ORIGINAL RESEARCH article
Front. Environ. Sci.
Sec. Environmental Informatics and Remote Sensing
Volume 13 - 2025 | doi: 10.3389/fenvs.2025.1686605
Research on landslide susceptibility and ecological vulnerability assessment in western Sichuan region considering spatial heterogeneity and feature optimization: A Case Study of Luding County
Provisionally accepted
- 1Taiyuan University of Technology, Taiyuan, China
- 2Wanjiang University of Technology, Ma'anshan, China
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Landslide frequency and ecological fragility jointly constrain Luding's development. In order to clarify the distribution pattern and ecological hazards of regional landslides, this study first considers internal and external geodynamic factors and analyzes the spatial distribution pattern of landslides in Luding County. Secondly, the Geographical Detector Model (GDM) was employed to quantify the influence of various factors on the distribution of landslides. Then, the RX-Stacking ensemble learning model was utilized to assess landslide susceptibility in Luding County. Finally, using land use type changes, the weight of ecosystem service value, and landslide occurrence probability three key factors, this study quantified the ecological damage induced by landslides and constructed an evaluation model for landslide-induced ecological damage. Based on this model and the susceptibility assessment results, an ecological vulnerability assessment of Luding County was conducted. The following conclusions were drawn: (1) Landslides in Luding County are densely distributed, with an over-all distribution density of 0.19 sites/km²; (2) Rainfall, distance to fault zones, and Bouguer gravity anomaly gradient had the most significant influence on the distribution of land-slides, with q values of 0.24, 0.18, and 0.10, respectively; (3) Interactions between factors exhibit a nonlinear enhancement effect, with any two-factor synergy significantly surpassing the influence of individual factors on landslide spatial distribution. Among these interactions, the one between rainfall and distance from the fault zone exerts the greatest influence, with a q value of 0.37; (4) Compared with the Random Forest (RF) model and Extreme Gradient Boosting (XGBoost) model, the RX-Stacking ensemble learning model has an AUC of 0.926, and its landslide susceptibility evaluation is better than the other two models, with good generalization; (5) The distribution of landslide susceptibility levels and ecological vulnerability levels exhibits a high degree of consistency. High/extremely high vulnerability zones are predominantly clustered in the eastern region with prominent ecosystem service functions, while low/moderate vulnerability zones are mainly clustered in the western region with weaker ecosystem service capacities. The results offer a scientific foundation for landslide prevention and ecological management in the region.
Keywords: landslide 1, western Sichuan 2, geographical detector model 3, ensemble learning model 4, susceptibility assessment 5, ecological vulnerability6
Received: 15 Aug 2025; Accepted: 26 Sep 2025.
Copyright: © 2025 Li and Shen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Lu Shen, wt14049@wjut.edu.cn
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