@book{OPUS4-18040, title = {Analysemethodik und Modellierung in der geographischen Handelsforschung}, editor = {Klein, Ralf and Rauh, J{\"u}rgen}, publisher = {L.I.S. Verlag}, address = {Passau}, isbn = {978-3-932820-32-8}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-180402}, publisher = {Universit{\"a}t W{\"u}rzburg}, pages = {153}, year = {2007}, abstract = {Methoden und Techniken sind in der geographischen Handelsforschung gleichermaßen in der Grundlagenforschung, in der universit{\"a}renAusbildung, in der praktischen Anwendung und der Fortbildung von hoher Bedeutung. Der vorliegende Band vertieft einige bekannte methodische Aspekte, setzt aber auch neue Akzente hinsichtlich Analysemethodik und Modellierung. Die Beitr{\"a}ge in dem vorliegenden Band zeigen weitergehende M{\"o}glichkeiten auf, in der geographischen Handelsforschung und insbesondere der Praxis bedeutsame Fragestellungen methodisch fassen und behandeln zu k{\"o}nnen. Die Reihenfolge der Beitr{\"a}ge ist thematisch gegliedert. Die Thematik wird zun{\"a}chst eher allgemein orientiert vorgestellt und dann mittels einer bestimmten Fragestellung oder Untersuchung konkretisiert. So wird der umfassende Beitrag von K. E. Klein zum Einsatz geographischer Informationssysteme im Einzelhandel durch die Studie von J. Scharfenberger zu mikrogeographischen Routing- und Marktpotenzialanalysen erg{\"a}nzt. Die Modellierung und Prognose von Marktgebieten im Einzelhandel wird von R. Klein zun{\"a}chst allgemein diskutiert und durch die Untersuchungen von C. Kanh{\"a}usser vertieft. Die Beitr{\"a}ge von R. Hesse / A. Schmid sowie J. Rauh / T. Schenk / M. Fehler / F. Kl{\"u}gl / F. Puppe zeigen mit Simulationsmodellen und der r{\"a}umlichen Optimierung neue methodische Anwendungsm{\"o}glichkeiten auf, die geeignet sind, die in der Regel vorhandene Trennung zwischen der individualistischen und der strukturellen Perspektive aufzul{\"o}sen. Erstgenannte wird in der Regel z.B. bei der Untersuchung des Konsumentenverhaltens eingenommen, die Letztgenannte bei der (Standort-)Analyse der Angebotsseite.}, subject = {Handelsforschung}, language = {de} } @article{AtaeeMaghsoudiLatifietal.2019, author = {Ataee, Mohammad Sadegh and Maghsoudi, Yasser and Latifi, Hooman and Fadaie, Farhad}, title = {Improving estimation accuracy of growing stock by multi-frequency SAR and multi-spectral data over Iran's heterogeneously-structured broadleaf Hyrcanian forests}, series = {Forests}, volume = {10}, journal = {Forests}, number = {8}, issn = {1999-4907}, doi = {10.3390/f10080641}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197212}, year = {2019}, abstract = {Via providing various ecosystem services, the old-growth Hyrcanian forests play a crucial role in the environment and anthropogenic aspects of Iran and beyond. The amount of growing stock volume (GSV) is a forest biophysical parameter with great importance in issues like economy, environmental protection, and adaptation to climate change. Thus, accurate and unbiased estimation of GSV is also crucial to be pursued across the Hyrcanian. Our goal was to investigate the potential of ALOS-2 and Sentinel-1's polarimetric features in combination with Sentinel-2 multi-spectral features for the GSV estimation in a portion of heterogeneously-structured and mountainous Hyrcanian forests. We used five different kernels by the support vector regression (nu-SVR) for the GSV estimation. Because each kernel differently models the parameters, we separately selected features for each kernel by a binary genetic algorithm (GA). We simultaneously optimized R\(^2\) and RMSE in a suggested GA fitness function. We calculated R\(^2\), RMSE to evaluate the models. We additionally calculated the standard deviation of validation metrics to estimate the model's stability. Also for models over-fitting or under-fitting analysis, we used mean difference (MD) index. The results suggested the use of polynomial kernel as the final model. Despite multiple methodical challenges raised from the composition and structure of the study site, we conclude that the combined use of polarimetric features (both dual and full) with spectral bands and indices can improve the GSV estimation over mixed broadleaf forests. This was partially supported by the use of proposed evaluation criterion within the GA, which helped to avoid the curse of dimensionality for the applied SVR and lowest over estimation or under estimation.}, language = {en} } @article{MayrKuenzerGessneretal.2019, author = {Mayr, Stefan and Kuenzer, Claudia and Gessner, Ursula and Klein, Igor and Rutzinger, Martin}, title = {Validation of earth observation time-series: a review for large-area and temporally dense land surface products}, series = {Remote Sensing}, volume = {11}, journal = {Remote Sensing}, number = {22}, issn = {2072-4292}, doi = {10.3390/rs11222616}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193202}, year = {2019}, abstract = {Large-area remote sensing time-series offer unique features for the extensive investigation of our environment. Since various error sources in the acquisition chain of datasets exist, only properly validated results can be of value for research and downstream decision processes. This review presents an overview of validation approaches concerning temporally dense time-series of land surface geo-information products that cover the continental to global scale. Categorization according to utilized validation data revealed that product intercomparisons and comparison to reference data are the conventional validation methods. The reviewed studies are mainly based on optical sensors and orientated towards global coverage, with vegetation-related variables as the focus. Trends indicate an increase in remote sensing-based studies that feature long-term datasets of land surface variables. The hereby corresponding validation efforts show only minor methodological diversification in the past two decades. To sustain comprehensive and standardized validation efforts, the provision of spatiotemporally dense validation data in order to estimate actual differences between measurement and the true state has to be maintained. The promotion of novel approaches can, on the other hand, prove beneficial for various downstream applications, although typically only theoretical uncertainties are provided.}, language = {en} } @article{AbdullahiWesselHuberetal.2019, author = {Abdullahi, Sahra and Wessel, Birgit and Huber, Martin and Wendleder, Anna and Roth, Achim and Kuenzer, Claudia}, title = {Estimating penetration-related X-band InSAR elevation bias: a study over the Greenland ice sheet}, series = {Remote Sensing}, volume = {11}, journal = {Remote Sensing}, number = {24}, issn = {2072-4292}, doi = {10.3390/rs11242903}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193902}, year = {2019}, abstract = {Accelerating melt on the Greenland ice sheet leads to dramatic changes at a global scale. Especially in the last decades, not only the monitoring, but also the quantification of these changes has gained considerably in importance. In this context, Interferometric Synthetic Aperture Radar (InSAR) systems complement existing data sources by their capability to acquire 3D information at high spatial resolution over large areas independent of weather conditions and illumination. However, penetration of the SAR signals into the snow and ice surface leads to a bias in measured height, which has to be corrected to obtain accurate elevation data. Therefore, this study purposes an easy transferable pixel-based approach for X-band penetration-related elevation bias estimation based on single-pass interferometric coherence and backscatter intensity which was performed at two test sites on the Northern Greenland ice sheet. In particular, the penetration bias was estimated using a multiple linear regression model based on TanDEM-X InSAR data and IceBridge laser-altimeter measurements to correct TanDEM-X Digital Elevation Model (DEM) scenes. Validation efforts yielded good agreement between observations and estimations with a coefficient of determination of R\(^2\) = 68\% and an RMSE of 0.68 m. Furthermore, the study demonstrates the benefits of X-band penetration bias estimation within the application context of ice sheet elevation change detection.}, language = {en} } @incollection{Appel2016, author = {Appel, Alexandra}, title = {Multi-Channel-Einzelhandel und Embeddedness - das Beispiel Migros Sanal Market in der T{\"u}rkei}, series = {Online-Handel ist Wandel}, booktitle = {Online-Handel ist Wandel}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-182662}, publisher = {Universit{\"a}t W{\"u}rzburg}, pages = {157-178}, year = {2016}, abstract = {No abstract available.}, language = {de} } @article{TrappeKneisel2019, author = {Trappe, Julian and Kneisel, Christof}, title = {Geophysical and sedimentological investigations of Peatlands for the assessment of lithology and subsurface water pathways}, series = {Geosciences}, volume = {9}, journal = {Geosciences}, number = {3}, doi = {10.3390/geosciences9030118}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-201699}, pages = {118}, year = {2019}, abstract = {Peatlands located on slopes (herein called slope bogs) are typical landscape units in the Hunsrueck, a low mountain range in Southwestern Germany. The pathways of the water feeding the slope bogs have not yet been documented and analyzed. The identification of the different mechanisms allowing these peatlands to originate and survive requires a better understanding of the subsurface lithology and hydrogeology. Hence, we applied a multi-method approach to two case study sites in order to characterize the subsurface lithology and to image the variable spatio-temporal hydrological conditions. The combination of Electrical Resistivity Tomography (ERT) and an ERT-Monitoring and Ground Penetrating Radar (GPR), in conjunction with direct methods and data (borehole drilling and meteorological data), allowed us to gain deeper insights into the subsurface characteristics and dynamics of the peatlands and their catchment area. The precipitation influences the hydrology of the peatlands as well as the interflow in the subsurface. Especially, the geoelectrical monitoring data, in combination with the precipitation and temperature data, indicate that there are several forces driving the hydrology and hydrogeology of the peatlands. While the water content of the uppermost layers changes with the weather conditions, the bottom layer seems to be more stable and changes to a lesser extent. At the selected case study sites, small differences in subsurface properties can have a huge impact on the subsurface hydrogeology and the water paths. Based on the collected data, conceptual models have been deduced for the two case study sites.}, language = {en} } @phdthesis{Hu2020, author = {Hu, Zhongyang}, title = {Earth Observation for the Assessment of Long-Term Snow Dynamics in European Mountains - Analysing 35-Year Snowline Dynamics in Europe Based on High Resolution Earth Observation Data between 1984 and 2018}, doi = {10.25972/OPUS-20044}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-200441}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2020}, abstract = {Worldwide, cold regions are undergoing significant alterations due to climate change. Snow, the most widely distributed cold region component, is highly sensitive to climate change. At the same time, snow itself profoundly impacts the Earth's energy budget, biodiversity, and natural hazards, as well as hydropower management, freshwater management, and winter tourism/sports. Large parts of the cold regions in Europe are mountain areas, which are densely populated because of the various ecosystem services and socioeconomic well-being in mountains. At present, severe consequences caused by climate change have been observed in European mountains and their surrounding areas. Yet, large knowledge gaps hinder the development of effective regional and local adaptation strategies. Long-term and evidence-based regional studies are urgently needed to enhance the comprehension of regional responses to climate change. Earth Observation (EO) provides long-term consistent records of the Earth's surface. It is a great alternative and/or supplement to conventional in-situ measurements which are usually time-consuming, cost-intensive and logistically demanding, particularly for the poor accessibility of cold regions. With the assistance of EO, land surface dynamics in cold regions can be observed in an objective, repeated, synoptic and consistent way. Thanks to free and open data policies, long-term archives such as Landsat Archive and Sentinel Archive can be accessed free-of-charge. The high- to medium-resolution remote sensing imagery from these freely accessible archives gives EO-based time series datasets the capability to depict snow dynamics in European mountains from the 1980s to the present. In order to compile such a dataset, it is necessary to investigate the spatiotemporal availability of EO data, and develop a spatiotemporally transferable framework from which one can investigate snow dynamics. Among the available EO image archives, the Landsat Archive has the longest uninterrupted records of the Earth's land surface. Furthermore, its 30 m spatial resolution fulfils the requirements for snow monitoring in complex terrains. Landsat data can yield a time series of snow dynamics in mountainous areas from 1984 to the present. However, severe Landsat data gaps have occurred across certain regions of Europe. Moreover, the Landsat Level 1 Precision and Terrain (L1TP) data is scarcer (up to 50\% less) in high-latitude mountainous areas than in low-latitude mountainous areas. Given the abovementioned facts, the Regional Snowline Elevation (RSE) is selected to characterize the snow dynamics in mountainous areas, as it can handle cloud obstructions in the optical images. In this thesis, I present a five-step framework to derive and densify RSE time series in European mountains, i.e. (1) pre-processing, (2) snow detection, (3) RSE retrieval, (4) time series densification, and (5) Regional Snowline Retreat Curve (RSRC) production. The results of the intra-annual RSE variations show a uniquely high variation in the beginning of the ablation seasons in the Alpine catchment Tagliamento, mainly toward higher elevation. As for inter-annual variations of RSE, median RSE increases in all selected catchments, with an average speed of around 4.66 m ∙ a-1 (median) and 5.87 m ∙ a-1 (at the beginning of the ablation season). The fastest significant retreat is observed in the catchment Drac (10.66 m ∙ a-1, at the beginning of the ablation season), and the slowest significant retreat is observed in the catchment Uzh (1.74 m ∙ a-1, at the beginning of the ablation season). The increase of RSEs at the beginning of the ablation season is faster than the median RSEs, whose average difference is nearly 1.21 m ∙ a-1, particularly in the catchment Drac (3.72 m ∙ a-1). The results of the RSRCs show a significant rise in RSEs at the beginning of the ablation season, except for the Alpine catchment Alpenrhein and Var, and the Pyrenean catchment Ariege. It indicates that 11.8 and 3.97 degrees Celsius less per year are needed for the regional snowlines to reach the middle point of the RSRC in the Tagliamento and Tysa, respectively. The variation of air temperature is regarded as an example of a potential climate driver in this thesis. The retrieved monthly mean RSEs are highly correlated (mean correlation coefficient "R" ̅ = 0.7) with the monthly temperature anomalies, which are more significant in months with extremely low/high temperature. Another case study that investigates the correlation between river discharges and RSEs is carried out to demonstrate the potential consequences of the derived snowline dynamics. The correlation analysis shows a good correlation between river discharges and RSEs (correlation coefficient, R=0.52). In this thesis, the developed framework signifies a better understanding of the snow dynamics in mountain areas, as well as their potential triggers and consequences. Nonetheless, an urgent need persists for: (1) validation data to assess long-term snow-related observations based on high-resolution EO data; (2) further studies to reveal interactions between snow and its ambient environment; and (3) regional and local adaptation-strategies coping with climate change. Further studies exploring the above-mentioned research gaps are urgently needed in the future.}, subject = {Fernerkundung}, language = {en} } @techreport{ConradMorperBuschNetzbandetal.2019, type = {Working Paper}, author = {Conrad, Christopher and Morper-Busch, Lucia and Netzband, Maik and Teucher, Mike and Sch{\"o}nbrodt-Stitt, Sarah and Schorcht, Gunther and Dukhovny, Viktor}, title = {Инструмент для выработки обоснованных решений в вопросах земле- и водопользования}, doi = {10.25972/OPUS-19200}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-192006}, pages = {12}, year = {2019}, abstract = {WUEMoCA — научный инструмент веб-кар¬тографирования для мониторинга эф¬фек¬тивности земле- и водопользования на территориях орошаемого земледелия стран трансграничного бассейна Араль¬ского моря (Казахстана, Кыргызстана, Таджикистана, Туркменистана, Узбеки¬стана и Афганистана). Путём интеграции спутниковых данных по землепользованию, растениеводству и потреблению воды с гидрологическими и экономическими данными создаётся целый набор показателей. Инструмент полезен для выработки масштабных решений в вопросах распределения воды и землепользования, а также может применяться во многих практических сферах, в которых требуются независимые данные о конкретных обширных территориях.}, language = {ru} } @article{Schamel2015, author = {Schamel, Johannes}, title = {Ableitung von Pr{\"a}ferenzen aus GPS-Trajektorien bei landschaftsbezogenen Erholungsaktivit{\"a}ten}, series = {AGIT - Journal f{\"u}r Angewandte Geoinformatik}, volume = {2015}, journal = {AGIT - Journal f{\"u}r Angewandte Geoinformatik}, number = {1}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-153590}, pages = {9}, year = {2015}, abstract = {No abstract available.}, language = {de} } @phdthesis{Ring2018, author = {Ring, Christoph}, title = {Entwicklung und Vergleich von Gewichtungsmetriken zur Analyse probabilistischer Klimaprojektionen aktueller Modellensembles}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-157294}, school = {Universit{\"a}t W{\"u}rzburg}, pages = {XII, 195}, year = {2018}, abstract = {Der anthropogene Klimawandel ist eine der gr{\"o}ßten Herausforderungen des 21. Jahrhunderts. Eine Hauptschwierigkeit liegt dabei in der Unsicherheit bez{\"u}glich der regionalen {\"A}nderung von Niederschlag und Temperatur. Hierdurch wird die Entwicklung geeigneter Anpassungsstrategien deutlich erschwert. In der vorliegenden Arbeit werden vier Evaluationsans{\"a}tze mit insgesamt 13 Metriken f{\"u}r aktuelle globale (zwei Generationen) und regionale Klimamodelle entwickelt und verglichen, um anschließend eine Analyse der Projektionsunsicherheit vorzunehmen. Basierend auf den erstellten Modellbewertungen werden durch Gewichtung Aussagen {\"u}ber den Unsicherheitsbereich des zuk{\"u}nftigen Klimas getroffen. Die Evaluation der Modelle wird im Mittelmeerraum sowie in acht Unterregionen durchgef{\"u}hrt. Dabei wird der saisonale Trend von Temperatur und Niederschlag im Evaluationszeitraum 1960-2009 ausgewertet. Zus{\"a}tzlich wird f{\"u}r bestimmte Metriken jeweils das klimatologische Mittel oder die harmonischen Zeitreiheneigenschaften evaluiert. Abschließend werden zum Test der {\"U}bertragbarkeit der Ergebnisse neben den Hauptuntersuchungsgebieten sechs global verteilte Regionen untersucht. Außerdem wird die zeitliche Konsistenz durch Analyse eines zweiten, leicht versetzten Evaluationszeitraums behandelt, sowie die Abh{\"a}ngigkeit der Modellbewertungen von verschiedenen Referenzdaten mit Hilfe von insgesamt drei Referenzdatens{\"a}tzen untersucht. Die Ergebnisse legen nahe, dass nahezu alle Metriken zur Modellevaluierung geeignet sind. Die Auswertung unterschiedlicher Variablen und Regionen erzeugt Modellbewertungen, die sich in den Kontext aktueller Forschungsergebnisse einf{\"u}gen. So wurde die Leistung der globalen Klimamodelle der neusten Generation (2013) im Vergleich zur Vorg{\"a}ngergeneration (2007) im Schnitt {\"a}hnlich hoch bzw. in vielen Situationen auch st{\"a}rker eingeordnet. Ein durchweg bestes Modell konnte nicht festgestellt werden. Der Großteil der entwickelten Metriken zeigt f{\"u}r {\"a}hnliche Situationen {\"u}bereinstimmende Modellbewertungen. Bei der Gewichtung hat sich der Niederschlag als besonders geeignet herausgestellt. Grund hierf{\"u}r sind die im Schnitt deutlichen Unterschiede der Modellleistungen in Zusammenhang mit einer geringeren Simulationsg{\"u}te. Umgekehrt zeigen die Metriken f{\"u}r die Modelle der Temperatur allgemein {\"u}berwiegend hohe Evaluationsergebnisse, wodurch nur wenig Informationsgewinn durch Gewichtung erreicht werden kann. W{\"a}hrend die Metriken gut f{\"u}r unterschiedliche Regionen und Skalenniveaus verwendet werden Evaluationszeitr{\"a}ume nicht grunds{\"a}tzlich gegeben. Zus{\"a}tzlich zeigen die Modellranglisten unterschiedlicher Regionen und Jahreszeiten h{\"a}ufig nur geringe Korrelationen. Dies gilt besonders f{\"u}r den Niederschlag. Bei der Temperatur sind hingegen leichte {\"U}bereinstimmungen auszumachen. Beim Vergleich der mittleren Ranglisten {\"u}ber alle Modellbewertungen und Situationen der Hauptregionen des Mittelmeerraums mit den Globalregionen besteht eine signifikante Korrelation von 0,39 f{\"u}r Temperatur, w{\"a}hrend sie f{\"u}r Niederschlag um null liegt. Dieses Ergebnis ist f{\"u}r alle drei verwendeten Referenzdatens{\"a}tze im Mittelmeerraum g{\"u}ltig. So schwankt die Korrelation der Modellbewertungen des Niederschlags f{\"u}r unterschiedliche Referenzdatens{\"a}tze immer um Null und die der Temperaturranglisten zwischen 0,36 und 0,44. Generell werden die Metriken als geeignete Evaluationswerkzeuge f{\"u}r Klimamodelle eingestuft. Daher k{\"o}nnen sie einen Beitrag zur {\"A}nderung des Unsicherheitsbereichs und damit zur St{\"a}rkung des Vertrauens in Klimaprojektionen leisten. Die Abh{\"a}ngigkeit der Modellbewertungen von Region und Untersuchungszeitraum muss dabei jedoch ber{\"u}cksichtigt werden. So besitzt die Analyse der Konsistenz von Modellbewertungen sowie der St{\"a}rken und Schw{\"a}chen der Klimamodelle großes Potential f{\"u}r folgende Studien, um das Vertrauen in Modellprojektionen weiter zu steigern.}, subject = {Anthropogene Klima{\"a}nderung}, language = {de} }