@phdthesis{Hoffmann2017, author = {Hoffmann, Susanne}, title = {Demographischer Wandel und innerst{\"a}dtische Einkaufszentren in Deutschland. Entwicklungen in Erlangen, Koblenz und Zwickau}, publisher = {W{\"u}rzburg University Press}, address = {W{\"u}rzburg}, isbn = {978-3-95826-062-7 (print)}, issn = {0510-9833}, doi = {10.25972/WUP-978-3-95826-062-7}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-148218}, school = {W{\"u}rzburg University Press}, pages = {380}, year = {2017}, abstract = {Der Anteil {\"a}lterer und alter Menschen an der Gesamtbev{\"o}lkerung steigt kontinuierlich an. Diese Entwicklung wird sich auch in den kommenden Jahren fortsetzen. So werden 2050 rund 40 \% der deutschen Bev{\"o}lkerung 60 Jahre oder {\"a}lter sein. Die Alterung der Bev{\"o}lkerung wirkt sich auf nahezu alle Lebensbereiche aus und stellt damit Planer und Entscheider auf staatlicher wie auf privater Seite vor neue Herausforderungen. Dies betrifft auch die Frage, wie innerst{\"a}dtische Einkaufsstandorte, und zwar traditionelle innerst{\"a}dtische Einkaufsstraßen und innerst{\"a}dtische Shopping Center, gestaltet werden m{\"u}ssen, um den Anforderungen und Bed{\"u}rfnissen m{\"o}glichst aller Altersgruppen und damit auch denjenigen der {\"a}lteren und alten Konsumenten zu entsprechen. Am Beispiel der St{\"a}dte Erlangen, Koblenz und Zwickau wird in vorliegender Untersuchung der Frage nachgegangen, wie {\"a}ltere und alte Menschen die verschiedenen innerst{\"a}dtischen Einkaufsstandorte wahrnehmen und nutzen, welche Unterschiede diesbez{\"u}glich zu j{\"u}ngeren Kundengruppen bestehen und welche Schlussfolgerungen sich daraus f{\"u}r eine zukunftsgerichtete Gestaltung der traditionellen Einkaufsstraßen und der innerst{\"a}dtischen Shopping Center ableiten lassen. F{\"u}r die Untersuchung kam ein breites methodisches Instrumentarium aus Zeitungsrecherchen, Kartierungen, qualitativen Beobachtungen, qualitativen Haushaltsbefragungen sowie quantitativen Passantenbefragungen zur Anwendung.}, subject = {Demographie}, 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 = {Izhodišča za usklajeno ohranjanje odprtega prostora na območju Alp: Načrtovalski priročnik projekta OpenSpaceAlps}, doi = {10.25972/OPUS-28651}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-286518}, pages = {114}, year = {2022}, abstract = {Na območju Alp že dalj časa poteka preobrazba odprtega prostora zaradi gradnje in širjenja naselij ter tehnične infrastrukture. Navedeni procesi povzročajo zlasti izgubo kmetijskih zemljišč, stalno pokritje površine tal z nepropustnimi snovmi in razdrobljenost krajine. Razdrobljenost je odvisna od vrst posegov in stopnje pozidanosti prostora, poglavitna negativna učinka pa sta izolacija naravnih habitatov in slabšanje ekološke povezljivosti. Opisana problematika je bila glavna tema projekta OpenSpaceAlps, v katerem so bili ob sodelovanju z deležniki na več pilotnih območjih razviti pristopi in rešitve, ki omogočajo trajnostno ohranjanje odprtega prostora. Načrtovalski priročnik povzema del rezultatov projekta. Namenjen je različnim deležnikom, zlasti načrtovalcem v javnih službah, kot pripomoček pri izvajanju načrtovalskih nalog in odločanju. V priročniku so predstavljeni analiza izzivov in okvirnih pogojev v Alpah ter opis in primerjava poglavitnih načel načrtovanja odprtega prostora, obravnavane pa so tudi celostne načrtovalske strategije za različne kategorije prostora.}, subject = {Raumordnung}, language = {mul} } @article{MayrKleinRutzingeretal.2021, author = {Mayr, Stefan and Klein, Igor and Rutzinger, Martin and Kuenzer, Claudia}, title = {Systematic water fraction estimation for a global and daily surface water time-series}, series = {Remote Sensing}, volume = {13}, journal = {Remote Sensing}, number = {14}, issn = {2072-4292}, doi = {10.3390/rs13142675}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-242586}, year = {2021}, abstract = {Fresh water is a vital natural resource. Earth observation time-series are well suited to monitor corresponding surface dynamics. The DLR-DFD Global WaterPack (GWP) provides daily information on globally distributed inland surface water based on MODIS (Moderate Resolution Imaging Spectroradiometer) images at 250 m spatial resolution. Operating on this spatiotemporal level comes with the drawback of moderate spatial resolution; only coarse pixel-based surface water quantification is possible. To enhance the quantitative capabilities of this dataset, we systematically access subpixel information on fractional water coverage. For this, a linear mixture model is employed, using classification probability and pure pixel reference information. Classification probability is derived from relative datapoint (pixel) locations in feature space. Pure water and non-water reference pixels are located by combining spatial and temporal information inherent to the time-series. Subsequently, the model is evaluated for different input sets to determine the optimal configuration for global processing and pixel coverage types. The performance of resulting water fraction estimates is evaluated on the pixel level in 32 regions of interest across the globe, by comparison to higher resolution reference data (Sentinel-2, Landsat 8). Results show that water fraction information is able to improve the product's performance regarding mixed water/non-water pixels by an average of 11.6\% (RMSE). With a Nash-Sutcliffe efficiency of 0.61, the model shows good overall performance. The approach enables the systematic provision of water fraction estimates on a global and daily scale, using only the reflectance and temporal information contained in the input time-series.}, language = {en} } @article{StereńczakLaurinChiricietal.2020, author = {Stereńczak, Krzysztof and Laurin, Gaia Vaglio and Chirici, Gherardo and Coomes, David A. and Dalponte, Michele and Latifi, Hooman and Puletti, Nicola}, title = {Global Airborne Laser Scanning Data Providers Database (GlobALS) — a new tool for monitoring ecosystems and biodiversity}, series = {Remote Sensing}, volume = {12}, journal = {Remote Sensing}, number = {11}, issn = {2072-4292}, doi = {10.3390/rs12111877}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-207819}, year = {2020}, abstract = {Protection and recovery of natural resource and biodiversity requires accurate monitoring at multiple scales. Airborne Laser Scanning (ALS) provides high-resolution imagery that is valuable for monitoring structural changes to vegetation, providing a reliable reference for ecological analyses and comparison purposes, especially if used in conjunction with other remote-sensing and field products. However, the potential of ALS data has not been fully exploited, due to limits in data availability and validation. To bridge this gap, the global network for airborne laser scanner data (GlobALS) has been established as a worldwide network of ALS data providers that aims at linking those interested in research and applications related to natural resources and biodiversity monitoring. The network does not collect data itself but collects metadata and facilitates networking and collaborative research amongst the end-users and data providers. This letter describes this facility, with the aim of broadening participation in GlobALS.}, language = {en} } @article{HuthGessnerKleinetal.2020, author = {Huth, Juliane and Gessner, Ursula and Klein, Igor and Yesou, Herv{\´e} and Lai, Xijun and Oppelt, Natascha and Kuenzer, Claudia}, title = {Analyzing water dynamics based on Sentinel-1 time series — a study for Dongting Lake wetlands in China}, series = {Remote Sensing}, volume = {12}, journal = {Remote Sensing}, number = {11}, issn = {2072-4292}, doi = {10.3390/rs12111761}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-205977}, year = {2020}, abstract = {In China, freshwater is an increasingly scarce resource and wetlands are under great pressure. This study focuses on China's second largest freshwater lake in the middle reaches of the Yangtze River — the Dongting Lake — and its surrounding wetlands, which are declared a protected Ramsar site. The Dongting Lake area is also a research region of focus within the Sino-European Dragon Programme, aiming for the international collaboration of Earth Observation researchers. ESA's Copernicus Programme enables comprehensive monitoring with area-wide coverage, which is especially advantageous for large wetlands that are difficult to access during floods. The first year completely covered by Sentinel-1 SAR satellite data was 2016, which is used here to focus on Dongting Lake's wetland dynamics. The well-established, threshold-based approach and the high spatio-temporal resolution of Sentinel-1 imagery enabled the generation of monthly surface water maps and the analysis of the inundation frequency at a 10 m resolution. The maximum extent of the Dongting Lake derived from Sentinel-1 occurred in July 2016, at 2465 km\(^2\), indicating an extreme flood year. The minimum size of the lake was detected in October, at 1331 km\(^2\). Time series analysis reveals detailed inundation patterns and small-scale structures within the lake that were not known from previous studies. Sentinel-1 also proves to be capable of mapping the wetland management practices for Dongting Lake polders and dykes. For validation, the lake extent and inundation duration derived from the Sentinel-1 data were compared with excerpts from the Global WaterPack (frequently derived by the German Aerospace Center, DLR), high-resolution optical data, and in situ water level data, which showed very good agreement for the period studied. The mean monthly extent of the lake in 2016 from Sentinel-1 was 1798 km\(^2\), which is consistent with the Global WaterPack, deviating by only 4\%. In summary, the presented analysis of the complete annual time series of the Sentinel-1 data provides information on the monthly behavior of water expansion, which is of interest and relevance to local authorities involved in water resource management tasks in the region, as well as to wetland conservationists concerned with the Ramsar site wetlands of Dongting Lake and to local researchers.}, language = {en} } @article{ForkuorUllmannGriesbeck2020, author = {Forkuor, Gerald and Ullmann, Tobias and Griesbeck, Mario}, title = {Mapping and monitoring small-scale mining activities in Ghana using Sentinel-1 time series (2015-2019)}, series = {Remote Sensing}, volume = {12}, journal = {Remote Sensing}, number = {6}, issn = {2072-4292}, doi = {10.3390/rs12060911}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-203204}, year = {2020}, abstract = {Illegal small-scale mining (galamsey) in South-Western Ghana has grown tremendously in the last decade and caused significant environmental degradation. Excessive cloud cover in the area has limited the use of optical remote sensing data to map and monitor the extent of these activities. This study investigated the use of annual time-series Sentinel-1 data to map and monitor illegal mining activities along major rivers in South-Western Ghana between 2015 and 2019. A change detection approach, based on three time-series features — minimum, mean, maximum — was used to compute a backscatter threshold value suitable to identify/detect mining-induced land cover changes in the study area. Compared to the mean and maximum, the minimum time-series feature (in both VH and VV polarization) was found to be more sensitive to changes in backscattering within the period of investigation. Our approach permitted the detection of new illegal mining areas on an annual basis. A backscatter threshold value of +1.65 dB was found suitable for detecting illegal mining activities in the study area. Application of this threshold revealed illegal mining area extents of 102 km\(^2\), 60 km\(^2\) and 33 km\(^2\) for periods 2015/2016-2016/2017, 2016/2017-2017/2018 and 2017/2018-2018/2019, respectively. The observed decreasing trend in new illegal mining areas suggests that efforts at stopping illegal mining yielded positive results in the period investigated. Despite the advantages of Synthetic Aperture Radar data in monitoring phenomena in cloud-prone areas, our analysis revealed that about 25\% of the Sentinel-1 data, mostly acquired in March and October (beginning and end of rainy season respectively), were unusable due to atmospheric effects from high intensity rainfall events. Further investigation in other geographies and climatic regions is needed to ascertain the susceptibility of Sentinel-1 data to atmospheric conditions.}, language = {en} } @book{Herb2022, author = {Herb, Christopher}, title = {Restrukturierung von Wertsch{\"o}pfungsketten in der Digitalisierung. Eine Analyse der deutschen Schuhbranche vom Hersteller bis zum Konsumenten}, publisher = {W{\"u}rzburg University Press}, address = {W{\"u}rzburg}, isbn = {978-3-95826-186-0}, issn = {2626-8906}, doi = {10.25972/WUP-978-3-95826-187-7}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-269564}, publisher = {W{\"u}rzburg University Press}, pages = {xii, 284}, year = {2022}, abstract = {Globale Wertsch{\"o}pfungsketten stellen nicht nur hochkomplexe Beziehungsgef{\"u}ge dar, sondern unterliegen auch einem st{\"a}ndigen Wandlungsprozess. Ein zentraler Treiber dieser Wandlungsprozesse ist der technologische Fortschritt. Moderne Informations- und Kommunikationstechnologien, insbesondere die Ph{\"a}nomene der Digitalisierung und des Online-Handels, sind derzeit von besonderer Bedeutung f{\"u}r Wertsch{\"o}pfungsketten, da unterschiedliche Fortschritte in der Digitalisierung nicht nur zu wirtschaftlichen Vor- und Nachteilen von Unternehmen f{\"u}hren k{\"o}nnen, sondern auch zu Up- bzw. Downgradingprozessen innerhalb der Wertsch{\"o}pfungsketten. In der vorliegenden Studie wird der Fokus auf den handels- bzw. konsumentennahen Teil von Wertsch{\"o}pfungsketten gelegt, um die Folgen der Digitalisierung f{\"u}r Hersteller, H{\"a}ndler und Konsumenten n{\"a}her zu betrachten. Als konkretes Forschungsbeispiel dient die deutsche Schuhbranche, da sich diese gegenw{\"a}rtig - von Industrie bis Handel - in einem umfassenden Strukturwandel befindet. Die Analyse zeigt, dass sich die Komplexit{\"a}t von Wertsch{\"o}pfungsketten im Zuge der Digitalisierung deutlich erh{\"o}ht (hat). In der Schuhbranche dr{\"a}ngen neue Akteure auf den Markt, bestehende Akteure m{\"u}ssen sich anpassen. Direkte Folgen sind nicht nur eine neue Akteurskonstellation, sondern auch ein sich neu bildendes Machtgef{\"u}ge. Es kommt somit zur Restrukturierung bisheriger Wertsch{\"o}pfungsketten.}, subject = {Wertsch{\"o}pfungskette}, language = {de} } @article{HoeserBachoferKuenzer2020, author = {Hoeser, Thorsten and Bachofer, Felix and Kuenzer, Claudia}, title = {Object detection and image segmentation with deep learning on Earth Observation data: a review — part II: applications}, series = {Remote Sensing}, volume = {12}, journal = {Remote Sensing}, number = {18}, issn = {2072-4292}, doi = {10.3390/rs12183053}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-213152}, year = {2020}, abstract = {In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by investigating aggregated classes. The increase in data with a very high spatial resolution enables investigations on a fine-grained feature level which can help us to better understand the dynamics of land surfaces by taking object dynamics into account. To extract fine-grained features and objects, the most popular deep-learning model for image analysis is commonly used: the convolutional neural network (CNN). In this review, we provide a comprehensive overview of the impact of deep learning on EO applications by reviewing 429 studies on image segmentation and object detection with CNNs. We extensively examine the spatial distribution of study sites, employed sensors, used datasets and CNN architectures, and give a thorough overview of applications in EO which used CNNs. Our main finding is that CNNs are in an advanced transition phase from computer vision to EO. Upon this, we argue that in the near future, investigations which analyze object dynamics with CNNs will have a significant impact on EO research. With a focus on EO applications in this Part II, we complete the methodological review provided in Part I.}, language = {en} } @article{HoeserKuenzer2020, author = {Hoeser, Thorsten and Kuenzer, Claudia}, title = {Object detection and image segmentation with deep learning on Earth observation data: a review-part I: evolution and recent trends}, series = {Remote Sensing}, volume = {12}, journal = {Remote Sensing}, number = {10}, issn = {2072-4292}, doi = {10.3390/rs12101667}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-205918}, year = {2020}, abstract = {Deep learning (DL) has great influence on large parts of science and increasingly established itself as an adaptive method for new challenges in the field of Earth observation (EO). Nevertheless, the entry barriers for EO researchers are high due to the dense and rapidly developing field mainly driven by advances in computer vision (CV). To lower the barriers for researchers in EO, this review gives an overview of the evolution of DL with a focus on image segmentation and object detection in convolutional neural networks (CNN). The survey starts in 2012, when a CNN set new standards in image recognition, and lasts until late 2019. Thereby, we highlight the connections between the most important CNN architectures and cornerstones coming from CV in order to alleviate the evaluation of modern DL models. Furthermore, we briefly outline the evolution of the most popular DL frameworks and provide a summary of datasets in EO. By discussing well performing DL architectures on these datasets as well as reflecting on advances made in CV and their impact on future research in EO, we narrow the gap between the reviewed, theoretical concepts from CV and practical application in EO.}, language = {en} } @article{HeinemannSiegmannThonfeldetal.2020, author = {Heinemann, Sascha and Siegmann, Bastian and Thonfeld, Frank and Muro, Javier and Jedmowski, Christoph and Kemna, Andreas and Kraska, Thorsten and Muller, Onno and Schultz, Johannes and Udelhoven, Thomas and Wilke, Norman and Rascher, Uwe}, title = {Land surface temperature retrieval for agricultural areas using a novel UAV platform equipped with a thermal infrared and multispectral sensor}, series = {Remote Sensing}, volume = {12}, journal = {Remote Sensing}, number = {7}, issn = {2072-4292}, doi = {10.3390/rs12071075}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-203557}, year = {2020}, abstract = {Land surface temperature (LST) is a fundamental parameter within the system of the Earth's surface and atmosphere, which can be used to describe the inherent physical processes of energy and water exchange. The need for LST has been increasingly recognised in agriculture, as it affects the growth phases of crops and crop yields. However, challenges in overcoming the large discrepancies between the retrieved LST and ground truth data still exist. Precise LST measurement depends mainly on accurately deriving the surface emissivity, which is very dynamic due to changing states of land cover and plant development. In this study, we present an LST retrieval algorithm for the combined use of multispectral optical and thermal UAV images, which has been optimised for operational applications in agriculture to map the heterogeneous and diverse agricultural crop systems of a research campus in Germany (April 2018). We constrain the emissivity using certain NDVI thresholds to distinguish different land surface types. The algorithm includes atmospheric corrections and environmental thermal emissions to minimise the uncertainties. In the analysis, we emphasise that the omission of crucial meteorological parameters and inaccurately determined emissivities can lead to a considerably underestimated LST; however, if the emissivity is underestimated, the LST can be overestimated. The retrieved LST is validated by reference temperatures from nearby ponds and weather stations. The validation of the thermal measurements indicates a mean absolute error of about 0.5 K. The novelty of the dual sensor system is that it simultaneously captures highly spatially resolved optical and thermal images, in order to construct the precise LST ortho-mosaics required to monitor plant diseases and drought stress and validate airborne and satellite data.}, language = {en} } @article{LatifiHeurich2019, author = {Latifi, Hooman and Heurich, Marco}, title = {Multi-scale remote sensing-assisted forest inventory: a glimpse of the state-of-the-art and future prospects}, series = {Remote Sensing}, volume = {11}, journal = {Remote Sensing}, number = {11}, issn = {2072-4292}, doi = {10.3390/rs11111260}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197358}, year = {2019}, abstract = {Advances in remote inventory and analysis of forest resources during the last decade have reached a level to be now considered as a crucial complement, if not a surrogate, to the long-existing field-based methods. This is mostly reflected in not only the use of multiple-band new active and passive remote sensing data for forest inventory, but also in the methodic and algorithmic developments and/or adoptions that aim at maximizing the predictive or calibration performances, thereby minimizing both random and systematic errors, in particular for multi-scale spatial domains. With this in mind, this editorial note wraps up the recently-published Remote Sensing special issue "Remote Sensing-Based Forest Inventories from Landscape to Global Scale", which hosted a set of state-of-the-art experiments on remotely sensed inventory of forest resources conducted by a number of prominent researchers worldwide.}, language = {en} } @article{AppelHardaker2021, author = {Appel, Alexandra and Hardaker, Sina}, title = {Strategies in Times of Pandemic Crisis — Retailers and Regional Resilience in W{\"u}rzburg, Germany}, series = {Sustainability}, volume = {13}, journal = {Sustainability}, number = {5}, issn = {2071-1050}, doi = {10.3390/su13052643}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-233991}, year = {2021}, abstract = {Research on the COVID-19 crisis and its implications on regional resilience is still in its infancy. To understand resilience on its aggregate level it is important to identify (non)resilient actions of individual actors who comprise regions. As the retail sector among others represents an important factor in an urban regions recovery, we focus on the resilience of (textile) retailers within the city of W{\"u}rzburg in Germany to the COVID-19 pandemic. To address the identified research gap, this paper applies the concept of resilience. Firstly, conducting expert interviews, the individual (textile) retailers' level and their strategies in coping with the crisis is considered. Secondly, conducting a contextual analysis of the German city of W{\"u}rzburg, we wish to contribute to the discussion of how the resilience of a region is influenced inter alia by actors. Our study finds three main strategies on the individual level, with retailers: (1) intending to "bounce back" to a pre-crisis state, (2) reorganising existing practices, as well as (3) closing stores and winding up business. As at the time of research, no conclusions regarding long-term impacts and resilience are possible, the results are limited. Nevertheless, detailed analysis of retailers' strategies contributes to a better understanding of regional resilience.}, language = {en} } @article{HolzwarthThonfeldAbdullahietal.2020, author = {Holzwarth, Stefanie and Thonfeld, Frank and Abdullahi, Sahra and Asam, Sarah and Da Ponte Canova, Emmanuel and Gessner, Ursula and Huth, Juliane and Kraus, Tanja and Leutner, Benjamin and Kuenzer, Claudia}, title = {Earth Observation based monitoring of forests in Germany: a review}, series = {Remote Sensing}, volume = {12}, journal = {Remote Sensing}, number = {21}, issn = {2072-4292}, doi = {10.3390/rs12213570}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-216334}, year = {2020}, abstract = {Forests in Germany cover around 11.4 million hectares and, thus, a share of 32\% of Germany's surface area. Therefore, forests shape the character of the country's cultural landscape. Germany's forests fulfil a variety of functions for nature and society, and also play an important role in the context of climate levelling. Climate change, manifested via rising temperatures and current weather extremes, has a negative impact on the health and development of forests. Within the last five years, severe storms, extreme drought, and heat waves, and the subsequent mass reproduction of bark beetles have all seriously affected Germany's forests. Facing the current dramatic extent of forest damage and the emerging long-term consequences, the effort to preserve forests in Germany, along with their diversity and productivity, is an indispensable task for the government. Several German ministries have and plan to initiate measures supporting forest health. Quantitative data is one means for sound decision-making to ensure the monitoring of the forest and to improve the monitoring of forest damage. In addition to existing forest monitoring systems, such as the federal forest inventory, the national crown condition survey, and the national forest soil inventory, systematic surveys of forest condition and vulnerability at the national scale can be expanded with the help of a satellite-based earth observation. In this review, we analysed and categorized all research studies published in the last 20 years that focus on the remote sensing of forests in Germany. For this study, 166 citation indexed research publications have been thoroughly analysed with respect to publication frequency, location of studies undertaken, spatial and temporal scale, coverage of the studies, satellite sensors employed, thematic foci of the studies, and overall outcomes, allowing us to identify major research and geoinformation product gaps.}, language = {en} } @article{UsmanMahmoodConradetal.2020, author = {Usman, Muhammad and Mahmood, Talha and Conrad, Christopher and Bodla, Habib Ullah}, title = {Remote Sensing and modelling based framework for valuing irrigation system efficiency and steering indicators of consumptive water use in an irrigated region}, series = {Sustainability}, volume = {12}, journal = {Sustainability}, number = {22}, issn = {2071-1050}, doi = {10.3390/su12229535}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-219358}, year = {2020}, abstract = {Water crises are becoming severe in recent times, further fueled by population increase and climate change. They result in complex and unsustainable water management. Spatial estimation of consumptive water use is vital for performance assessment of the irrigation system using Remote Sensing (RS). For this study, its estimation is done using the Soil Energy Balance Algorithm for Land (SEBAL) approach. Performance indicators including equity, adequacy, and reliability were worked out at various spatiotemporal scales. Moreover, optimization and sustainable use of water resources are not possible without knowing the factors mainly influencing consumptive water use of major crops. For that purpose, random forest regression modelling was employed using various sets of factors for site-specific, proximity, and cropping system. The results show that the system is underperforming both for Kharif (i.e., summer) and Rabi (i.e., winter) seasons. Performance indicators highlight poor water distribution in the system, a shortage of water supply, and unreliability. The results are relatively good for Rabi as compared to Kharif, with an overall poor situation for both seasons. Factors importance varies for different crops. Overall, distance from canal, road density, canal density, and farm approachability are the most important factors for explaining consumptive water use. Auditing of consumptive water use shows the potential for resource optimization through on-farm water management by the targeted approach. The results are based on the present situation without considering future changes in canal water supply and consumptive water use under climate change.}, language = {en} } @phdthesis{Bangelesa2022, author = {Bangelesa, Freddy Fefe}, title = {Impacts of climate variability and change on Maize (\(Zea\) \(mays\)) production in tropical Africa}, doi = {10.25972/OPUS-25934}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-259347}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2022}, abstract = {Climate change is undeniable and constitutes one of the major threats of the 21st century. It impacts sectors of our society, usually negatively, and is likely to worsen towards the middle and end of the century. The agricultural sector is of particular concern, for it is the primary source of food and is strongly dependent on the weather. Considerable attention has been given to the impact of climate change on African agriculture because of the continent's high vulnerability, which is mainly due to its low adaptation capac- ity. Several studies have been implemented to evaluate the impact of climate change on this continent. The results are sometimes controversial since the studies are based on different approaches, climate models and crop yield datasets. This study attempts to contribute substantially to this large topic by suggesting specific types of climate pre- dictors. The study focuses on tropical Africa and its maize yield. Maize is considered to be the most important crop in this region. To estimate the effect of climate change on maize yield, the study began by developing a robust cross-validated multiple linear regression model, which related climate predictors and maize yield. This statistical trans- fer function is reputed to be less prone to overfitting and multicollinearity problems. It is capable of selecting robust predictors, which have a physical meaning. Therefore, the study combined: large-scale predictors, which were derived from the principal component analysis of the monthly precipitation and temperature; traditional local-scale predictors, mainly, the mean precipitation, mean temperature, maximum temperature and minimum temperature; and the Water Requirement Satisfaction Index (WRSI), derived from the specific crop (maize) water balance model. The projected maize-yield change is forced by a regional climate model (RCM) REMO under two emission scenarios: high emission scenario (RCP8.5) and mid-range emission scenario (RCP4.5). The different effects of these groups of predictors in projecting the future maize-yield changes were also assessed. Furthermore, the study analysed the impact of climate change on the global WRSI. The results indicate that almost 27 \% of the interannual variability of maize production of the entire region is explained by climate variables. The influence of climate predictors on maize-yield production is more pronounced in West Africa, reaching 55 \% in some areas. The model projection indicates that the maize yield in the entire region is expected to decrease by the middle of the century under an RCP8.5 emission scenario, and from the middle of the century to the end of the century, the production will slightly recover but will remain negative (around -10 \%). However, in some regions of East Africa, a slight increase in maize yield is expected. The maize-yield projection under RCP4.5 remains relatively unchanged compared to the baseline period (1982-2016). The results further indicate that large-scale predictors are the most critical drivers of the global year-to-year maize-yield variability, and ENSO - which is highly correlated with the most important predictor (PC2) - seems to be the physical process underlying this variability. The effects of local predictors are more pronounced in the eastern parts of the region. The impact of the future climate change on WRSI reveals that the availability of maize water is expected to decrease everywhere, except in some parts of eastern Africa.}, language = {en} } @phdthesis{Karama2021, author = {Karama, Alphonse}, title = {East African Seasonal Rainfall prediction using multiple linear regression and regression with ARIMA errors models}, doi = {10.25972/OPUS-25183}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-251831}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2021}, abstract = {The detrimental impacts of climate variability on water, agriculture, and food resources in East Africa underscore the importance of reliable seasonal climate prediction. To overcome this difficulty RARIMAE method were evolved. Applications RARIMAE in the literature shows that amalgamating different methods can be an efficient and effective way to improve the forecasts of time series under consideration. With these motivations, attempt have been made to develop a multiple linear regression model (MLR) and a RARIMAE models for forecasting seasonal rainfall in east Africa under the following objectives: 1. To develop MLR model for seasonal rainfall prediction in East Africa. 2. To develop a RARIMAE model for seasonal rainfall prediction in East Africa. 3. Comparison of model's efficiency under consideration In order to achieve the above objectives, the monthly precipitation data covering the period from 1949 to 2000 was obtained from Climate Research Unit (CRU). Next to that, the first differenced climate indices were used as predictors. In the first part of this study, the analyses of the rainfall fluctuation in whole Central- East Africa region which span over a longitude of 15 degrees East to 55 degrees East and a latitude of 15 degrees South to 15 degrees North was done by the help of maps. For models' comparison, the R-squared values for the MLR model are subtracted from the R-squared values of RARIMAE model. The results show positive values which indicates that R-squared is improved by RARIMAE model. On the other side, the root mean square errors (RMSE) values of the RARIMAE model are subtracted from the RMSE values of the MLR model and the results show negative value which indicates that RMSE is reduced by RARIMAE model for training and testing datasets. For the second part of this study, the area which is considered covers a longitude of 31.5 degrees East to 41 degrees East and a latitude of 3.5 degrees South to 0.5 degrees South. This region covers Central-East of the Democratic Republic of Congo (DRC), north of Burundi, south of Uganda, Rwanda, north of Tanzania and south of Kenya. Considering a model constructed based on the average rainfall time series in this region, the long rainfall season counts the nine months lead of the first principal component of Indian sea level pressure (SLP_PC19) and the nine months lead of Dipole Mode Index (DMI_LR9) as selected predictors for both statistical and predictive model. On the other side, the short rainfall season counts the three months lead of the first principal component of Indian sea surface temperature (SST_PC13) and the three months lead of Southern Oscillation Index (SOI_SR3) as predictors for predictive model. For short rainfall season statistical model SAOD current time series (SAOD_SR0) was added on the two predictors in predictive model. By applying a MLR model it is shown that the forecast can explain 27.4\% of the total variation and has a RMSE of 74.2mm/season for long rainfall season while for the RARIMAE the forecast explains 53.6\% of the total variation and has a RMSE of 59.4mm/season. By applying a MLR model it is shown that the forecast can explain 22.8\% of the total variation and has a RMSE of 106.1 mm/season for short rainfall season predictive model while for the RARIMAE the forecast explains 55.1\% of the total variation and has a RMSE of 81.1 mm/season. From such comparison, a significant rise in R-squared, a decrease of RMSE values were observed in RARIMAE models for both short rainfall and long rainfall season averaged time series. In terms of reliability, RARIMAE outperformed its MLR counterparts with better efficiency and accuracy. Therefore, whenever the data suffer from autocorrelation, we can go for MLR with ARIMA error, the ARIMA error part is more to correct the autocorrelation thereby improving the variance and productiveness of the model.}, subject = {Regression}, language = {en} } @article{JobWilliMayeretal.2020, author = {Job, Hubert and Willi, Gero and Mayer, Marius and P{\"u}tz, Marco}, title = {Open Spaces in Alpine Countries: Analytical Concepts and Preservation Strategies in Spatial Planning}, series = {Mountain Research and Development}, volume = {40}, journal = {Mountain Research and Development}, number = {3}, doi = {10.1659/MRD-JOURNAL-D-20-00016.1}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-259338}, pages = {D1-D11}, year = {2020}, abstract = {Open spaces in the Alps are becoming noticeably scarcer, and the long-term consequences for humans and the environment are often overlooked. Open spaces preserve ecosystem services but are under pressure in many Alpine valleys due to demographic and economic development as well as corresponding technical and tourism infrastructure. This article conceptualizes and measures open spaces in Alpine environments. In addition to analyzing existing spatial planning instruments and the open spaces resulting from 2 of them-the Bavarian Alpenplan in Germany and the Tyrolean Ruhegebiete in Austria-we identify open spaces in Switzerland using a geographic information system. More generally, we discuss how spatial planning deals with open spaces. Results show that both the Alpenplan and the Ruhegebiete have contributed significantly to the protection of open spaces in the Bavarian and Tyrolean Alps since the 1970s. Indeed, both approaches prevented several development projects. In the Swiss Alps, open spaces cover 41.9\% of the Alpine Convention area. A share of 40.3\% vegetation-free open spaces shows that they are concentrated in high alpine areas. Of the open spaces identified, 64.6\% are covered by protected areas. Hence, about one third of the open spaces still existing in the Swiss Alps need preservation, not only for ecological connectivity reasons but also to preserve them for generations to come. We conclude that different sectoral approaches for the conservation of open spaces for people and natural heritage in the Alps and other high mountain ranges should be better coordinated. In addition, much more intensive crossborder cooperation in spatial development and planning is needed to preserve open spaces throughout the Alpine arc.}, language = {en} } @article{AsamGessnerAlmengorGonzalezetal.2022, author = {Asam, Sarah and Gessner, Ursula and Almengor Gonz{\´a}lez, Roger and Wenzl, Martina and Kriese, Jennifer and Kuenzer, Claudia}, title = {Mapping crop types of Germany by combining temporal statistical metrics of Sentinel-1 and Sentinel-2 time series with LPIS data}, series = {Remote Sensing}, volume = {14}, journal = {Remote Sensing}, number = {13}, issn = {2072-4292}, doi = {10.3390/rs14132981}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-278969}, year = {2022}, abstract = {Nationwide and consistent information on agricultural land use forms an important basis for sustainable land management maintaining food security, (agro)biodiversity, and soil fertility, especially as German agriculture has shown high vulnerability to climate change. Sentinel-1 and Sentinel-2 satellite data of the Copernicus program offer time series with temporal, spatial, radiometric, and spectral characteristics that have great potential for mapping and monitoring agricultural crops. This paper presents an approach which synergistically uses these multispectral and Synthetic Aperture Radar (SAR) time series for the classification of 17 crop classes at 10 m spatial resolution for Germany in the year 2018. Input data for the Random Forest (RF) classification are monthly statistics of Sentinel-1 and Sentinel-2 time series. This approach reduces the amount of input data and pre-processing steps while retaining phenological information, which is crucial for crop type discrimination. For training and validation, Land Parcel Identification System (LPIS) data were available covering 15 of the 16 German Federal States. An overall map accuracy of 75.5\% was achieved, with class-specific F1-scores above 80\% for winter wheat, maize, sugar beet, and rapeseed. By combining optical and SAR data, overall accuracies could be increased by 6\% and 9\%, respectively, compared to single sensor approaches. While no increase in overall accuracy could be achieved by stratifying the classification in natural landscape regions, the class-wise accuracies for all but the cereal classes could be improved, on average, by 7\%. In comparison to census data, the crop areas could be approximated well with, on average, only 1\% of deviation in class-specific acreages. Using this streamlined approach, similar accuracies for the most widespread crop types as well as for smaller permanent crop classes were reached as in other Germany-wide crop type studies, indicating its potential for repeated nationwide crop type mapping.}, language = {en} } @article{AppelHardaker2022, author = {Appel, Alexandra and Hardaker, Sina}, title = {Einzelhandel als Katalysator f{\"u}r nachhaltige urbane Radlogistik? - W{\"u}Livery, ein Fallbeispiel aus W{\"u}rzburg}, series = {Standort}, volume = {46}, journal = {Standort}, number = {1}, issn = {1432-220X}, doi = {10.1007/s00548-021-00758-y}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-268437}, pages = {9-15}, year = {2022}, abstract = {Die Covid-19-Pandemie gilt in vielen gesellschaftlichen Teilbereichen als Beschleuniger f{\"u}r Transformationsprozesse. Auch im Bereich der Organisation urbaner Logistik und Einzelhandelslandschaften etablieren sich neue Akteur*innen und Funktionen. Logistiker*innen integrieren lokale Onlinemarktpl{\"a}tze in ihre Profile und der station{\"a}re Einzelhandel generiert Wettbewerbsf{\"a}higkeit gegen{\"u}ber großen Onlineh{\"a}ndler*innen {\"u}ber die Nutzung lokaler Radlogistiknetzwerke, mittels derer Lieferungen noch am Tag der Bestellung (Same-Day-Delivery) verteilt werden k{\"o}nnen. Damit leisten die involvierten Akteur*innen potenziell auch einen Beitrag zur Nachhaltigkeitstransformation im Bereich urbaner Logistiksysteme. Im Fokus steht das Fallbeispiel W{\"u}Livery, ein Kooperationsprojekt des Stadtmarketingvereins, der Wirtschaftsf{\"o}rderung, Radlogistiker*innen sowie Einzelh{\"a}ndler*innen in W{\"u}rzburg, welches w{\"a}hrend des zweiten coronabedingten Lockdowns im November 2020 umgesetzt wurde. Die entstehenden Dynamiken und Organisationsformen werden auf Basis von 11 Expert*inneninterviews dargestellt und analysiert. Es kann gezeigt werden, dass st{\"a}dtische Akteur*innen grundlegende Mediator*innen f{\"u}r Transformationsprozesse darstellen und Einzelh{\"a}ndler*innen und lokale Onlinemarktpl{\"a}tze als Katalysator*innen fungieren k{\"o}nnen. Das ist auch vor dem Hintergrund planerischer und politischer Kommunikationsprozesse zur Legitimation neuer Verkehrsinfrastrukturen nutzbar, da die einzelnen Akteur*innengruppen in Austausch kommen und ein gesteigertes Bewusstsein f{\"u}r die jeweiligen Bedarfe entsteht.}, language = {de} } @article{WehnerHuchlerFritz2022, author = {Wehner, Helena and Huchler, Katharina and Fritz, Johannes}, title = {Quantification of foraging areas for the Northern Bald Ibis (Geronticus eremita) in the northern Alpine foothills: a random forest model fitted with optical and actively sensed earth observation data}, series = {Remote Sensing}, volume = {14}, journal = {Remote Sensing}, number = {4}, issn = {2072-4292}, doi = {10.3390/rs14041015}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-262245}, year = {2022}, abstract = {The Northern Bald Ibis (Geronticus eremita, NBI) is an endangered migratory species, which went extinct in Europe in the 17th century. Currently, a translocation project in the frame of the European LIFE program is carried out, to reintroduce a migratory population with breeding colonies in the northern and southern Alpine foothills and a common wintering area in southern Tuscany. The population meanwhile consists of about 200 individuals, with about 90\% of them carrying a GPS device on their back. We used biologging data from 2021 to model the habitat suitability for the species in the northern Alpine foothills. To set up a species distribution model, indices describing environmental conditions were calculated from satellite images of Landsat-8, and in addition to the well-proven use of optical remote sensing data, we also included Sentinel-1 actively sensed observation data, as well as climate and urbanization data. A random forest model was fitted on NBI GPS positions, which we used to identify regions with high predicted foraging suitability within the northern Alpine foothills. The model resulted in 84.5\% overall accuracy. Elevation and slope had the highest predictive power, followed by grass cover and VV intensity of Sentinel-1 radar data. The map resulting from the model predicts the highest foraging suitability for valley floors, especially of Inn, Rhine, and Salzach-Valley as well as flatlands, like the Swiss Plateau and the agricultural areas surrounding Lake Constance. Areas with a high suitability index largely overlap with known historic breeding sites. This is particularly noteworthy because the model only refers to foraging habitats without considering the availability of suitable breeding cliffs. Detailed analyses identify the transition zone from extensive grassland management to intensive arable farming as the northern range limit. The modeling outcome allows for defining suitable areas for further translocation and management measures in the frame of the European NBI reintroduction program. Although required in the international IUCN translocation guidelines, the use of models in the context of translocation projects is still not common and in the case of the Northern Bald Ibis not considered in the present Single Species Action Plan of the African-Eurasian Migratory Water bird Agreement. Our species distribution model represents a contemporary snapshot, but sustainability is essential for conservation planning, especially in times of climate change. In this regard, a further model could be optimized by investigating sustainable land use, temporal dynamics, and climate change scenarios.}, language = {en} }