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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 -