Switzerland map shows detailed mix of tree species from aerial images
National-scale mapping of tree species composition in Switzerland from high-resolution aerial imagery and multimodal data
Computer Vision and Pattern Recognition
Summary
Forests are made up of many tree species, which affect how healthy and strong they are against things like drought or pests. Most maps only show the main tree type in each area and miss the smaller species. The authors created a detailed map of Switzerland’s main tree species using high-resolution aerial pictures, laser scans, satellite data, and climate info. Their model can tell the share of different tree types in small patches of forest, offering a more complete picture than before. This helps with better forest care and planning for climate changes.
What this means in practice
- •For forest management teams: Map detailed mixtures of tree species to improve management of mixed forests under climate change stress.
- •For environmental monitoring agencies: Use spatially continuous species-share maps for national forest monitoring to better track ecosystem health over time.
Authors
Yixin Zhou, Luca Ferrari, Verena C. Griess, Nataliia Rehush, Konrad Schindler, Mirela Beloiu
Abstract
Tree-species composition shapes forest productivity and how forests respond to climate disturbances such as drought, windthrow and insect outbreaks. Yet most national forest maps assign each pixel a single dominant species and cannot show the share of accompanying species. We mapped the basal-area shares of ten main tree species and a residual Other class across Switzerland at 30 m resolution. A deep-learning model combining 0.1 m aerial RGB imagery, airborne laser scanning (ALS), annual AlphaEarth satellite embeddings, and climate variables was trained on basal-area observations from 3,992 Swiss National Forest Inventory (NFI) sites. In spatial cross-validation, the model reached a macro-$R^2$ of 0.485 across the 11 classes. On an independent forest-reserve dataset, the map reached a macro-$R^2$ of 0.552 for individual 30 m pixels and 0.733 for plots, and identified the dominant class in 73.2% of pixels and 81.1% of plots. The map shows maple and ash as widespread minor components that dominant-class maps omit. It also reproduces the elevational sequence from broadleaved lowland stands through spruce to larch and Arolla pine near the treeline. By resolving the species mixture within each 30 m pixel, the map complements field inventory statistics with spatially continuous information that can support forest monitoring, the management of mixed stands, and climate-adaptation planning across Switzerland.