A continuous tree species-specific reflectance anomaly index reveals declining forest condition between 2016 and 2022 in Germany

Abstract

Large areas of Europe have been repeatedly affected by severe droughts. Stressed trees suffered from direct drought impacts such as water stress or heat and were also more susceptible to other biotic and abiotic stress agents and calamities. Monitoring such vulnerable forests area-wide is crucial to assess the highly dynamic climate change induced impacts not captured by traditional ground-based monitoring approaches. However, most remote sensing studies dealing with forest condition are either not species-specific, not accounting for morphological and climatic conditions across different regions, not considering natural variations in phenology or not including multiple disturbance agents. Here, we extract species-specific reflectance time series separately for seven natural regions covering Germany for 2016 to 2022. The seasonal evolution of these time series serves as reference for the detection of forest condition anomalies. We calculated a similarity metric – further called forest condition anomaly index (FCA) – between each single reflectance observation and the respective measurements within the reference time series, also considering the natural temporal deviations caused by phenology. Temporal aggregation of the FCA allows the generation of spatially comprehensive forest condition anomaly maps. We demonstrate that the FCA shows patterns related to fires, storms and insect infestations and found an overall agreement with state-of-the-art forest disturbance products using a threshold of FCA=−0.15 for forest loss. Consequently, the FCA can be used to detect forest disturbances or linked with vegetation models to assess e.g. forest biomass or carbon flux.