Extraction of Landslide Information Based on Object-Oriented Approach and Cause Analysis in Shuicheng, China

In China, landslides are abundant, widespread, and regular, destroying villages and agriculture and sometimes posing a threat to people's lives. The question of how to rapidly detect and attain landslide data is a significant topic of research, yet traditional measurement using medium-resolution remote sensing data is problematic. Object-oriented categorization is utilized in this research to extract landside data from high-resolution GF-1 and Sentinel-2 data. Data preprocessing begins with orthophoto correction, image matching, and data fusion, followed by band enhancement, which comprises band synthesis, principal component analysis, and filtering, and finally landside extraction using an object-oriented technique. The impact of geology, lithology, rainfall, and human activities on the occurrence of landslides in the study area is explored utilizing DEM data, visualization tools, remote sensing interpretation map, and other associated data. The studies are conducted in Shuicheng County, Guizhou Province, China, with a segmentation scale of 25 pixels and 14 classification feature parameters. Following that, the landslide mass is extracted and categorization findings of nearby characteristics are acquired. Finally, the destructiveness of the landslide is determined by comparing the results of object-oriented classification before and after the landslide. With a Kappa coefficient of 0.76 and a landslide extraction accuracy of 79.8%, the overall classification accuracy is 87%. Combined with the geological structure, rock lithology, spatial location, landslide occurrence process, elevation of the study area, precipitation and the impact of human activities, the causes of the landslide are discussed and analyzed. The early warning of other unknown landslides can be obtained by analyzing the features of the aforementioned components.

Automated summary

This study utilizes object-oriented classification to extract landslide data from high-resolution remote sensing data and explores the impact of geology, lithology, rainfall, and human activities on landslide occurrence. The study found a Kappa coefficient of 0.76, landslide extraction accuracy of 79.8%, and an overall classification accuracy of 87%. The causes of landslides are discussed and early warning information for unknown landslides can be obtained through feature analysis.