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Forest & Vegetation Products

The EOC provides comprehensive information on the structure and composition of forest areas and vegetation.
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Forest & Vegetation Products

fCover - Fractional Vegetation Cover Netherlands based on Sentinel-2 Data

The Sentinel-2 fractional vegetation cover (fCover) product for the Netherlands was produced as part of the NextGEOSS project at the German Aerospace Center (DLR). The goal is to derive abundance maps from atmospherically corrected Sentinel-2 multispectral images for photosynthetically active vegetation (PV), combined non-photosynthetically active vegetation (NPV) and bare soil (BS).


Forest Structure - Germany, 2017-2022

The product shows forest structure information on canopy height, total canopy cover and Above-ground biomass density (AGBD) in Germany as annual products from 2017 to 2022 in 10 m spatial resolution. The products were generated using a machine learning modelling approach that combines complementary spaceborne remote sensing sensors, namely GEDI (Global Ecosystem Dynamics Investigation; NASA; full-waveform LiDAR), Sentinel-1 (Synthetic-Aperture-Radar; ESA, C-band) and Sentinel-2 (Multispectral Instrument; ESA; VIS-NIR-SWIR).


Geo-ForPy - Forest cover Paraguayan Chaco

This repository contains data on the forest structure and forest cover dynamics in the Paraguayan Chaco (northeastern part of Paraguay) between 1987 and 2020.


TanDEM-X Forest/Non-Forest Map

The TanDEM-X Forest/Non-Forest Map is a project developed by the Microwaves and Radar Institute at the German Aerospace Center (DLR), within the activities of the TanDEM-X mission. The goal is the derivation of a global forest/non-forest classification mosaic from TanDEM-X bistatic interferometric synthetic aperture radar (InSAR) data, acquired for the generation of the global digital elevation model (DEM) in Stripmap single polarization (HH) mode.


Tree Canopy Cover Loss - Germany, 2018-2021

This repository contains tree canopy cover loss information between January 2018 and April 2021 for Germany at monthly resolution. The analysis is based on monthly image composites of the disturbance index (DI) derived from Sentinel-2 and Landsat-8 time series. Deviations from a 2017 reference median DI image exceeding a threshold are recorded as losses. The method used to derive this product as well as the mapping results are described in detail in Thonfeld et al. (2022). The map depicts areas of natural disturbances (windthrow, fire, droughts, insect infestation) as well as sanitation and salvage logging, and regular forest harvest without explicitly differentiating these drivers.