High-Resolution Forest Biomass Mapping in Japan Using Canopy Height Estimation from Remote Sensing and Machine Learning

Continuous monitoring of forest biomass is indispensable for establishing transparent carbon budgets and ensuring sustainable forest management toward achieving carbon neutrality. While satellite data has traditionally been used for wide-area biomass estimation, signal saturation in high-biomass regions has posed a significant challenge to accuracy. To address this saturation issue and enhance the precision of carbon budget estimations, this study develops a new methodology for estimating forest above-ground biomass (AGB). First, a training dataset was constructed by integrating airborne LiDAR data from across Japan with various satellite datasets, such as PALSAR-2 and Sentinel-2. Machine learning (XGBoost) was then employed to generate a nationwide canopy height map, achieving a high coefficient of determination (R2=0.594). Subsequently, allometric equations with parameters optimized for specific forest types (evergreen coniferous, evergreen broadleaf, deciduous coniferous, and deciduous broadleaf) were derived from the relationship between estimated canopy height and AGB to create a nationwide AGB map. Validation results indicated that the resulting AGB map demonstrated higher estimation accuracy (R2=0.265) compared to existing global products (ESA CCI Biomass), with significant improvements in mitigating underestimation (saturation) in high-biomass areas. By combining canopy height estimation with forest-type-specific allometry, this approach enables high-precision mapping that reflects the unique characteristics of Japanese forests and is expected to contribute to more reliable carbon budget assessments.