Forest Structure - Sentinel-1/2, GEDI - 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). Sample estimates on forest structure from GEDI were modelled in 10 m spatial resolution as annual products based on spatio-temporal composites from Sentinel-1 and -2 for six years (2017 to 2022). The derived products are the first consistent data sets on canopy height, total canopy cover and AGBD for Germany which enable a quantitative assessment of recent forest structure dynamics, e.g. in the context of repeated drought events since 2018. The full description of the method and results can be found in the publication of Kacic et al. (2023).本数据集为2017至2022年德国区域的森林结构年度产品，空间分辨率达10米，涵盖冠层高度、总冠层覆盖率以及地上生物量密度（Above-ground biomass density, AGBD）相关的森林结构参数。该产品基于机器学习建模方法生成，融合了多颗星载遥感传感器的互补观测数据，具体包括GEDI（全球生态系统动态调查，美国国家航空航天局，全波形激光雷达）、Sentinel-1（合成孔径雷达，欧洲空间局，C波段）以及Sentinel-2（多光谱仪，欧洲空间局，可见光-近红外-短波红外）。研究以Sentinel-1与Sentinel-2的时空合成数据为基础，将GEDI获取的森林结构样本估算值以10米空间分辨率建模为2017至2022年共六年的年度产品。本次生成的数据集为德国首套覆盖冠层高度、总冠层覆盖率与地上生物量密度（AGBD）的一致性森林结构数据集，可用于定量评估近年森林结构动态变化，例如2018年以来持续发生的干旱事件背景下的森林响应与变化。该研究的方法与结果完整细节可参见Kacic等人（2023）发表的学术文献。