Integrating global canopy height models with satellite data for improved forest inventory in Ukraine

Accurate forest monitoring in Ukraine faces unprecedented challenges due to ongoing Russian aggression and limited access to traditional inventory methods, requiring robust satellite-based alternatives for National Forest Inventory operations. This study develops a comprehensive framework integrating state-of-the-art canopy height models with harmonized multi-sensor satellite data to achieve airborne LiDAR-comparable accuracy in forest structural parameter estimation.We evaluated six CHMs against 2,634 in-situ canopy height measurements across Ukraine. The most recent European-scale CHM by Pauls et al. (2025) demonstrated superior performance with R2 = 0.68 and RMSE = 4.65 m. Using this CHM as input alongside harmonized optical (Sentinel-2) and radar (Sentinel-1, ALOS PALSAR) observations, we developed a machine learning framework testing 20 algorithms to address whether satellite-derived canopy height enhances forest structural parameter estimation and whether country-trained models match regionally specific approaches. Feature importance analysis revealed CHM-derived height as the dominant predictor (45-60%), followed by Sentinel-2 shortwave infrared bands and vegetation indices. This approach achieved relative RMSE values of 8.8-12.3% across four forest structural parameters: growing stock volume (R2 = 0.77), basal area (R2 = 0.68), stand age (R2 = 0.53), and diameter at breast height (R2 = 0.48). Performance improvements of 49-144% were observed compared to models without height information, matching operational airborne LiDAR accuracy in Finland and USA.The framework successfully generates wall-to-wall forest inventory maps with robust transferability across diverse forest conditions. This research establishes satellite-based forest inventory as a viable alternative for conflict-affected regions and data-scarce environments worldwide.