Forest Digital Terrain Model from Copernicus GLO-30 DEM and GEDI L2A Using an Explainable Boosting Machine

Accurate ground elevation beneath forest canopies is important for terrain and forest-structure analysis, yet satellite-based elevation data can be affected by canopy and terrain conditions, while spaceborne light detection and ranging (LiDAR) provides ground elevations only at discrete locations. Here, the elevation offset between the Copernicus GLO-30 DEM (CopDEM) and Global Ecosystem Dynamics Investigation (GEDI) L2A ground elevation was estimated using an Explainable Boosting Machine (EBM) with topographic, canopy-structure, and CopDEM-uncertainty predictors to generate continuous forest digital terrain models (DTMs) for two regions in the Republic of Korea. Validation against independent airborne LiDAR data showed root mean square errors (RMSEs) of the proposed DTM of 4.10 m in the central-western region and 3.67 m in the eastern region. In the EBM, the CopDEM height error mask (HEM), canopy cover, and aspect contributed most to the estimated offset, and the magnitude and direction of their contributions varied across canopy–terrain conditions and between regions. Residual bias increased with canopy height and became more negative on steep slopes, where RMSE also increased. These results show that the proposed approach can generate continuous forest DTMs while quantifying the contributions of the main predictors to the offset, their variation across environmental conditions, and the conditions under which residual errors increase.