Changes of surface recovery at coseismic landslides and their driving factors in the Wenchuan earthquake-affected area

Chen, Ming; Tang, Chuan; Li, Mingwei; Xiong, Jiang; Luo, Yuting; Shi, Qingyun; Zhang, Xian‐Zheng; Tie, Yu; Feng, Qiang

doi:10.1016/j.catena.2021.105871

Cited by 23 publications

(6 citation statements)

References 53 publications

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“…The 2008, the Wenchuan earthquake triggered large-scale collapses and landslides, resulting in significant damage to the ecosystem of the Qipan catchment. Over time, the ecosystem in the study area showed a gradual recovery trend [15,30]. In March 2013, a 4.5 magnitude earthquake further triggered many new landslides, providing a rich material source for debris flows.…”

Section: Landslide Interpretationmentioning

confidence: 97%

“…Therefore, many scholars have studied the regional stability of vegetation restoration after earthquakes and secondary disasters. For example, the vegetation restoration process in the Wenchuan earthquake area was predicted using the long-term sequence change in the NDVI as an indicator [29,30], but these studies ignored the internal factors influencing debris flow occurrence. If the characteristic factors of debris flow areas are included in regional stability assessments, the judgment and evaluation of debris flow susceptibility can be significantly improved.…”

Section: Introductionmentioning

confidence: 99%

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Variation in Debris-Flow-Prone Areas with Ecosystem Stability: A Case Study of the Qipan Catchment in the Wenchuan Earthquake Region

Zhan,

Hu,

Jing

et al. 2024

Sustainability

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The spatial distribution of vegetation in a basin has a far-reaching influence on the potential for sediment separation and transport capacity. However, many landslides induced by strong earthquakes have greatly changed the existing pattern, which further increases the probability of debris flow in a basin during heavy rainfall and has a significant impact on the stability of the basin. Thus, this study selected the debris flow basin in the Qipan catchment of the Wenchuan earthquake area as the research object. Multisource and high-precision remote sensing images were used to analyze the land use changes in the basin, and the index of connectivity (IC) was introduced to analyze the evolution of sediment transport capacity. An ecosystem stability assessment method suitable for post-earthquake debris flow basins was proposed. Through quantitative assessment of the ecosystem stability of the basin after the Wenchuan earthquake in 2008 and the two debris flow events after the earthquake, the dynamic relationship between the debris-flow-prone area and the ecosystem stability of the basin was revealed. The results showed that the stability of the ecosystem in the Qipan catchment increased annually, indicating a stable and substable state. The spatial distribution characteristics were lower in the north and south and greater in the middle. By comparing the evaluation results with the actual terrain change trend, the accuracy and feasibility of the evaluation method are verified. The results of this study provide a scientific basis for the formulation of regional disaster prevention strategies and help to accelerate the improvement of regional stability in debris-flow-prone areas.

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Section: Landslide Interpretationmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Variation in Debris-Flow-Prone Areas with Ecosystem Stability: A Case Study of the Qipan Catchment in the Wenchuan Earthquake Region

Zhan,

Hu,

Jing

et al. 2024

Sustainability

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“…( 9) Normalized Vegetation Index (NDVI) NDVI (vegetation index) reflects the development status of surface vegetation. The higher the vegetation index, the better the development of the surface vegetation, the greater the biomass, and the stronger the ability of the area to prevent wind damage, fix the soil, and resist the scouring of the surface by rainwater [37]. It is generally believed that the higher the vegetation index, the lower the probability of geological disasters [41] (Figure 2i).…”

Section: Data 221 Data Selection Basismentioning

confidence: 99%

“…There is a strong relationship between the occurrence of geological hazards and the lithology of the strata [37][38][39][40][41] (Figure 2d). On the one hand, lithology affects the development of joint fissures.…”

Section: (4) Stratigraphic Lithologymentioning

confidence: 99%

“…It is normal for the ranking of importance of the disaster, causing factors to vary between studies because of the differences in geological and environmental conditions in different study areas, as well as the differences in the intensity of human engineering activities and rainfall intensity. For example, Mingyong Liao et al (2022) found that rainfall, elevation, and lithology have significant effects on landslide occurrence when studying the influence of raster resolution on landslide susceptibility assessments [37]; Mohammed Amin Benbouras (2022) used a neural network algorithm in their landslide susceptibility assessment, and the results show that distance from a river is the most important factor affecting landslide susceptibility, while lithology and distance from the road also have some influence [50]. However, these studies are consistent in their general conclusions, in that they find that topography, rainfall, distance from the road, and slope are the main factors that induce landslide geological hazards [51], which conclusion is basically consistent with the results of this study.…”

Section: Differences In the Importance Of Disaster-causing Factorsmentioning

confidence: 99%

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Analysis of Geological Hazard Susceptibility of Landslides in Muli County Based on Random Forest Algorithm

Song

Chen

et al. 2023

Sustainability

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Landslides seriously threaten human life and property. The rapid and accurate prediction of landslide geological hazard susceptibility is the key to disaster prevention and mitigation. Traditional landslide susceptibility evaluation methods have disadvantages in terms of factor classification and subjective weight determination. Based on this, this paper uses a random forest model built using Python language to predict the landslide susceptibility of Muli County in western Sichuan and outputs the factor weight and model accuracy. The results show that (1) the three most important factors are elevation, distance from the road, and average annual rainfall, and the sum of their weights is 67.54%; (2) the model’s performance is good, with ACC = 99.43%, precision = 99.3%, recall = 99.48%, and F1 = 99.39%; (3) the landslide development and susceptibility zoning factors are basically the same. Therefore, this model can effectively and accurately evaluate regional landslide susceptibility. However, there are some limitations: (1) the landslide information statistical table is incomplete; (2) there are demanding requirements in terms of training concentration relating to the definition of landslide and non-landslide point sets, and the landslide range should be accurately delineated according to field surveys.

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Identifying potential debris flow hazards after the 2022 Mw 6.8 Luding earthquake in southwestern China

Chen,

Chang,

et al. 2024

Bull Eng Geol Environ

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Changes of surface recovery at coseismic landslides and their driving factors in the Wenchuan earthquake-affected area

Cited by 23 publications

References 53 publications

Variation in Debris-Flow-Prone Areas with Ecosystem Stability: A Case Study of the Qipan Catchment in the Wenchuan Earthquake Region

Variation in Debris-Flow-Prone Areas with Ecosystem Stability: A Case Study of the Qipan Catchment in the Wenchuan Earthquake Region

Analysis of Geological Hazard Susceptibility of Landslides in Muli County Based on Random Forest Algorithm

Identifying potential debris flow hazards after the 2022 Mw 6.8 Luding earthquake in southwestern China

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