Speaker
Description
Climate change is increasing the drought frequency and intensity, placing growing pressure on forest ecosystems. Assessing forest responses to such disturbances requires observations that capture both fine spatial variability and long-term coverage of multiple disturbance events. Existing approaches rely either on tree-ring data, which are spatially sparse, or on remote sensing products that often lack either long-term coverage or sufficient spatial resolution to resolve small-scale disturbances. To address this gap we present a high-resolution (30 m) long-term time series of forest condition across Germany from 2000 to 2022. The dataset is based on a satellite fusion framework that combines Landsat and MODIS satellite observations to achieve both a high spatial and temporal resolution. Applying the already established Forest Condition Monitor Framework, we calculated the forest condition anomaly index (FCA) covering a 22-year time series. In contrast to many existing approaches, it accounts for species and region-specific reflectance behavior and natural phenological variability. Validation against forest loss products yields an F-score of 0.83 using a damage threshold of -0.15. Additionally, FCA shows strong agreement (concordance correlation coefficient of 0.85) with an existing Sentinel-2-based FCA dataset during the overlapping period. Results show that FCA changes correspond well with major drought events experienced in 2003, 2015, and 2018-2021. We further analyze relationships between drought response and biodiversity using the Shannon index derived from tree species distributions. The dataset enables large-scale analysis of forest condition dynamics and supports high-resolution investigations of forest responses to drought and other disturbances. The proposed framework is transferable and can be extended to understudied regions globally.
| Status Group | Doctoral Researcher |
|---|---|
| FOR TALKS: Poster Presentation Option | Yes, I’m willing to present as a poster. |