National-scale tree species mapping with deep learning reveals forest management insights in Germany

Abstract

Accurate tree species distribution is essential for biodiversity assessment, sustainable forest management, and environmental policy. However, mapping species over large areas with satellite data is challenging due to spectral mixing and complex spatial distribution. To address this, we developed a novel deep learning model, ForestFormer, using Sentinel-2 time series data to map eight dominant tree species in Germany. ForestFormer’s dual-branch network with spectral and spatial attention modules improves classification by highlighting species-specific characteristics. Cross-validation in 2,364 National Forest Inventory plots shows that ForestFormer achieves species classification accuracy ranging from 69% to 92%, with an average accuracy of 84%, outperforming existing baseline methods. The developed ForestFormer model can help generate a large-scale and reliable tree species map for Germany, which in turn provides crucial insights into the diverse characteristics of tree species to support forest management. Our analysis of results shows that Pine is the species most resistant to disturbances, while Douglas fir is the least. Northeastern regions of Germany exhibit particularly low levels of forest biodiversity, especially in the states of Brandenburg and Berlin, followed by neighboring states such as Sachsen–Anhalt, Mecklenburg–Vorpommern, Sachsen, and Niedersachsen. In addition, climatic factors, especially water deficit, are shown to play a very important role in determining tree species distribution patterns, followed by topographic and soil factors. These findings are anticipated to provide a critical basis for environmental policy formulation, particularly in forest management strategies responding to ongoing climate change.