Satellite light detection and ranging (LiDAR) technology enables efficient large-scale monitoring of forest canopy structure, which is crucial for understanding ecosystem dynamics and carbon cycling. The Global Ecosystem Dynamics Investigation (GEDI) mission provides unprecedented global coverage of forest canopy height measurements. However, GEDI-derived canopy height estimates and their spatial characteristics are not yet fully understood. In this study, we demonstrated that GEDI canopy height estimates exhibited significant spatial heterogeneity in their accuracy when compared to high-resolution airborne LiDAR data over the city of Nikko, Japan. We found that GEDI relative height metrics (RH98) correlated with airborne LiDAR canopy heights (r＝0.31), with mean absolute errors of 6.9 m. Importantly, we revealed substantial spatial variability in estimation errors using geographically weighted error metrics. Large overestimation errors were found in flat areas dominated by evergreen conifers, while underestimation occurred in steep terrain with deciduous conifers. Areas with deciduous broadleaf forests showed relatively small errors. These spatial patterns in accuracy were not captured by conventional global error metrics, highlighting the importance of local context when interpreting GEDI canopy height results. Our findings clarify how satellite LiDAR performs according to forest type and terrain, which provides crucial insights for improving global forest structure monitoring. This study introduces a novel spatial error assessment framework for satellite-derived forest metrics that can enhance our understanding of uncertainties in forest monitoring.
