Geospatial Foundational Model for Canopy Height Estimates Across Kenya's Ecoregions

Abstract

Maximizing carbon sequestration in trees across different ecoregions has the potential to support carbon markets while improving forest restoration and preserving biodiversity. Tree height is a predictor of carbon stored in its biomass, but estimating tree height using publicly available remote sensing datasets remains challenging. Artificial intelligence can play an important role in improving estimates of vegetation canopy height, especially in regions with limited local measurements. This study compares a transformer-based Geospatial Foundation Model (GFM) and a baseline deep learning model (U-Net) for predicting tree canopy height across Kenya's diverse ecoregions. The models use cloudfree mosaics from the Harmonized Landsat and Sentinel-2 (HLS) product as predictors and space-borne GEDI laser data for canopy height reference. Both models had similar root mean square error (RMSE) scores: GFM at 6.05 m and UNet at 5.80 m for the most prevalent small to medium-sized trees. In a second experiment, the models trained in Kenya were applied to a Mozambique study area. In this challenging set-up, GFM generalized better to the different ecoregions.