Landslide Hazard Assessment Combined with InSAR Deformation: A Case Study in the Zagunao River Basin, Sichuan Province, Southwestern China

Abstract

Landslides are common natural disasters that cause serious damage to ecosystems and human societies. To effectively prevent and mitigate these disasters, an accurate assessment of landslide hazards is necessary. However, most traditional landslide hazard assessment methods rely on static assessment factors while ignoring the dynamic changes in landslides, which may lead to false-positive errors in the assessment results. This paper presents a novel landslide hazard assessment method for the Zagunao River basin, China. In this study, an updated landslide inventory was obtained for the Zagunao River basin using data from interferometric synthetic aperture radar (InSAR) and optical images. Based on this inventory, a landslide susceptibility map was developed using a random forest algorithm. Finally, an evaluation matrix was created by combining the results of deformation rates from both ascending and descending data to establish a hazard level that considers surface deformation. The method presented in this study can reflect recent landslide hazards in the region and produce dynamic assessments of regional landslide hazards. It provides a basis for the government to identify and manage high-risk areas.

Research on a Multi-Dimensional Indicator Assessment Model for Evaluating Landslide Risk near Large Alpine Reservoirs

“…For the 1200 sample datasets categorized by risk level, the output label, d i , is denoted as 1 for slopes at the alarm level and 0 for slopes at other levels. The corresponding Boolean result matrix is represented as D, as illustrated in Equation (6).…”

“…Larger values represent a higher impact of the indicator on landslides. (6) We can determine the gray relation coefficients between each evaluation indicator and landslides based on matrix B and matrix D, as shown in Equation (7).…”

“…The authors of [5] used the XGBoost algorithm to analyze 24 landslide impact factors and identified distance to roads, slope length, and lithology as the primary causative factors in urban areas, all of which increase the probability of landslide occurrence by about 10% compared with a single impact variable. The authors of [6] selected nine factors to predict the sensitivity of slope landslides and combined them with ArcGIS technology to make a dynamic risk assessment of the monitoring area. The authors of [7] evaluated landslide danger and susceptibility using twelve hazard factors and nine vulnerable evaluation factors.…”