TY - JOUR A1 - van Toor, Mariëlle L. A1 - Newman, Scott H. A1 - Takekawa, John Y. A1 - Wegmann, Martin A1 - Safi, Kamran T1 - Temporal segmentation of animal trajectories informed by habitat use JF - Ecosphere N2 - Most animals live in seasonal environments and experience very different conditions throughout the year. Behavioral strategies like migration, hibernation, and a life cycle adapted to the local seasonality help to cope with fluctuations in environmental conditions. Thus, how an individual utilizes the environment depends both on the current availability of habitat and the behavioral prerequisites of the individual at that time. While the increasing availability and richness of animal movement data has facilitated the development of algorithms that classify behavior by movement geometry, changes in the environmental correlates of animal movement have so far not been exploited for a behavioral annotation. Here, we suggest a method that uses these changes in individual–environment associations to divide animal location data into segments of higher ecological coherence, which we term niche segmentation. We use time series of random forest models to evaluate the transferability of habitat use over time to cluster observational data accordingly. We show that our method is able to identify relevant changes in habitat use corresponding to both changes in the availability of habitat and how it was used using simulated data, and apply our method to a tracking data set of common teal (Anas crecca). The niche segmentation proved to be robust, and segmented habitat suitability outperformed models neglecting the temporal dynamics of habitat use. Overall, we show that it is possible to classify animal trajectories based on changes of habitat use similar to geometric segmentation algorithms. We conclude that such an environmentally informed classification of animal trajectories can provide new insights into an individuals' behavior and enables us to make sensible predictions of how suitable areas might be connected by movement in space and time. KW - Anas crecca KW - animal movement KW - common teal KW - habitat use KW - life history KW - migration KW - niche dynamics KW - random forest models KW - segmentation KW - simulation KW - species distribution model KW - transferability Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-164970 VL - 7 IS - 10 ER - TY - JOUR A1 - Walz, Yvonne A1 - Wegmann, Martin A1 - Leutner, Benjamin A1 - Dech, Stefan A1 - Vounatsou, Penelope A1 - N'Goran, Eliézer K. A1 - Raso, Giovanna A1 - Utzinger, Jürg T1 - Use of an ecologically relevant modelling approach to improve remote sensing-based schistosomiasis risk profiling JF - Geospatial Health N2 - Schistosomiasis is a widespread water-based disease that puts close to 800 million people at risk of infection with more than 250 million infected, mainly in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and the frequency, duration and extent of human bodies exposed to infested water sources during human water contact. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. Since schistosomiasis risk profiling based on remote sensing data inherits a conceptual drawback if school-based disease prevalence data are directly related to the remote sensing measurements extracted at the location of the school, because the disease transmission usually does not exactly occur at the school, we took the local environment around the schools into account by explicitly linking ecologically relevant environmental information of potential disease transmission sites to survey measurements of disease prevalence. Our models were validated at two sites with different landscapes in Côte d’Ivoire using high- and moderateresolution remote sensing data based on random forest and partial least squares regression. We found that the ecologically relevant modelling approach explained up to 70% of the variation in Schistosoma infection prevalence and performed better compared to a purely pixelbased modelling approach. Furthermore, our study showed that model performance increased as a function of enlarging the school catchment area, confirming the hypothesis that suitable environments for schistosomiasis transmission rarely occur at the location of survey measurements. KW - Côte d’Ivoire KW - schistosomiasis KW - spatial risk profiling KW - remote sensing KW - ecological relevant model Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-126148 VL - 10 IS - 2 ER - TY - JOUR A1 - Walz, Yvonne A1 - Wegmann, Martin A1 - Dech, Stefan A1 - Vounastou, Penelope A1 - Poda, Jean-Noel A1 - N'Goran, Eliézer K. A1 - Raso, Giovanna A1 - Utzinger, Jürg T1 - Modeling and Validation of Environmental Suitability for Schistosomiasis Transmission Using Remote Sensing JF - PLoS Neglected Tropical Diseases N2 - Background Schistosomiasis is the most widespread water-based disease in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and human water contact patterns. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. We investigated the potential of remote sensing to characterize habitat conditions of parasite and intermediate host snails and discuss the relevance for public health. Methodology We employed high-resolution remote sensing data, environmental field measurements, and ecological data to model environmental suitability for schistosomiasis-related parasite and snail species. The model was developed for Burkina Faso using a habitat suitability index (HSI). The plausibility of remote sensing habitat variables was validated using field measurements. The established model was transferred to different ecological settings in Côte d’Ivoire and validated against readily available survey data from school-aged children. Principal Findings Environmental suitability for schistosomiasis transmission was spatially delineated and quantified by seven habitat variables derived from remote sensing data. The strengths and weaknesses highlighted by the plausibility analysis showed that temporal dynamic water and vegetation measures were particularly useful to model parasite and snail habitat suitability, whereas the measurement of water surface temperature and topographic variables did not perform appropriately. The transferability of the model showed significant relations between the HSI and infection prevalence in study sites of Côte d’Ivoire. Conclusions/Significance A predictive map of environmental suitability for schistosomiasis transmission can support measures to gain and sustain control. This is particularly relevant as emphasis is shifting from morbidity control to interrupting transmission. Further validation of our mechanistic model needs to be complemented by field data of parasite- and snail-related fitness. Our model provides a useful tool to monitor the development of new hotspots of potential schistosomiasis transmission based on regularly updated remote sensing data. KW - schistosomiasis KW - Burkina Faso KW - remote sensing KW - surface water KW - habitats KW - agricultural irrigation KW - rivers KW - snails Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-125845 VL - 9 IS - 11 ER - TY - JOUR A1 - Naidoo, Robin A1 - Du Preez, Pierre A1 - Stuart-Hill, Greg A1 - Jago, Mark A1 - Wegmann, Martin T1 - Home on the Range: Factors Explaining Partial Migration of African Buffalo in a Tropical Environment JF - PLoS One N2 - 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. KW - Savannas KW - utilization distributions KW - movement ecology KW - predation risk KW - animal ecology KW - South Africa KW - size KW - conservation KW - Serengeti KW - ecosystem Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-134935 VL - 7 IS - 5 ER - TY - JOUR A1 - Wohlfart, Christian A1 - Wegmann, Martin A1 - Leimgruber, Peter T1 - Mapping threatened dry deciduous dipterocarp forest in South-east Asia for conservation management JF - Tropical Conservation Science N2 - Habitat loss is the primary reason for species extinction, making habitat conservation a critical strategy for maintaining global biodiversity. Major habitat types, such as lowland tropical evergreen forests or mangrove forests, are already well represented in many conservation priorities, while others are underrepresented. This is particularly true for dry deciduous dipterocarp forests (DDF), a key forest type in Asia that extends from the tropical to the subtropical regions in South-east Asia (SE Asia), where high temperatures and pronounced seasonal precipitation patterns are predominant. DDF are a unique forest ecosystem type harboring a wide range of important and endemic species and need to be adequately represented in global biodiversity conservation strategies. One of the greatest challenges in DDF conservation is the lack of detailed and accurate maps of their distribution due to inaccurate open-canopy seasonal forest mapping methods. Conventional land cover maps therefore tend to perform inadequately with DDF. Our study accurately delineates DDF on a continental scale based on remote sensing approaches by integrating the strong, characteristic seasonality of DDF. We also determine the current conservation status of DDF throughout SE Asia. We chose SE Asia for our research because its remaining DDF are extensive in some areas but are currently degrading and under increasing pressure from significant socio-economic changes throughout the region. Phenological indices, derived from MODIS vegetation index time series, served as input variables for a Random Forest classifier and were used to predict the spatial distribution of DDF. The resulting continuous fields maps of DDF had accuracies ranging from R-2 = 0.56 to 0.78. We identified three hotspots in SE Asia with a total area of 156,000 km(2), and found Myanmar to have more remaining DDF than the countries in SE Asia. Our approach proved to be a reliable method for mapping DDF and other seasonally influenced ecosystems on continental and regional scales, and is very valuable for conservation management in this region. KW - remote sensing KW - vegetation phenology KW - modis NDVI KW - time series analysis KW - Costa Rica KW - time series KW - Burma KW - Myanmar KW - continous fields KW - research priorities KW - deer cervus-eldi KW - land-cover KW - tropical forest KW - biodiversity conservation KW - habitat preferences KW - tropical dry forest conservation Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-117782 SN - 1940-0829 VL - 7 IS - 4 ER - TY - THES A1 - Wegmann, Martin T1 - Analyse von räumlichen Landschaftsmustern und deren Determinanten mittels Fernerkundungsdaten : am Beispiel von Regenwaldfragmenten in Westafrika T1 - Analysis of spatial landscape pattern and determining factors by the means of remote sensing data N2 - In den letzten Jahrzehnten ist eine verstärkte Veränderung der Landoberfläche beobachtet worden. Diese Prozesse sind direkten und indirekten anthropogenen Einflüssen zuzuschreiben, wie Deforestation oder Klimawandel. Mit dieser Entwicklung geht der Verlust und die Fragmentation von naturnahen Flächen einher. Für das Fortbestehen von Populationen verschiedenster Organismen in einer derartig geformten Landschaft ist entscheidend, inwieweit die Migration zwischen bestehenden Fragmenten gewährleistet ist. Diese wird von der Eignung der umgebenden Landschaft beeinflusst. Im Kontext einer klimatischen Veränderung und verstärkter anthropogener Landnutzung ist die Analyse der räumlichen Anordnung von Habitatfragmenten und der Qualität der umgebenden Landschaft besonders für die globale Aufrechterhaltung der Biodiversität wichtig. Großräumige Muster der Landschaftsveränderung können mit Hilfe von Satellitendaten analysiert werden, da es nur diese ermöglichen die Landbedeckung flächendeckend, reproduzierbar und auf einer adäquaten räumlichen Auflösung zu kartieren. Besonders zeitlich hochaufgelöste Daten liefern wertvolle Informationen bezüglich der Dynamik der Landbedeckung. Diese Arbeit beschäftigt sich mit der Analyse der Fragmentation in Westafrika und der potentiellen Bedeutung von singulären Fragmenten und deren potentiellen Auswirkungen auf die Biodiversität. Dafür wurden zeitlich hoch- und räumlich mittelaufgelöste Daten des Aufnahmesystems MODIS verwendet, mit denen für das Untersuchungsgebiet Westafrika die Landbedeckung klassifziert wurde. Für die darauf folgenden Analysen der räumlichen Konfiguration der Fragmente wurde der Fokus auf Regenwaldgebiete gelegt. Die Analyse von räumlichen Mustern der Regenwaldfragmente liefert weiterführende qualitative Informationen der individuellen Teilbereiche. Die räumliche Anordnung wurde sowohl mit etablierten Maßen als auch mittels in dieser Arbeit erstellter robuster und übertragbarer Indizes quantifiziert. Es konnte gezeigt werden, dass die Verwendung von aussagekräftigen Indizes, besonders, wenn sie alle benachbarten Fragmente und die Qualität der umgebenden Matrix berücksichtigen, die räumliche Differenzierung von Fragmenten verbessert. Jedoch ist die Anwendung dieser Maße abhängig von den Ansprüchen einer Art. Daher muss die artspezifische Perzeptionen der Landschaft auf der Basis der Indizes implementiert werden, da die Übertragung der Ergebnisse einzelner Indizes auf andere räumliche Auflösungen und andere Regionen nur begrenzt möglich war. Des Weiteren wurden potentielle Einflussfaktoren auf die räumlichen Muster mittels Neutraler Landschaftsmodelle untersucht. Hierbei ergaben sich je nach Region und Index unterschiedliche Ergebnisse, allerdings konnte der Einfluss anthropogen induzierter Veränderungen auf die Landbedeckung postuliert werden. Die große Bedeutung der räumlichen Attribution von Landbedeckungsklassen konnte in dieser Arbeit aufgezeigt werden. Der alleinige Fokus auf die Kartierung von z. B. Waldfragmenten ohne deren räumliche Anordnung zu berücksichtigen, kann zu falschen Schlüssen bezüglich deren ökologischen, hydrologischen und klimatologischen Bedeutung führen. N2 - In a century where climate change is unquestioned and the anthropogenic induced loss of habitats of many species has reached tremendous rates, it becomes important to analyse the effects of these changes. Especially the division of formerly continuous habitats into smaller parts and the resulting changes of area or shape are important due to their effects on e.g. viable populations sizes. Consequently does the connectivity between patches change, which is also influenced by the environmental condition inbetween patches. Generally, the loss and fragmentation of habitat will constrain species migration, which in the long run will result in less genetic diversity within populations and decline and potentially in a loss of biodiversity. Broad scale analysis of these changes can be achieved using satellite imagery due to their capabilities to deliver information repeatedly for a large area with an ecological adequate spatial resolution which can be used to generate a landcover map. This study focuses on the effects of habitat fragmentation in West Africa. The importance of patches for the retention of the overall landscape connectivity and therefore their importance for the maintenance of biodiversity is in the focus of this work. For the analysis of fragmentation on a landscape level, temporal high- and spatial low-resolution imagery provided by MODIS are used for the necessary landcover classification. The analyses focus on the rainforest in West Africa and use various established and novel indices to classify the fragments due to their spatial attributes. The novel indices invented in this study aimed at being transferrable to other region and species and at being robust and applicable on large data sets. Moreover Neutral Landscapemodels have been generated to analyse the behaviour of indices across different scales and spatial arrangments. This study showed, that the differentiation of fragments based on their spatial attributes can be achieved best using indices which incorporate all neigbouring patches and the quality of the surrounding landscape. However all indices and their application are highly depending on species specific habitat requirements and perception range as well as the spatial scale, when using spatial pattern analysis. Moreover, analysing the causes for the existing spatial patterns showed, that the factors explaining the existing patterns differed by region and used indices, but generally the human impact contributed to a high degree to the existing spatial arrangement of forest fragments. This study showed that using solely the extent of a certain landcover without considering their spatial arrangement might lead to wrong conclusions concerning its importance for biodiversity. The results depend highly on the complex interactions between spatial and thematic resolution of the landcover data, spatial arrangement of patches, the surrounding landscapes and species requirements which can be accounted for using the novel applied indices in this study. Hence the importance to analyse the spatial attributes of landcover data is shown in this study. KW - Fragmentierung KW - Fernerkundung KW - Geoinformationssystem KW - Regenwald KW - Fragmentation KW - räumliche Muster KW - Tropen KW - Fragmentatin KW - GIS KW - remote sensing KW - spatial pattern analysis Y1 - 2009 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-36532 ER - TY - JOUR A1 - Alavipanah, Sadroddin A1 - Wegmann, Martin A1 - Qureshi, Salman A1 - Weng, Qihao A1 - Koellner, Thomas T1 - The role of vegetation in mitigating urban land surface temperatures: a case study of Munich, Germany during the warm season JF - Sustainability N2 - 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. KW - Surface Urban Heat Island (SUHI) KW - cities KW - buildings KW - Land Surface Temperature (LST) KW - urban vegetation KW - climate change KW - heat waves Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-143447 VL - 7 ER - TY - JOUR A1 - Lausch, Angela A1 - Borg, Erik A1 - Bumberger, Jan A1 - Dietrich, Peter A1 - Heurich, Marco A1 - Huth, Andreas A1 - Jung, András A1 - Klenke, Reinhard A1 - Knapp, Sonja A1 - Mollenhauer, Hannes A1 - Paasche, Hendrik A1 - Paulheim, Heiko A1 - Pause, Marion A1 - Schweitzer, Christian A1 - Schmulius, Christiane A1 - Settele, Josef A1 - Skidmore, Andrew K. A1 - Wegmann, Martin A1 - Zacharias, Steffen A1 - Kirsten, Toralf A1 - Schaepman, Michael E. T1 - Understanding forest health with remote sensing, part III: requirements for a scalable multi-source forest health monitoring network based on data science approaches JF - Remote Sensing N2 - 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. KW - forest health KW - in situ forest monitoring KW - remote sensing KW - data science KW - digitalization KW - big data KW - semantic web KW - linked open data KW - FAIR KW - multi-source forest health monitoring network Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-197691 SN - 2072-4292 VL - 10 IS - 7 ER - TY - JOUR A1 - Fa, John E. A1 - Olivero, Jesús A1 - Real, Raimundo A1 - Farfán, Miguel A. A1 - Márquez, Ana L. A1 - Vargas, J. Mario A1 - Ziegler, Stefan A1 - Wegmann, Martin A1 - Brown, David A1 - Margetts, Barrie A1 - Nasi, Robert T1 - Disentangling the relative effects of bushmeat availability on human nutrition in central Africa JF - Scientific Reports N2 - 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. KW - plant species richness KW - development policy KW - Congo Basin KW - conservation KW - dependence KW - wildlife consumption KW - food security KW - forests KW - biodiversity KW - hotspots Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-144110 VL - 5 IS - 8168 ER - TY - RPRT A1 - Müller, Jörg A1 - Scherer-Lorenzen, Michael A1 - Ammer, Christian A1 - Eisenhauer, Nico A1 - Seidel, Dominik A1 - Schuldt, Bernhard A1 - Biedermann, Peter A1 - Schmitt, Thomas A1 - Künzer, Claudia A1 - Wegmann, Martin A1 - Cesarz, Simone A1 - Peters, Marcell A1 - Feldhaar, Heike A1 - Steffan-Dewenter, Ingolf A1 - Claßen, Alice A1 - Bässler, Claus A1 - von Oheimb, Goddert A1 - Fichtner, Andreas A1 - Thorn, Simon A1 - Weisser, Wolfgang T1 - BETA-FOR: Erhöhung der strukturellen Diversität zwischen Waldbeständen zur Erhöhung der Multidiversität und Multifunktionalität in Produktionswäldern. Antragstext für die DFG Forschungsgruppe FOR 5375 T1 - BETA-FOR: Enhancing the structural diversity between patches for improving multidiversity and multifunctionality in production forests. Proposal for DFG Research Unit FOR 5375 BT - β\(_4\) : Proposal for the 1st phase (2022-2026) of the DFG Research Unit FOR 5375/1 (DFG Forschergruppe FOR 5375/1 – BETA-FOR), Fabrikschleichach, October 2021 N2 - Der in jüngster Zeit beobachtete kontinuierliche Verlust der β-Diversität in Ökosystemen deutet auf homogene Gemeinschaften auf Landschaftsebene hin, was hauptsächlich auf die steigende Landnutzungsintensität zurückgeführt wird. Biologische Vielfalt ist mit zahlreichen Funktionen und der Stabilität von Ökosystemen verknüpft. Es ist daher zu erwarten, dass eine abnehmende β-Diversität auch die Multifunktionalität verringert. Wir kombinieren hier Fachwissen aus der Forstwissenschaft, der Ökologie, der Fernerkundung, der chemischen Ökologie und der Statistik in einem gemeinschaftlichen und experimentellen β-Diversitätsdesign, um einerseits die Auswirkungen der Homogenisierung zu bewerten und andererseits Konzepte zu entwickeln, um negative Auswirkungen durch Homogenisierung in Wäldern rückgängig zu machen. Konkret werden wir uns mit der Frage beschäftigen, ob die Verbesserung der strukturellen β-Komplexität (ESBC) in Wäldern durch Waldbau oder natürliche Störungen die Biodiversität und Multifunktionalität in ehemals homogenen Produktionswäldern erhöhen kann. Unser Ansatz wird mögliche Mechanismen hinter den beobachteten Homogenisierungs-Diversitäts-Beziehungen identifizieren und zeigen, wie sich diese auf die Multifunktionalität auswirken. An elf Standorten in ganz Deutschland haben wir dazu zwei Waldbestände als zwei kleine "Waldlandschaften" ausgewählt. In einem dieser beiden Bestände haben wir ESBC (Enhancement of Structural Beta Complexity)-Behandlungen durchgeführt. 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 Ökosystemfunktionen, einschließlich der wichtigsten Funktionen in Wäldern der gemäßigten Zonen, messen. Der statistische Rahmen wird eine umfassende Analyse der Biodiversität ermöglichen, indem verschiedenen Aspekte (taxonomische, funktionelle und phylogenetische Vielfalt) auf verschiedenen Skalenebenen (α-, β-, γ-Diversität) quantifiziert werden. Um die Gesamtdiversität zu kombinieren, werden wir das Konzept der Multidiversität auf die 18 Taxa anwenden. Wir werden neue Ansätze zur Quantifizierung und Aufteilung der Multifunktionalität auf α- und β-Skalen verwenden und entwickeln. Durch die experimentelle Beschreibung des Zusammenhangs zwischen β-Diversität und Multifunktionalität in einer Reallandschaft wird unsere Forschung einen neuen Weg einschlagen. Darüber hinaus werden wir dazu beitragen, verbesserte Leitlinien für waldbauliche Konzepte und für das Management natürlicher Störungen zu entwickeln, um Homogenisierungseffekte der Vergangenheit umzukehren. N2 - The recently observed consistent loss of β-diversity across ecosystems indicates increasingly homogeneous communities in patches of landscapes, mainly caused by increasing land-use intensity. Biodiversity is related to numerous ecosystem functions and stability. Therefore, decreasing β-diversity is also expected to reduce multifunctionality. To assess the impact of homogenization and to develop guidelines to reverse its potentially negative effects, we combine expertise from forest science, ecology, remote sensing, chemical ecology and statistics in a collaborative and experimental β-diversity approach. Specifically, we will address the question whether the Enhancement of Structural Beta Complexity (ESBC) in forests by silviculture or natural disturbances will increase biodiversity and multifunctionality in formerly homogeneously structured production forests. Our approach will identify potential mechanisms behind observed homogenization-diversity-relationships and show how these translate into effects on multifunctionality. At eleven forest sites throughout Germany, we selected two districts as two types of small ‘forest landscapes’. In one of these two districts, we established ESBC treatments (nine differently treated 50x50 m patches with a focus on canopy cover and deadwood features). In the second, the control district, we will establish nine patches without ESBC. By a comprehensive sampling, we will monitor 18 taxonomic groups and measure 21 ecosystem functions, including key functions in temperate forests, on all patches. The statistical framework will allow a comprehensive biodiversity assessment by quantifying the different aspects of multitrophic biodiversity (taxonomical, functional and phylogenetic diversity) on different levels of biodiversity (α-, β-, γ-diversity). To combine overall diversity, we will apply the concept of multidiversity across the 18 taxa. We will use and develop new approaches for quantification and partitioning of multifunctionality at α- and β- scales. Overall, our study will herald a new research avenue, namely by experimentally describing the link between β-diversity and multifunctionality. Furthermore, we will help to develop guidelines for improved silvicultural concepts and concepts for management of natural disturbances in temperate forests reversing past homogenization effects. KW - Waldökosystem KW - Biodiversität KW - BETA-Multifunktionalität KW - beta-multifunctionality KW - BETA-Diversität KW - beta diversity KW - Forschungsstation Fabrikschleichach Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-290849 ER - TY - JOUR A1 - Ulloa-Torrealba, Yrneh A1 - Stahlmann, Reinhold A1 - Wegmann, Martin A1 - Koellner, Thomas T1 - Over 150 years of change: object-oriented analysis of historical land cover in the Main river catchment, Bavaria/Germany JF - Remote Sensing N2 - The monitoring of land cover and land use change is critical for assessing the provision of ecosystem services. One of the sources for long-term land cover change quantification is through the classification of historical and/or current maps. Little research has been done on historical maps using Object-Based Image Analysis (OBIA). This study applied an object-based classification using eCognition tool for analyzing the land cover based on historical maps in the Main river catchment, Upper Franconia, Germany. This allowed land use change analysis between the 1850s and 2015, a time span which covers the phase of industrialization of landscapes in central Europe. The results show a strong increase in urban area by 2600%, a severe loss of cropland (−24%), a moderate reduction in meadows (−4%), and a small gain in forests (+4%). The method proved useful for the application on historical maps due to the ability of the software to create semantic objects. The confusion matrix shows an overall accuracy of 82% for the automatic classification compared to manual reclassification considering all 17 sample tiles. The minimum overall accuracy was 65% for historical maps of poor quality and the maximum was 91% for very high-quality ones. Although accuracy is between high and moderate, coarse land cover patterns in the past and trends in land cover change can be analyzed. We conclude that such long-term analysis of land cover is a prerequisite for quantifying long-term changes in ecosystem services. KW - historical KW - land cover change KW - object-based classification KW - eCognition Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-220029 SN - 2072-4292 VL - 12 IS - 24 ER - TY - JOUR A1 - Walz, Yvonne A1 - Wegmann, Martin A1 - Dech, Stefan A1 - Raso, Giovanna A1 - Utzinger, Jürg T1 - Risk profiling of schistosomiasis using remote sensing: approaches, challenges and outlook JF - Parasites & Vectors N2 - Background: Schistosomiasis is a water-based disease that affects an estimated 250 million people, mainly in sub-Saharan Africa. The transmission of schistosomiasis is spatially and temporally restricted to freshwater bodies that contain schistosome cercariae released from specific snails that act as intermediate hosts. Our objective was to assess the contribution of remote sensing applications and to identify remaining challenges in its optimal application for schistosomiasis risk profiling in order to support public health authorities to better target control interventions. Methods: We reviewed the literature (i) to deepen our understanding of the ecology and the epidemiology of schistosomiasis, placing particular emphasis on remote sensing; and (ii) to fill an identified gap, namely interdisciplinary research that bridges different strands of scientific inquiry to enhance spatially explicit risk profiling. As a first step, we reviewed key factors that govern schistosomiasis risk. Secondly, we examined remote sensing data and variables that have been used for risk profiling of schistosomiasis. Thirdly, the linkage between the ecological consequence of environmental conditions and the respective measure of remote sensing data were synthesised. Results: We found that the potential of remote sensing data for spatial risk profiling of schistosomiasis is - in principle - far greater than explored thus far. Importantly though, the application of remote sensing data requires a tailored approach that must be optimised by selecting specific remote sensing variables, considering the appropriate scale of observation and modelling within ecozones. Interestingly, prior studies that linked prevalence of Schistosoma infection to remotely sensed data did not reflect that there is a spatial gap between the parasite and intermediate host snail habitats where disease transmission occurs, and the location (community or school) where prevalence measures are usually derived from. Conclusions: Our findings imply that the potential of remote sensing data for risk profiling of schistosomiasis and other neglected tropical diseases has yet to be fully exploited. KW - ecology KW - scale KW - remote sensing KW - risk profiling KW - spatial modelling KW - schistosomiasis KW - geographical information system KW - intermediate host snail KW - epidemology Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-148778 VL - 8 IS - 163 ER - TY - JOUR A1 - Philipp, Marius A1 - Wegmann, Martin A1 - Kübert-Flock, Carina T1 - Quantifying the Response of German Forests to Drought Events via Satellite Imagery JF - Remote Sensing N2 - 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. KW - time-series KW - harmonic analysis KW - z-score KW - scPDSI KW - drought KW - vegetation response KW - forest ecosystems KW - Google Earth Engine Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-239575 SN - 2072-4292 VL - 13 IS - 9 ER - TY - JOUR A1 - Remelgado, Ruben A1 - Safi, Kamran A1 - Wegmann, Martin T1 - From ecology to remote sensing: using animals to map land cover JF - Remote Sensing in Ecology and Conservation N2 - 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. KW - Animal Tracking KW - land cover KW - land use KW - movement ecology KW - R KW - remote sensing Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-225200 VL - 6 IS - 1 ER - TY - JOUR A1 - Fisser, Henrik A1 - Khorsandi, Ehsan A1 - Wegmann, Martin A1 - Baier, Frank T1 - Detecting moving trucks on roads using Sentinel-2 data JF - Remote Sensing N2 - 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. KW - Sentinel-2 KW - truck detection KW - road traffic KW - machine learning Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-267174 SN - 2072-4292 VL - 14 IS - 7 ER - TY - JOUR A1 - Halbgewachs, Magdalena A1 - Wegmann, Martin A1 - da Ponte, Emmanuel T1 - A spectral mixture analysis and landscape metrics based framework for monitoring spatiotemporal forest cover changes: a case study in Mato Grosso, Brazil JF - Remote Sensing N2 - 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. KW - Landsat KW - Google Earth Engine KW - spectral mixture analysis KW - deforestation KW - forest degradation KW - landscape metrics KW - forest fragmentaion KW - Mato Grosso Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-270644 SN - 2072-4292 VL - 14 IS - 8 ER - TY - JOUR A1 - Schwalb‐Willmann, Jakob A1 - Remelgado, Ruben A1 - Safi, Kamran A1 - Wegmann, Martin T1 - moveVis: Animating movement trajectories in synchronicity with static or temporally dynamic environmental data in R JF - Methods in Ecology and Evolution N2 - Visualizing movement data is challenging: While traditional spatial data can be sufficiently displayed as two‐dimensional plots or maps, movement trajectories require the representation of time in a third dimension. To address this, we present moveVis, an R package, which provides tools to animate movement trajectories, overlaying simultaneous uni‐ or multi‐temporal raster imagery or vector data. moveVis automates the processing of movement and environmental data to turn such into an animation. This includes (a) the regularization of movement trajectories enforcing uniform time instances and intervals across all trajectories, (b) the frame‐wise mapping of movement trajectories onto temporally static or dynamic environmental layers, (c) the addition of customizations, for example, map elements or colour scales and (d) the rendering of frames into an animation encoded as GIF or video file. moveVis is designed to display interactions and concurrencies of animal movement and environmental data. We present examples and use cases, ranging from data exploration to visualizing scientific findings. Static spatial plots of movement data disregard the temporal dimension that distinguishes movement from other spatial data. In contrast, animations allow to display relocation in both time and space. We deem animations a powerful way to visually explore movement data, frame analytical findings and display potential interactions with spatially continuous and temporally dynamic environmental covariates. KW - animal tracking KW - animation KW - data visualization KW - movement data KW - movement ecology KW - spatio‐temporal data Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-214856 VL - 11 IS - 5 SP - 664 EP - 669 ER - TY - JOUR A1 - Remelgado, Ruben A1 - Leutner, Benjamin A1 - Safi, Kamran A1 - Sonnenschein, Ruth A1 - Kuebert, Carina A1 - Wegmann, Martin T1 - Linking animal movement and remote sensing - mapping resource suitability from a remote sensing perspective JF - Remote Sensing in Ecology and Conservation N2 - 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. KW - Landsat KW - movement ecology KW - optical remote sensing KW - resource mapping KW - resource suitability KW - surface reflectances Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-225199 VL - 4 IS - 3 ER -