Stratified AGB Inversion Driven by DGTHI: Quantifying Topographic Controls on Biomass Prediction Across Tree Species

Abstract Accurate forest aboveground biomass (AGB) estimation is crucial for global carbon cycle research. While existing studies have utilized topographic factors in remote sensing, they often fail to systematically quantify multi-dimensional heterogeneity or address species-specific responses. This study pioneers the application of the Digital Elevation Model (DEM) Grid Topographic Heterogeneity Index (DGTHI)—a composite metric integrating elevation variability, relief, surface roughness, and mean slope—to enhance AGB inversion models by explicitly accounting for terrain-vegetation interactions. Using airborne Light detection and ranging (LiDAR) and 8,804 field-measured trees in Mengyin County, Linyi City, Shandong Province, China, we developed a DGTHI-stratified modeling framework to dissect how topographic heterogeneity governs species-level AGB estimation accuracy at the county scale.Results demonstrate: (1) DGTHI outperformed conventional single-factor topographic corrections, with heterogeneity effects on feature selection following a species hierarchy: acacia > pine > cypress > poplar; (2) DGTHI-driven stratification significantly improved model accuracy, increasing R² by 0.08–0.17 versus unstratified models; (3) Spatial AGB patterns (27–217 t/ha in May 2023) revealed southwest–northeast highs and northwest–southeast lows, directly modulated by DGTHI-mapped heterogeneity. As the first integration of DGTHI into species-specific AGB inversion, this work provides a transferable paradigm for precision carbon mapping in topographically complex forests.