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Characterization and Geomorphic Change Detection of Landslides Using UAV Multi-Temporal Imagery in the Himalayas, Pakistan

Author

Listed:
  • Naseem Ahmad

    (National Centre of Excellence in Geology, University of Peshawar, Peshawar 25000, Pakistan
    GIS and Space Applications in Geosciences (G-SAG), National Centre of GIS and Space Application (NCGSA), University of Peshawar, Peshawar 25000, Pakistan)

  • Muhammad Shafique

    (National Centre of Excellence in Geology, University of Peshawar, Peshawar 25000, Pakistan
    GIS and Space Applications in Geosciences (G-SAG), National Centre of GIS and Space Application (NCGSA), University of Peshawar, Peshawar 25000, Pakistan)

  • Mian Luqman Hussain

    (National Centre of Excellence in Geology, University of Peshawar, Peshawar 25000, Pakistan
    GIS and Space Applications in Geosciences (G-SAG), National Centre of GIS and Space Application (NCGSA), University of Peshawar, Peshawar 25000, Pakistan)

  • Fakhrul Islam

    (Department of Geology, Khushal Khan Khattak University, Karak 27200, Pakistan)

  • Aqil Tariq

    (Department of Wildlife, Fisheries and Aquaculture, College of Forest Resource, Mississippi State University, Mississippi State, MS 39762-9690, USA)

  • Walid Soufan

    (Plant Production Department, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, Riyadh 11451, Saudi Arabia)

Abstract

Multi-temporal unmanned aerial vehicle (UAV) imagery and topographic data were used to characterize and evaluate the geomorphic changes of two active landslides (Nara and Nokot) in Pakistan. Ortho-mosaic images and field-based investigations were utilized to assess the geomorphological changes, including the Topographic Wetness Index, slope, and displacement. Volumetric changes in specific areas of the landslides were measured using the Geomorphic Change Detection (GCD) tool. The depletion zone of the Nara landslide was characterized by failures of the main scarps, resulting in landslides causing erosional displacements exceeding 201.6 m. In contrast, for the Nokot landslide, the erosional displacement ranged from −201.05 m to −64.98 m. The transition zone of the slide experienced many slow earth flows that re-mobilized displaced material from the middle portion of the landslide, ultimately reaching the accumulation zone. Volumetric analysis of the Nara landslide indicated overall erosion of landslide material with a volume of approximately 4,565,274.96 m 3 , while the accumulated and surface-raising material volume was approximately 185,544.53 m 3 . Similarly, for the Nokot landslide, the overall erosion of landslide material was estimated to be 6,486,121.30 m 3 , with an accumulated volume and surface-raising material of 117.98 m 3 . This study has demonstrated the efficacy of the GCD tool as a robust and repeatable method for mapping and monitoring landslide dynamics with UAVs over a relatively long time series.

Suggested Citation

  • Naseem Ahmad & Muhammad Shafique & Mian Luqman Hussain & Fakhrul Islam & Aqil Tariq & Walid Soufan, 2024. "Characterization and Geomorphic Change Detection of Landslides Using UAV Multi-Temporal Imagery in the Himalayas, Pakistan," Land, MDPI, vol. 13(7), pages 1-28, June.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:7:p:904-:d:1419754
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    References listed on IDEAS

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    1. Haoyuan Hong & Himan Shahabi & Ataollah Shirzadi & Wei Chen & Kamran Chapi & Baharin Bin Ahmad & Majid Shadman Roodposhti & Arastoo Yari Hesar & Yingying Tian & Dieu Tien Bui, 2019. "Landslide susceptibility assessment at the Wuning area, China: a comparison between multi-criteria decision making, bivariate statistical and machine learning methods," 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. 96(1), pages 173-212, March.
    2. Suhua Zhou & Shuaikang Zhou & Xin Tan, 2020. "Nationwide Susceptibility Mapping of Landslides in Kenya Using the Fuzzy Analytic Hierarchy Process Model," Land, MDPI, vol. 9(12), pages 1-22, December.
    3. Lirong Yin & Lei Wang & Jianqiang Li & Siyu Lu & Jiawei Tian & Zhengtong Yin & Shan Liu & Wenfeng Zheng, 2023. "YOLOV4_CSPBi: Enhanced Land Target Detection Model," Land, MDPI, vol. 12(9), pages 1-17, September.
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