ArticleFrontiers in plant science2026
Regional forest stock volume mapping using GEDI-based interpolation, multi-source remote sensing, and a multi-level stacking ensemble model in complex terrain.
Article in Frontiers in plant science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Forest Stock Volume (FSV) is a key indicator of forest ecosystem functions, including productivity and carbon storage. Although spaceborne LiDAR such as GEDI provides high-resolution information on forest vertical structure, its discrete sampling pattern poses challenges for generating continuous spatial representations. Methods: This study developed an integrated framework for FSV estimation in Results: Among the tested interpolation methods, Sequential Gaussian Conditional Simulation (SGCS) performed best, and the SGCS-derived variables generally achieved R Discussion: These results demonstrate the value of combining interpolated GEDI structural information with multi-source remote-sensing predictors for regional FSV mapping in complex mountainous terrain. The resulting spatially explicit FSV map can provide useful support for forest inventory updating, productivity monitoring, and management prioritization.
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