Identifying recurrent and persistent landslides using satellite imagery and deep learning: A 30-year analysis of the Himalaya

Chen, Tzu-Hsin Karen; Kincey, Mark E.; Rosser, Nick J.; Seto, Karen C.

Abstract

This paper presents a remote sensing-based method to efficiently generate multi-temporal landslide inventories and identify recurrent and persistent landslides. We used free data from Landsat, nighttime lights, digital elevation models, and a convolutional neural network model to develop the first multi-decadal inventory of landslides across the Himalaya, spanning from 1992 to 2021. The model successfully delineated >265,000 landslides, accurately identifying 83 % of manually mapped landslide areas and 94 % of reported landslide events in the region. Surprisingly, only 14 % of landslide areas each year were first occurrences, 55–83 % of landslide areas were persistent and 3–24 % had reactivated. On average, a landslide-affected pixel persisted for 4.7 years before recovery, a duration shorter than findings from small-scale studies following a major earthquake event. Among the recovered areas, 50 % of them experienced recurrent landslides after an average of five years. In fact, 22 % of landslide areas in the Himalaya experienced at least three episodes of landslides within 30 years. Disparities in landslide persistence across the Himalaya were pronounced, with an average recovery time of 6 years for Western India and Nepal, compared to 3 years for Bhutan and Eastern India. Slope and elevation emerged as significant controls of persistent and recurrent landslides. Road construction, afforestation policies, and seismic and monsoon activities were related to changes in landslide patterns in the Himalaya.

Citation

Chen, T. K., Kincey, M. E., Rosser, N. J., & Seto, K. C. (2024). Identifying recurrent and persistent landslides using satellite imagery and deep learning: A 30-year analysis of the Himalaya. Science of the Total Environment, 922, Article 171161. https://doi.org/10.1016/j.scitotenv.2024.171161

• Journal Article Type: Article
• Acceptance Date: Feb 19, 2024
• Online Publication Date: Feb 21, 2024
• Publication Date: Apr 20, 2024
• Deposit Date: Feb 26, 2024
• Publicly Available Date: Feb 28, 2024
• Journal: Science of The Total Environment
• Print ISSN: 0048-9697
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 922
• Article Number: 171161
• DOI: https://doi.org/10.1016/j.scitotenv.2024.171161
• Keywords: Landslide evolution, Machine learning, Spatiotemporal analysis, Multi-temporal, Landslide inventory, Vegetation recovery
• Public URL: https://durham-repository.worktribe.com/output/2287543