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Analysis of Changes in Landslide Susceptibility according to Land Use over 38 Years in Lixian County, China

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  • Jie Liu

    (Lanzhou Geophysical National Field Scientific Observation and Research Station, Lanzhou Geotechnical and Seismological Research Institute, Earthquake Administration, Earthquake Administration of Gansu Province, Lanzhou 730000, China)

  • Zhen Wu

    (Lanzhou Geophysical National Field Scientific Observation and Research Station, Lanzhou Geotechnical and Seismological Research Institute, Earthquake Administration, Earthquake Administration of Gansu Province, Lanzhou 730000, China)

  • Huiwen Zhang

    (State Key Laboratory Breeding Base of Desertification and Aeolian Sand Disaster Combating, Gansu Desert Control Research Institute, Lanzhou 730070, China)

Abstract

Landslides occur frequently in Lixian County, China, and land use has changed significantly in recent decades. We obtained land use data for the years 1980, 2000, and 2018, as well as three landslide susceptibility maps from a Random Forest model. Agricultural land, low coverage grassland, water area, and urban, rural and other construction land were prone to landslides. Landslide susceptibility was low in areas of woodland, moderate and high coverage grassland, bare rock land, desert and tundra. Areas with high landslide susceptibility were mainly located in the catchment of the study region, and a 2.61% decrease in high landslide susceptibility areas over the 38-year period was primarily driven by changes in agricultural and rural land. By contrast, a 1.42% increase in low landslide susceptibility areas over the 38-year period was driven by changes in moderate and high coverage woodland and moderate coverage grassland. There is a need for effective management measures to be implemented because areas with high landslide susceptibility are still present. We also found that human aggregations, or the absence of these, vary in their effects on the areas of Lixian County most susceptible to landslides.

Suggested Citation

  • Jie Liu & Zhen Wu & Huiwen Zhang, 2021. "Analysis of Changes in Landslide Susceptibility according to Land Use over 38 Years in Lixian County, China," Sustainability, MDPI, vol. 13(19), pages 1-23, September.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:19:p:10858-:d:646894
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    References listed on IDEAS

    as
    1. Yongwei Li & Xianmin Wang & Hang Mao, 2020. "Influence of human activity on landslide susceptibility development in the Three Gorges area," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(3), pages 2115-2151, December.
    2. Emmanouil Psomiadis & Andreas Papazachariou & Konstantinos X. Soulis & Despoina-Simoni Alexiou & Ioannis Charalampopoulos, 2020. "Landslide Mapping and Susceptibility Assessment Using Geospatial Analysis and Earth Observation Data," Land, MDPI, vol. 9(5), pages 1-26, April.
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    1. Parinaz Rashidi & Sopan D. Patil & Aafke M. Schipper & Rob Alkemade & Isabel Rosa, 2023. "Downscaling Global Land-Use Scenario Data to the National Level: A Case Study for Belgium," Land, MDPI, vol. 12(9), pages 1-19, September.
    2. Evelina Volpe & Stefano Luigi Gariano & Francesca Ardizzone & Federica Fiorucci & Diana Salciarini, 2022. "A Heuristic Method to Evaluate the Effect of Soil Tillage on Slope Stability: A Pilot Case in Central Italy," Land, MDPI, vol. 11(6), pages 1-15, June.
    3. Chelsea Dandridge & Thomas Stanley & Dalia Kirschbaum & Pukar Amatya & Venkataraman Lakshmi, 2023. "The influence of land use and land cover change on landslide susceptibility in the Lower Mekong River Basin," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 115(2), pages 1499-1523, January.
    4. Siti Norsakinah Selamat & Nuriah Abd Majid & Aizat Mohd Taib, 2023. "A Comparative Assessment of Sampling Ratios Using Artificial Neural Network (ANN) for Landslide Predictive Model in Langat River Basin, Selangor, Malaysia," Sustainability, MDPI, vol. 15(1), pages 1-21, January.

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