@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} } @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} }