Spatial Pattern of Extreme Rainfall-Induced Forest Aboveground Biomass Loss and Its Influencing Factors in the Tableland-Gully Region of the Loess Plateau, China

Extreme rainfall-induced forest aboveground biomass (AGB) loss poses a serious threat to ecosystem stability and carbon stocks amid intensifying climate extremes. However, quantitative assessments of this loss remain scarce, and the spatial patterns of biomass loss and the nonlinear effects of its controlling factors have been insufficiently explored. This study used a typical extreme rainfall event (26–29 July 2023) in the tableland-gully region of the Loess Plateau as a case study to quantify the spatial patterns of forest AGB loss and to disentangle the nonlinear responses of its controlling factors. GF-7 (0.65 m) and GF-2 (1 m) imagery, combined with band differencing and object-based image analysis (OBIA), were used to identify forest AGB loss patches. To enable patch-level loss estimation, the 30 m AGB dataset was statistically downscaled to 1 m. Global Moran’s I and Getis-Ord Gi* statistics were applied to characterize spatial clustering, and an XGBoost model coupled with Shapley Additive Explanations (SHAP) was employed to identify dominant predictors and their nonlinear responses. This event triggered a total of 67,855 forest AGB loss patches (overall accuracy = 0.93; F1 score = 0.93), with a cumulative area of 17.29 km2 and a total loss of 119,825.38 Mg. AGB loss exhibited significant spatial clustering, displaying a west-high–east-low gradient consistent with rainfall distribution. Cumulative rainfall was the dominant predictor; the median grain size of the Last Glacial Maximum loess unit (L1-1 MD), elevation, proximity to roads and rivers, fractional vegetation cover (FVC), and slope aspect contributed additional spatial variation through distinct nonlinear relationships. These findings provide a quantitative basis for event-scale assessment of extreme rainfall-induced forest carbon loss and for informing vegetation restoration and carbon conservation strategies under climate extremes.