@article{AlavipanahWegmannQureshietal.2015,
  author    = {Alavipanah, Sadroddin and Wegmann, Martin and Qureshi, Salman and Weng, Qihao and Koellner, Thomas},
  title     = {The role of vegetation in mitigating urban land surface temperatures: a case study of Munich, Germany during the warm season},
  series = {Sustainability},
  volume    = {7},
  journal   = {Sustainability},
  doi       = {10.3390/su7044689},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-143447},
  pages     = {4689-4706},
  year      = {2015},
  abstract  = {The Urban Heat Island (UHI) is the phenomenon of altered increased temperatures in urban areas compared to their rural surroundings. UHIs grow and intensify under extreme hot periods, such as during heat waves, which can affect human health and also increase the demand for energy for cooling. This study applies remote sensing and land use/land cover (LULC) data to assess the cooling effect of varying urban vegetation cover, especially during extreme warm periods, in the city of Munich, Germany. To compute the relationship between Land Surface Temperature (LST) and Land Use Land Cover (LULC), MODIS eight-day interval LST data for the months of June, July and August from 2002 to 2012 and the Corine Land Cover (CLC) database were used. Due to similarities in the behavior of surface temperature of different CLCs, some classes were reclassified and combined to form two major, rather simplified, homogenized classes: one of built-up area and one of urban vegetation. The homogenized map was merged with the MODIS eight-day interval LST data to compute the relationship between them. The results revealed that (i) the cooling effect accrued from urban vegetation tended to be non-linear; and (ii) a remarkable and stronger cooling effect in terms of LST was identified in regions where the proportion of vegetation cover was between seventy and almost eighty percent per square kilometer. The results also demonstrated that LST within urban vegetation was affected by the temperature of the surrounding built-up and that during the well-known European 2003 heat wave, suburb areas were cooler from the core of the urbanized region. This study concluded that the optimum green space for obtaining the lowest temperature is a non-linear trend. This could support urban planning strategies to facilitate appropriate applications to mitigate heat-stress in urban area.},
  language  = {en}
}
@article{FaOliveroRealetal.2015,
  author    = {Fa, John E. and Olivero, Jes{\´u}s and Real, Raimundo and Farf{\´a}n, Miguel A. and M{\´a}rquez, Ana L. and Vargas, J. Mario and Ziegler, Stefan and Wegmann, Martin and Brown, David and Margetts, Barrie and Nasi, Robert},
  title     = {Disentangling the relative effects of bushmeat availability on human nutrition in central Africa},
  series = {Scientific Reports},
  volume    = {5},
  journal   = {Scientific Reports},
  number    = {8168},
  doi       = {10.1038/srep08168},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-144110},
  year      = {2015},
  abstract  = {We studied links between human malnutrition and wild meat availability within the Rainforest Biotic Zone in central Africa. We distinguished two distinct hunted mammalian diversity distributions, one in the rainforest areas (Deep Rainforest Diversity, DRD) containing taxa of lower hunting sustainability, the other in the northern rainforest-savanna mosaic, with species of greater hunting potential (Marginal Rainforest Diversity, MRD). Wild meat availability, assessed by standing crop mammalian biomass, was greater in MRD than in DRD areas. Predicted bushmeat extraction was also higher in MRD areas. Despite this, stunting of children, a measure of human malnutrition, was greater in MRD areas. Structural equation modeling identified that, in MRD areas, mammal diversity fell away from urban areas, but proximity to these positively influenced higher stunting incidence. In DRD areas, remoteness and distance from dense human settlements and infrastructures explained lower stunting levels. Moreover, stunting was higher away from protected areas. Our results suggest that in MRD areas, forest wildlife rational use for better human nutrition is possible. By contrast, the relatively low human populations in DRD areas currently offer abundant opportunities for the continued protection of more vulnerable mammals and allow dietary needs of local populations to be met.},
  language  = {en}
}
@article{FisserKhorsandiWegmannetal.2022,
  author    = {Fisser, Henrik and Khorsandi, Ehsan and Wegmann, Martin and Baier, Frank},
  title     = {Detecting moving trucks on roads using Sentinel-2 data},
  series = {Remote Sensing},
  volume    = {14},
  journal   = {Remote Sensing},
  number    = {7},
  issn      = {2072-4292},
  doi       = {10.3390/rs14071595},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-267174},
  year      = {2022},
  abstract  = {In most countries, freight is predominantly transported by road cargo trucks. We present a new satellite remote sensing method for detecting moving trucks on roads using Sentinel-2 data. The method exploits a temporal sensing offset of the Sentinel-2 multispectral instrument, causing spatially and spectrally distorted signatures of moving objects. A random forest classifier was trained (overall accuracy: 84\%) on visual-near-infrared-spectra of 2500 globally labelled targets. Based on the classification, the target objects were extracted using a developed recursive neighbourhood search. The speed and the heading of the objects were approximated. Detections were validated by employing 350 globally labelled target boxes (mean F\(_1\) score: 0.74). The lowest F\(_1\) score was achieved in Kenya (0.36), the highest in Poland (0.88). Furthermore, validated at 26 traffic count stations in Germany on in sum 390 dates, the truck detections correlate spatio-temporally with station figures (Pearson r-value: 0.82, RMSE: 43.7). Absolute counts were underestimated on 81\% of the dates. The detection performance may differ by season and road condition. Hence, the method is only suitable for approximating the relative truck traffic abundance rather than providing accurate absolute counts. However, existing road cargo monitoring methods that rely on traffic count stations or very high resolution remote sensing data have limited global availability. The proposed moving truck detection method could fill this gap, particularly where other information on road cargo traffic are sparse by employing globally and freely available Sentinel-2 data. It is inferior to the accuracy and the temporal detail of station counts, but superior in terms of spatial coverage.},
  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{LauschBorgBumbergeretal.2018,
  author    = {Lausch, Angela and Borg, Erik and Bumberger, Jan and Dietrich, Peter and Heurich, Marco and Huth, Andreas and Jung, Andr{\´a}s and Klenke, Reinhard and Knapp, Sonja and Mollenhauer, Hannes and Paasche, Hendrik and Paulheim, Heiko and Pause, Marion and Schweitzer, Christian and Schmulius, Christiane and Settele, Josef and Skidmore, Andrew K. and Wegmann, Martin and Zacharias, Steffen and Kirsten, Toralf and Schaepman, Michael E.},
  title     = {Understanding forest health with remote sensing, part III: requirements for a scalable multi-source forest health monitoring network based on data science approaches},
  series = {Remote Sensing},
  volume    = {10},
  journal   = {Remote Sensing},
  number    = {7},
  issn      = {2072-4292},
  doi       = {10.3390/rs10071120},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197691},
  pages     = {1120},
  year      = {2018},
  abstract  = {Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.},
  language  = {en}
}
@techreport{MuellerSchererLorenzenAmmeretal.2022,
  author    = {M{\"u}ller, J{\"o}rg and Scherer-Lorenzen, Michael and Ammer, Christian and Eisenhauer, Nico and Seidel, Dominik and Schuldt, Bernhard and Biedermann, Peter and Schmitt, Thomas and K{\"u}nzer, Claudia and Wegmann, Martin and Cesarz, Simone and Peters, Marcell and Feldhaar, Heike and Steffan-Dewenter, Ingolf and Claßen, Alice and B{\"a}ssler, Claus and von Oheimb, Goddert and Fichtner, Andreas and Thorn, Simon and Weisser, Wolfgang},
  title     = {BETA-FOR: Erh{\"o}hung der strukturellen Diversit{\"a}t zwischen Waldbest{\"a}nden zur Erh{\"o}hung der Multidiversit{\"a}t und Multifunktionalit{\"a}t in Produktionsw{\"a}ldern. Antragstext f{\"u}r die DFG Forschungsgruppe FOR 5375},
  doi       = {10.25972/OPUS-29084},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-290849},
  pages     = {210},
  year      = {2022},
  abstract  = {Der in j{\"u}ngster Zeit beobachtete kontinuierliche Verlust der β-Diversit{\"a}t in {\"O}kosystemen deutet auf homogene Gemeinschaften auf Landschaftsebene hin, was haupts{\"a}chlich auf die steigende Landnutzungsintensit{\"a}t zur{\"u}ckgef{\"u}hrt wird. Biologische Vielfalt ist mit zahlreichen Funktionen und der Stabilit{\"a}t von {\"O}kosystemen verkn{\"u}pft. Es ist daher zu erwarten, dass eine abnehmende β-Diversit{\"a}t auch die Multifunktionalit{\"a}t verringert. Wir kombinieren hier Fachwissen aus der Forstwissenschaft, der {\"O}kologie, der Fernerkundung, der chemischen {\"O}kologie und der Statistik in einem gemeinschaftlichen und experimentellen β-Diversit{\"a}tsdesign, um einerseits die Auswirkungen der Homogenisierung zu bewerten und andererseits Konzepte zu entwickeln, um negative Auswirkungen durch Homogenisierung in W{\"a}ldern r{\"u}ckg{\"a}ngig zu machen. Konkret werden wir uns mit der Frage besch{\"a}ftigen, ob die Verbesserung der strukturellen β-Komplexit{\"a}t (ESBC) in W{\"a}ldern durch Waldbau oder nat{\"u}rliche St{\"o}rungen die Biodiversit{\"a}t und Multifunktionalit{\"a}t in ehemals homogenen Produktionsw{\"a}ldern erh{\"o}hen kann. Unser Ansatz wird m{\"o}gliche Mechanismen hinter den beobachteten Homogenisierungs-Diversit{\"a}ts-Beziehungen identifizieren und zeigen, wie sich diese auf die Multifunktionalit{\"a}t auswirken. An elf Standorten in ganz Deutschland haben wir dazu zwei Waldbest{\"a}nde als zwei kleine "Waldlandschaften" ausgew{\"a}hlt. In einem dieser beiden Best{\"a}nde haben wir ESBC (Enhancement of Structural Beta Complexity)-Behandlungen durchgef{\"u}hrt. Im zweiten, dem Kontrollbestand, werden wir die gleich Anzahl 50x50m Parzellen ohne ESBC einrichten. Auf allen Parzellen werden wir 18 taxonomische Artengruppen aller trophischer Ebenen und 21 {\"O}kosystemfunktionen, einschließlich der wichtigsten Funktionen in W{\"a}ldern der gem{\"a}ßigten Zonen, messen. Der statistische Rahmen wird eine umfassende Analyse der Biodiversit{\"a}t erm{\"o}glichen, indem verschiedenen Aspekte (taxonomische, funktionelle und phylogenetische Vielfalt) auf verschiedenen Skalenebenen (α-, β-, γ-Diversit{\"a}t) quantifiziert werden. Um die Gesamtdiversit{\"a}t zu kombinieren, werden wir das Konzept der Multidiversit{\"a}t auf die 18 Taxa anwenden. Wir werden neue Ans{\"a}tze zur Quantifizierung und Aufteilung der Multifunktionalit{\"a}t auf α- und β-Skalen verwenden und entwickeln. Durch die experimentelle Beschreibung des Zusammenhangs zwischen β-Diversit{\"a}t und Multifunktionalit{\"a}t in einer Reallandschaft wird unsere Forschung einen neuen Weg einschlagen. Dar{\"u}ber hinaus werden wir dazu beitragen, verbesserte Leitlinien f{\"u}r waldbauliche Konzepte und f{\"u}r das Management nat{\"u}rlicher St{\"o}rungen zu entwickeln, um Homogenisierungseffekte der Vergangenheit umzukehren.},
  subject      = {Wald{\"o}kosystem},
  language  = {en}
}
@article{NaidooDuPreezStuartHilletal.2012,
  author    = {Naidoo, Robin and Du Preez, Pierre and Stuart-Hill, Greg and Jago, Mark and Wegmann, Martin},
  title     = {Home on the Range: Factors Explaining Partial Migration of African Buffalo in a Tropical Environment},
  series = {PLoS One},
  volume    = {7},
  journal   = {PLoS One},
  number    = {5},
  doi       = {10.1371/journal.pone.0036527},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-134935},
  pages     = {e36527},
  year      = {2012},
  abstract  = {Partial migration (when only some individuals in a population undertake seasonal migrations) is common in many species and geographical contexts. Despite the development of modern statistical methods for analyzing partial migration, there have been no studies on what influences partial migration in tropical environments. We present research on factors affecting partial migration in African buffalo (Syncerus caffer) in northeastern Namibia. Our dataset is derived from 32 satellite tracking collars, spans 4 years and contains over 35,000 locations. We used remotely sensed data to quantify various factors that buffalo experience in the dry season when making decisions on whether and how far to migrate, including potential man-made and natural barriers, as well as spatial and temporal heterogeneity in environmental conditions. Using an information-theoretic, non-linear regression approach, our analyses showed that buffalo in this area can be divided into 4 migratory classes: migrants, non-migrants, dispersers, and a new class that we call "expanders". Multimodel inference from least-squares regressions of wet season movements showed that environmental conditions (rainfall, fires, woodland cover, vegetation biomass), distance to the nearest barrier (river, fence, cultivated area) and social factors (age, size of herd at capture) were all important in explaining variation in migratory behaviour. The relative contributions of these variables to partial migration have not previously been assessed for ungulates in the tropics. Understanding the factors driving migratory decisions of wildlife will lead to better-informed conservation and land-use decisions in this area.},
  language  = {en}
}
@article{PhilippWegmannKuebertFlock2021,
  author    = {Philipp, Marius and Wegmann, Martin and K{\"u}bert-Flock, Carina},
  title     = {Quantifying the Response of German Forests to Drought Events via Satellite Imagery},
  series = {Remote Sensing},
  volume    = {13},
  journal   = {Remote Sensing},
  number    = {9},
  issn      = {2072-4292},
  doi       = {10.3390/rs13091845},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-239575},
  year      = {2021},
  abstract  = {Forest systems provide crucial ecosystem functions to our environment, such as balancing carbon stocks and influencing the local, regional and global climate. A trend towards an increasing frequency of climate change induced extreme weather events, including drought, is hereby a major challenge for forest management. Within this context, the application of remote sensing data provides a powerful means for fast, operational and inexpensive investigations over large spatial scales and time. This study was dedicated to explore the potential of satellite data in combination with harmonic analyses for quantifying the vegetation response to drought events in German forests. The harmonic modelling method was compared with a z-score standardization approach and correlated against both, meteorological and topographical data. Optical satellite imagery from Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS) was used in combination with three commonly applied vegetation indices. Highest correlation scores based on the harmonic modelling technique were computed for the 6th harmonic degree. MODIS imagery in combination with the Normalized Difference Vegetation Index (NDVI) generated hereby best results for measuring spectral response to drought conditions. Strongest correlation between remote sensing data and meteorological measures were observed for soil moisture and the self-calibrated Palmer Drought Severity Index (scPDSI). Furthermore, forests regions over sandy soils with pine as the dominant tree type were identified to be particularly vulnerable to drought. In addition, topographical analyses suggested mitigated drought affects along hill slopes. While the proposed approaches provide valuable information about vegetation dynamics as a response to meteorological weather conditions, standardized in-situ measurements over larger spatial scales and related to drought quantification are required for further in-depth quality assessment of the used methods and data.},
  language  = {en}
}
@article{RemelgadoLeutnerSafietal.2018,
  author    = {Remelgado, Ruben and Leutner, Benjamin and Safi, Kamran and Sonnenschein, Ruth and Kuebert, Carina and Wegmann, Martin},
  title     = {Linking animal movement and remote sensing - mapping resource suitability from a remote sensing perspective},
  series = {Remote Sensing in Ecology and Conservation},
  volume    = {4},
  journal   = {Remote Sensing in Ecology and Conservation},
  number    = {3},
  doi       = {10.1002/rse2.70},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-225199},
  pages     = {211-224},
  year      = {2018},
  abstract  = {Optical remote sensing is an important tool in the study of animal behavior providing ecologists with the means to understand species-environment interactions in combination with animal movement data. However, differences in spatial and temporal resolution between movement and remote sensing data limit their direct assimilation. In this context, we built a data-driven framework to map resource suitability that addresses these differences as well as the limitations of satellite imagery. It combines seasonal composites of multiyear surface reflectances and optimized presence and absence samples acquired with animal movement data within a cross-validation modeling scheme. Moreover, it responds to dynamic, site-specific environmental conditions making it applicable to contrasting landscapes. We tested this framework using five populations of White Storks (Ciconia ciconia) to model resource suitability related to foraging achieving accuracies from 0.40 to 0.94 for presences and 0.66 to 0.93 for absences. These results were influenced by the temporal composition of the seasonal reflectances indicated by the lower accuracies associated with higher day differences in relation to the target dates. Additionally, population differences in resource selection influenced our results marked by the negative relationship between the model accuracies and the variability of the surface reflectances associated with the presence samples. Our modeling approach spatially splits presences between training and validation. As a result, when these represent different and unique resources, we face a negative bias during validation. Despite these inaccuracies, our framework offers an important basis to analyze species-environment interactions. As it standardizes site-dependent behavioral and environmental characteristics, it can be used in the comparison of intra- and interspecies environmental requirements and improves the analysis of resource selection along migratory paths. Moreover, due to its sensitivity to differences in resource selection, our approach can contribute toward a better understanding of species requirements.},
  language  = {en}
}
@article{RemelgadoSafiWegmann2020,
  author    = {Remelgado, Ruben and Safi, Kamran and Wegmann, Martin},
  title     = {From ecology to remote sensing: using animals to map land cover},
  series = {Remote Sensing in Ecology and Conservation},
  volume    = {6},
  journal   = {Remote Sensing in Ecology and Conservation},
  number    = {1},
  doi       = {10.1002/rse2.126},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-225200},
  pages     = {93-104},
  year      = {2020},
  abstract  = {Land cover is a key variable in monitoring applications and new processing technologies made deriving this information easier. Yet, classification algorithms remain dependent on samples collected on the field and field campaigns are limited by financial, infrastructural and political boundaries. Here, animal tracking data could be an asset. Looking at the land cover dependencies of animal behaviour, we can obtain land cover samples over places that are difficult to access. Following this premise, we evaluated the potential of animal movement data to map land cover. Specifically, we used 13 White Storks (Cicona cicona) individuals of the same population to map agriculture within three test regions distributed along their migratory track. The White Stork has adapted to foraging over agricultural lands, making it an ideal source of samples to map this land use. We applied a presence-absence modelling approach over a Normalized Difference Vegetation Index (NDVI) time series and validated our classifications, with high-resolution land cover information. Our results suggest White Stork movement is useful to map agriculture, however, we identified some limitations. We achieved high accuracies (F1-scores > 0.8) for two test regions, but observed poor results over one region. This can be explained by differences in land management practices. The animals preferred agriculture in every test region, but our data showed a biased distribution of training samples between irrigated and non-irrigated land. When both options occurred, the animals disregarded non-irrigated land leading to its misclassification as non-agriculture. Additionally, we found difference between the GPS observation dates and the harvest times for non-irrigated crops. Given the White Stork takes advantage of managed land to search for prey, the inactivity of these fields was the likely culprit of their underrepresentation. Including more species attracted to agriculture - with other land-use dependencies and observation times - can contribute to better results in similar applications.},
  language  = {en}
}