Tag: Random Forest
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New paper on the potential of simulated laser scanning and field data to train forest biomass models
In great collaboration with colleagues from Karlsruhe (DE), Vienna (AT), Brno (CZ), Leipzig (DE), Raszyn (PL), and Berlin (DE), we published a paper investigating approaches to improve LiDAR-based biomass models when only limited sample plots with field data are available. The main work was carried out by PhD student Jannika Schäfer (IFGG, Karlsruhe Institute of…
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Use of TanDEM-X and Sentinel products to derive gully activity maps in Kunene Region (Namibia) based on automatic iterative Random Forest approach
Gullies are landforms with specific patterns of shape, topography, hydrology, vegetation, and soil characteristics. Remote sensing products (TanDEM-X, Sentinel-1 and Sentinel-2) serve as inputs into an iterative algorithm, initialized using a micro-mapping simulation as training data, to map gullies in the northwestern of Namibia. A Random Forest Classifier examines pixels with similar characteristics in a…