Application of RS & GIS in Regional Landslides Susceptibility Mapping and Spatiotemporal Characteristics Analysis

Abstract

Landslide susceptibility mapping is one of the most important aspects of landslide early warning and prediction. Therefore, it is very necessary to improve the accuracy of landslide susceptibility assessment by focusing on the mapping results. Besides, as the obvious spatial and temporal heterogeneity of regional landslide susceptibility, revealing and mastering the spatial and temporal distribution characteristics of landslide susceptibility and analyzing their possible genetic mechanisms, have significant guidance for the development of landslide prevention measurements. With the integrated application of RS and GIS technology, firstly, this paper acquires and manages the landslide impact factors in the study area, then the regional landslide susceptibility zonation map is drawn, and finally, the spatiotemporal characteristics of the regional landslide susceptibility distribution are analyzed. It is shown that RS and GIS can play very important roles in the regional landslide susceptibility mapping and spatiotemporal characteristics analysis.

Debris Flow Prediction in Mountainous Terrain Using Remote Sensing and GIS: A Case Study of Al-Hada Road, Makkah Province, Saudi Arabia

“…This method involves collecting, integrating, and analyzing various data types and information from different sources, such as geological, geomorphological, hydrological, and meteorological domains. To better comprehend and delineate the susceptibility of debris flow in a spatial manner, datasets such as Sentinel-1 satellite imagery and digital elevation model (DEM) 12.5 are employed to generate and derive essential variables such as drainage density, slope angle, and aspect direction [16]. Moreover, raster data such as geology, soil type, precipitation rate, and land use/cover datasets are also incorporated and included to enhance the accuracy and reliability of the debris-flowsusceptibility mapping [17].…”

Landslide Prediction in Mountainous Terrain Using Remote Sensing and GIS: A Case Study of Al-Hada Road, Makkah Province, Saudi Arabia

“…This method involves collecting, integrating, and analyzing various data types and information from different sources, such as geological, geomorphological, hydrological, and meteorological domains. To better comprehend and delineate the susceptibility to landslides in a spatial manner, datasets such as Sentinel-1 satellite imagery and digital elevation model (DEM) 12.5 are employed to generate and derive essential variables such as drainage density, slope angle, and aspect direction [20]. Moreover, raster data such as geology, soil type, precipitation rate, and land use/cover datasets are also incorporated and included to enhance the accuracy and reliability of the landslide susceptibility mapping [21].…”