Distribution laws of landslides and theirs influencing factors in the Qiaojia segment of Jinsha River, China
The landslide disaster database is a prerequisite for regional landslide disaster research, and summarizing and analyzing the distribution pattern and influencing factors of landslide disasters is of great significance for carrying out the susceptibility and hazard assessment. The study area is a ty...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co. Ltd.
2025-03-01
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| Series: | Natural Hazards Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666592124000519 |
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| Summary: | The landslide disaster database is a prerequisite for regional landslide disaster research, and summarizing and analyzing the distribution pattern and influencing factors of landslide disasters is of great significance for carrying out the susceptibility and hazard assessment. The study area is a typical southwest mountainous area, and geological disasters such as landslides are very serious. A total of 3573 landslides were identified after a combination of image interpretation and field investigation in an area of 8.4 km2.This paper conducted a spatial analysis to reveal the distribution laws of landslides and analyzed the relationship between landslide and 13 influencing factors such as elevation, slope, slope aspect, topographic relief, soil, land use, lithology, annual average rainfall, ground peak ground acceleration (PGA). It can be concluded that the landslide showed the characteristics of non-uniformity and zonal distribution. A statistics analysis indicates that landslides are significantly correlated with elevation, slope gradient, slope direction, distance from faults, lithology, rivers, highways and so on. Therefore, when constructing engineering in alpine and canyon areas, it is essential to avoid the areas with steep slopes, large height difference, active faults, and the area being distributed by soft or broken hard rock masses to reduce disaster risks. |
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| ISSN: | 2666-5921 |