Miao Zelang, Xiong Yaopeng, Cheng Zhiwei, Wu Bin, Wang Wei, Peng Zuwu
School of Geoscience and Info-Physics, Central South University, Changsha 410083, China.
Laboratory of Geo-Hazards Perception, Cognition and Predication, Central South University, Changsha 410083, China.
Sensors (Basel). 2025 Jul 6;25(13):4221. doi: 10.3390/s25134221.
This study investigates the influence of root cohesion spatial heterogeneity on rainfall-induced landslide distribution across the Loess Plateau, addressing limitations in existing methods that oversimplify root reinforcement. Leveraging Landsat and GaoFen satellite images, we developed a regional root cohesion inversion model that quantifies spatial heterogeneity using tree height (derived from time series Landsat imagery) and above-ground biomass (from 30 m resolution satellite products). This approach, integrated with land use-specific hydrological parameters and an infinite slope stability model, significantly improves landslide susceptibility predictions compared to models ignoring root cohesion or using uniform assignments. High-resolution pre- and post-rainfall GaoFen satellite imagery validated landslide inventories, revealing dynamic susceptibility patterns: farmland exhibited the highest risk, followed by artificial and secondary forests, with susceptibility escalating post-rainfall. This study underscores the critical role of remote sensing-driven root cohesion mapping in landslide risk assessment, offering actionable insights for land use planning and disaster mitigation on the Loess Plateau.
本研究调查了根系凝聚力空间异质性对黄土高原降雨诱发滑坡分布的影响，解决了现有方法中过度简化根系加固作用的局限性。利用Landsat和高分卫星图像，我们开发了一个区域根系凝聚力反演模型，该模型使用树高（从Landsat时间序列图像中得出）和地上生物量（来自30米分辨率卫星产品）来量化空间异质性。与忽略根系凝聚力或使用统一赋值的模型相比，这种方法与特定土地利用的水文参数和无限斜坡稳定性模型相结合，显著提高了滑坡易发性预测。高分辨率降雨前后的高分卫星图像验证了滑坡清单，揭示了动态易发性模式：农田风险最高，其次是人工林和次生林，降雨后易发性增加。本研究强调了遥感驱动的根系凝聚力测绘在滑坡风险评估中的关键作用，为黄土高原的土地利用规划和减灾提供了可操作的见解。
