Using remote sensing to support forest policies in Bavaria, Germany

Abstract. In order to mitigate climate change, the German Federal Government set goals to reduce greenhouse gas emissions and reach climate neutrality by 2045. To implement it, the federal and local governments have made a series of policies to improve the forest conditions in Bavaria. In this paper, we generated annual high-resolution dominant leaf type (DLT) and above-ground biomass density maps over Bavaria to support policy-making. Specifically, two U-Net-based models were trained to predict the DLT and biomass density separately from multispectral Sentinel-2 data based on deep learning. The model achieved 92.5 % DLT segmentation accuracy and an R² of 0.62 biomass estimation accuracy on the test set. Then, the trained model is used to derive annual DLT and biomass density maps from 2015 to 2025, where a post-processing step was proposed to exclude noisy fluctuating predictions. The results show a clear increase in tree area and broadleaved area, but this has slowed down since 2020. Besides, biomass loss due to tree degradation is higher than that due to deforestation, as suggested by the results. Subsequently, the time-series maps are used to identify hotspots in Bavaria, which is of interest to policymakers. We analyzed the tree cover and biomass loss for different administrative regions, and found that for most administrative areas, the increase of broadleaf tree areas is noticeably larger than the loss of that, except for Upper Franconia. Besides, continuous increases in both forest area and biomass amount in mountainous regions were observed. A landscape metrics-based analysis suggests that forest cover across the entire state has become increasingly fragmented. The results provide good insights into the tree status in Bavaria and suggest a new focus for forest management policies.