Abstract
The topography and landforms of Guizhou Province in China are complicated, and the climatic conditions of heavy precipitation make landslide disasters in Guizhou Province occur frequently. To avoid damage to people’s lives and economic property caused by disasters, a reliable early landslide identification method and landslide monitoring method are urgently needed. Traditional landslide identification and monitoring methods have limitations. InSAR technology has unique advantages in large-scale landslide identification and monitoring, but landslide identification results based on a single deformation value are one-sided. Therefore, this paper uses Sentinel-1A radar satellite image data and uses InSAR technology and optical remote sensing technology to carry out large-scale surface deformation monitoring and identification of dangerous deformation areas in Liupanshui City, Tongren City, Guiyang City and other regions in Guizhou Province. The potential landslide identification methods based on the time series normalized difference vegetation index and landslide development environment elements are combined to investigate hidden landslide hazards in the study area. In this paper, time series InSAR technology is used to monitor three key landslides in Jichang Town, Yujiaying and Fana, to grasp the movement status of the landslide in time. The method of landslide identification and monitoring in this paper is of great significance for disaster prevention and management in Guizhou Province.
Highlights
China is a country where geological disasters occur frequently
The terrain of Guizhou Province is low from west to East, carbonate rocks are widely distributed, and karst landscape is widely distributed
According to the analysis of geological environment factors, there is a great possibility of geological disasters in this province
Summary
China is a country where geological disasters occur frequently. In addition to earthquakes, landslides are one of the most natural disasters that threaten the lives and property of the country and people. The monitoring results obtained by these methods are all based on single-point measurement results, and require a lot of manpower and financial resources. These methods are high-risk and low-efficiency, and lack of continuous macromonitoring of disaster-prone areas. Optical remote sensing technology has the advantages of large coverage and recognition by human-computer interaction in identifying and monitoring landslides, and the accuracy of the recognition results is high [1]. There is an urgent need to explore methods for early identification and monitoring of landslides in a large area. InSAR technology and optical remote sensing technology are combined to identify and classify dangerous deformation zones. This article is of great significance for landslide disaster monitoring and early warning, regional disaster prevention and mitigation, and can provide technical reference for landslide disaster research in similar regions
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