@article{HoehnFrimmelDebailleetal.2021,
  author    = {H{\"o}hn, Stefan and Frimmel, Hartwig E. and Debaille, Vinciane and Price, Westley},
  title     = {Pre-Klondikean oxidation prepared the ground for Broken Hill-type mineralization in South Africa},
  series = {Terra Nova},
  volume    = {33},
  journal   = {Terra Nova},
  number    = {2},
  doi       = {10.1111/ter.12502},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-218545},
  pages     = {168 -- 173},
  year      = {2021},
  abstract  = {New Cu isotope data obtained on chalcopyrite from the Black Mountain and the Broken Hill deposits in the medium- to high-grade metamorphic Aggeneys-Gamsberg ore district (South Africa) require a revision of our understanding of the genesis of metamorphic Broken Hill-type massive sulphide deposits. Chalcopyrite from both deposits revealed unusually wide ranges in δ\(^{65}\)Cu (-2.41 to 2.84 per mille NIST 976 standard) in combination with distinctly positive mean values (0.27 and 0.94 per mille, respectively). This is interpreted to reflect derivation from various silicate and oxide precursor minerals in which Cu occurred in higher oxidation states. Together with the observation of a typical supergene base metal distribution within the deposits and their spatial association with an unconformity only meters above the ore horizon, our new data are best explained by supergene oxidation of originally possibly SEDEX deposits prior to metamorphic sulphide formation, between the Okiepian (1,210-1,180 Ma) and Klondikean (1,040-1,020 Ma) orogenic events.},
  language  = {en}
}
@article{JobBittlingmaierMayeretal.2021,
  author    = {Job, Hubert and Bittlingmaier, Sarah and Mayer, Marius and von Ruschkowski, Eick and Woltering, Manuel},
  title     = {Park-People Relationships: The Socioeconomic Monitoring of National Parks in Bavaria, Germany},
  series = {Sustainability},
  volume    = {13},
  journal   = {Sustainability},
  number    = {16},
  issn      = {2071-1050},
  doi       = {10.3390/su13168984},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-245061},
  year      = {2021},
  abstract  = {Questions about park-people relationships and the understanding and handling of the conflicts that may result from the creation and management of national parks in the surrounding area are prerequisites for both successful park management and sustainable rural tourism development. This paper analyzes the roles that research may play in relation to park-people relationships in the context of the two oldest German national parks located in Bavaria. The different fields of action of national parks are used to identify the potential for conflict, using detailed case studies from the Bavarian Forest and Berchtesgaden National Parks using quantitative population surveys carried out in 2018. The overall attitude towards both national parks is overwhelmingly positive, with trust towards park administrations and the perceived economic benefits from rural tourism being the attitudes most strongly correlated to the overall level of park-people relationships. Nevertheless, some points of contention still exist, like the ecological integrity approach towards strict nature conservation and related landscape changes (e.g., deadwood cover). A comparison over time shows in both cases that the spatial proximity to the protected area negatively influences people's attitudes towards the parks, but less so than in the past. Recommendations for national park management include communicating proactively and with greater transparency with locals and decision-makers, to identify conflicts earlier and, where possible, to eliminate them. Furthermore, developing a standardized method to monitor park-people relationships in Germany is a must and would benefit integrated approaches in research and management based on conservation social science.},
  language  = {en}
}
@article{PhilippDietzBucheltetal.2021,
  author    = {Philipp, Marius and Dietz, Andreas and Buchelt, Sebastian and Kuenzer, Claudia},
  title     = {Trends in satellite earth observation for permafrost related analyses — A review},
  series = {Remote Sensing},
  volume    = {13},
  journal   = {Remote Sensing},
  number    = {6},
  doi       = {10.3390/rs13061217},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-234198},
  year      = {2021},
  abstract  = {Climate change and associated Arctic amplification cause a degradation of permafrost which in turn has major implications for the environment. The potential turnover of frozen ground from a carbon sink to a carbon source, eroding coastlines, landslides, amplified surface deformation and endangerment of human infrastructure are some of the consequences connected with thawing permafrost. Satellite remote sensing is hereby a powerful tool to identify and monitor these features and processes on a spatially explicit, cheap, operational, long-term basis and up to circum-Arctic scale. By filtering after a selection of relevant keywords, a total of 325 articles from 30 international journals published during the last two decades were analyzed based on study location, spatio- temporal resolution of applied remote sensing data, platform, sensor combination and studied environmental focus for a comprehensive overview of past achievements, current efforts, together with future challenges and opportunities. The temporal development of publication frequency, utilized platforms/sensors and the addressed environmental topic is thereby highlighted. The total number of publications more than doubled since 2015. Distinct geographical study hot spots were revealed, while at the same time large portions of the continuous permafrost zone are still only sparsely covered by satellite remote sensing investigations. Moreover, studies related to Arctic greenhouse gas emissions in the context of permafrost degradation appear heavily underrepresented. New tools (e.g., Google Earth Engine (GEE)), methodologies (e.g., deep learning or data fusion etc.)and satellite data (e.g., the Methane Remote Sensing LiDAR Mission (Merlin) and the Sentinel-fleet)will thereby enable future studies to further investigate the distribution of permafrost, its thermal state and its implications on the environment such as thermokarst features and greenhouse gas emission rates on increasingly larger spatial and temporal scales.},
  language  = {en}
}
@article{Hardaker2022,
  author    = {Hardaker, Sina},
  title     = {More Than Infrastructure Providers - Digital Platforms' Role and Power in Retail Digitalisation in Germany},
  series = {Tijdschrift voor Economische en Sociale Geografie},
  volume    = {113},
  journal   = {Tijdschrift voor Economische en Sociale Geografie},
  number    = {3},
  doi       = {10.1111/tesg.12511},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-287297},
  pages     = {310 -- 328},
  year      = {2022},
  abstract  = {Digital platforms, such as Amazon, represent the major beneficiaries of the Covid-19 crisis. This study examines the role of digital platforms and their engagement in digitalisation initiatives targeting (small) brick-and-mortar retailers in Germany, thereby contributing to a better understanding of how digital platforms augment, substitute or reorganise physical retail spaces. This study applies a mixed-method approach based on qualitative interviews, participant observation as well as media analysis. First, the study illustrates the controversial role of digital platforms by positioning themselves as supporting partners of the (offline) retailers, while simultaneously shifting power towards the platforms themselves. Second, digital platforms have established themselves not only as infrastructure providers but also as actors within these infrastructures, framing digital as well as physical retail spaces, inter alia due to their role as publicly legitimised retail advisers. Third, while institutions want to help retailers to survive, they simultaneously enhance retailers' dependency on digital platforms.},
  language  = {en}
}
@phdthesis{Keupp2024,
  author    = {Keupp, Luzia Esther},
  title     = {Hochaufgel{\"o}ste Erfassung zuk{\"u}nftiger Klimarisiken f{\"u}r Land- und Forstwirtschaft in Unterfranken},
  doi       = {10.25972/OPUS-34735},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-347350},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2024},
  abstract  = {Das Klima und seine Ver{\"a}nderungen wirken sich direkt auf die Land- und Forstwirtschaft aus. Daher ist die Untersuchung der zuk{\"u}nftigen Klimarisiken f{\"u}r diese Sektoren von hoher Relevanz. Dies ist auch und vor allem f{\"u}r den schon heute weitr{\"a}umig trockheitsgepr{\"a}gten und vom Klimawandel besonders betroffenen nordwestbayerischen Regierungsbezirk Unterfranken der Fall, dessen Gebiet zu {\"u}ber 80 \% land- oder forstwirtschaftlich genutzt wird. Zur Untersuchung der Zukunft in hoher r{\"a}umlicher Aufl{\"o}sung werden Projektionen von regionalen Klimamodellen genutzt. Da diese jedoch Defizite in der Repr{\"a}sentation des beobachteten Klimas der Vergangenheit aufweisen, sollte vor der weiteren Verwendung eine Anpassung der Daten erfolgen. Dies geschieht in der vorliegenden Arbeit am Beispiel des regionalen Klimamodells REMO im Bezug auf klimatische Kennwerte f{\"u}r Trockenheit, Starkniederschlag, Hitze sowie (Sp{\"a}t-)Frost, die alle eine hohe land- und forstwirtschaftliche Bedeutung besitzen. Die Datenanpassung erfolgt durch zwei verschiedene Ans{\"a}tze. Zum Einen wird eine Biaskorrektur der aus Globalmodell-angetriebenen REMO-Daten berechneten Indizes durch additive und multiplikative Linearskalierung sowie empirische und parametrische Verteilungsanpassung durchgef{\"u}hrt. Zum Anderen wird ein exploratives Verfahren auf Basis von Model Output Statistics angewandt: Lokale und großr{\"a}umige atmosph{\"a}rische Variablen von REMO mit Reanalyseantrieb, die eine zeitliche Korrespondenz zu den Beobachtungen aufweisen, dienen als Pr{\"a}diktoren f{\"u}r die Aufstellung von Transferfunktionen zur Simulation der Indizes. Diese Transferfunktionen werden sowohl mithilfe Multipler Linearer Regression als auch mit verschiedenen Generalisierten Linearen Modellen konstruiert. Sie werden anschließend genutzt, um Analysen auf Basis von biaskorrigierten Globalmodell-angetriebenen REMO-Pr{\"a}diktoren durchzuf{\"u}hren. Sowohl f{\"u}r die Biaskorrektur als auch die Model Output Statistics wird eine Kreuzvalidierung durchgef{\"u}hrt, um die Ergebnisse unabh{\"a}ngig vom jeweiligen Trainingszeitraum zu untersuchen und die jeweils besten Varianten zu finden. Werden beide Verfahren mit ihren Unterkategorien f{\"u}r den gesamten historischen Modellzeitraum verglichen, so weist f{\"u}r alle Monat-Kennwert-Kombinationen eine der beiden Verteilungskorrekturen die besten Ergebnisse auf. Die Zukunftsprojektionen unter Verwendung der jeweils erfolgreichsten Methode zeigen im regionalen Durchschnitt f{\"u}r das 21. Jahrhundert negative Trends der (Sp{\"a}t-)Frost- und Eis- sowie positive Trends der Hitzetageh{\"a}ufigkeit. Winterliche Starkregenereignisse nehmen hinsichtlich ihrer Anzahl zu, im Sommer verst{\"a}rkt sich die Trockenheit. Die Hinzunahme zwei weiterer regionaler Klimamodelle best{\"a}tigt die allgemeinen Zukunftstrends, jedoch ergeben sich beim Sp{\"a}tfrost Widerspr{\"u}che, wenn dieser hinsichtlich der thermisch abgegrenzten Vegetationsperiode definiert wird. Zus{\"a}tzlich werden die Model Output Statistics auf gleiche Weise mit bodennahen Pr{\"a}diktoren zur Simulation von Ertr{\"a}gen aus Acker- und Weinbau wiederholt. Die G{\"u}te kann aufgrund mangelnder Beobachtungsdatenl{\"a}nge nur anhand der Reanalyse-angetriebenen REMO-Daten abgesch{\"a}tzt werden, ist hierbei jedoch deutlich besser als im Bezug auf die Kennwertsimulation. Die Zukunftsprojektionen von REMO sowie drei weiterer Regionalmodelle zeigen im Mittel {\"u}ber alle Landkreise Unterfrankens steigende Winter- sowie sinkende Sommerfeldfruchtertr{\"a}ge. Hinsichtlich der Frankenweinertr{\"a}ge widersprechen sich die Ergebnisse der drei Klassen Weiß-, Rot- und Gesamtwein insofern, als dass REMO und ein weiteres Modell negative Weiß- und Rotweinertragstrends, jedoch positive Gesamtweinertragstrends simulieren. Die zwei anderen verwendeten Modelle f{\"u}hren durch positive Trendvorzeichen f{\"u}r den Weißwein zu insgesamt koh{\"a}renten Ergebnissen. Die Resultate im Bezug auf die land- und forstwirtschaftlich relevanten klimatischen Kennwerte bedeuten, dass Anpassungsmaßnahmen gegen{\"u}ber Hitze sowie im Speziellen gegen{\"u}ber Trockenheit in Zukunft im ohnehin trockenheitsgepr{\"a}gten Unterfranken an Bedeutung gewinnen werden. Auch die unsicheren Projektionen im Bezug auf die Sp{\"a}tfrostgefahr m{\"u}ssen im Blick behalten werden. Die Trends der Feldfruchtertr{\"a}ge deuten in die gleiche Richtung, da Sommergetreide eine h{\"o}here Trockenheitsanf{\"a}lligkeit besitzen. Die unklaren Ergebnisse der Weinertr{\"a}ge hingegen lassen keine eindeutigen Schl{\"u}sse zu. Der starke anthropogene Einfluss auf die Erntemengen sowie die großen Unterschiede der Rebsorten hinsichtlich der klimatischen Eignung k{\"o}nnten ein Grund hierf{\"u}r sein.},
  subject      = {Klima},
  language  = {de}
}
@phdthesis{MeyerHeintze2024,
  author    = {Meyer-Heintze, Simon},
  title     = {Holocene pedosedimentary sequences as archives for paleoenvironmental reconstructions},
  doi       = {10.25972/OPUS-34909},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-349098},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2024},
  abstract  = {Human-environment interaction has significantly altered the pedosphere since the Neolithic, if not since the early Holocene. In the course of clearance, agriculture, and (wood) pasture soils have been deeply modified or eroded. These types of land use practices but above all forms of sedentariness spread alongside floodplains and trajectories were oriented towards loess covered areas where fertile soils could develop. Besides this, also peripheral / marginal regions were settled due to population pressure or other factors. Evidence for landscape history and development can be found within archeological sites but also overbank deposits and anthropogenic slope deposits document vast transformation processes. The presented investigations took place within the natural region of the Windsheimer Bucht which is locat-ed in the district of Middle Franconia in northern Bavaria, Germany. In this area, Holocene soils predomi-nantly developed within mudstones of the Middle to Upper Triassic. The soil texture is extremely clay-rich which renders the soils problematic with regard to cultivation management. As a peculiarity, the gypsum underlying the mudstones is prone to karstification processes and resulting proceeding geomorphological processes shape the surface of the landscape. In the course of gypsum mining the karst forms are being exposed and archeological findings are being documented. The latter mainly date back to a span from the Neolithic to the Iron Age, but partly are of Younger Paleolithic origin. Especially subsidence sinkholes are capable of storing pedosediments of several meters in thickness. Despite the high clay content and connect-ed pedoturbation processes, the excavated sequences are stratigraphically and pedologically well-differentiated. The archives occur in the context of settlement structures such as pits and postholes; there-fore, they developed at the interface of natural developments and human impact on their surroundings. The main original research questions that were formulated within the general frame of a project funded by the Deutsche Forschungsgemeinschaft (DFG-projects Te295/15-1 and -2 and Fa390/9-1 and -2) focused on the attractors of the peripheral region for early settlers, the pedological conditions before land use, but also the impact of humans on soils and karst dynamics through time. In the course of the in hand study, the pedosedimentary archives have been approached with a multimethodological toolset which consisted of field analyses, soil morphological analyses from micro- to macro-scale, spectrophotometric (color), (laser) granulometric, and (iron-) pedochemical analyses. The numerical chronological frame was spanned by radiocarbon dating of different organic remains and bulk material if soil organic carbon was supposed-ly high. The result is a multi-dimensional data set that consists of analyses on different spatial scales but also on different levels of measurement. Thus, qualitative, semi-quantitative, and quantitative data consti-tute the basis for discussion. While the grain-size analyses underline the general sedimentological differen-tiation of the records and further affirm the high clay content within the pedosedimentary layers, iron-pedochemical analyses indicate an interplay between oxidation of iron and its chemical reduction. This is also manifested within the spectrophotometric record. Especially the versatile pedogenic characteristics that have been identified by field analyses are confirmed within the thin sections and, by considering all different analyses, the polygenic character of the pedosediments is emphasized. After stressing the general pedological specificities among the different investigated sites within the re-search area, for the collected data, the research further branches into the subjects of general notions on pedogenesis in clayey material and the classification of the respective pedosediments according to paleo-pedological concepts but also recent schemes. Concerning the latter, it becomes evident that established principles cannot be applied to the studied pedosediments without major adaptions. This underlines the specific characteristics of the material. The basis for further interpretations is the evaluation of the multi-level data set for the single records with regard to profile development and pedogenic processes. Hereby, the main drivers of pedogenesis could be identified, which are karst dynamics, land use, and subtle changes in parent material due to the admixture of slope deposits that contain allochthonous eolian material. The latter underlines the importance of Pleis-tocene preconditioning for understanding Holocene landscape dynamics. At the same time, a differentia-tion between the mentioned factors and Holocene climate development is difficult. The following compila-tion of record and localities within the given time frame unveils synchronous as well as asynchronous de-velopments; however, a clear connection between phases of Holocene climate and pedogenesis within the pedosediments cannot be established. Instead, it becomes evident that site specific factors or those that act on the scale of the micro-catchment of the investigated records are decisive. The aforementioned main topics of the project are also considered in the in hand study from a soil-geographic perspective: it is possible that before land use, there was an insular or thin cover by loess sedi-ments or at least upper layers (according to the concept of periglacial cover beds) which constituted the parent material for Holocene soil formation. The according soils, which were superior for agricultural purposes compared to those developed on the autochthonous mudstones, were eroded which exposed the clayey Upper to Middle Triassic beds. Erosion was aggravated due to the impermeable mudstones which enhanced overland flow and interflow within the overlying silty (loessic) material. This is further support-ed by the notions on erodibility of the clayey material that are derived from the comparison of conven-tional and laser granulometric analyses: probably, the clayey pedosediments are capable of forming micro-aggregates that can easily be eroded during heavy rainfall events despite the general consent that material with heavy texture should be rather resistant. The study presents a comprehensive view on clay-rich pedosediments and the complex effects of human-environment interaction on pedogenic as well as sedimentary processes through time that have not been investigated in such detail before. In this context, the multi-level soil morphological analyses and their necessity for a genetic interpretation with regard to the influence of natural versus anthropogenic factors need to be emphasized. Based on quantitative laboratory analytical data only, a respective differentiation would not be possible. This underlines the importance of the chosen soil-geographic multi-methodological approach for answering questions with regard to human-environment interaction but also geoarcheology in general.},
  subject      = {Geoarch{\"a}ologie},
  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{Ibebuchi2021,
  author    = {Ibebuchi, Chibuike Chiedozie},
  title     = {Revisiting the 1992 severe drought episode in South Africa: the role of El Ni{\~n}o in the anomalies of atmospheric circulation types in Africa south of the equator},
  series = {Theoretical and Applied Climatology},
  volume    = {146},
  journal   = {Theoretical and Applied Climatology},
  number    = {1-2},
  issn      = {1434-4483},
  doi       = {10.1007/s00704-021-03741-7},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-268569},
  pages     = {723-740},
  year      = {2021},
  abstract  = {During strong El Ni{\~n}o events, below-average rainfall is expected in large parts of southern Africa. The 1992 El Ni{\~n}o season was associated with one of the worst drought episodes in large parts of South Africa. Using reanalysis data set from NCEP-NCAR, this study examined circulation types (CTs) in Africa south of the equator that are statistically related to the El Ni{\~n}o signal in the southwest Indian Ocean and the implication of this relationship during the 1992 drought episode in South Africa. A statistically significant correlation was found between the above-average Nino 3.4 index and a CT that features widespread cyclonic activity in the tropical southwest Indian Ocean, coupled with a weaker state of the south Indian Ocean high-pressure. During the analysis period, it was found that the El Ni{\~n}o signal enhanced the amplitude of the aforementioned CT. The impacts of the El Ni{\~n}o signal on CTs in southern Africa, which could have contributed to the 1992 severe drought episode in South Africa, were reflected in (i) robust decrease in the frequency of occurrence of the austral summer climatology pattern of atmospheric circulation that favors southeasterly moisture fluxes, advected by the South Indian Ocean high-pressure; (ii) modulation of easterly moisture fluxes, advected by the South Atlantic Ocean high-pressure, ridging south of South Africa; (iii) and enhancement of the amplitude of CTs that both enhances subsidence over South Africa, and associated with the dominance of westerlies across the Agulhas current. Under the ssp585 scenario, the analyzed climate models suggested that the impact of radiative heating on the CT significantly related to El Ni{\~n}o might result in an anomalous increase in surface pressure at the eastern parts of South Africa.},
  language  = {en}
}
@article{KacicKuenzer2022,
  author    = {Kacic, Patrick and Kuenzer, Claudia},
  title     = {Forest biodiversity monitoring based on remotely sensed spectral diversity — a review},
  series = {Remote Sensing},
  volume    = {14},
  journal   = {Remote Sensing},
  number    = {21},
  issn      = {2072-4292},
  doi       = {10.3390/rs14215363},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-290535},
  year      = {2022},
  abstract  = {Forests are essential for global environmental well-being because of their rich provision of ecosystem services and regulating factors. Global forests are under increasing pressure from climate change, resource extraction, and anthropologically-driven disturbances. The results are dramatic losses of habitats accompanied with the reduction of species diversity. There is the urgent need for forest biodiversity monitoring comprising analysis on α, β, and γ scale to identify hotspots of biodiversity. Remote sensing enables large-scale monitoring at multiple spatial and temporal resolutions. Concepts of remotely sensed spectral diversity have been identified as promising methodologies for the consistent and multi-temporal analysis of forest biodiversity. This review provides a first time focus on the three spectral diversity concepts "vegetation indices", "spectral information content", and "spectral species" for forest biodiversity monitoring based on airborne and spaceborne remote sensing. In addition, the reviewed articles are analyzed regarding the spatiotemporal distribution, remote sensing sensors, temporal scales and thematic foci. We identify multispectral sensors as primary data source which underlines the focus on optical diversity as a proxy for forest biodiversity. Moreover, there is a general conceptual focus on the analysis of spectral information content. In recent years, the spectral species concept has raised attention and has been applied to Sentinel-2 and MODIS data for the analysis from local spectral species to global spectral communities. Novel remote sensing processing capacities and the provision of complementary remote sensing data sets offer great potentials for large-scale biodiversity monitoring in the future.},
  language  = {en}
}
@article{DhillonDahmsKuebertFlocketal.2022,
  author    = {Dhillon, Maninder Singh and Dahms, Thorsten and K{\"u}bert-Flock, Carina and Steffan-Dewenter, Ingolf and Zhang, Jie and Ullmann, Tobias},
  title     = {Spatiotemporal Fusion Modelling Using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria},
  series = {Remote Sensing},
  volume    = {14},
  journal   = {Remote Sensing},
  number    = {3},
  issn      = {2072-4292},
  doi       = {10.3390/rs14030677},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-323471},
  year      = {2022},
  abstract  = {The increasing availability and variety of global satellite products provide a new level of data with different spatial, temporal, and spectral resolutions; however, identifying the most suited resolution for a specific application consumes increasingly more time and computation effort. The region's cloud coverage additionally influences the choice of the best trade-off between spatial and temporal resolution, and different pixel sizes of remote sensing (RS) data may hinder the accurate monitoring of different land cover (LC) classes such as agriculture, forest, grassland, water, urban, and natural-seminatural. To investigate the importance of RS data for these LC classes, the present study fuses 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, eight day)) using the spatial and temporal adaptive reflectance fusion model (STARFM), which fills regions' cloud or shadow gaps without losing spatial information. These eight synthetic NDVI STARFM products (2: high pair multiply 4: low pair) offer a spatial resolution of 10 or 30 m and temporal resolution of 1, 8, or 16 days for the entire state of Bavaria (Germany) in 2019. Due to their higher revisit frequency and more cloud and shadow-free scenes (S = 13, L = 9), Sentinel-2 (overall R\(^2\) = 0.71, and RMSE = 0.11) synthetic NDVI products provide more accurate results than Landsat (overall R\(^2\) = 0.61, and RMSE = 0.13). Likewise, for the agriculture class, synthetic products obtained using Sentinel-2 resulted in higher accuracy than Landsat except for L-MOD13Q1 (R\(^2\) = 0.62, RMSE = 0.11), resulting in similar accuracy preciseness as S-MOD13Q1 (R\(^2\) = 0.68, RMSE = 0.13). Similarly, comparing L-MOD13Q1 (R\(^2\) = 0.60, RMSE = 0.05) and S-MOD13Q1 (R\(^2\) = 0.52, RMSE = 0.09) for the forest class, the former resulted in higher accuracy and precision than the latter. Conclusively, both L-MOD13Q1 and S-MOD13Q1 are suitable for agricultural and forest monitoring; however, the spatial resolution of 30 m and low storage capacity makes L-MOD13Q1 more prominent and faster than that of S-MOD13Q1 with the 10-m spatial resolution.},
  language  = {en}
}
@article{GhasemiLatifiPourhashemi2022,
  author    = {Ghasemi, Marziye and Latifi, Hooman and Pourhashemi, Mehdi},
  title     = {A novel method for detecting and delineating coppice trees in UAV images to monitor tree decline},
  series = {Remote Sensing},
  volume    = {14},
  journal   = {Remote Sensing},
  number    = {23},
  issn      = {2072-4292},
  doi       = {10.3390/rs14235910},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-297258},
  year      = {2022},
  abstract  = {Monitoring tree decline in arid and semi-arid zones requires methods that can provide up-to-date and accurate information on the health status of the trees at single-tree and sample plot levels. Unmanned Aerial Vehicles (UAVs) are considered as cost-effective and efficient tools to study tree structure and health at small scale, on which detecting and delineating tree crowns is the first step to extracting varied subsequent information. However, one of the major challenges in broadleaved tree cover is still detecting and delineating tree crowns in images. The frequent dominance of coppice structure in degraded semi-arid vegetation exacerbates this problem. Here, we present a new method based on edge detection for delineating tree crowns based on the features of oak trees in semi-arid coppice structures. The decline severity in individual stands can be analyzed by extracting relevant information such as texture from the crown area. Although the method presented in this study is not fully automated, it returned high performances including an F-score = 0.91. Associating the texture indices calculated in the canopy area with the phenotypic decline index suggested higher correlations of the GLCM texture indices with tree decline at the tree level and hence a high potential to be used for subsequent remote-sensing-assisted tree decline studies.},
  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}
}
@article{KleinCoccoUereyenetal.2022,
  author    = {Klein, Igor and Cocco, Arturo and Uereyen, Soner and Mannu, Roberto and Floris, Ignazio and Oppelt, Natascha and Kuenzer, Claudia},
  title     = {Outbreak of Moroccan locust in Sardinia (Italy): a remote sensing perspective},
  series = {Remote Sensing},
  volume    = {14},
  journal   = {Remote Sensing},
  number    = {23},
  issn      = {2072-4292},
  doi       = {10.3390/rs14236050},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-297232},
  year      = {2022},
  abstract  = {The Moroccan locust has been considered one of the most dangerous agricultural pests in the Mediterranean region. The economic importance of its outbreaks diminished during the second half of the 20th century due to a high degree of agricultural industrialization and other human-caused transformations of its habitat. Nevertheless, in Sardinia (Italy) from 2019 on, a growing invasion of this locust species is ongoing, being the worst in over three decades. Locust swarms destroyed crops and pasture lands of approximately 60,000 ha in 2022. Drought, in combination with increasing uncultivated land, contributed to forming the perfect conditions for a Moroccan locust population upsurge. The specific aim of this paper is the quantification of land cover land use (LCLU) influence with regard to the recent locust outbreak in Sardinia using remote sensing data. In particular, the role of untilled, fallow, or abandoned land in the locust population upsurge is the focus of this case study. To address this objective, LCLU was derived from Sentinel-2A/B Multispectral Instrument (MSI) data between 2017 and 2021 using time-series composites and a random forest (RF) classification model. Coordinates of infested locations, altitude, and locust development stages were collected during field observation campaigns between March and July 2022 and used in this study to assess actual and previous land cover situation of these locations. Findings show that 43\% of detected locust locations were found on untilled, fallow, or uncultivated land and another 23\% within a radius of 100 m to such areas. Furthermore, oviposition and breeding sites are mostly found in sparse vegetation (97\%). This study demonstrates that up-to-date remote sensing data and target-oriented analyses can provide valuable information to contribute to early warning systems and decision support and thus to minimize the risk concerning this agricultural pest. This is of particular interest for all agricultural pests that are strictly related to changing human activities within transformed habitats.},
  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{AnsahAbuKleemannetal.2022,
  author    = {Ansah, Christabel Edena and Abu, Itohan-Osa and Kleemann, Janina and Mahmoud, Mahmoud Ibrahim and Thiel, Michael},
  title     = {Environmental contamination of a biodiversity hotspot — action needed for nature conservation in the Niger Delta, Nigeria},
  series = {Sustainability},
  volume    = {14},
  journal   = {Sustainability},
  number    = {21},
  issn      = {2071-1050},
  doi       = {10.3390/su142114256},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-297214},
  year      = {2022},
  abstract  = {The Niger Delta belongs to the largest swamp and mangrove forests in the world hosting many endemic and endangered species. Therefore, its conservation should be of highest priority. However, the Niger Delta is confronted with overexploitation, deforestation and pollution to a large extent. In particular, oil spills threaten the biodiversity, ecosystem services, and local people. Remote sensing can support the detection of spills and their potential impact when accessibility on site is difficult. We tested different vegetation indices to assess the impact of oil spills on the land cover as well as to detect accumulations (hotspots) of oil spills. We further identified which species, land cover types, and protected areas could be threatened in the Niger Delta due to oil spills. The results showed that the Enhanced Vegetation Index, the Normalized Difference Vegetation Index, and the Soil Adjusted Vegetation Index were more sensitive to the effects of oil spills on different vegetation cover than other tested vegetation indices. Forest cover was the most affected land-cover type and oil spills also occurred in protected areas. Threatened species are inhabiting the Niger Delta Swamp Forest and the Central African Mangroves that were mainly affected by oil spills and, therefore, strong conservation measures are needed even though security issues hamper the monitoring and control.},
  language  = {en}
}
@phdthesis{Weigand2024,
  author    = {Weigand, Matthias Johann},
  title     = {Fernerkundung und maschinelles Lernen zur Erfassung von urbanem Gr{\"u}n - Eine Analyse am Beispiel der Verteilungsgerechtigkeit in Deutschland},
  doi       = {10.25972/OPUS-34961},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-349610},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2024},
  abstract  = {Gr{\"u}nfl{\"a}chen stellen einen der wichtigsten Umwelteinfl{\"u}sse in der Wohnumwelt der Menschen dar. Einerseits wirken sie sich positiv auf die physische und mentale Gesundheit der Menschen aus, andererseits k{\"o}nnen Gr{\"u}nfl{\"a}chen auch negative Wirkungen anderer Faktoren abmildern, wie beispielsweise die im Laufe des Klimawandels zunehmenden Hitzeereignisse. Dennoch sind Gr{\"u}nfl{\"a}chen nicht f{\"u}r die gesamte Bev{\"o}lkerung gleichermaßen zug{\"a}nglich. Bestehende Forschung im Kontext der Umweltgerechtigkeit (UG) konnte bereits aufzeigen, dass unterschiedliche sozio-{\"o}konomische und demographische Gruppen der deutschen Bev{\"o}lkerung unterschiedlichen Zugriff auf Gr{\"u}nfl{\"a}chen haben. An bestehenden Analysen von Umwelteinfl{\"u}ssen im Kontext der UG wird kritisiert, dass die Auswertung geographischer Daten h{\"a}ufig auf zu stark aggregiertem Level geschieht, wodurch lokal spezifische Expositionen nicht mehr genau abgebildet werden. Dies trifft insbesondere f{\"u}r großfl{\"a}chig angelegte Studien zu. So werden wichtige r{\"a}umliche Informationen verloren. Doch moderne Erdbeobachtungs- und Geodaten sind so detailliert wie nie und Methoden des maschinellen Lernens erm{\"o}glichen die effiziente Verarbeitung zur Ableitung h{\"o}herwertiger Informationen. Das {\"u}bergeordnete Ziel dieser Arbeit besteht darin, am Beispiel von Gr{\"u}nfl{\"a}chen in Deutschland methodische Schritte der systematischen Umwandlung umfassender Geodaten in relevante Geoinformationen f{\"u}r die großfl{\"a}chige und hochaufgel{\"o}ste Analyse von Umwelteigenschaften aufzuzeigen und durchzuf{\"u}hren. An der Schnittstelle der Disziplinen Fernerkundung, Geoinformatik, Sozialgeographie und Umweltgerechtigkeitsforschung sollen Potenziale moderner Methoden f{\"u}r die Verbesserung der r{\"a}umlichen und semantischen Aufl{\"o}sung von Geoinformationen erforscht werden. Hierf{\"u}r werden Methoden des maschinellen Lernens eingesetzt, um Landbedeckung und -nutzung auf nationaler Ebene zu erfassen. Diese Entwicklungen sollen dazu beitragen bestehende Datenl{\"u}cken zu schließen und Aufschluss {\"u}ber die Verteilungsgerechtigkeit von Gr{\"u}nfl{\"a}chen zu bieten. Diese Dissertation gliedert sich in drei konzeptionelle Teilschritte. Im ersten Studienteil werden Erdbeobachtungsdaten der Sentinel-2 Satelliten zur deutschlandweiten Klassifikation von Landbedeckungsinformationen verwendet. In Kombination mit punktuellen Referenzdaten der europaweiten Erfassung f{\"u}r Landbedeckungs- und Landnutzungsinformationen des Land Use and Coverage Area Frame Survey (LUCAS) wird ein maschinelles Lernverfahren trainiert. In diesem Kontext werden verschiedene Vorverarbeitungsschritte der LUCAS-Daten und deren Einfluss auf die Klassifikationsgenauigkeit beleuchtet. Das Klassifikationsverfahren ist in der Lage Landbedeckungsinformationen auch in komplexen urbanen Gebieten mit hoher Genauigkeit abzuleiten. Ein Ergebnis des Studienteils ist eine deutschlandweite Landbedeckungsklassifikation mit einer Gesamtgenauigkeit von 93,07 \%, welche im weiteren Verlauf der Arbeit genutzt wird, um gr{\"u}ne Landbedeckung (GLC) r{\"a}umlich zu quantifizieren. Im zweiten konzeptionellen Teil der Arbeit steht die differenzierte Betrachtung von Gr{\"u}nfl{\"a}chen anhand des Beispiels {\"o}ffentlicher Gr{\"u}nfl{\"a}chen (PGS), die h{\"a}ufig Gegenstand der UG-Forschung ist, im Vordergrund. Doch eine h{\"a}ufig verwendete Quelle f{\"u}r r{\"a}umliche Daten zu {\"o}ffentlichen Gr{\"u}nfl{\"a}chen, der European Urban Atlas (EUA), wird bisher nicht fl{\"a}chendeckend f{\"u}r Deutschland erhoben. Dieser Studienteil verfolgt einen datengetriebenen Ansatz, die Verf{\"u}gbarkeit von {\"o}ffentlichem Gr{\"u}n auf der r{\"a}umlichen Ebene von Nachbarschaften f{\"u}r ganz Deutschland zu ermitteln. Hierf{\"u}r dienen bereits vom EUA erfasste Gebiete als Referenz. Mithilfe einer Kombination von Erdbeobachtungsdaten und Informationen aus dem OpenStreetMap-Projekt wird ein Deep Learning -basiertes Fusionsnetzwerk erstellt, welche die verf{\"u}gbare Fl{\"a}che von {\"o}ffentlichem Gr{\"u}n quantifiziert. Das Ergebnis dieses Schrittes ist ein Modell, welches genutzt wird, um die Menge {\"o}ffentlicher Gr{\"u}nfl{\"a}chen in der Nachbarschaft zu sch{\"a}tzen (𝑅 2 = 0.952). Der dritte Studienteil greift die Ergebnisse der ersten beiden Studienteile auf und betrachtet die Verteilung von Gr{\"u}nfl{\"a}chen in Deutschland unter Hinzunahme von georeferenzierten Bev{\"o}lkerungsdaten. Diese exemplarische Analyse unterscheidet dabei Gr{\"u}nfl{\"a}chen nach zwei Typen: GLC und PGS. Zun{\"a}chst wird mithilfe deskriptiver Statistiken die generelle Gr{\"u}nfl{\"a}chenverteilung in der Bev{\"o}lkerung Deutschlands beleuchtet. Daraufhin wird die Verteilungsgerechtigkeit anhand g{\"a}ngiger Gerechtigkeitsmetriken bestimmt. Abschließend werden die Zusammenh{\"a}nge zwischen der demographischen Komposition der Nachbarschaft und der verf{\"u}gbaren Menge von Gr{\"u}nfl{\"a}chen anhand dreier exemplarischer soziodemographischer Gesellschaftsgruppen untersucht. Die Analyse zeigt starke Unterschiede der Verf{\"u}gbarkeit von PGS zwischen st{\"a}dtischen und l{\"a}ndlichen Gebieten. Ein h{\"o}herer Prozentsatz der Stadtbev{\"o}lkerung hat Zugriff das Mindestmaß von PGS gemessen an der Vorgabe der Weltgesundheitsorganisation. Die Ergebnisse zeigen auch einen deutlichen Unterschied bez{\"u}glich der Verteilungsgerechtigkeit zwischen GLC und PGS und verdeutlichen die Relevanz der Unterscheidung von Gr{\"u}nfl{\"a}chentypen f{\"u}r derartige Untersuchungen. Die abschließende Betrachtung verschiedener Bev{\"o}lkerungsgruppen arbeitet Unterschiede auf soziodemographischer Ebene auf. In der Zusammenschau demonstriert diese Arbeit wie moderne Geodaten und Methoden des maschinellen Lernens genutzt werden k{\"o}nnen bisherige Limitierungen r{\"a}umlicher Datens{\"a}tze zu {\"u}berwinden. Am Beispiel von Gr{\"u}nfl{\"a}chen in der Wohnumgebung der Bev{\"o}lkerung Deutschlands wird gezeigt, dass landesweite Analysen zur Umweltgerechtigkeit durch hochaufgel{\"o}ste und lokal feingliedrige geographische Informationen bereichert werden k{\"o}nnen. Diese Arbeit verdeutlicht, wie die Methoden der Erdbeobachtung und Geoinformatik einen wichtigen Beitrag leisten k{\"o}nnen, die Ungleichheit der Wohnumwelt der Menschen zu identifizieren und schlussendlich den nachhaltigen Siedlungsbau in Form von objektiven Informationen zu unterst{\"u}tzen und {\"u}berwachen.},
  subject      = {Geografie},
  language  = {de}
}
@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}
}
@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}
}
@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{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{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{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{RedlichZhangBenjaminetal.2022,
  author    = {Redlich, Sarah and Zhang, Jie and Benjamin, Caryl and Dhillon, Maninder Singh and Englmeier, Jana and Ewald, J{\"o}rg and Fricke, Ute and Ganuza, Cristina and Haensel, Maria and Hovestadt, Thomas and Kollmann, Johannes and Koellner, Thomas and K{\"u}bert-Flock, Carina and Kunstmann, Harald and Menzel, Annette and Moning, Christoph and Peters, Wibke and Riebl, Rebekka and Rummler, Thomas and Rojas-Botero, Sandra and Tobisch, Cynthia and Uhler, Johannes and Uphus, Lars and M{\"u}ller, J{\"o}rg and Steffan-Dewenter, Ingolf},
  title     = {Disentangling effects of climate and land use on biodiversity and ecosystem services—A multi-scale experimental design},
  series = {Methods in Ecology and Evolution},
  volume    = {13},
  journal   = {Methods in Ecology and Evolution},
  number    = {2},
  doi       = {10.1111/2041-210X.13759},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-258270},
  pages     = {514-527},
  year      = {2022},
  abstract  = {Climate and land-use change are key drivers of environmental degradation in the Anthropocene, but too little is known about their interactive effects on biodiversity and ecosystem services. Long-term data on biodiversity trends are currently lacking. Furthermore, previous ecological studies have rarely considered climate and land use in a joint design, did not achieve variable independence or lost statistical power by not covering the full range of environmental gradients. Here, we introduce a multi-scale space-for-time study design to disentangle effects of climate and land use on biodiversity and ecosystem services. The site selection approach coupled extensive GIS-based exploration (i.e. using a Geographic information system) and correlation heatmaps with a crossed and nested design covering regional, landscape and local scales. Its implementation in Bavaria (Germany) resulted in a set of study plots that maximise the potential range and independence of environmental variables at different spatial scales. Stratifying the state of Bavaria into five climate zones (reference period 1981-2010) and three prevailing land-use types, that is, near-natural, agriculture and urban, resulted in 60 study regions (5.8 × 5.8 km quadrants) covering a mean annual temperature gradient of 5.6-9.8°C and a spatial extent of ~310 × 310 km. Within these regions, we nested 180 study plots located in contrasting local land-use types, that is, forests, grasslands, arable land or settlement (local climate gradient 4.5-10°C). This approach achieved low correlations between climate and land use (proportional cover) at the regional and landscape scale with |r ≤ 0.33| and |r ≤ 0.29| respectively. Furthermore, using correlation heatmaps for local plot selection reduced potentially confounding relationships between landscape composition and configuration for plots located in forests, arable land and settlements. The suggested design expands upon previous research in covering a significant range of environmental gradients and including a diversity of dominant land-use types at different scales within different climatic contexts. It allows independent assessment of the relative contribution of multi-scale climate and land use on biodiversity and ecosystem services. Understanding potential interdependencies among global change drivers is essential to develop effective restoration and mitigation strategies against biodiversity decline, especially in expectation of future climatic changes. Importantly, this study also provides a baseline for long-term ecological monitoring programs.},
  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}
}
@article{StanglRauchRauhetal.2021,
  author    = {Stangl, Stephanie and Rauch, Sebastian and Rauh, J{\"u}rgen and Meyer, Martin and M{\"u}ller-Nordhorn, Jacqueline and Wildner, Manfred and W{\"o}ckel, Achim and Heuschmann, Peter U.},
  title     = {Disparities in Accessibility to Evidence-Based Breast Cancer Care Facilities by Rural and Urban Areas in Bavaria, Germany},
  series = {Cancer},
  volume    = {127},
  journal   = {Cancer},
  number    = {13},
  doi       = {10.1002/cncr.33493},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-239854},
  pages     = {2319 -- 2332},
  year      = {2021},
  abstract  = {Background Breast cancer (BC), which is most common in elderly women, requires a multidisciplinary and continuous approach to care. With demographic changes, the number of patients with chronic diseases such as BC will increase. This trend will especially hit rural areas, where the majority of the elderly live, in terms of comprehensive health care. Methods Accessibility to several cancer facilities in Bavaria, Germany, was analyzed with a geographic information system. Facilities were identified from the national BC guideline and from 31 participants in a proof-of-concept study from the Breast Cancer Care for Patients With Metastatic Disease registry. The timeframe for accessibility was defined as 30 or 60 minutes for all population points. The collection of address information was performed with different sources (eg, a physician registry). Routine data from the German Census 2011 and the population-based Cancer Registry of Bavaria were linked at the district level. Results Females from urban areas (n = 2,938,991 [ie, total of females living in urban areas]) had a higher chance for predefined accessibility to the majority of analyzed facilities in comparison with females from rural areas (n = 3,385,813 [ie, total number of females living in rural areas]) with an odds ratio (OR) of 9.0 for cancer information counselling, an OR of 17.2 for a university hospital, and an OR of 7.2 for a psycho-oncologist. For (inpatient) rehabilitation centers (OR, 0.2) and genetic counselling (OR, 0.3), women from urban areas had lower odds of accessibility within 30 or 60 minutes. Conclusions Disparities in accessibility between rural and urban areas exist in Bavaria. The identification of underserved areas can help to inform policymakers about disparities in comprehensive health care. Future strategies are needed to deliver high-quality health care to all inhabitants, regardless of residence.},
  language  = {en}
}
@article{IbebuchiPaeth2021,
  author    = {Ibebuchi, Chibuike Chiedozie and Paeth, Heiko},
  title     = {The Imprint of the Southern Annular Mode on Black Carbon AOD in the Western Cape Province},
  series = {Atmosphere},
  volume    = {12},
  journal   = {Atmosphere},
  number    = {10},
  issn      = {2073-4433},
  doi       = {10.3390/atmos12101287},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-248387},
  year      = {2021},
  abstract  = {This study examines the relationship between variations of the Southern Annular Mode (SAM) and black carbon (BC) at 550 nm aerosol optical depth (AOD) in the Western Cape province (WC). Variations of the positive (negative) phase of the SAM are found to be related to regional circulation types (CTs) in southern Africa, associated with suppressed (enhanced) westerly wind over the WC through the southward (northward) migration of Southern Hemisphere mid-latitude cyclones. The CTs related to positive (negative) SAM anomalies induce stable (unstable) atmospheric conditions over the southwestern regions of the WC, especially during the austral winter and autumn seasons. Through the control of CTs, positive (negative) SAM phases tend to contribute to the build-up (dispersion and dilution) of BC in the study region because they imply dry (wet) conditions which favor the build-up (washing out) of pollutant particles in the atmosphere. Indeed, recent years with an above-average frequency of CTs related to positive (negative) SAM anomalies are associated with a high (low) BC AOD over southwesternmost Africa.},
  language  = {en}
}
@book{Guth2020,
  author    = {Guth, Denis},
  title     = {Zur Sicherstellung der ‚Vertr{\"a}glichkeit' innerst{\"a}dtischer Einkaufszentren - Raumbezogene Diskurs- und Kalkulationsordnungen am Beispiel der Mainzer Innenstadt},
  publisher = {W{\"u}rzburg University Press},
  address   = {W{\"u}rzburg},
  isbn      = {978-3-95826-130-3},
  doi       = {10.25972/WUP-978-3-95826-131-0},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-192670},
  publisher      = {W{\"u}rzburg University Press},
  pages     = {248},
  year      = {2020},
  abstract  = {Der Begriff der ‚Vertr{\"a}glichkeit' spielt eine zentrale Rolle f{\"u}r die politisch-planerische Steuerung von Einzelhandels- und Stadtentwicklung. Besonders kontrovers wird v.a. seit Mitte der 1990er Jahre die Frage der ‚Vertr{\"a}glichkeit' innerst{\"a}dtischer Einkaufszentren diskutiert. Die vorliegende Studie untersucht anhand ehemaliger Shopping-Center-Planungen f{\"u}r die Mainzer Innenstadt, wie der Vertr{\"a}glichkeitsbegriff in der Praxis gef{\"u}llt wird und welche planerischen Steuerungslogiken hieraus hervorgehen. Die Arbeit setzt sich kritisch mit der Frage auseinander, auf welche normativen Wissensordnungen {\"u}ber den innerst{\"a}dtischen Raum sich die politisch-planerische Bearbeitung der Vertr{\"a}glichkeitsproblematik st{\"u}tzt und welche Machtwirkungen hiermit einhergehen. Ausgehend von einer poststrukturalistisch inspirierten, diskurstheoretischen Perspektive verschiebt die Studie damit den geographischen Blick auf die Vertr{\"a}glichkeitsfrage: Was ‚Vertr{\"a}glichkeit' f{\"u}r die politisch-planerische Praxis konkret bedeutet, ob ein geplantes Einkaufszentrum als ‚(innenstadt)vertr{\"a}glich' gelten kann bzw. welche konkreten Interventionen dies erfordert, h{\"a}ngt demzufolge weniger von objektiven {\"o}konomischen, r{\"a}umlichen oder st{\"a}dtebaulichen Gegebenheiten ab - vielmehr zeigt die Studie, dass eine ganzen Reihe von Techniken raumbezogener Wissensproduktion mobilisiert werden m{\"u}ssen, damit die Vertr{\"a}glichkeitsfrage {\"u}berhaupt als eine objektivierbare Frage erscheinen kann.},
  subject      = {Einkaufszentrum},
  language  = {de}
}
@techreport{MeyerJobLaneretal.2022,
  author    = {Meyer, Constantin and Job, Hubert and Laner, Peter and Omizzolo, Andrea and Kollmann, Nadia and Clare, Jasmin and Vesely, Philipp and Riedler, Walter and Plassmann, Guido and Coronado, Oriana and Praper Gulič, Sergeja and Gulič, Andrej and Koblar, Simon and Teofili, Corrado and Rohringer, Verena and Schoßleitner, Richard and Ainz, Gerhard},
  title     = {OpenSpaceAlps - Manuale di Pianificazione: Prospettive per la salvaguardia coerente degli Spazi Aperti nella regione alpina},
  doi       = {10.25972/OPUS-27704},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-277042},
  pages     = {114},
  year      = {2022},
  abstract  = {Nella regione alpina, si pu{\`o} osservare il continuo consumo di spazi aperti a causa dell'aumento di aree di insediamento e di infrastrutture tecniche e la conseguente impermeabilizzazione del suolo. Questo fenomeno porta principalmente alla perdita di suolo agricolo. A seconda dell'estensione dello sviluppo, si riscontra anche una maggiore frammentazione del paesaggio, che {\`e} associata all'isolamento degli habitat naturali e alla perdita della connettivit{\`a} ecologica, cos{\`i} come ad altre conseguenze negative. Il progetto OpenSpaceAlps ha affrontato questo problema e, sulla base di procedure cooperative partecipate attuate in diverse regioni pilota alpine, ha sviluppato approcci e strategie di soluzione per la salvaguardia sostenibile degli spazi aperti. Questo manuale supporta le attivit{\`a} e il processo decisionale di vari stakeholder, in primo luogo i pianificatori delle autorit{\`a} pubbliche di pianificazione. Sulla base di un'analisi delle sfide e delle condizioni generali nella regione alpina, il manuale presenta e confronta i "principi" centrali della pianificazione degli spazi aperti. Inoltre, vengono discusse strategie di pianificazione integrata per diverse categorie spaziali.},
  subject      = {Raumordnung},
  language  = {it}
}
@article{ReinermannAsamKuenzer2020,
  author    = {Reinermann, Sophie and Asam, Sarah and Kuenzer, Claudia},
  title     = {Remote Sensing of Grassland Production and Management - A Review},
  series = {Remote Sensing},
  volume    = {12},
  journal   = {Remote Sensing},
  number    = {12},
  issn      = {2072-4292},
  doi       = {10.3390/rs12121949},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-207799},
  year      = {2020},
  abstract  = {Grasslands cover one third of the earth's terrestrial surface and are mainly used for livestock production. The usage type, use intensity and condition of grasslands are often unclear. Remote sensing enables the analysis of grassland production and management on large spatial scales and with high temporal resolution. Despite growing numbers of studies in the field, remote sensing applications in grassland biomes are underrepresented in literature and less streamlined compared to other vegetation types. By reviewing articles within research on satellite-based remote sensing of grassland production traits and management, we describe and evaluate methods and results and reveal spatial and temporal patterns of existing work. In addition, we highlight research gaps and suggest research opportunities. The focus is on managed grasslands and pastures and special emphasize is given to the assessment of studies on grazing intensity and mowing detection based on earth observation data. Grazing and mowing highly influence the production and ecology of grassland and are major grassland management types. In total, 253 research articles were reviewed. The majority of these studies focused on grassland production traits and only 80 articles were about grassland management and use intensity. While the remote sensing-based analysis of grassland production heavily relied on empirical relationships between ground-truth and satellite data or radiation transfer models, the used methods to detect and investigate grassland management differed. In addition, this review identified that studies on grassland production traits with satellite data often lacked including spatial management information into the analyses. Studies focusing on grassland management and use intensity mostly investigated rather small study areas with homogeneous intensity levels among the grassland parcels. Combining grassland production estimations with management information, while accounting for the variability among grasslands, is recommended to facilitate the development of large-scale continuous monitoring and remote sensing grassland products, which have been rare thus far.},
  language  = {en}
}
@article{RieserVesteThieletal.2021,
  author    = {Rieser, Jakob and Veste, Maik and Thiel, Michael and Sch{\"o}nbrodt-Stitt, Sarah},
  title     = {Coverage and Rainfall Response of Biological Soil Crusts Using Multi-Temporal Sentinel-2 Data in a Central European Temperate Dry Acid Grassland},
  series = {Remote Sensing},
  volume    = {13},
  journal   = {Remote Sensing},
  number    = {16},
  issn      = {2072-4292},
  doi       = {10.3390/rs13163093},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-245006},
  year      = {2021},
  abstract  = {Biological soil crusts (BSCs) are thin microbiological vegetation layers that naturally develop in unfavorable higher plant conditions (i.e., low precipitation rates and high temperatures) in global drylands. They consist of poikilohydric organisms capable of adjusting their metabolic activities depending on the water availability. However, they, and with them, their ecosystem functions, are endangered by climate change and land-use intensification. Remote sensing (RS)-based studies estimated the BSC cover in global drylands through various multispectral indices, and few of them correlated the BSCs' activity response to rainfall. However, the allocation of BSCs is not limited to drylands only as there are areas beyond where smaller patches have developed under intense human impact and frequent disturbance. Yet, those areas were not addressed in RS-based studies, raising the question of whether the methods developed in extensive drylands can be transferred easily. Our temperate climate study area, the 'Lieberoser Heide' in northeastern Germany, is home to the country's largest BSC-covered area. We applied a Random Forest (RF) classification model incorporating multispectral Sentinel-2 (S2) data, indices derived from them, and topographic information to spatiotemporally map the BSC cover for the first time in Central Europe. We further monitored the BSC response to rainfall events over a period of around five years (June 2015 to end of December 2020). Therefore, we combined datasets of gridded NDVI as a measure of photosynthetic activity with daily precipitation data and conducted a change detection analysis. With an overall accuracy of 98.9\%, our classification proved satisfactory. Detected changes in BSC activity between dry and wet conditions were found to be significant. Our study emphasizes a high transferability of established methods from extensive drylands to BSC-covered areas in the temperate climate. Therefore, we consider our study to provide essential impulses so that RS-based biocrust mapping in the future will be applied beyond the global drylands.},
  language  = {en}
}
@article{DhillonDahmsKuebertFlocketal.2020,
  author    = {Dhillon, Maninder Singh and Dahms, Thorsten and Kuebert-Flock, Carina and Borg, Erik and Conrad, Christopher and Ullmann, Tobias},
  title     = {Modelling Crop Biomass from Synthetic Remote Sensing Time Series: Example for the DEMMIN Test Site, Germany},
  series = {Remote Sensing},
  volume    = {12},
  journal   = {Remote Sensing},
  number    = {11},
  issn      = {2072-4292},
  doi       = {10.3390/rs12111819},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-207845},
  year      = {2020},
  abstract  = {This study compares the performance of the five widely used crop growth models (CGMs): World Food Studies (WOFOST), Coalition for Environmentally Responsible Economies (CERES)-Wheat, AquaCrop, cropping systems simulation model (CropSyst), and the semi-empiric light use efficiency approach (LUE) for the prediction of winter wheat biomass on the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site, Germany. The study focuses on the use of remote sensing (RS) data, acquired in 2015, in CGMs, as they offer spatial information on the actual conditions of the vegetation. Along with this, the study investigates the data fusion of Landsat (30 m) and Moderate Resolution Imaging Spectroradiometer (MODIS) (500 m) data using the spatial and temporal reflectance adaptive reflectance fusion model (STARFM) fusion algorithm. These synthetic RS data offer a 30-m spatial and one-day temporal resolution. The dataset therefore provides the necessary information to run CGMs and it is possible to examine the fine-scale spatial and temporal changes in crop phenology for specific fields, or sub sections of them, and to monitor crop growth daily, considering the impact of daily climate variability. The analysis includes a detailed comparison of the simulated and measured crop biomass. The modelled crop biomass using synthetic RS data is compared to the model outputs using the original MODIS time series as well. On comparison with the MODIS product, the study finds the performance of CGMs more reliable, precise, and significant with synthetic time series. Using synthetic RS data, the models AquaCrop and LUE, in contrast to other models, simulate the winter wheat biomass best, with an output of high R2 (>0.82), low RMSE (<600 g/m\(^2\)) and significant p-value (<0.05) during the study period. However, inputting MODIS data makes the models underperform, with low R2 (<0.68) and high RMSE (>600 g/m\(^2\)). The study shows that the models requiring fewer input parameters (AquaCrop and LUE) to simulate crop biomass are highly applicable and precise. At the same time, they are easier to implement than models, which need more input parameters (WOFOST and CERES-Wheat).},
  language  = {en}
}
@article{FakhriLatifi2021,
  author    = {Fakhri, Seyed Arvin and Latifi, Hooman},
  title     = {A consumer grade UAV-based framework to estimate structural attributes of coppice and high oak forest stands in semi-arid regions},
  series = {Remote Sensing},
  volume    = {13},
  journal   = {Remote Sensing},
  number    = {21},
  issn      = {2072-4292},
  doi       = {10.3390/rs13214367},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-248469},
  year      = {2021},
  abstract  = {Semi-arid tree covers, in both high and coppice growth forms, play an essential role in protecting water and soil resources and provides multiple ecosystem services across fragile ecosystems. Thus, they require continuous inventories. Quantification of forest structure in these tree covers provides important measures for their management and biodiversity conservation. We present a framework, based on consumer-grade UAV photogrammetry, to separately estimate primary variables of tree height (H) and crown area (A) across diverse coppice and high stands dominated by Quercus brantii Lindl. along the latitudinal gradient of Zagros mountains of western Iran. Then, multivariate linear regressions were parametrized with H and A to estimate the diameter at breast height (DBH) of high trees because of its importance to accelerate the existing practical DBH inventories across Zagros Forests. The estimated variables were finally applied to a model tree aboveground biomass (AGB) for both vegetative growth forms by local allometric equations and Random Forest models. In each step, the estimated variables were evaluated against the field reference values, indicating practically high accuracies reaching root mean square error (RMSE) of 0.68 m and 4.74 cm for H and DBH, as well as relative RMSE < 10\% for AGB estimates. The results generally suggest an effective framework for single tree-based attribute estimation over mountainous, semi-arid coppice, and high stands.},
  language  = {en}
}
@article{ReinermannGessnerAsametal.2022,
  author    = {Reinermann, Sophie and Gessner, Ursula and Asam, Sarah and Ullmann, Tobias and Schucknecht, Anne and Kuenzer, Claudia},
  title     = {Detection of grassland mowing events for Germany by combining Sentinel-1 and Sentinel-2 time series},
  series = {Remote Sensing},
  volume    = {14},
  journal   = {Remote Sensing},
  number    = {7},
  issn      = {2072-4292},
  doi       = {10.3390/rs14071647},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-267164},
  year      = {2022},
  abstract  = {Grasslands cover one-third of the agricultural area in Germany and play an important economic role by providing fodder for livestock. In addition, they fulfill important ecosystem services, such as carbon storage, water purification, and the provision of habitats. These ecosystem services usually depend on the grassland management. In central Europe, grasslands are grazed and/or mown, whereby the management type and intensity vary in space and time. Spatial information on the mowing timing and frequency on larger scales are usually not available but would be required in order to assess the ecosystem services, species composition, and grassland yields. Time series of high-resolution satellite remote sensing data can be used to analyze the temporal and spatial dynamics of grasslands. Within this study, we aim to overcome the drawbacks identified by previous studies, such as optical data availability and the lack of comprehensive reference data, by testing the time series of various Sentinel-2 (S2) and Sentinal-1 (S1) parameters and combinations of them in order to detect mowing events in Germany in 2019. We developed a threshold-based algorithm by using information from a comprehensive reference dataset of heterogeneously managed grassland parcels in Germany, obtained by RGB cameras. The developed approach using the enhanced vegetation index (EVI) derived from S2 led to a successful mowing event detection in Germany (60.3\% of mowing events detected, F1-Score = 0.64). However, events shortly before, during, or shortly after cloud gaps were missed and in regions with lower S2 orbit coverage fewer mowing events were detected. Therefore, S1-based backscatter, InSAR, and PolSAR features were investigated during S2 data gaps. From these, the PolSAR entropy detected mowing events most reliably. For a focus region, we tested an integrated approach by combining S2 and S1 parameters. This approach detected additional mowing events, but also led to many false positive events, resulting in a reduction in the F1-Score (from 0.65 of S2 to 0.61 of S2 + S1 for the focus region). According to our analysis, a majority of grasslands in Germany are only mown zero to two times (around 84\%) and are probably additionally used for grazing. A small proportion is mown more often than four times (3\%). Regions with a generally higher grassland mowing frequency are located in southern, south-eastern, and northern Germany.},
  language  = {en}
}
@article{MayrKleinRutzingeretal.2021,
  author    = {Mayr, Stefan and Klein, Igor and Rutzinger, Martin and Kuenzer, Claudia},
  title     = {Determining temporal uncertainty of a global inland surface water time series},
  series = {Remote Sensing},
  volume    = {13},
  journal   = {Remote Sensing},
  number    = {17},
  issn      = {2072-4292},
  doi       = {10.3390/rs13173454},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-245234},
  year      = {2021},
  abstract  = {Earth observation time series are well suited to monitor global surface dynamics. However, data products that are aimed at assessing large-area dynamics with a high temporal resolution often face various error sources (e.g., retrieval errors, sampling errors) in their acquisition chain. Addressing uncertainties in a spatiotemporal consistent manner is challenging, as extensive high-quality validation data is typically scarce. Here we propose a new method that utilizes time series inherent information to assess the temporal interpolation uncertainty of time series datasets. For this, we utilized data from the DLR-DFD Global WaterPack (GWP), which provides daily information on global inland surface water. As the time series is primarily based on optical MODIS (Moderate Resolution Imaging Spectroradiometer) images, the requirement of data gap interpolation due to clouds constitutes the main uncertainty source of the product. With a focus on different temporal and spatial characteristics of surface water dynamics, seven auxiliary layers were derived. Each layer provides probability and reliability estimates regarding water observations at pixel-level. This enables the quantification of uncertainty corresponding to the full spatiotemporal range of the product. Furthermore, the ability of temporal layers to approximate unknown pixel states was evaluated for stratified artificial gaps, which were introduced into the original time series of four climatologic diverse test regions. Results show that uncertainty is quantified accurately (>90\%), consequently enhancing the product's quality with respect to its use for modeling and the geoscientific community.},
  language  = {en}
}
@article{WeiBlaschke2018,
  author    = {Wei, Chunzhu and Blaschke, Thomas},
  title     = {Pixel-wise vs. object-based impervious surface analysis from remote sensing: correlations with land surface temperature and population density},
  series = {Urban Science},
  volume    = {2},
  journal   = {Urban Science},
  number    = {1},
  issn      = {2413-8851},
  doi       = {10.3390/urbansci2010002},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197829},
  pages     = {2},
  year      = {2018},
  abstract  = {Impervious surface areas (ISA) are heavily influenced by urban structure and related structural features. We examined the effects of object-based impervious surface spatial pattern analysis on land surface temperature and population density in Guangzhou, China, in comparison to classic per-pixel analyses. An object-based support vector machine (SVM) and a linear spectral mixture analysis (LSMA) were integrated to estimate ISA fraction using images from the Chinese HJ-1B satellite for 2009 to 2011. The results revealed that the integrated object-based SVM-LSMA algorithm outperformed the traditional pixel-wise LSMA algorithm in classifying ISA fraction. More specifically, the object-based ISA spatial patterns extracted were more suitable than pixel-wise patterns for urban heat island (UHI) studies, in which the UHI areas (landscape surface temperature >37 °C) generally feature high ISA fraction values (ISA fraction >50\%). In addition, the object-based spatial patterns enable us to quantify the relationship of ISA with population density (correlation coefficient >0.2 in general), with global human settlement density (correlation coefficient >0.2), and with night-time light map (correlation coefficient >0.4), and, whereas pixel-wise ISA did not yield significant correlations. These results indicate that object-based spatial patterns have a high potential for UHI detection and urbanization monitoring. Planning measures that aim to reduce the urbanization impacts and UHI intensities can be better supported.},
  language  = {en}
}
@article{UereyenKuenzer2019,
  author    = {Uereyen, Soner and Kuenzer, Claudia},
  title     = {A review of earth observation-based analyses for major river basins},
  series = {Remote Sensing},
  volume    = {11},
  journal   = {Remote Sensing},
  number    = {24},
  issn      = {2072-4292},
  doi       = {10.3390/rs11242951},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193849},
  pages     = {2951},
  year      = {2019},
  abstract  = {Regardless of political boundaries, river basins are a functional unit of the Earth's land surface and provide an abundance of resources for the environment and humans. They supply livelihoods supported by the typical characteristics of large river basins, such as the provision of freshwater, irrigation water, and transport opportunities. At the same time, they are impacted i.e., by human-induced environmental changes, boundary conflicts, and upstream-downstream inequalities. In the framework of water resource management, monitoring of river basins is therefore of high importance, in particular for researchers, stake-holders and decision-makers. However, land surface and surface water properties of many major river basins remain largely unmonitored at basin scale. Several inventories exist, yet consistent spatial databases describing the status of major river basins at global scale are lacking. Here, Earth observation (EO) is a potential source of spatial information providing large-scale data on the status of land surface properties. This review provides a comprehensive overview of existing research articles analyzing major river basins primarily using EO. Furthermore, this review proposes to exploit EO data together with relevant open global-scale geodata to establish a database and to enable consistent spatial analyses and evaluate past and current states of major river basins.},
  language  = {en}
}
@article{NillUllmannKneiseletal.2019,
  author    = {Nill, Leon and Ullmann, Tobias and Kneisel, Christof and Sobiech-Wolf, Jennifer and Baumhauer, Roland},
  title     = {Assessing Spatiotemporal Variations of Landsat Land Surface Temperature and Multispectral Indices in the Arctic Mackenzie Delta Region between 1985 and 2018},
  series = {Remote Sensing},
  volume    = {11},
  journal   = {Remote Sensing},
  number    = {19},
  issn      = {2072-4292},
  doi       = {10.3390/rs11192329},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193301},
  year      = {2019},
  abstract  = {Air temperatures in the Arctic have increased substantially over the last decades, which has extensively altered the properties of the land surface. Capturing the state and dynamics of Land Surface Temperatures (LSTs) at high spatial detail is of high interest as LST is dependent on a variety of surficial properties and characterizes the land-atmosphere exchange of energy. Accordingly, this study analyses the influence of different physical surface properties on the long-term mean of the summer LST in the Arctic Mackenzie Delta Region (MDR) using Landsat 30 m-resolution imagery between 1985 and 2018 by taking advantage of the cloud computing capabilities of the Google Earth Engine. Multispectral indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Tasseled Cap greenness (TCG), brightness (TCB), and wetness (TCW) as well as topographic features derived from the TanDEM-X digital elevation model are used in correlation and multiple linear regression analyses to reveal their influence on the LST. Furthermore, surface alteration trends of the LST, NDVI, and NDWI are revealed using the Theil-Sen (T-S) regression method. The results indicate that the mean summer LST appears to be mostly influenced by the topographic exposition as well as the prevalent moisture regime where higher evapotranspiration rates increase the latent heat flux and cause a cooling of the surface, as the variance is best explained by the TCW and northness of the terrain. However, fairly diverse model outcomes for different regions of the MDR (R2 from 0.31 to 0.74 and RMSE from 0.51 °C to 1.73 °C) highlight the heterogeneity of the landscape in terms of influential factors and suggests accounting for a broad spectrum of different factors when modeling mean LSTs. The T-S analysis revealed large-scale wetting and greening trends with a mean decadal increase of the NDVI/NDWI of approximately +0.03 between 1985 and 2018, which was mostly accompanied by a cooling of the land surface given the inverse relationship between mean LSTs and vegetation and moisture conditions. Disturbance through wildfires intensifies the surface alterations locally and lead to significantly cooler LSTs in the long-term compared to the undisturbed surroundings.},
  language  = {en}
}
@article{AsareKyeiForkuorVenus2015,
  author    = {Asare-Kyei, Daniel and Forkuor, Gerald and Venus, Valentijn},
  title     = {Modeling Flood Hazard Zones at the Sub-District Level with the Rational Model Integrated with GIS and Remote Sensing Approaches},
  series = {Water},
  volume    = {7},
  journal   = {Water},
  doi       = {10.3390/w7073531},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-151581},
  pages     = {3531 -- 3564},
  year      = {2015},
  abstract  = {Robust risk assessment requires accurate flood intensity area mapping to allow for the identification of populations and elements at risk. However, available flood maps in West Africa lack spatial variability while global datasets have resolutions too coarse to be relevant for local scale risk assessment. Consequently, local disaster managers are forced to use traditional methods such as watermarks on buildings and media reports to identify flood hazard areas. In this study, remote sensing and Geographic Information System (GIS) techniques were combined with hydrological and statistical models to delineate the spatial limits of flood hazard zones in selected communities in Ghana, Burkina Faso and Benin. The approach involves estimating peak runoff concentrations at different elevations and then applying statistical methods to develop a Flood Hazard Index (FHI). Results show that about half of the study areas fall into high intensity flood zones. Empirical validation using statistical confusion matrix and the principles of Participatory GIS show that flood hazard areas could be mapped at an accuracy ranging from 77\% to 81\%. This was supported with local expert knowledge which accurately classified 79\% of communities deemed to be highly susceptible to flood hazard. The results will assist disaster managers to reduce the risk to flood disasters at the community level where risk outcomes are first materialized.},
  language  = {en}
}
@article{UllmannSauerbreyHoffmeisteretal.2019,
  author    = {Ullmann, Tobias and Sauerbrey, Julia and Hoffmeister, Dirk and May, Simon Matthias and Baumhauer, Roland and Bubenzer, Olaf},
  title     = {Assessing Spatiotemporal Variations of Sentinel-1 InSAR Coherence at Different Time Scales over the Atacama Desert (Chile) between 2015 and 2018},
  series = {Remote Sensing},
  volume    = {11},
  journal   = {Remote Sensing},
  number    = {24},
  issn      = {2072-4292},
  doi       = {10.3390/rs11242960},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193836},
  pages     = {2960},
  year      = {2019},
  abstract  = {This study investigates synthetic aperture radar (SAR) time series of the Sentinel-1 mission acquired over the Atacama Desert, Chile, between March 2015 and December 2018. The contribution analyzes temporal and spatial variations of Sentinel-1 interferometric SAR (InSAR) coherence and exemplarily illustrates factors that are responsible for observed signal differences. The analyses are based on long temporal baselines (365-1090 days) and temporally dense time series constructed with short temporal baselines (12-24 days). Results are compared to multispectral data of Sentinel-2, morphometric features of the digital elevation model (DEM) TanDEM-X WorldDEM™, and to a detailed governmental geographic information system (GIS) dataset of the local hydrography. Sentinel-1 datasets are suited for generating extensive, nearly seamless InSAR coherence mosaics covering the entire Atacama Desert (>450 × 1100 km) at a spatial resolution of 20 × 20 meter per pixel. Temporal baselines over several years lead only to very minor decorrelation, indicating a very high signal stability of C-Band in this region, especially in the hyperarid uplands between the Coastal Cordillera and the Central Depression. Signal decorrelation was associated with certain types of surface cover (e.g., water or aeolian deposits) or with actual surface dynamics (e.g., anthropogenic disturbance (mining) or fluvial activity and overland flow). Strong rainfall events and fluvial activity in the periods 2015 to 2016 and 2017 to 2018 caused spatial patterns with significant signal decorrelation; observed linear coherence anomalies matched the reference channel network and indicated actual episodic and sporadic discharge events. In the period 2015-2016, area-wide loss of coherence appeared as strip-like patterns of more than 80 km length that matched the prevailing wind direction. These anomalies, and others observed in that period and in the period 2017-2018, were interpreted to be caused by overland flow of high magnitude, as their spatial location matched well with documented heavy rainfall events that showed cumulative precipitation amounts of more than 20 mm.},
  language  = {en}
}
@article{BaumhoerDietzKneiseletal.2019,
  author    = {Baumhoer, Celia A. and Dietz, Andreas J. and Kneisel, C. and Kuenzer, C.},
  title     = {Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning},
  series = {Remote Sensing},
  volume    = {11},
  journal   = {Remote Sensing},
  number    = {21},
  issn      = {2072-4292},
  doi       = {10.3390/rs11212529},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193150},
  pages     = {2529},
  year      = {2019},
  abstract  = {Sea level rise contribution from the Antarctic ice sheet is influenced by changes in glacier and ice shelf front position. Still, little is known about seasonal glacier and ice shelf front fluctuations as the manual delineation of calving fronts from remote sensing imagery is very time-consuming. The major challenge of automatic calving front extraction is the low contrast between floating glacier and ice shelf fronts and the surrounding sea ice. Additionally, in previous decades, remote sensing imagery over the often cloud-covered Antarctic coastline was limited. Nowadays, an abundance of Sentinel-1 imagery over the Antarctic coastline exists and could be used for tracking glacier and ice shelf front movement. To exploit the available Sentinel-1 data, we developed a processing chain allowing automatic extraction of the Antarctic coastline from Seninel-1 imagery and the creation of dense time series to assess calving front change. The core of the proposed workflow is a modified version of the deep learning architecture U-Net. This convolutional neural network (CNN) performs a semantic segmentation on dual-pol Sentinel-1 data and the Antarctic TanDEM-X digital elevation model (DEM). The proposed method is tested for four training and test areas along the Antarctic coastline. The automatically extracted fronts deviate on average 78 m in training and 108 m test areas. Spatial and temporal transferability is demonstrated on an automatically extracted 15-month time series along the Getz Ice Shelf. Between May 2017 and July 2018, the fronts along the Getz Ice Shelf show mostly an advancing tendency with the fastest moving front of DeVicq Glacier with 726 ± 20 m/yr.},
  language  = {en}
}
@article{PhilippLevick2019,
  author    = {Philipp, Marius B. and Levick, Shaun R.},
  title     = {Exploring the potential of C-Band SAR in contributing to burn severity mapping in tropical savanna},
  series = {Remote Sensing},
  volume    = {12},
  journal   = {Remote Sensing},
  number    = {1},
  issn      = {2072-4292},
  doi       = {10.3390/rs12010049},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193789},
  pages     = {49},
  year      = {2019},
  abstract  = {The ability to map burn severity and to understand how it varies as a function of time of year and return frequency is an important tool for landscape management and carbon accounting in tropical savannas. Different indices based on optical satellite imagery are typically used for mapping fire scars and for estimating burn severity. However, cloud cover is a major limitation for analyses using optical data over tropical landscapes. To address this pitfall, we explored the suitability of C-band Synthetic Aperture Radar (SAR) data for detecting vegetation response to fire, using experimental fires in northern Australia. Pre- and post-fire results from Sentinel-1 C-band backscatter intensity data were compared to those of optical satellite imagery and were corroborated against structural changes on the ground that we documented through terrestrial laser scanning (TLS). Sentinel-1 C-band backscatter (VH) proved sensitive to the structural changes imparted by fire and was correlated with the Normalised Burn Ratio (NBR) derived from Sentinel-2 optical data. Our results suggest that C-band SAR holds potential to inform the mapping of burn severity in savannas, but further research is required over larger spatial scales and across a broader spectrum of fire regime conditions before automated products can be developed. Combining both Sentinel-1 SAR and Sentinel-2 multi-spectral data will likely yield the best results for mapping burn severity under a range of weather conditions.},
  language  = {en}
}
@article{SognoKleinKuenzer2022,
  author    = {Sogno, Patrick and Klein, Igor and Kuenzer, Claudia},
  title     = {Remote sensing of surface water dynamics in the context of global change — a review},
  series = {Remote Sensing},
  volume    = {14},
  journal   = {Remote Sensing},
  number    = {10},
  issn      = {2072-4292},
  doi       = {10.3390/rs14102475},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-275274},
  year      = {2022},
  abstract  = {Inland surface water is often the most accessible freshwater source. As opposed to groundwater, surface water is replenished in a comparatively quick cycle, which makes this vital resource — if not overexploited — sustainable. From a global perspective, freshwater is plentiful. Still, depending on the region, surface water availability is severely limited. Additionally, climate change and human interventions act as large-scale drivers and cause dramatic changes in established surface water dynamics. Actions have to be taken to secure sustainable water availability and usage. This requires informed decision making based on reliable environmental data. Monitoring inland surface water dynamics is therefore more important than ever. Remote sensing is able to delineate surface water in a number of ways by using optical as well as active and passive microwave sensors. In this review, we look at the proceedings within this discipline by reviewing 233 scientific works. We provide an extensive overview of used sensors, the spatial and temporal resolution of studies, their thematic foci, and their spatial distribution. We observe that a wide array of available sensors and datasets, along with increasing computing capacities, have shaped the field over the last years. Multiple global analysis-ready products are available for investigating surface water area dynamics, but so far none offer high spatial and temporal resolution.},
  language  = {en}
}
@article{LappeUllmannBachofer2022,
  author    = {Lappe, Ronja and Ullmann, Tobias and Bachofer, Felix},
  title     = {State of the Vietnamese coast — assessing three decades (1986 to 2021) of coastline dynamics using the Landsat archive},
  series = {Remote Sensing},
  volume    = {14},
  journal   = {Remote Sensing},
  number    = {10},
  issn      = {2072-4292},
  doi       = {10.3390/rs14102476},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-275281},
  year      = {2022},
  abstract  = {Vietnam's 3260 km coastline is densely populated, experiences rapid urban and economic growth, and faces at the same time a high risk of coastal hazards. Satellite archives provide a free and powerful opportunity for long-term area-wide monitoring of the coastal zone. This paper presents an automated analysis of coastline dynamics from 1986 to 2021 for Vietnam's entire coastal zone using the Landsat archive. The proposed method is implemented within the cloud-computing platform Google Earth Engine to only involve publicly and globally available datasets and tools. We generated annual coastline composites representing the mean-high water level and extracted sub-pixel coastlines. We further quantified coastline change rates along shore-perpendicular transects, revealing that half of Vietnam's coast did not experience significant change, while the remaining half is classified as erosional (27.7\%) and accretional (27.1\%). A hotspot analysis shows that coastal segments with the highest change rates are concentrated in the low-lying deltas of the Mekong River in the south and the Red River in the north. Hotspots with the highest accretion rates of up to +47 m/year are mainly associated with the construction of artificial coastlines, while hotspots with the highest erosion rates of -28 m/year may be related to natural sediment redistribution and human activity.},
  language  = {en}
}
@article{HalbgewachsWegmanndaPonte2022,
  author    = {Halbgewachs, Magdalena and Wegmann, Martin and da Ponte, Emmanuel},
  title     = {A spectral mixture analysis and landscape metrics based framework for monitoring spatiotemporal forest cover changes: a case study in Mato Grosso, Brazil},
  series = {Remote Sensing},
  volume    = {14},
  journal   = {Remote Sensing},
  number    = {8},
  issn      = {2072-4292},
  doi       = {10.3390/rs14081907},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-270644},
  year      = {2022},
  abstract  = {An increasing amount of Brazilian rainforest is being lost or degraded for various reasons, both anthropogenic and natural, leading to a loss of biodiversity and further global consequences. Especially in the Brazilian state of Mato Grosso, soy production and large-scale cattle farms led to extensive losses of rainforest in recent years. We used a spectral mixture approach followed by a decision tree classification based on more than 30 years of Landsat data to quantify these losses. Research has shown that current methods for assessing forest degradation are lacking accuracy. Therefore, we generated classifications to determine land cover changes for each year, focusing on both cleared and degraded forest land. The analyses showed a decrease in forest area in Mato Grosso by 28.8\% between 1986 and 2020. In order to measure changed forest structures for the selected period, fragmentation analyses based on diverse landscape metrics were carried out for the municipality of Colniza in Mato Grosso. It was found that forest areas experienced also a high degree of fragmentation over the study period, with an increase of 83.3\% of the number of patches and a decrease of the mean patch area of 86.1\% for the selected time period, resulting in altered habitats for flora and fauna.},
  language  = {en}
}
@article{YangYaoLietal.2022,
  author    = {Yang, Xuting and Yao, Wanqiang and Li, Pengfei and Hu, Jinfei and Latifi, Hooman and Kang, Li and Wang, Ningjing and Zhang, Dingming},
  title     = {Changes of SOC content in China's Shendong coal mining area during 1990-2020 investigated using remote sensing techniques},
  series = {Sustainability},
  volume    = {14},
  journal   = {Sustainability},
  number    = {12},
  issn      = {2071-1050},
  doi       = {10.3390/su14127374},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-278939},
  year      = {2022},
  abstract  = {Coal mining, an important human activity, disturbs soil organic carbon (SOC) accumulation and decomposition, eventually affecting terrestrial carbon cycling and the sustainability of human society. However, changes of SOC content and their relation with influential factors in coal mining areas remained unclear. In the study, predictive models of SOC content were developed based on field sampling and Landsat images for different land-use types (grassland, forest, farmland, and bare land) of the largest coal mining area in China (i.e., Shendong). The established models were employed to estimate SOC content across the Shendong mining area during 1990-2020, followed by an investigation into the impacts of climate change and human disturbance on SOC content by a Geo-detector. Results showed that the models produced satisfactory results (R\(^2\) > 0.69, p < 0.05), demonstrating that SOC content over a large coal mining area can be effectively assessed using remote sensing techniques. Results revealed that average SOC content in the study area rose from 5.67 gC·kg\(^{-1}\) in 1990 to 9.23 gC·kg\(^{-1}\) in 2010 and then declined to 5.31 gC·Kg\(^{-1}\) in 2020. This could be attributed to the interaction between the disturbance of soil caused by coal mining and the improvement of eco-environment by land reclamation. Spatially, the SOC content of farmland was the highest, followed by grassland, and that of bare land was the lowest. SOC accumulation was inhibited by coal mining activities, with the effect of high-intensity mining being lower than that of moderate- and low-intensity mining activities. Land use was found to be the strongest individual influencing factor for SOC content changes, while the interaction between vegetation coverage and precipitation exerted the most significant influence on the variability of SOC content. Furthermore, the influence of mining intensity combined with precipitation was 10 times higher than that of mining intensity alone.},
  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{EmmertKneisel2021,
  author    = {Emmert, Adrian and Kneisel, Christof},
  title     = {Internal structure and palsa development at Orravatnsr{\´u}stir Palsa Site (Central Iceland), investigated by means of integrated resistivity and ground-penetrating radar methods},
  series = {Permafrost and Periglacial Processes},
  volume    = {32},
  journal   = {Permafrost and Periglacial Processes},
  number    = {3},
  doi       = {10.1002/ppp.2106},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-238933},
  pages     = {503 -- 519},
  year      = {2021},
  abstract  = {The natural cyclical development of palsas makes it difficult to use visible signs of decay as reference points for environmental change. Thus, to determine the actual development stage of a palsa, investigations of the internal structure are crucial. Our study presents 2-D and 3-D electrical resistivity imaging (ERI) and 2-D ground-penetrating radar (GPR) results, measurements of surface and subsurface temperatures, and of the soil matric potential from Orravatnsr{\´u}stir Palsa Site in Central Iceland. By a joint interpretation of the results, we deduce the internal structure (i.e., thickness of thaw zone and permafrost, ice/water content) of five palsas of different size and shape. The results differentiate between initial and mature development stages and show that palsas of different development stages can exist in close proximity. While internal characteristics indicate undisturbed development of four palsas, one palsa shows indications of environmental change. Our study shows the value of the multimethod geophysical approach and introduces measurements of the soil matric potential as a promising method to assess the current state of the subsurface.},
  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{GeyerLanding2021,
  author    = {Geyer, Gerd and Landing, Ed},
  title     = {The Souss lagerstatte of the Anti-Atlas, Morocco: discovery of the first Cambrian fossil lagerstatte from Africa},
  series = {Scientific Reports},
  volume    = {11},
  journal   = {Scientific Reports},
  number    = {1},
  doi       = {10.1038/s41598-021-82546-0},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-259236},
  pages     = {3107},
  year      = {2021},
  abstract  = {Episodic low oxygenated conditions on the sea-floor are likely responsible for exceptional preservation of animal remains in the upper Amouslek Formation (lower Cambrian, Stage 3) on the northern slope of the western Anti-Atlas, Morocco. This stratigraphic interval has yielded trilobite, brachiopod, and hyolith fossils with preserved soft parts, including some of the oldest known trilobite guts. The "Souss fossil lagerstatte" (newly proposed designation) represents the first Cambrian fossil lagerstatte in Cambrian strata known from Africa and is one of the oldest trilobite-bearing fossil lagerstatten on Earth. Inter-regional correlation of the Souss fossil lagerstatte in West Gondwana suggests its development during an interval of high eustatic levels recorded by dark shales that occur in informal upper Cambrian Series 2 in Siberia, South China, and East Gondwana.},
  language  = {en}
}