Dept. of Civil Engineering, Punjab Engineering College, Sector 12, Chandigarh, 160012, India.
Environ Monit Assess. 2024 Feb 13;196(3):257. doi: 10.1007/s10661-024-12440-5.
Landslide susceptibility zonation (LSZ) mapping is used to delineate areas prone to landslides and is critical for effective landslide hazard management. The existing methodologies for generating such maps tend to neglect the influence of dynamic environmental variables on landslide occurrences, which may lead to obsolete and erroneous estimates of landslide susceptibility (LS) for a concerned area. Although recent studies have started to report the effects of Land Use/ Land Cover (LULC) variation on LSZ mapping, variations in other dynamic variables like rainfall, soil moisture, and evapotranspiration apart from LULC may also influence slope stability in mountainous regions. The present study investigates the impact of variations in these four variables on the LS distribution, of a selected Indian Himalayan region between 2017 and 2021. Random Forest (RF) susceptibility models are utilized for evaluating the LS for the selected years and geospatial technologies are employed for LS change detection. The results indicate up to 19% variations in the spatial extent for some of the zones of the generated LSZ maps. The research findings of this study are crucial since they reveal the impact of dynamic behavior on LS, which has not been previously documented in the literature.
滑坡易发性分区(LSZ)制图用于划定易发生滑坡的区域,对于有效进行滑坡灾害管理至关重要。现有的生成此类地图的方法往往忽略了动态环境变量对滑坡发生的影响,这可能导致对相关区域滑坡易发性(LS)的过时和错误估计。尽管最近的研究已经开始报告土地利用/土地覆盖(LULC)变化对 LSZ 制图的影响,但除了 LULC 之外,其他动态变量(如降雨、土壤湿度和蒸散)的变化也可能影响山区的边坡稳定性。本研究调查了这四个变量的变化对 2017 年至 2021 年期间选定的印度喜马拉雅地区 LS 分布的影响。随机森林(RF)易感性模型用于评估所选年份的 LS,并利用地理空间技术进行 LS 变化检测。结果表明,一些生成的 LSZ 地图区域的空间范围变化高达 19%。本研究的研究结果至关重要,因为它们揭示了动态行为对 LS 的影响,这在文献中尚未有记载。