Nationwide Tree Species Mapping in Germany With Forestformer: Assessing Forest Resilience and Unveiling Distribution Patterns
MCML Authors
Xiaoxiang Zhu
Prof. Dr.
Principal Investigator
Abstract
Xiaoxiang Zhu
Prof. Dr.
Principal Investigator
Abstract
Accurate information on tree species distribution is crucial for biodiversity assessment, effective forest management, and evidence-informed environmental policy-making. However, achieving high-resolution discrimination of tree species over large areas is challenging, especially in heterogeneous forest ecosystems where multiple species coexist, leading to spectral mixing and spatial complexity. To overcome these challenges, we developed a novel ForestFormer model using Sentinel-2 time series data for mapping eight dominant tree species (Beech, Oak, Other deciduous, Larch, Spruce, Pine, Fir, and Douglas fir) in Germany at 10 m resolution. ForestFormer employs a dual-branch network with attention modules in both spectral and spatial domains, enhancing classification accuracy effectively by highlighting key spectral and spatial characteristics unique to individual species. Cross validation on 9,456 National Forest Inventory (NFI) data plots indicates that the proposed ForestFormer achieves an overall average accuracy of 83.94%, outperforming several state-of-the-art methods. The developed ForestFormer model can aid in generating high-resolution tree species distribution maps for Germany, which in turn can provide crucial insights into the diverse characteristics of tree species. For instance, our analysis of results shows that the Pine is the species most resilient to disturbances, while Douglas fir is the least. Northeastern regions of Germany exhibit particularly low levels of 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, topographic, and soil factors are shown to play a very important role in determining tree species distribution patterns, followed by human activity factors. These findings are anticipated to provide a critical basis for environmental policy formulation, particularly in forest management strategies responding to ongoing climate change.
BibTeXKey: MGS+24a