Yixin Zhou

Swiss forest composition was previously shown as one dominant species per pixel, erasing the share of accompanying species. A deep-learning model anchored on 0.1 m aerial RGB imagery and combined with airborne laser scanning, annual AlphaEarth satellite embeddings and climate variables was trained on basal-area observations from 3,992 Swiss National Forest Inventory sites to output basal-area shares for ten main tree species and an Other class on a 30 m grid. Spatial cross-validation gave a macro coefficient of determination of 0.485; on forest-reserve data not used in model development, the macro value was 0.552 at 30 m pixel support and 0.733 at plot support, with dominant-class identification in 73.2% of pixels and 81.1% of plots.

Swiss forest composition was previously shown as one dominant species per pixel, erasing the share of accompanying species. A deep-learning model anchored on 0.1 m aerial RGB imagery and combined with airborne laser scanning, annual AlphaEarth satellite embeddings and climate variables was trained on basal-area observations from 3,992 Swiss National Forest Inventory sites to output basal-area shares for ten main tree species and an Other class on a 30 m grid. Spatial cross-validation gave a macro coefficient of determination of 0.485; on forest-reserve data not used in model development, the macro value was 0.552 at 30 m pixel support and 0.733 at plot support, with dominant-class identification in 73.2% of pixels and 81.1% of plots.