Probabilistic estimation of vegetation greenness recovery timeline in a landslide-prone region affected by the 2008 Wenchuan earthquake

作者：Mingxuan Wan, Jiujiang Wu, Yi Yang, Upama Koju, Wei Zhao · 发表于：Environmental and Sustainability Indicators · 年份：2026 · DOI：10.1016/j.indic.2026.101300 · 被引用次数：1 · 研究领域：Ecosystem dynamics and resilience、Remote Sensing in Agriculture、Landslides and related hazards

Seismically triggered landslides disrupt montane ecosystems, where vegetation recovery is critical to ecological resilience and mitigating secondary hazards. However, the theoretical potential for vegetation regrowth remains poorly quantified. To quantitatively investigate this recovery process, this study proposes a survival-analysis-based inference framework to characterize and evaluate a newly introduced metric: the Potential Vegetation Greenness Recovery Timeline (Potential-VGRT). Unlike traditional regression or other machine learning-based estimation approaches, this study employs the Random Survival Forests (RSF) model to integrate both recovered events and right-censored observations (unrecovered areas), thereby capturing the observed spectrum of recovery dynamics within the observed environmental conditions. Focusing on Maoxian County—a region heavily impacted by the 2008 Wenchuan earthquake, the RSF-based estimation model demonstrated robust spatial discrimination (C-index = 0.82; Mean Time-dependent AUC = 0.89), effectively characterizing spatial gradients between rapid regeneration and arrested succession. To investigate the drivers of Potential-VGRT, we applied Shapley Additive Explanations (SHAP) to quantify both global nonlinear responses and spatially explicit local contributions. SHAP revealed strong nonlinear threshold responses and interactions among precipitation, temperature, solar radiation, and potential evapotranspiration, while spatial mapping of SHAP...