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Utilizing Remote Sensing and Random Forests to Identify Optimal Land Use Scenarios and Address the Increase in Landslide Susceptibility

Author

Listed:
  • Aditya Nugraha Putra

    (Department of Soil Science, Faculty of Agriculture, Brawijaya University, Malang 65145, Indonesia)

  • Jaenudin

    (Department of Soil Science, Faculty of Agriculture, Brawijaya University, Malang 65145, Indonesia)

  • Novandi Rizky Prasetya

    (Department of Soil Science, Faculty of Agriculture, Brawijaya University, Malang 65145, Indonesia)

  • Michelle Talisia Sugiarto

    (Department of Soil Science, Faculty of Agriculture, Brawijaya University, Malang 65145, Indonesia)

  • Sudarto

    (Department of Soil Science, Faculty of Agriculture, Brawijaya University, Malang 65145, Indonesia)

  • Cahyo Prayogo

    (Department of Soil Science, Faculty of Agriculture, Brawijaya University, Malang 65145, Indonesia)

  • Febrian Maritimo

    (Geospatial Information Agency of Indonesia, Bogor 16911, Indonesia)

  • Fandy Tri Admajaya

    (Geospatial Information Agency of Indonesia, Bogor 16911, Indonesia)

Abstract

Massive land use changes in Indonesia driven by deforestation, agricultural expansion, and urbanization have significantly increased landslide susceptibility in upper watersheds. This study focuses on the Sumber Brantas and Kali Konto sub-watersheds where rapid land conversion has destabilized slopes and disrupted ecological balance. By integrating remote sensing, Cellular Automata-Markov (CA-Markov), and Random Forest (RF) models, the research aims to identify optimal land use scenarios for mitigating landslide hazards. Three scenarios were analyzed: business as usual (BAU), land capability classification (LCC), and regional spatial planning (RSP) using 400 field-validated landslide data points alongside 22 topographic, geological, environmental, and anthropogenic parameters. Land use analysis from 2017 to 2022 revealed a 1% decline in natural forest cover, which corresponded to a 1% increase in high and very high landslide hazard areas. From 2017 to 2022, landslide risk increased as the “High” category rose from 33.95% to 37.59% and “Very High” from 10.24% to 12.18%; under BAU 2025, they reached 40.89% and 12.48%, while RSP and LCC reduced the “High” category to 44.12% and 34.44%, respectively. These findings highlight the critical role of integrating geospatial analysis and machine learning in regional planning to promote sustainable land use, reduce landslide hazards, and enhance watershed resilience with high model accuracy (>81%).

Suggested Citation

  • Aditya Nugraha Putra & Jaenudin & Novandi Rizky Prasetya & Michelle Talisia Sugiarto & Sudarto & Cahyo Prayogo & Febrian Maritimo & Fandy Tri Admajaya, 2025. "Utilizing Remote Sensing and Random Forests to Identify Optimal Land Use Scenarios and Address the Increase in Landslide Susceptibility," Sustainability, MDPI, vol. 17(9), pages 1-23, May.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:9:p:4227-:d:1650823
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    References listed on IDEAS

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    1. Yue Wang & Deliang Sun & Haijia Wen & Hong Zhang & Fengtai Zhang, 2020. "Comparison of Random Forest Model and Frequency Ratio Model for Landslide Susceptibility Mapping (LSM) in Yunyang County (Chongqing, China)," IJERPH, MDPI, vol. 17(12), pages 1-39, June.
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