Rapid post-landslide vegetation regrowth detected by multi-temporal satellite imagery in the Southern part of Mt. Rinjani National Park, Lombok, Indonesia

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Novia Lusiana, Galuh Egalita Adliya, Luhur Akbar Devianto, Nur Azuan Husin

2026 Natural Hazards Vol. 122 Issue 5 Article Cited by 0

Abstract

Landslides frequently produce significant damage to the vegetation ecosystem, consequently reducing slope stability. Monitoring the periods of vegetation recovery following landslide events is crucial for assessing the re-establishment of slope stability. While previous studies have investigated vegetation recovery periods in non-tropical regions, limited studies have been conducted in tropical environments such as Indonesia. This study aims to estimate the duration for the full recovery of vegetation after landslide occurrences and to determine the most affecting factors in vegetation recovery in the southern part of Mount Rinjani National Park (MNRP), Lombok. Landslide areas in MNRP were identified by Google Earth, and vegetation recovery was assessed using Normalized Difference Vegetation Index (NDVI) derived from Sentinel 2 imagery from 2018 to 2024. Elevation, slope gradient, and slope aspects were selected as potential contributing factors in vegetation recovery. Based on linear regression and s-logistic regression analysis, the results indicate that approximately 6 years are required for full vegetation recovery, shorter than reported periods in non-tropical regions. Decision tree analysis identified elevation as the most contributing factor in vegetation recovery. Elevation of 1667 to 1863 m with slope gradients less than 18.89° indicates experiencing rapid vegetation recovery. Additionally, slope aspects facing north, south, southeast, and southwest tend to be more conducive to vegetation regrowth. © The Author(s), under exclusive licence to Springer Nature B.V. 2026.

Affiliations

Environmental Engineering Study Program, Faculty of Agricultural Technology, Universitas Brawijaya, Malang, 65145, Indonesia; Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Selangor, Serdang, 43400, Malaysia; Smart Farming Technology Research Centre, Universiti Putra Malaysia, Serdang, Selangor, 43400, Malaysia; Institute of Plantation Studies, Universiti Putra Malaysia, Serdang, Selangor, 43400, Malaysia