}sung. So sollten subskalige Informationen des Bodens und der Orographie ber{\"u}cksichtigt werden. Dies dient einerseits der Repr{\"a}sentation vorliegender Heterogenit{\"a}ten und kann andererseits, wie am Beispiel der Infiltrationsschemata dargelegt, zur Verbesserung bestehender Prozesse beitragen. Da die simulierte Drainage durch das mehrschichtige Bodenschema im gleichen Maße zu- wie der Oberfl{\"a}chenabfluss abnimmt und das Wasser dem Modell in der Folge nicht weiter zur Verf{\"u}gung steht, sollte zuk{\"u}nftig auch Grundwasser im Modell ber{\"u}cksichtigt werden. Eine Vielzahl von Studien konnte einen Mehrwert durch die Implementierung dieser Variable und damit verbundener Prozesse feststellen. Mittelfristig ist jedoch insgesamt die Kopplung an ein hydrologisches Modell zu empfehlen, um die bei hochaufl{\"o}senden Simulationen relevanten Prozesse angemessen repr{\"a}sentieren zu k{\"o}nnen. Hierf{\"u}r bieten sich beispielsweise ParFlow oder mHM an. Insgesamt ist festzuhalten, dass das mehrschichtige Bodenschema einen Mehrwert liefert, da schwer zu simulierende und in der Postprozessierung zu korrigierende Variablen wie die Evapotranspiration und der Oberfl{\"a}chenabfluss deutlich besser modelliert werden k{\"o}nnen als mit dem einschichtigen Schema. Dies gilt auch f{\"u}r die Extremtemperaturen. Beides ist klar auf die Schichtung des Bodens und damit einhergehender Prozesse zur{\"u}ckzuf{\"u}hren. Bez{\"u}glich der Daten zeigt sich, dass die Wurzeltiefe, die Ber{\"u}cksichtigung von SoilGrids und die vertikale Bodeninformation f{\"u}r die weitere Optimierung verantwortlich sind. Dar{\"u}ber hinaus ist der h{\"o}here Informationsgehalt, der anhand der geschichteten Bodenfeuchte zur Verf{\"u}gung steht, ebenfalls als Mehrwert einzustufen.},
  subject      = {Klima},
  language  = {de}
}
@article{BellKleinRieseretal.2023,
  author    = {Bell, Alexandra and Klein, Doris and Rieser, Jakob and Kraus, Tanja and Thiel, Michael and Dech, Stefan},
  title     = {Scientific evidence from space — a review of spaceborne remote sensing applications at the science-policy interface},
  series = {Remote Sensing},
  volume    = {15},
  journal   = {Remote Sensing},
  number    = {4},
  issn      = {2072-4292},
  doi       = {10.3390/rs15040940},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-303925},
  year      = {2023},
  abstract  = {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.},
  language  = {en}
}
@phdthesis{Dhillon2023,
  author    = {Dhillon, Maninder Singh},
  title     = {Potential of Remote Sensing in Modeling Long-Term Crop Yields},
  doi       = {10.25972/OPUS-32258},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-322581},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2023},
  abstract  = {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.},
  subject      = {Ernteertrag},
  language  = {en}
}
@phdthesis{Dhillon2023,
  author    = {Dhillon, Maninder Singh},
  title     = {Potential of Remote Sensing in Modeling Long-Term Crop Yields},
  doi       = {10.25972/OPUS-33052},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-330529},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2023},
  abstract  = {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.},
  subject      = {Ernteertrag},
  language  = {en}
}
@article{DhillonDahmsKuebertFlocketal.2023,
  author    = {Dhillon, Maninder Singh and Dahms, Thorsten and Kuebert-Flock, Carina and Rummler, Thomas and Arnault, Joel and Steffan-Dewenter, Ingolf and Ullmann, Tobias},
  title     = {Integrating random forest and crop modeling improves the crop yield prediction of winter wheat and oil seed rape},
  series = {Frontiers in Remote Sensing},
  volume    = {3},
  journal   = {Frontiers in Remote Sensing},
  issn      = {2673-6187},
  doi       = {10.3389/frsen.2022.1010978},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-301462},
  year      = {2023},
  abstract  = {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.},
  language  = {en}
}
@article{DhillonDahmsKuebertFlocketal.2023,
  author    = {Dhillon, Maninder Singh and Dahms, Thorsten and K{\"u}bert-Flock, Carina and Liepa, Adomas and Rummler, Thomas and Arnault, Joel and Steffan-Dewenter, Ingolf and Ullmann, Tobias},
  title     = {Impact of STARFM on crop yield predictions: fusing MODIS with Landsat 5, 7, and 8 NDVIs in Bavaria Germany},
  series = {Remote Sensing},
  volume    = {15},
  journal   = {Remote Sensing},
  number    = {6},
  issn      = {2072-4292},
  doi       = {10.3390/rs15061651},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-311092},
  year      = {2023},
  abstract  = {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.},
  language  = {en}
}
@article{DhillonKuebertFlockDahmsetal.2023,
  author    = {Dhillon, Maninder Singh and K{\"u}bert-Flock, Carina and Dahms, Thorsten and Rummler, Thomas and Arnault, Joel and Steffan-Dewenter, Ingolf and Ullmann, Tobias},
  title     = {Evaluation of MODIS, Landsat 8 and Sentinel-2 data for accurate crop yield predictions: a case study using STARFM NDVI in Bavaria, Germany},
  series = {Remote Sensing},
  volume    = {15},
  journal   = {Remote Sensing},
  number    = {7},
  issn      = {2072-4292},
  doi       = {10.3390/rs15071830},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-311132},
  year      = {2023},
  abstract  = {The increasing availability and variety of global satellite products and the rapid development of new algorithms has provided great potential to generate a new level of data with different spatial, temporal, and spectral resolutions. However, the ability of these synthetic spatiotemporal datasets to accurately map and monitor our planet on a field or regional scale remains underexplored. This study aimed to support future research efforts in estimating crop yields by identifying the optimal spatial (10 m, 30 m, or 250 m) and temporal (8 or 16 days) resolutions on a regional scale. The current study explored and discussed the suitability of four different synthetic (Landsat (L)-MOD13Q1 (30 m, 8 and 16 days) and Sentinel-2 (S)-MOD13Q1 (10 m, 8 and 16 days)) and two real (MOD13Q1 (250 m, 8 and 16 days)) NDVI products combined separately to two widely used crop growth models (CGMs) (World Food Studies (WOFOST), and the semi-empiric Light Use Efficiency approach (LUE)) for winter wheat (WW) and oil seed rape (OSR) yield forecasts in Bavaria (70,550 km\(^2\)) for the year 2019. For WW and OSR, the synthetic products' high spatial and temporal resolution resulted in higher yield accuracies using LUE and WOFOST. The observations of high temporal resolution (8-day) products of both S-MOD13Q1 and L-MOD13Q1 played a significant role in accurately measuring the yield of WW and OSR. For example, L- and S-MOD13Q1 resulted in an R\(^2\) = 0.82 and 0.85, RMSE = 5.46 and 5.01 dt/ha for WW, R\(^2\) = 0.89 and 0.82, and RMSE = 2.23 and 2.11 dt/ha for OSR using the LUE model, respectively. Similarly, for the 8- and 16-day products, the simple LUE model (R\(^2\) = 0.77 and relative RMSE (RRMSE) = 8.17\%) required fewer input parameters to simulate crop yield and was highly accurate, reliable, and more precise than the complex WOFOST model (R\(^2\) = 0.66 and RRMSE = 11.35\%) with higher input parameters. Conclusively, both S-MOD13Q1 and L-MOD13Q1, in combination with LUE, were more prominent for predicting crop yields on a regional scale than the 16-day products; however, L-MOD13Q1 was advantageous for generating and exploring the long-term yield time series due to the availability of Landsat data since 1982, with a maximum resolution of 30 m. In addition, this study recommended the further use of its findings for implementing and validating the long-term crop yield time series in different regions of the world.},
  language  = {en}
}
@phdthesis{Engelbauer2023,
  author    = {Engelbauer, Manuel},
  title     = {Global assessment of recent UNESCO Biosphere Reserve quality enhancement strategies and interlinkages with other UNESCO labels},
  publisher = {W{\"u}rzburg University Press},
  address   = {W{\"u}rzburg},
  isbn      = {978-3-95826-196-9},
  doi       = {10.25972/WUP-978-3-95826-197-6},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-286538},
  school      = {W{\"u}rzburg University Press},
  pages     = {XVI, 187},
  year      = {2023},
  abstract  = {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.},
  subject      = {Naturschutz},
  language  = {en}
}
@article{GeyerLandingMeieretal.2023,
  author    = {Geyer, Gerd and Landing, Ed and Meier, Stefan and H{\"o}hn, Stefan},
  title     = {Oldest known West Gondwanan graptolite: Ovetograptus? sp. (lower Agdzian/lowest Wuliuan; basal Middle Cambrian) of the Franconian Forest, Germany, and review of pre-Furongian graptolithoids},
  series = {Pal{\"a}ontologische Zeitschrift},
  volume    = {97},
  journal   = {Pal{\"a}ontologische Zeitschrift},
  number    = {4},
  issn      = {0031-0220},
  doi       = {10.1007/s12542-022-00627-5},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-324099},
  pages     = {677-686},
  year      = {2023},
  abstract  = {The occurrence of a likely graptolite in lowest Wuliuan strata of the Franconian Forest almost certainly records the oldest known graptolithoid hemichordate in West Gondwana and possibly the oldest graptolite presently known. The fossil is a delicate, erect, apparently unbranched rhabdosome with narrow thecae tentatively assigned to the poorly known genus Ovetograptus of the Dithecodendridae. This report includes an overview of pre-Furongian graptolithoids with slight corrections on the stratigraphic position of earlier reported species.},
  language  = {en}
}
@phdthesis{Ibebuchi2023,
  author    = {Ibebuchi, Chibuike Chiedozie},
  title     = {Bias correction of climate model output for Germany},
  doi       = {10.25972/OPUS-31264},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-312647},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2023},
  abstract  = {Regional climate models (RCMs) are tools used to project future climate change at a regional scale. Despite their high horizontal resolution, RCMs are characterized by systematic biases relative to observations, which can result in unrealistic interpretations of future climate change signals. On the other hand, bias correction (BC) is a popular statistical post-processing technique applied to improve the usability of output from climate models. Like every other statistical technique, BC has its strengths and weaknesses. Hence, within the regional context of Germany, and for temperature and precipitation, this study is dedicated to the assessment of the impact of different BC techniques on the RCM output. The focuses are on the impact of BC on the RCM's statistical characterization, and physical consistency defined as the spatiotemporal consistency between the bias-corrected variable and the simulated physical mechanisms governing the variable, as well as the correlations between the bias-corrected variable and other (simulated) climate variables. Five BC techniques were applied in adjusting the systematic biases in temperature and precipitation RCM outputs. The BC techniques are linear scaling, empirical quantile mapping, univariate quantile delta mapping, multivariate quantile delta mapping that considers inter-site dependencies, and multivariate quantile delta mapping that considers inter-variable dependencies (MBCn). The results show that each BC technique adds value in reducing the biases in the statistics of the RCM output, though the added value depends on several factors such as the temporal resolution of the data, choice of RCM, climate variable, region, and the metric used in evaluating the BC technique. Further, the raw RCMs reproduced portions of the observed modes of atmospheric circulation in Western Europe, and the observed temperature, and precipitation meteorological patterns in Germany. After the BC, generally, the spatiotemporal configurations of the simulated meteorological patterns as well as the governing large-scale mechanisms were reproduced. However, at a more localized spatial scale for the individual meteorological patterns, the BC changed the simulated co-variability of some grids, especially for precipitation. Concerning the co-variability among the variables, a physically interpretable positive correlation was found between temperature and precipitation during boreal winter in both models and observations. For most grid boxes in the study domain and on average, the BC techniques that do not adjust inter-variable dependency did not notably change the simulated correlations between the climate variables. However, depending on the grid box, the (univariate) BC techniques tend to degrade the simulated temporal correlations between temperature and precipitation. Further, MBCn which adjusts biases in inter-variable dependency has the skill to improve the correlations between the simulated variables towards observations.},
  language  = {en}
}
@article{Ibebuchi2023,
  author    = {Ibebuchi, Chibuike Chiedozie},
  title     = {On the representation of atmospheric circulation modes in regional climate models over Western Europe},
  series = {International Journal of Climatology},
  volume    = {43},
  journal   = {International Journal of Climatology},
  number    = {1},
  doi       = {10.1002/joc.7807},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-312424},
  pages     = {668 -- 682},
  year      = {2023},
  abstract  = {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.},
  language  = {en}
}
@article{Ibebuchi2023,
  author    = {Ibebuchi, Chibuike Chiedozie},
  title     = {Circulation patterns linked to the positive sub-tropical Indian Ocean dipole},
  series = {Advances in Atmospheric Sciences},
  volume    = {40},
  journal   = {Advances in Atmospheric Sciences},
  number    = {1},
  issn      = {0256-1530},
  doi       = {10.1007/s00376-022-2017-2},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-324119},
  pages     = {110-128},
  year      = {2023},
  abstract  = {The positive phase of the subtropical Indian Ocean dipole (SIOD) is one of the climatic modes in the subtropical southern Indian Ocean that influences the austral summer inter-annual rainfall variability in parts of southern Africa. This paper examines austral summer rain-bearing circulation types (CTs) in Africa south of the equator that are related to the positive SIOD and the dynamics through which specific rainfall regions in southern Africa can be influenced by this relationship. Four austral summer rain-bearing CTs were obtained. Among the four CTs, the CT that featured (i) enhanced cyclonic activity in the southwest Indian Ocean; (ii) positive widespread rainfall anomaly in the southwest Indian Ocean; and (iii) low-level convergence of moisture fluxes from the tropical South Atlantic Ocean, tropical Indian Ocean, and the southwest Indian Ocean, over the south-central landmass of Africa, was found to be related to the positive SIOD climatic mode. The relationship also implies that positive SIOD can be expected to increase the amplitude and frequency of occurrence of the aforementioned CT. The linkage between the CT related to the positive SIOD and austral summer homogeneous regions of rainfall anomalies in Africa south of the equator showed that it is the principal CT that is related to the inter-annual rainfall variability of the south-central regions of Africa, where the SIOD is already known to significantly influence its rainfall variability. Hence, through the large-scale patterns of atmospheric circulation associated with the CT, the SIOD can influence the spatial distribution and intensity of rainfall over the preferred landmass through enhanced moisture convergence.},
  language  = {en}
}
@article{KacicThonfeldGessneretal.2023,
  author    = {Kacic, Patrick and Thonfeld, Frank and Gessner, Ursula and Kuenzer, Claudia},
  title     = {Forest structure characterization in Germany: novel products and analysis based on GEDI, Sentinel-1 and Sentinel-2 data},
  series = {Remote Sensing},
  volume    = {15},
  journal   = {Remote Sensing},
  number    = {8},
  issn      = {2072-4292},
  doi       = {10.3390/rs15081969},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-313727},
  year      = {2023},
  abstract  = {Monitoring forest conditions is an essential task in the context of global climate change to preserve biodiversity, protect carbon sinks and foster future forest resilience. Severe impacts of heatwaves and droughts triggering cascading effects such as insect infestation are challenging the semi-natural forests in Germany. As a consequence of repeated drought years since 2018, large-scale canopy cover loss has occurred calling for an improved disturbance monitoring and assessment of forest structure conditions. The present study demonstrates the potential of complementary remote sensing sensors to generate wall-to-wall products of forest structure for Germany. The combination of high spatial and temporal resolution imagery from Sentinel-1 (Synthetic Aperture Radar, SAR) and Sentinel-2 (multispectral) with novel samples on forest structure from the Global Ecosystem Dynamics Investigation (GEDI, LiDAR, Light detection and ranging) enables the analysis of forest structure dynamics. Modeling the three-dimensional structure of forests from GEDI samples in machine learning models reveals the recent changes in German forests due to disturbances (e.g., canopy cover degradation, salvage logging). This first consistent data set on forest structure for Germany from 2017 to 2022 provides information of forest canopy height, forest canopy cover and forest biomass and allows estimating recent forest conditions at 10 m spatial resolution. The wall-to-wall maps of the forest structure support a better understanding of post-disturbance forest structure and forest resilience.},
  language  = {en}
}
@article{KanmegneTamgaLatifiUllmannetal.2023,
  author    = {Kanmegne Tamga, Dan and Latifi, Hooman and Ullmann, Tobias and Baumhauer, Roland and Thiel, Michael and Bayala, Jules},
  title     = {Modelling the spatial distribution of the classification error of remote sensing data in cocoa agroforestry systems},
  series = {Agroforestry Systems},
  volume    = {97},
  journal   = {Agroforestry Systems},
  number    = {1},
  issn      = {0167-4366},
  doi       = {10.1007/s10457-022-00791-2},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-324139},
  pages     = {109-119},
  year      = {2023},
  abstract  = {Cocoa growing is one of the main activities in humid West Africa, which is mainly grown in pure stands. It is the main driver of deforestation and encroachment in protected areas. Cocoa agroforestry systems which have been promoted to mitigate deforestation, needs to be accurately delineated to support a valid monitoring system. Therefore, the aim of this research is to model the spatial distribution of uncertainties in the classification cocoa agroforestry. The study was carried out in C{\^o}te d'Ivoire, close to the Ta{\"i} National Park. The analysis followed three steps (i) image classification based on texture parameters and vegetation indices from Sentinel-1 and -2 data respectively, to train a random forest algorithm. A classified map with the associated probability maps was generated. (ii) Shannon entropy was calculated from the probability maps, to get the error maps at different thresholds (0.2, 0.3, 0.4 and 0.5). Then, (iii) the generated error maps were analysed using a Geographically Weighted Regression model to check for spatial autocorrelation. From the results, a producer accuracy (0.88) and a user's accuracy (0.91) were obtained. A small threshold value overestimates the classification error, while a larger threshold will underestimate it. The optimal value was found to be between 0.3 and 0.4. There was no evidence of spatial autocorrelation except for a smaller threshold (0.2). The approach differentiated cocoa from other landcover and detected encroachment in forest. Even though some information was lost in the process, the method is effective for mapping cocoa plantations in C{\^o}te d'Ivoire.},
  language  = {en}
}
@phdthesis{Kraff2023,
  author    = {Kraff, Nicolas Johannes},
  title     = {Analyse raumzeitlicher Ver{\"a}nderungen und ontologische Kategorisierung morphologischer Armutserscheinungen - Eine globale Betrachtung mithilfe von Satellitenbildern und manueller Bildinterpretation},
  doi       = {10.25972/OPUS-32026},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-320264},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2023},
  abstract  = {Die st{\"a}dtische Umwelt ist in steter Ver{\"a}nderung, vor allem durch den Bau, aber auch durch die Zerst{\"o}rung von st{\"a}dtischen Elementen. Die formelle Entwicklung ist ein Prozess mit langen Planungszeitr{\"a}umen und die bebaute Landschaft wirkt daher statisch. Dagegen unterliegen informelle oder spontane Siedlungen aufgrund ihrer stets unvollendeten st{\"a}dtischen Form einer hohen Dynamik - so wird in der Literatur berichtet. Allerdings sind Dynamik und die morphologischen Merkmale der physischen Transformation in solchen Siedlungen, die st{\"a}dtische Armut morphologisch repr{\"a}sentieren, auf globaler Ebene bisher kaum mit einer konsistenten Datengrundlage empirisch untersucht worden. Hier setzt die vorliegende Arbeit an. Unter der Annahme, dass die erforschte zeitliche Dynamik in Europa geringer ausf{\"a}llt, stellt sich die generelle Frage nach einer katalogisierten Erfassung physischer Wohnformen von Armut speziell in Europa. Denn Wohnformen der Armut werden oft ausschließlich mit dem ‚Globalen S{\"u}den' assoziiert, insbesondere durch die Darstellung von Slums. Tats{\"a}chlich ist Europa sogar die Wiege der Begriffe ‚Slum' und ‚Ghetto', die vor Jahrhunderten zur Beschreibung von Missst{\"a}nden und Unterdr{\"u}ckung auftauchten. Bis heute weist dieser facettenreiche Kontinent eine enorme Vielfalt an physischen Wohnformen der Armut auf, die ihre Wurzeln in unterschiedlichen Politiken, Kulturen, Geschichten und Lebensstilen haben. Um {\"u}ber diese genannten Aspekte Aufschluss zu erlangen, bedarf es u.a. der Bildanalyse durch Satellitenbilder. Diese Arbeit wird daher mittels Fernerkundung bzw. Erdbeobachtung (EO) sowie zus{\"a}tzlicher Literaturrecherchen und einer empirischen Erhebung erstellt. Um Unsicherheiten konzeptionell und in der Erfassung offenzulegen, ist die Methode der manuellen Bildinterpretation von Armutsgebieten kritisch zu hinterfragen. Das {\"u}bergeordnete Ziel dieser Arbeit ist eine bessere Wissensbasis {\"u}ber Armut zu schaffen, um Maßnahmen zur Reduzierung von Armut entwickeln zu k{\"o}nnen. Die Arbeit dient dabei als eine Antwort auf die Nachhaltigkeitsziele der Vereinten Nationen. Es wird Grundlagenforschung betrieben, indem Wissensl{\"u}cken in der Erdbeobachtung zu physisch-baulichen bzw. morphologischen Erscheinungen von Armut auf Geb{\"a}ude-Ebene explorativ analysiert werden. Die Arbeit wird in drei Forschungsthemen bzw. Studienteile untergliedert: Ziel des ersten Studienteils ist die globale raumzeitliche Erfassung von Dynamiken durch Ankn{\"u}pfung an bisherige Kategorisierungen von Armutsgebieten. Die bisherige Wissensl{\"u}cke soll gef{\"u}llt werden, indem {\"u}ber einen Zeitraum von etwa sieben Jahren in 16 dokumentierten Manifestationen st{\"a}dtischer Armut anhand von Erdbeobachtungsdaten eine zeitliche Analyse der bebauten Umwelt durchgef{\"u}hrt wird. Neben einer global verteilten Gebietsauswahl wird die visuelle Bildinterpretation (MVII) unter Verwendung von hochaufl{\"o}senden optischen Satellitendaten genutzt. Dies geschieht in Kombination mit in-situ- und Google Street View-Bildern zur Ableitung von 3D-Stadtmodellen. Es werden physische Raumstrukturen anhand von sechs r{\"a}umlichen morphologischen Variablen gemessen: Anzahl, Gr{\"o}ße, H{\"o}he, Ausrichtung und Dichte der Geb{\"a}ude sowie Heterogenit{\"a}t der Bebauung. Diese ‚temporale Analyse' zeigt zun{\"a}chst sowohl inter- als auch intra-urbane Unterschiede. Es lassen sich unterschiedliche, aber generell hohe morphologische Dynamiken zwischen den Untersuchungsgebieten finden. Dies dr{\"u}ckt sich in vielf{\"a}ltiger Weise aus: von abgerissenen und rekonstruierten Gebieten bis hin zu solchen, wo Ver{\"a}nderungen innerhalb der gegebenen Strukturen auftreten. Geographisch gesehen resultiert in der Stichprobe eine fortgeschrittene Dynamik, insbesondere in Gebieten des Globalen S{\"u}dens. Gleichzeitig l{\"a}sst sich eine hohe r{\"a}umliche Variabilit{\"a}t der morphologischen Transformationen innerhalb der untersuchten Gebiete beobachten. Trotz dieser teilweise hohen morphologischen Dynamik sind die r{\"a}umlichen Muster von Geb{\"a}udefluchten, Straßen und Freifl{\"a}chen {\"u}berwiegend konstant. Diese ersten Ergebnisse deuten auf einen geringen Wandel in Europa hin, weshalb diese europ{\"a}ischen Armutsgebiete im folgenden Studienteil von Grund auf erhoben und kategorisiert werden. Ziel des zweiten Studienteils ist die Erschaffung einer neuen Kategorisierung, speziell f{\"u}r das in der Wissenschaft unterrepr{\"a}sentierte Europa. Die verschiedenen Formen nicht indizierter Wohnungsmorphologien werden erforscht und kategorisiert, um das bisherige globale wissenschaftliche ontologische Portfolio f{\"u}r Europa zu erweitern. Hinsichtlich dieses zweiten Studienteils bietet eine Literaturrecherche mit mehr als 1.000 gesichteten Artikeln die weitere Grundlage f{\"u}r den folgenden Fokus auf Europa. Auf der Recherche basierend werden mittels der manuellen visuellen Bildinterpretation (engl.: MVII) erneut Satellitendaten zur Erfassung der physischen Morphologien von Wohnformen genutzt. Weiterhin kommen selbst definierte geographische Indikatoren zu Lage, Struktur und formellem Status zum Einsatz. Dar{\"u}ber hinaus werden gesellschaftliche Hintergr{\"u}nde, die durch Begriffe wie ‚Ghetto', ‚Wohnwagenpark', ‚ethnische Enklave' oder ‚Fl{\"u}chtlingslager' beschrieben werden, recherchiert und implementiert. Sie sollen als Erkl{\"a}rungsansatz f{\"u}r Armutsviertel in Europa dienen. Die Stichprobe der europ{\"a}ischen, insgesamt aber unbekannten Grundgesamtheit verdeutlicht eine große Vielfalt an physischen Formen: Es wird f{\"u}r Europa eine neue Kategorisierung von sechs Hauptklassen entwickelt, die von ‚einfachsten Wohnst{\"a}tten' (z. B. Zelten) {\"u}ber ‚behelfsm{\"a}ßige Unterk{\"u}nfte ' (z. B. Baracken, Container) bis hin zu ‚mehrst{\"o}ckigen Bauten' - als allgemeine Taxonomie der Wohnungsnot in Europa - reicht. Die Untersuchung zeigt verschiedene Wohnformen wie z. B. unterirdische oder mobile Typen, verfallene Wohnungen oder große Wohnsiedlungen, die die Armut im Europa des 21. Jahrhunderts widerspiegeln. {\"U}ber die Wohnungsmorphologie hinaus werden diese Klassen durch die Struktur und ihren rechtlichen Status beschrieben - entweder als geplante oder als organisch-gewachsene bzw. weiterhin als formelle, informelle oder hybride (halblegale) Formen. Geographisch lassen sich diese {\"a}rmlichen Wohnformen sowohl in st{\"a}dtischen als auch in l{\"a}ndlichen Gebieten finden, mit einer Konzentration in S{\"u}deuropa. Der Hintergrund bei der Mehrheit der Morphologien betrifft Fl{\"u}chtlinge, ethnische Minderheiten und sozio{\"o}konomisch benachteiligte Menschen - die ‚Unterprivilegierten'. Ziel des dritten Studienteils ist eine kritische Analyse der Methode. Zur Erfassung all dieser Siedlungen werden heutzutage Satellitenbilder aufgrund der Fortschritte bei den Bildklassifizierungsmethoden meist automatisch ausgewertet. Dennoch spielt die MVII noch immer eine wichtige Rolle, z.B. um Trainingsdaten f{\"u}r Machine-Learning-Algorithmen zu generieren oder f{\"u}r Validierungszwecke. In bestimmten st{\"a}dtischen Umgebungen jedoch, z.B. solchen mit h{\"o}chster Dichte und struktureller Komplexit{\"a}t, fordern spektrale und textur-basierte Verflechtungen von {\"u}berlappenden Dachstrukturen den menschlichen Interpreten immer noch heraus, wenn es darum geht einzelne Geb{\"a}udestrukturen zu erfassen. Die kognitive Wahrnehmung und die Erfahrung aus der realen Welt sind nach wie vor unumg{\"a}nglich. Vor diesem Hintergrund zielt die Arbeit methodisch darauf ab, Unsicherheiten speziell bei der Kartierung zu quantifizieren und zu interpretieren. Kartiert werden Dachfl{\"a}chen als ‚Fußabdr{\"u}cke' solcher Gebiete. Der Fokus liegt dabei auf der {\"U}bereinstimmung zwischen mehreren Bildinterpreten und welche Aspekte der Wahrnehmung und Elemente der Bildinterpretation die Kartierung beeinflussen. Um letztlich die Methode der MVII als drittes Ziel selbstkritisch zu reflektieren, werden Experimente als sogenannte ‚Unsicherheitsanalyse' geschaffen. Dabei digitalisieren zehn Testpersonen bzw. Probanden/Interpreten sechs komplexe Gebiete. Hierdurch werden quantitative Informationen {\"u}ber r{\"a}umliche Variablen von Geb{\"a}uden erzielt, um systematisch die Konsistenz und Kongruenz der Ergebnisse zu {\"u}berpr{\"u}fen. Ein zus{\"a}tzlicher Fragebogen liefert subjektive qualitative Informationen {\"u}ber weitere Schwierigkeiten. Da die Grundlage der hierf{\"u}r bisher genutzten Kategorisierungen auf der subjektiven Bildinterpretation durch den Menschen beruht, m{\"u}ssen etwaige Unsicherheiten und damit Fehleranf{\"a}lligkeiten offengelegt werden. Die Experimente zu dieser Unsicherheitsanalyse erfolgen quantifiziert und qualifiziert. Es lassen sich generell große Unterschiede zwischen den Kartierungsergebnissen der Probanden, aber eine hohe Konsistenz der Ergebnisse bei ein und demselben Probanden feststellen. Steigende Abweichungen korrelieren mit einer steigenden baustrukturellen (morphologischen) Komplexit{\"a}t. Ein hoher Grad an Individualit{\"a}t bei den Probanden {\"a}ußert sich in Aspekten wie z.B. Zeitaufwand beim Kartieren, in-situ Vorkenntnissen oder Vorkenntnissen beim Umgang mit Geographischen Informationssystemen (GIS). Nennenswert ist hierbei, dass die jeweilige Datenquelle das Kartierungsverfahren meist beeinflusst. Mit dieser Studie soll also auch an der Stelle der angewandten Methodik eine weitere Wissensl{\"u}cke gef{\"u}llt werden. Die bisherige Forschung komplexer urbaner Areale unter Nutzung der manuellen Bildinterpretation implementiert oftmals keine Unsicherheitsanalyse oder Quantifizierung von Kartierungsfehlern. Fernerkundungsstudien sollten k{\"u}nftig zur Validierung nicht nur zweifelsfrei auf MVII zur{\"u}ckgreifen k{\"o}nnen, sondern vielmehr sind Daten und Methoden notwendig, um Unsicherheiten auszuschließen. Zusammenfassend tr{\"a}gt diese Arbeit zur bisher wenig erforschten morphologischen Dynamik von Armutsgebieten bei. Es werden inter- wie auch intra-urbane Unterschiede auf globaler Ebene pr{\"a}sentiert. Dabei sind allgemein hohe morphologische Transformationen zwischen den selektierten Gebieten festzustellen. Die Ergebnisse deuten auf einen grundlegenden Kenntnismangel in Europa hin, weshalb an dieser Stelle angekn{\"u}pft wird. Eine {\"u}ber Europa verteilte Stichprobe erlaubt eine neue morphologische Kategorisierung der großen Vielfalt an gefundenen physischen Formen. Die Menge an Gebieten erschließt sich in einer unbekannten Grundgesamtheit. Zur Datenaufbereitung bisheriger Analysen m{\"u}ssen Satellitenbilder manuell interpretiert werden. Das Verfahren birgt Unsicherheiten. Als kritische Selbstreflexion zeigt eine Reihe von Experimenten signifikante Unterschiede zwischen den Ergebnissen der Probanden auf, verdeutlicht jedoch bei ein und derselben Person Best{\"a}ndigkeit.},
  subject      = {Slum},
  language  = {de}
}
@article{KunzUllmannKneiseletal.2023,
  author    = {Kunz, Julius and Ullmann, T. and Kneisel, C. and Baumhauer, R.},
  title     = {Three-dimensional subsurface architecture and its influence on the spatiotemporal development of a retrogressive thaw slump in the Richardson Mountains, Northwest Territories, Canada},
  series = {Arctic, Antarctic, and Alpine Research},
  volume    = {55},
  journal   = {Arctic, Antarctic, and Alpine Research},
  number    = {1},
  issn      = {1523-0430},
  doi       = {10.1080/15230430.2023.2167358},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-350147},
  year      = {2023},
  abstract  = {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.},
  language  = {en}
}
@article{Libanda2023,
  author    = {Libanda, Brigadier},
  title     = {Performance assessment of CORDEX regional climate models in wind speed simulations over Zambia},
  series = {Modeling Earth Systems and Environment},
  volume    = {9},
  journal   = {Modeling Earth Systems and Environment},
  number    = {1},
  issn      = {2363-6203},
  doi       = {10.1007/s40808-022-01504-5},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-324147},
  pages     = {253-262},
  year      = {2023},
  abstract  = {There is no single solution to cutting emissions, however, renewable energy projects that are backed by rigorous ex-ante assessments play an important role in these efforts. An inspection of literature reveals critical knowledge gaps in the understanding of future wind speed variability across Zambia, thus leading to major uncertainties in the understanding of renewable wind energy potential over the country. Several model performance metrics, both statistical and graphical were used in this study to examine the performance of CORDEX Africa Regional Climate Models (RCMs) in simulating wind speed across Zambia. Results indicate that wind speed is increasing at the rate of 0.006 m s\(^{-1}\) per year. RCA4-GFDL-ESM2M, RCA4-HadGEM2-ES, RCA4-IPSL-CM5A-MR, and RCA4-CSIRO-MK3.6.0 were found to correctly simulate wind speed increase with varying magnitudes on the Sen's estimator of slope. All the models sufficiently reproduce the annual cycle of wind speed with a steady increase being observed from April reaching its peak around August/September and beginning to drop in October. Apart from RegCM4-MPI-ESM and RegCM4-HadGEM2, the performance of RCMs in simulating spatial wind speed patterns is generally good although they overestimate it by ~ 1 m s\(^{-1}\) in the western and southern provinces of the country. Model performance metrics indicate that with a correlation coefficient of 0.5, a root mean square error of 0.4 m s\(^{-1}\), an RSR value of 7.7 and a bias of 19.9\%, RCA4-GFDL-ESM2M outperforms all other models followed by RCA4-HadGEM2, and RCA4-CM5A-MR respectively. These results, therefore, suggest that studies that use an ensemble of RCA4-GFDL-ESM2M, RCA4-HadGEM2, and RCA4-CM5A-MR would yield useful results for informing future renewable wind energy potential in Zambia.},
  language  = {en}
}
@article{LibandaPaeth2023,
  author    = {Libanda, Brigadier and Paeth, Heiko},
  title     = {Modelling wind speed across Zambia: Implications for wind energy},
  series = {International Journal of Climatology},
  volume    = {43},
  journal   = {International Journal of Climatology},
  number    = {2},
  doi       = {10.1002/joc.7826},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-312134},
  pages     = {772 -- 786},
  year      = {2023},
  abstract  = {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.},
  language  = {en}
}
@phdthesis{Majewski2023,
  author    = {Majewski, Lisa},
  title     = {Input-Output-Analyse zur Ermittlung der regional{\"o}konomischen Effekte des Tourismus in Schutzgebieten : Eine Adaption der Methodik an internationale Standards am Fallbeispiel Biosph{\"a}rengebiet Schwarzwald},
  publisher = {W{\"u}rzburg University Press},
  address   = {W{\"u}rzburg},
  isbn      = {978-3-95826-216-4},
  issn      = {0510-9833},
  doi       = {10.25972/WUP-978-3-95826-217-1},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-316545},
  school      = {W{\"u}rzburg University Press},
  pages     = {XVI, 444},
  year      = {2023},
  abstract  = {Schutzgebiete gelten laut der Convention on Biological Diversity als Fl{\"a}cheninstrument zum Schutz der Biodiversit{\"a}t. Menschen profitieren davon unter anderem durch die Nutzung als touristische Attraktion. Sch{\"a}tzungen zufolge werden weltweit etwa acht Mrd. Besuche zur Wahrnehmung des Naturerlebnisangebots der Schutzgebiete erreicht, woraus direkte Besucherausgaben in H{\"o}he von 600 Mrd. US-\$ resultieren. Schutzgebiete sind damit auch bedeutende Wirtschaftsmotoren der regionalen {\"O}konomien. Deutschlands Nationalparks z{\"a}hlen j{\"a}hrlich etwa 53 Mio. Besuchstage, deren t{\"a}gliche Ausgaben vor Ort einen Bruttoumsatz in H{\"o}he von 2,78 Mrd. € generieren. Die touristische Wertsch{\"o}pfung betr{\"a}gt 1,45 Mrd. €. Die weiteren 65 Mio. Besuchstage in deutschen Biosph{\"a}renreservaten erwirtschaften einen Bruttoumsatz in H{\"o}he von 2,94 Mrd. €. Das Einkommen von 172.000 Personen ist vom Tourismus in deutschen Nationalparken und Biosph{\"a}renreservaten abh{\"a}ngig. Im Rahmen einer ersten Studie zu den regional{\"o}konomischen Effekten des Nationalparks Berchtesgaden im Jahr 2002 wurde die touristische Wertsch{\"o}pfungsanalyse als Standardmethode der deutschen Schutzgebietsforschung etabliert. Im Laufe der Jahre wurde sie dahingehend modifiziert, ein vergleichbares, weil standardisiertes Vorgehen anwenden zu k{\"o}nnen. Die internationale Forschung manifestiert mit der Herausgabe eines Leitfadens einen anderen Standard zur regional{\"o}konomischen Wirkungsanalyse des Tourismus in Schutzgebieten: die Input-Output-Analyse. Schutzgebietsverwaltungen in den USA, Kanada, Brasilien, Namibia, S{\"u}dafrika und Finnland f{\"u}hren f{\"u}r ihr Besuchermonitoring Input-Output-Analysen durch. Diese sind im Vergleich zur Wertsch{\"o}pfungsanalyse als der validiere Ansatz einzustufen, weil damit ein Rechenwerk gegeben ist, womit indirekte Vorleistungs- und induzierte Konsumwirkungen zuverl{\"a}ssig quantifiziert werden k{\"o}nnen. Die Wertsch{\"o}pfungsanalyse arbeitet hingegen mit pauschalen Wertsch{\"o}pfungsquoten f{\"u}r alle touristischen Wirtschaftszweige und auf jeder Maßstabsebene. Aufgrund der fehlenden Datenverf{\"u}gbarkeit konnte die Input-Output-Analyse in Deutschland bisher nicht angewandt werden. Eine potenzielle Datenquelle er{\"o}ffnete sich durch das US-amerikanische Modellierungsunternehmen IMPLAN, welches Input-Output-Tabellen f{\"u}r die regionale Ebene der EU anbot. IMPLAN-Daten werden auch vom US-amerikanischen National Park Service verwendet. Die vorliegende Arbeit versteht sich als methodische Weiterentwicklung regional{\"o}konomischer Wirkungsanalysen in Deutschlands Schutzgebieten zur Adaption an internationale Standards. Dazu erfolgt die Applikation der Input-Output-Analyse f{\"u}r das Fallbeispiel des Biosph{\"a}rengebiets Schwarzwald, dessen Regional{\"o}konomie einen {\"u}berschaubaren Analyserahmen bietet. Der Nationalpark Schwarzwald wurde als Vergleichsregion zur Validierung der Ergebnisse untersucht. F{\"u}r eine erweiterte Einordnung der touristischen Multiplikatorwirkung in der Schwarzwaldregion wurde eine multiregionale Input-Output-Analyse durchgef{\"u}hrt, die sich auf die Gebietsabgrenzung der beiden Naturparke Schwarzwald Mitte/Nord und S{\"u}dschwarzwald bezieht. Zur Quantifizierung der direkten Wirkungsebene wurden touristische Kenngr{\"o}ßen der amtlichen Statistik entnommen. Die Berechnung von direkten Wertsch{\"o}pfungsquoten erfolgte gem{\"a}ß ihrer Definition als die in der Region verbleibende Wertsch{\"o}pfung am touristischen Produktionswert. Mittels der Input-Output-Analyse wurden die indirekten und induzierten Effekte des Tourismus im Biosph{\"a}rengebiet Schwarzwald ermittelt. Aus den regionalen Input-Output-Tabellen wurden inverse Koeffizienten abgeleitet, welche die regional{\"o}konomischen Multiplikatoren anzeigen. Zwei Multiplikatortypen wurden f{\"u}r die touristischen Kenngr{\"o}ßen Output, Wertsch{\"o}pfung und Besch{\"a}ftigung berechnet: Typ I-Multiplikatoren bemessen die indirekten Vorleistungseffekte touristischer Ausgaben. Typ II-Multiplikatoren inkludieren auch die induzierten Konsumeffekte. In einem mehrstufigen Prozess der Analyse von weiteren Fallbeispielen k{\"o}nnen regional{\"o}konomische Multiplikatoren f{\"u}r verschiedene Gebietseinheiten validiert und so ganzheitlich abgestimmt f{\"u}r das deutsche Schutzgebietssystem adaptiert werden. Dadurch k{\"o}nnte die Input-Output-Analyse als neue Standardmethode f{\"u}r ein dauerhaftes regional{\"o}konomisches Monitoring in deutschen Schutzgebieten etabliert werden.},
  language  = {de}
}
@article{MeistervonSuchodoletzZeeden2023,
  author    = {Meister, Julia and von Suchodoletz, Hans and Zeeden, Christian},
  title     = {Preface: Quaternary research from and inspired by the first virtual DEUQUA conference},
  series = {E\&G Quaternary Science Journal},
  volume    = {72},
  journal   = {E\&G Quaternary Science Journal},
  number    = {2},
  doi       = {10.5194/egqsj-72-185-2023},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-350157},
  pages     = {185-187},
  year      = {2023},
  abstract  = {No abstract available.},
  language  = {en}
}
@article{OuedraogoHackmanThieletal.2023,
  author    = {Ouedraogo, Valentin and Hackman, Kwame Oppong and Thiel, Michael and Dukiya, Jaiye},
  title     = {Intensity analysis for urban Land Use/Land Cover dynamics characterization of Ouagadougou and Bobo-Dioulasso in Burkina Faso},
  series = {Land},
  volume    = {12},
  journal   = {Land},
  number    = {5},
  issn      = {2073-445X},
  doi       = {10.3390/land12051063},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-319397},
  year      = {2023},
  abstract  = {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.},
  language  = {en}
}
@phdthesis{Philipp2023,
  author    = {Philipp, Marius Balthasar},
  title     = {Quantifying the Effects of Permafrost Degradation in Arctic Coastal Environments via Satellite Earth Observation},
  doi       = {10.25972/OPUS-34563},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-345634},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2023},
  abstract  = {Permafrost degradation is observed all over the world as a consequence of climate change and the associated Arctic amplification, which has severe implications for the environment. Landslides, increased rates of surface deformation, rising likelihood of infrastructure damage, amplified coastal erosion rates, and the potential turnover of permafrost from a carbon sink to a carbon source are thereby exemplary implications linked to the thawing of frozen ground material. In this context, satellite earth observation is a potent tool for the identification and continuous monitoring of relevant processes and features on a cheap, long-term, spatially explicit, and operational basis as well as up to a circumpolar scale. A total of 325 articles published in 30 different international journals during the past two decades were investigated on the basis of studied environmental foci, remote sensing platforms, sensor combinations, applied spatio-temporal resolutions, and study locations in an extensive review on past achievements, current trends, as well as future potentials and challenges of satellite earth observation for permafrost related analyses. The development of analysed environmental subjects, utilized sensors and platforms, and the number of annually published articles over time are addressed in detail. Studies linked to atmospheric features and processes, such as the release of greenhouse gas emissions, appear to be strongly under-represented. Investigations on the spatial distribution of study locations revealed distinct study clusters across the Arctic. At the same time, large sections of the continuous permafrost domain are only poorly covered and remain to be investigated in detail. A general trend towards increasing attention in satellite earth observation of permafrost and related processes and features was observed. The overall amount of published articles hereby more than doubled since the year 2015. New sources of satellite data, such as the Sentinel satellites and the Methane Remote Sensing LiDAR Mission (Merlin), as well as novel methodological approaches, such as data fusion and deep learning, will thereby likely improve our understanding of the thermal state and distribution of permafrost, and the effects of its degradation. Furthermore, cloud-based big data processing platforms (e.g. Google Earth Engine (GEE)) will further enable sophisticated and long-term analyses on increasingly larger scales and at high spatial resolutions. In this thesis, a specific focus was put on Arctic permafrost coasts, which feature increasing vulnerability to environmental parameters, such as the thawing of frozen ground, and are therefore associated with amplified erosion rates. In particular, a novel monitoring framework for quantifying Arctic coastal erosion rates within the permafrost domain at high spatial resolution and on a circum-Arctic scale is presented within this thesis. Challenging illumination conditions and frequent cloud cover restrict the applicability of optical satellite imagery in Arctic regions. In order to overcome these limitations, Synthetic Aperture RADAR (SAR) data derived from Sentinel-1 (S1), which is largely independent from sun illumination and weather conditions, was utilized. Annual SAR composites covering the months June-September were combined with a Deep Learning (DL) framework and a Change Vector Analysis (CVA) approach to generate both a high-quality and circum-Arctic coastline product as well as a coastal change product that highlights areas of erosion and build-up. Annual composites in the form of standard deviation (sd) and median backscatter were computed and used as inputs for both the DL framework and the CVA coastal change quantification. The final DL-based coastline product covered a total of 161,600 km of Arctic coastline and featured a median accuracy of ±6.3 m to the manually digitized reference data. Annual coastal change quantification between 2017-2021 indicated erosion rates of up to 67 m per year for some areas based on 400 m coastal segments. In total, 12.24\% of the investigated coastline featured an average erosion rate of 3.8 m per year, which corresponds to 17.83 km2 of annually eroded land area. Multiple quality layers associated to both products, the generated DL-coastline and the coastal change rates, are provided on a pixel basis to further assess the accuracy and applicability of the proposed data, methods, and products. Lastly, the extracted circum-Arctic erosion rates were utilized as a basis in an experimental framework for estimating the amount of permafrost and carbon loss as a result of eroding permafrost coastlines. Information on permafrost fraction, Active Layer Thickness (ALT), soil carbon content, and surface elevation were thereby combined with the aforementioned erosion rates. While the proposed experimental framework provides a valuable outline for quantifying the volume loss of frozen ground and carbon release, extensive validation of the utilized environmental products and resulting volume loss numbers based on 200 m segments are necessary. Furthermore, data of higher spatial resolution and information of carbon content for deeper soil depths are required for more accurate estimates.},
  subject      = {Dauerfrostboden},
  language  = {en}
}
@article{PhilippDietzUllmannetal.2023,
  author    = {Philipp, Marius and Dietz, Andreas and Ullmann, Tobias and Kuenzer, Claudia},
  title     = {A circum-Arctic monitoring framework for quantifying annual erosion rates of permafrost coasts},
  series = {Remote Sensing},
  volume    = {15},
  journal   = {Remote Sensing},
  number    = {3},
  issn      = {2072-4292},
  doi       = {10.3390/rs15030818},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-304447},
  year      = {2023},
  abstract  = {This study demonstrates a circum-Arctic monitoring framework for quantifying annual change of permafrost-affected coasts at a spatial resolution of 10 m. Frequent cloud coverage and challenging lighting conditions, including polar night, limit the usability of optical data in Arctic regions. For this reason, Synthetic Aperture RADAR (SAR) data in the form of annual median and standard deviation (sd) Sentinel-1 (S1) backscatter images covering the months June-September for the years 2017-2021 were computed. Annual composites for the year 2020 were hereby utilized as input for the generation of a high-quality coastline product via a Deep Learning (DL) workflow, covering 161,600 km of the Arctic coastline. The previously computed annual S1 composites for the years 2017 and 2021 were employed as input data for the Change Vector Analysis (CVA)-based coastal change investigation. The generated DL coastline product served hereby as a reference. Maximum erosion rates of up to 67 m per year could be observed based on 400 m coastline segments. Overall highest average annual erosion can be reported for the United States (Alaska) with 0.75 m per year, followed by Russia with 0.62 m per year. Out of all seas covered in this study, the Beaufort Sea featured the overall strongest average annual coastal erosion of 1.12 m. Several quality layers are provided for both the DL coastline product and the CVA-based coastal change analysis to assess the applicability and accuracy of the output products. The predicted coastal change rates show good agreement with findings published in previous literature. The proposed methods and data may act as a valuable tool for future analysis of permafrost loss and carbon emissions in Arctic coastal environments.},
  language  = {en}
}
@phdthesis{Reinermann2023,
  author    = {Reinermann, Sophie},
  title     = {Earth Observation Time Series for Grassland Management Analyses - Development and large-scale Application of a Framework to detect Grassland Mowing Events in Germany},
  doi       = {10.25972/OPUS-32273},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-322737},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2023},
  abstract  = {Grasslands shape many landscapes of the earth as they cover about one-third of its surface. They are home and provide livelihood for billions of people and are mainly used as source of forage for animals. However, grasslands fulfill many additional ecosystem functions next to fodder production, such as storage of carbon, water filtration, provision of habitats and cultural values. They play a role in climate change (mitigation) and in preserving biodiversity and ecosystem functions on a global scale. The degree to what these ecosystem functions are present within grassland ecosystems is largely determined by the management. Individual management practices and the use intensity influence the species composition as well as functions, like carbon storage, while higher use intensities (e.g. high mowing frequencies) usually show a negative impact. Especially in Central European countries, like in Germany, the determining influence of grassland management on its physiognomy and ecosystem functions leads to a large variability and small-scale alternations of grassland parcels. Large-scale information on the management and use intensity of grasslands is not available. Consequently, estimations of grassland ecosystem functions are challenging which, however, would be required for large-scale assessments of the status of grassland ecosystems and optimized management plans for the future. The topic of this thesis tackles this gap by investigating the major grassland management practice in Germany, which is mowing, for multiple years, in high spatial resolution and on a national scale. Earth Observation (EO) has the advantage of providing information of the earth's surface on multi-temporal time steps. An extensive literature review on the use of EO for grassland management and production analyses, which was part of this thesis, showed that in particular research on grasslands consisting of small parcels with a large variety of management and use intensity, like common in Central Europe, is underrepresented. Especially the launch of the Sentinel satellites in the recent past now enables the analyses of such grasslands due to their high spatial and temporal resolution. The literature review specifically on the investigation of grassland mowing events revealed that most previous studies focused on small study areas, were exploratory, only used one sensor type and/or lacked a reference data set with a complete range of management options. Within this thesis a novel framework to detect grassland mowing events over large areas is presented which was applied and validated for the entire area of Germany for multiple years (2018-2021). The potential of both sensor types, optical (Sentinel-2) and Synthetic Aperture Radar (SAR) (Sentinel-1) was investigated regarding grassland mowing event detection. Eight EO parameters were investigated, namely the Enhanced Vegetation Index (EVI), the backscatter intensity and the interferometric (InSAR) temporal coherence for both available polarization modes (VV and VH), and the polarimetric (PolSAR) decomposition parameters Entropy, K0 and K1. An extensive reference data set was generated based on daily images of webcams distributed in Germany which resulted in mowing information for grasslands with the entire possible range of mowing frequencies - from one to six in Germany - and in 1475 reference mowing events for the four years of interest. For the first time a observation-driven mowing detection approach including data from Sentinel-2 and Sentinel-1 and combining the two was developed, applied and validated on large scale. Based on a subset of the reference data (13 grassland parcels with 44 mowing events) from 2019 the EO parameters were investigated and the detection algorithm developed and parameterized. This analysis showed that a threshold-based change detection approach based on EVI captured grassland mowing events best, which only failed during periods of clouds. All SAR-based parameters showed a less consistent behavior to mowing events, with PolSAR Entropy and InSAR Coherence VH, however, revealing the highest potential among them. A second, combined approach based on EVI and a SARbased parameter was developed and tested for PolSAR Entropy and InSAR VH. To avoid additional false positive detections during periods in which mowing events are anyhow reliably detected using optical data, the SAR-based mowing detection was only initiated during long gaps within the optical time series (< 25 days). Application and validation of these approaches in a focus region revealed that only using EVI leads to the highest accuracies (F1-Score = 0.65) as combining this approach with SAR-based detection led to a strong increase in falsely detected mowing events resulting in a decrease of accuracies (EVI + PolSAR ENT F1-Score = 0.61; EVI + InSAR COH F1-Score = 0.61). The mowing detection algorithm based on EVI was applied for the entire area of Germany for the years 2018-2021. It was revealed that the largest share of grasslands with high mowing frequencies (at least four mowing events) can be found in southern/south-eastern Germany. Extensively used grassland (mown up to two times) is distributed within the entire country with larger shares in the center and north-eastern parts of Germany. These patterns stay constant in general, but small fluctuations between the years are visible. Early mown grasslands can be found in southern/south-eastern Germany - in line with high mowing frequency areas - but also in central-western parts. The years 2019 and 2020 revealed higher accuracies based on the 1475 mowing events of the multi-annual validation data set (F1-Scores of 0.64 and 0.63), 2018 and 2021 lower ones (F1-Score of 0.52 and 0.50). Based on this new, unprecedented data set, potential influencing factors on the mowing dynamics were investigated. Therefore, climate, topography, soil data and information on conservation schemes were related to mowing dynamics for the year 2020, which showed a high number of valid observations and detection accuracy. It was revealed that there are no strong linear relationships between the mowing frequency or the timing of the first mowing event and the investigated variables. However, it was found that for intensive grassland usage certain climatic and topographic conditions have to be fulfilled, while extensive grasslands appear on the entire spectrum of these variables. Further, higher mowing frequencies occur on soils with influence of ground water and lower mowing frequencies in protected areas. These results show the complex interplay between grassland mowing dynamics and external influences and highlight the challenges of policies aiming to protect grassland ecosystem functions and their need to be adapted to regional circumstances.},
  subject      = {Gr{\"u}nland},
  language  = {en}
}
@article{ReinermannAsamGessneretal.2023,
  author    = {Reinermann, Sophie and Asam, Sarah and Gessner, Ursula and Ullmann, Tobias and Kuenzer, Claudia},
  title     = {Multi-annual grassland mowing dynamics in Germany},
  series = {Frontiers in Environmental Science},
  volume    = {11},
  journal   = {Frontiers in Environmental Science},
  issn      = {2296-665X},
  doi       = {10.3389/fenvs.2023.1040551},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-320700},
  year      = {2023},
  abstract  = {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.},
  language  = {en}
}
@article{ReinersSobrinoKuenzer2023,
  author    = {Reiners, Philipp and Sobrino, Jos{\´e} and Kuenzer, Claudia},
  title     = {Satellite-derived land surface temperature dynamics in the context of global change — a review},
  series = {Remote Sensing},
  volume    = {15},
  journal   = {Remote Sensing},
  number    = {7},
  issn      = {2072-4292},
  doi       = {10.3390/rs15071857},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-311120},
  year      = {2023},
  abstract  = {Satellite-derived Land Surface Temperature (LST) dynamics have been increasingly used to study various geophysical processes. This review provides an extensive overview of the applications of LST in the context of global change. By filtering a selection of relevant keywords, a total of 164 articles from 14 international journals published during the last two decades were analyzed based on study location, research topic, applied sensor, spatio-temporal resolution and scale and employed analysis methods. It was revealed that China and the USA were the most studied countries and those that had the most first author affiliations. The most prominent research topic was the Surface Urban Heat Island (SUHI), while the research topics related to climate change were underrepresented. MODIS was by far the most used sensor system, followed by Landsat. A relatively small number of studies analyzed LST dynamics on a global or continental scale. The extensive use of MODIS highly determined the study periods: A majority of the studies started around the year 2000 and thus had a study period shorter than 25 years. The following suggestions were made to increase the utilization of LST time series in climate research: The prolongation of the time series by, e.g., using AVHRR LST, the better representation of LST under clouds, the comparison of LST to traditional climate change measures, such as air temperature and reanalysis variables, and the extension of the validation to heterogenous sites.},
  language  = {en}
}
@article{SchaeferFaethKneiseletal.2023,
  author    = {Sch{\"a}fer, Christian and F{\"a}th, Julian and Kneisel, Christof and Baumhauer, Roland and Ullmann, Tobias},
  title     = {Multidimensional hydrological modeling of a forested catchment in a German low mountain range using a modular runoff and water balance model},
  series = {Frontiers in Forests and Global Change},
  volume    = {6},
  journal   = {Frontiers in Forests and Global Change},
  doi       = {10.3389/ffgc.2023.1186304},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-357358},
  year      = {2023},
  abstract  = {Sufficient plant-available water is one of the most important requirements for vital, stable, and well-growing forest stands. In the face of climate change, there are various approaches to derive recommendations considering tree species selection based on plant-available water provided by measurements or simulations. Owing to the small-parcel management of Central European forests as well as small-spatial variation of soil and stand properties, in situ data collection for individual forest stands of large areas is not feasible, considering time and cost effort. This problem can be addressed using physically based modeling, aiming to numerically simulate the water balance. In this study, we parameterized, calibrated, and verified the hydrological multidimensional WaSiM-ETH model to assess the water balance at a spatial resolution of 30 m in a German forested catchment area (136.4 km2) for the period 2000-2021 using selected in situ data, remote sensing products, and total runoff. Based on the model output, drought-sensitive parameters, such as the difference between potential and effective stand transpiration (Tdiff) and the water balance, were deduced from the model, analyzed, and evaluated. Results show that the modeled evapotranspiration (ET) correlated significantly (R2 = 0.80) with the estimated ET using MODIS data (MOD16A2GFv006). Compared with observed daily, monthly, and annual runoff data, the model shows a good performance (R2: 0.70|0.77|0.73; Kling-Gupta efficiency: 0.59|0.62|0.83; volumetric efficiency: 0.52|0.60|0.83). The comparison with in situ data from a forest monitoring plot, established at the end of 2020, indicated good agreement between observed and simulated interception and soil water content. According to our results, WaSiM-ETH is a potential supplement for forest management, owing to its multidimensionality and the ability to model soil water balance for large areas at comparable high spatial resolution. The outputs offer, compared to non-distributed models (like LWF-Brook90), spatial differentiability, which is important for small-scale parceled forests, regarding stand structure and soil properties. Due to the spatial component offered, additional verification possibilities are feasible allowing a reliable and profound verification of the model and its parameterization.},
  language  = {en}
}
@article{SenaratneMuehlbauerKiefletal.2023,
  author    = {Senaratne, Hansi and M{\"u}hlbauer, Martin and Kiefl, Ralph and C{\´a}rdenas, Andrea and Prathapan, Lallu and Riedlinger, Torsten and Biewer, Carolin and Taubenb{\"o}ck, Hannes},
  title     = {The Unseen — an investigative analysis of thematic and spatial coverage of news on the ongoing refugee crisis in West Africa},
  series = {ISPRS International Journal of Geo-Information},
  volume    = {12},
  journal   = {ISPRS International Journal of Geo-Information},
  number    = {4},
  issn      = {2220-9964},
  doi       = {10.3390/ijgi12040175},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-313607},
  year      = {2023},
  abstract  = {The fastest growing regional crisis is happening in West Africa today, with over 8 million people considered persons of concern. A culmination of identity politics, climate-driven disasters, and extreme poverty has led to this humanitarian crisis in the region and is exacerbated by a lack of political will and misplaced media attention. The current state of the art does not present sufficient investigations of the thematic and spatial coverage of news media of this crisis in this region. This paper studies the spatial coverage of this crisis as reported in the media, and the themes associated with those locations, based on a curated dataset. For the time frame 12 March to 15 September 2021, 2017 news articles related to the refugee crisis in West Africa were examined and manually coded based on (1) the geographical locations mentioned in each article; (2) the themes found in the articles in reference to a location (e.g., Relocation of people in Abuja). The dataset introduces a thematic dimension, as never achieved before, to the conflict-ridden areas in West Africa. A comparative analysis with UNHCR (United Nations High Commissioner for Refugees) data showed that 96.8\% of refugee-related locations in West Africa were not covered by news during the considered time frame. Contrastingly, 80.4\% of locations mentioned in the news do not appear in the UNHCR repository. Most news articles published during this time frame reported on Development aid or Political statements. Linear multiple regression analysis showed GDP per capita and political stability to be among the most influential determinants of news coverage.},
  language  = {en}
}
@article{SteiningerAbelZiegleretal.2023,
  author    = {Steininger, Michael and Abel, Daniel and Ziegler, Katrin and Krause, Anna and Paeth, Heiko and Hotho, Andreas},
  title     = {ConvMOS: climate model output statistics with deep learning},
  series = {Data Mining and Knowledge Discovery},
  volume    = {37},
  journal   = {Data Mining and Knowledge Discovery},
  number    = {1},
  issn      = {1384-5810},
  doi       = {10.1007/s10618-022-00877-6},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-324213},
  pages     = {136-166},
  year      = {2023},
  abstract  = {Climate models are the tool of choice for scientists researching climate change. Like all models they suffer from errors, particularly systematic and location-specific representation errors. One way to reduce these errors is model output statistics (MOS) where the model output is fitted to observational data with machine learning. In this work, we assess the use of convolutional Deep Learning climate MOS approaches and present the ConvMOS architecture which is specifically designed based on the observation that there are systematic and location-specific errors in the precipitation estimates of climate models. We apply ConvMOS models to the simulated precipitation of the regional climate model REMO, showing that a combination of per-location model parameters for reducing location-specific errors and global model parameters for reducing systematic errors is indeed beneficial for MOS performance. We find that ConvMOS models can reduce errors considerably and perform significantly better than three commonly used MOS approaches and plain ResNet and U-Net models in most cases. Our results show that non-linear MOS models underestimate the number of extreme precipitation events, which we alleviate by training models specialized towards extreme precipitation events with the imbalanced regression method DenseLoss. While we consider climate MOS, we argue that aspects of ConvMOS may also be beneficial in other domains with geospatial data, such as air pollution modeling or weather forecasts.},
  subject      = {Klima},
  language  = {en}
}
@article{WeismannMoeckelPaethetal.2023,
  author    = {Weismann, Dirk and M{\"o}ckel, Martin and Paeth, Heiko and Slagman, Anna},
  title     = {Modelling variations of emergency attendances using data on community mobility, climate and air pollution},
  series = {Scientific Reports},
  volume    = {13},
  journal   = {Scientific Reports},
  doi       = {10.1038/s41598-023-47857-4},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-357578},
  year      = {2023},
  abstract  = {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.},
  language  = {en}
}
@book{Wiedemann2023,
  author    = {Wiedemann, Cathrin},
  title     = {Picken, Packen, Radeln? Betriebsformen, Standorte, Arbeitsprozesse und deren Auswirkungen auf Besch{\"a}ftigte im Lebensmittelonlinehandel in Deutschland},
  edition   = {1. Auflage},
  publisher = {W{\"u}rzburg University Press},
  address   = {W{\"u}rzburg},
  isbn      = {978-3-95826-208-9},
  issn      = {2196-5811},
  doi       = {10.25972/WUP-978-3-95826-209-6},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-298886},
  publisher      = {W{\"u}rzburg University Press},
  pages     = {XV, 171},
  year      = {2023},
  abstract  = {Der Lebensmittelonlinehandel in Deutschland gewann, verst{\"a}rkt durch die Covid-19-Pandemie, an Umsatzanteilen im Lebensmitteleinzelhandel. Hierdurch wurden neue Anforderungen an Arbeit und Besch{\"a}ftigung in Deutschland geschaffen. Insbesondere in urbanen R{\"a}umen hat die Lebensmittelzustellung durch neu entstandene Betriebsformen zugenommen. So entstehen durch das Versprechen der Betriebe, Lebensmittel in kurzen Zeitr{\"a}umen zu liefern, verschiedene Logistikstandorte und u.a. urbane Fahrradlieferdienste. W{\"a}hrend Medien und Gewerkschaften bereits vor der Entstehung prek{\"a}rer Arbeitsbedingungen warnen, sind die genauen Auswirkungen des Lebensmittelonlinehandels auf die Entwicklung neuer Arbeitsstandorte und die dort stattfindende Besch{\"a}ftigung nur unzureichend bekannt. Diese Arbeit untersucht den Lebensmittelonlinehandel anhand seiner Betriebsformen, Standorte und Arbeitsprozesse sowie deren Auswirkungen auf Besch{\"a}ftigte in Deutschland. Den konzeptionellen Hintergrund bilden Arbeiten der geographischen Handelsforschung sowie Debatten zu Arbeitsplatzqualit{\"a}t und Besch{\"a}ftigung. F{\"u}r die Analyse sind Prim{\"a}rdaten und Sekund{\"a}rdaten erhoben worden. Es zeigt sich, dass teilweise komplexe Betriebsformen entstehen, bei denen sich die Arbeit und Arbeitsorte ver{\"a}ndern. Zudem entstehen neue Herausforderungen f{\"u}r die Besch{\"a}ftigten (u.a. physische und psychische Belastung), welche in dieser Arbeit identifiziert werden.},
  subject      = {Arbeitsprozess},
  language  = {de}
}