兰州市主城区活动滑坡空间分布及其主导驱动因子识别

Spatial Distribution of Active Landslides and Identification of Dominant Driving Factor in the Main Urban Area of Lanzhou City

  • 摘要: 随着城市化进程的加快和极端气候事件频率的上升,城市区域活动滑坡灾害日益加剧,严重威胁人民生命财产安全。以甘肃省兰州市主城区为例,开展活动滑坡识别与主导驱动因子分析研究。首先,基于通用型大气改正在线服务(generic atmospheric correction online service for InSAR,GACOS)辅助下的干涉影像堆叠技术(interferometric synthetic aperture radar stacking,InSAR Stacking)获取视线向地表形变速率,并将其投影到沿斜坡方向,通过聚类方法自动识别活动滑坡,用于构建活动滑坡密度图;其次,综合选取地形地貌、地质、气候水文、土地覆被与人类工程活动等影响因素,开展斯皮尔曼相关性分析与多重共线性诊断,剔除冗余变量,保留具有较高独立性的驱动因子;最后,利用优势分析方法,定量评估各驱动因子对活动滑坡空间分布的主导程度。结果表明,兰州市主城区共有活动滑坡331处,呈空间集聚分布,主要集中在关山沟-徐家湾、晏家坪、阿干镇柳树湾和大砂坪-福儿沟区域。优势分析结果表明,距河流距离、人类活动强度和坡度是影响兰州市主城区活动滑坡空间分布排名前三的驱动因子。该研究成果有助于揭示黄土高原城市区活动滑坡空间分布机制,可为兰州市滑坡灾害防控和城市规划提供科学支撑。

     

    Abstract: Objectives: With accelerating urbanization and the increasing frequency of extreme climate events, active landslides in urban areas have intensified, posing serious threats to human life and property. This study focuses on the main urban area of Lanzhou City, Gansu Province, to identify active landslides and analyze their dominant driving factors. Methods: Firstly, surface deformation rates in the satellite radar line of sight (LOS) were derived using generic atmospheric correction online service for InSAR (GACOS) assisted InSAR Stacking, and then projected along the slope direction. Active landslides were automatically identified through clustering methods, and an active landslide density map was constructed. Secondly, a comprehensive analysis of driving factors, including topography, geology, climate, hydrology, land cover, and anthropogenic activities, was conducted. Spearman correlation analysis and multicollinearity diagnostics were performed to identify and eliminate redundant variables, while retaining those with high independence. Finally, dominance Analysis was employed to quantitatively evaluate the relative importance of each factor in controlling the spatial distribution of active landslides. Results: A total of 331 active landslides were detected in the main urban area of Lanzhou, exhibiting clustered spatial distribution patterns, primarily concentrated in the Guanshangou–Xujiawan, Yanjia Ping, Agan Town–Liushuwan, and Dashaping–Fuergou regions. Dominance analysis revealed that distance to rivers, human activity intensity index (HAII), and slope are the top three driving factors influencing the spatial distribution of active landslides in the study area. Conclusions: These findings help clarify the spatial distribution mechanisms of active landslides in loess-covered urban areas, providing scientific support for landslide disaster mitigation and urban planning in Lanzhou City.

     

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