@article{HolzwarthThonfeldAbdullahietal.2020, author = {Holzwarth, Stefanie and Thonfeld, Frank and Abdullahi, Sahra and Asam, Sarah and Da Ponte Canova, Emmanuel and Gessner, Ursula and Huth, Juliane and Kraus, Tanja and Leutner, Benjamin and Kuenzer, Claudia}, title = {Earth Observation based monitoring of forests in Germany: a review}, series = {Remote Sensing}, volume = {12}, journal = {Remote Sensing}, number = {21}, issn = {2072-4292}, doi = {10.3390/rs12213570}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-216334}, year = {2020}, abstract = {Forests in Germany cover around 11.4 million hectares and, thus, a share of 32\% of Germany's surface area. Therefore, forests shape the character of the country's cultural landscape. Germany's forests fulfil a variety of functions for nature and society, and also play an important role in the context of climate levelling. Climate change, manifested via rising temperatures and current weather extremes, has a negative impact on the health and development of forests. Within the last five years, severe storms, extreme drought, and heat waves, and the subsequent mass reproduction of bark beetles have all seriously affected Germany's forests. Facing the current dramatic extent of forest damage and the emerging long-term consequences, the effort to preserve forests in Germany, along with their diversity and productivity, is an indispensable task for the government. Several German ministries have and plan to initiate measures supporting forest health. Quantitative data is one means for sound decision-making to ensure the monitoring of the forest and to improve the monitoring of forest damage. In addition to existing forest monitoring systems, such as the federal forest inventory, the national crown condition survey, and the national forest soil inventory, systematic surveys of forest condition and vulnerability at the national scale can be expanded with the help of a satellite-based earth observation. In this review, we analysed and categorized all research studies published in the last 20 years that focus on the remote sensing of forests in Germany. For this study, 166 citation indexed research publications have been thoroughly analysed with respect to publication frequency, location of studies undertaken, spatial and temporal scale, coverage of the studies, satellite sensors employed, thematic foci of the studies, and overall outcomes, allowing us to identify major research and geoinformation product gaps.}, 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{TimmermansvanderTolTimmermansetal.2015, author = {Timmermans, Wim J. and van der Tol, Christiaan and Timmermans, Joris and Ucer, Murat and Chen, Xuelong and Alonso, Luis and Moreno, Jose and Carrara, Arnaud and Lopez, Ramon and Fernando de la Cruz, Tercero and Corcoles, Horacio L. and de Miguel, Eduardo and Sanchez, Jose A. G. and Perez, Irene and Belen, Perez and Munoz, Juan-Carlos J. and Skokovic, Drazen and Sobrino, Jose and Soria, Guillem and MacArthur, Alasdair and Vescovo, Loris and Reusen, Ils and Andreu, Ana and Burkart, Andreas and Cilia, Chiara and Contreras, Sergio and Corbari, Chiara and Calleja, Javier F. and Guzinski, Radoslaw and Hellmann, Christine and Herrmann, Ittai and Kerr, Gregoire and Lazar, Adina-Laura and Leutner, Benjamin and Mendiguren, Gorka and Nasilowska, Sylwia and Nieto, Hector and Pachego-Labrador, Javier and Pulanekar, Survana and Raj, Rahul and Schikling, Anke and Siegmann, Bastian and von Bueren, Stefanie and Su, Zhongbo (Bob)}, title = {An Overview of the Regional Experiments for Land-atmosphere Exchanges 2012 (REFLEX 2012) Campaign}, series = {Acta Geophysica}, volume = {63}, journal = {Acta Geophysica}, number = {6}, doi = {10.2478/s11600-014-0254-1}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-136491}, pages = {1465-1484}, year = {2015}, abstract = {The REFLEX 2012 campaign was initiated as part of a training course on the organization of an airborne campaign to support advancement of the understanding of land-atmosphere interaction processes. This article describes the campaign, its objectives and observations, remote as well as in situ. The observations took place at the experimental Las Tiesas farm in an agricultural area in the south of Spain. During the period of ten days, measurements were made to capture the main processes controlling the local and regional land-atmosphere exchanges. Apart from multi-temporal, multi-directional and multi-spatial space-borne and airborne observations, measurements of the local meteorology, energy fluxes, soil temperature profiles, soil moisture profiles, surface temperature, canopy structure as well as leaf-level measurements were carried out. Additional thermo-dynamical monitoring took place at selected sites. After presenting the different types of measurements, some examples are given to illustrate the potential of the observations made.}, language = {en} } @article{WalzWegmannLeutneretal.2015, author = {Walz, Yvonne and Wegmann, Martin and Leutner, Benjamin and Dech, Stefan and Vounatsou, Penelope and N'Goran, Eli{\´e}zer K. and Raso, Giovanna and Utzinger, J{\"u}rg}, title = {Use of an ecologically relevant modelling approach to improve remote sensing-based schistosomiasis risk profiling}, series = {Geospatial Health}, volume = {10}, journal = {Geospatial Health}, number = {2}, doi = {10.4081/gh.2015.398}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-126148}, pages = {398}, year = {2015}, abstract = {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 C{\^o}te 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.}, language = {en} }