Benchmarking Large-scale Forest Disturbance Products

Forest disturbances are among the main drivers of global carbon emissions.  These disturbances are associated with various human-related and natural drivers, including unsustainable resource extraction, fires, overgrazing and extreme weather events. Such disturbances vary in intensity and type, ranging from stand-replacing disturbances following clear-cuts or windthrow to more scattered disturbance patterns involving standing deadwood resulting from drought-induced mortality or small-scale canopy removal from selective logging. In recent years, multiple Earth observation products have been generated from Landsat and Sentinel missions to monitor such disturbances. These products vary in their methodological approaches and in their global and temporal coverage. However, there are currently no consistent benchmarks with which to evaluate their performance under different disturbance regimes and drivers. This study aims to evaluate and compare the accuracy and operational applicability of satellite-based forest disturbance products. We compared eight large-scale satellite products for detecting various forest disturbances, such as scattered tree mortality, large-scale removal and natural hazards. The disturbance products compared include Global Forest Change (GFC), DIST-ALERT, DeadTrees.Earth and the European Forest Disturbance Atlas (EFDA), amongst others. The products were compared qualitatively and quantitatively using reference data on disturbance events obtained from globally distributed aerial imagery acquired using unmanned aircraft systems (UAS).  We use a total of 35 aerial orthomosaics acquired between 2015 and 2024, obtained from the DeadTrees.Earth platform. We identify forest disturbance types and quantify their extent using visual interpretation. This study advances our understanding of the strengths and limitations of current forest disturbance products by systematically assessing their performance across diverse disturbance types and environmental contexts.