Fusing Multi-Source Remote Sensing Data and MGWR to Unravel Spatial Heterogeneity of Bamboo Forest Carbon Stocks in Mountainous Regions: A Case from Zixi, China

Quantifying mountain forest carbon stocks and elucidating their spatially heterogeneous driving mechanisms are both critical for terrestrial carbon management under the global carbon neutrality agenda. Conventional single-source remote sensing approaches can neither fully exploit multi-source data synergies nor adequately resolve spatial heterogeneity in complex terrains. This study develops an integrated framework combining multi-source remote sensing classification, InVEST-based carbon estimation, and multiscale geographically weighted regression (MGWR) and applies it to Zixi County, a subtropical mountainous bamboo-abundant region in southeastern China. Sentinel-2 imagery, PlanetScope data, and DEM derivatives were fused with an optimized Random Forest classifier, achieving an overall accuracy of 0.8565 (Kappa = 0.7065). Carbon stocks were then estimated via the InVEST model. MGWR analysis (adjusted R2 = 0.930, AICc = 594.032) substantially outperformed the global OLS model (adjusted R2 = 0.795, AICc = 1717.450), confirming strong spatial non-stationarity across all drivers. Canopy density exhibited the strongest positive local effect (coefficient range: 0.343–0.768); slope position showed predominantly negative regulation with localized positive reversals (−0.778 to 0.270); elevation displayed a broad-scale positive gradient (0.133–0.140); and total vegetation cover exhibited bidirectional effects (−0.134 to 0.208) with pronounced east–west divergence. This framework not only provides a robust methodological reference for carbon stock assessment in complex mountain landscapes but also supports targeted forest management and carbon sequestration strategies through spatially explicit driver identification.