Frank Thonfeld

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Assessing and mapping patterns of (semi-)natural vegetation types at a large spatial scale is a difficult task. The challenge increases if the floristic variation within vegetation types (i.e., subtype variation of species composition) is the target. A desirable way to deal with this task may be to address such vegetation patterns with remote-sensing(More)
Image time series of high temporal and spatial resolution capture land surface dynamics of heterogeneous landscapes. We applied the ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) algorithm to multi-spectral images covering two semi-arid heterogeneous rangeland study sites located in South Africa. MODIS 250 m resolution and(More)
Based on orthorectified, bi-temporal 2,000×2,000 5 m pixel multispectral RapidEye data [1] short-term changes are detected associated with land-use and reclamation in connection with open pit mining in North Rhine-Westphalia, Germany. The changes are found automatically by means of a combination of the iteratively re-weighted MAD method [2], which produces(More)
Moderate Resolution Imaging Spectroradiometer (MODIS) data forms the basis for numerous land use and land cover (LULC) mapping and analysis frameworks at regional scale. Compared to other satellite sensors, the spatial, temporal and spectral specifications of MODIS are considered as highly suitable for LULC classifications which support many different(More)
Acquiring multi-temporal spatial information on vegetation condition at scales appropriate for site-specific agricultural management is often complicated by the need for meticulous field measurements. Understanding spatial/temporal crop cover heterogeneity within irrigated croplands may support sustainable land use, specifically in areas affected by land(More)
Automated monitoring systems that can capture wetlands’ high spatial and temporal variability are essential for their management. SAR-based change detection approaches offer a great opportunity to enhance our understanding of complex and dynamic ecosystems. We test a recently-developed time series change detection approach (S1-omnibus) using Sentinel-1(More)
Frank Thonfeld 1,2,*,†, Andreas Rienow 1,3,†, Olena Dubovyk 2,†, Ayman Abdel-Hamid 2, Agatha Akpeokhai 1, Esther Amler 4, Georg Bareth 2,5, Amit Kumar Basukala 6, Morton Canty 2, Manfred Denich 2,6, Tomasz Dobrzeniecki 7, Jessica Ferner 2, Hendrik Flügel 1, Gohar Ghazaryan 2, Ellen Götz 2, Valerie Graw 2, Klaus Greve 2,8, Reginald Guuroh 9, Sascha Heinemann(More)