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

Aditya Nugraha Putra (), Jaenudin, Novandi Rizky Prasetya, Michelle Talisia Sugiarto, Sudarto, Cahyo Prayogo, Febrian Maritimo and Fandy Tri Admajaya
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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

Sustainability, 2025, vol. 17, issue 9, 1-23

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%).

Keywords: land use changes; landslide susceptibility; Cellular Automata-Markov (CA-Markov); random forest models; sustainable spatial planning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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