Study on characteristics and impact of Kalikhola landslide, Manipur, NE India, using UAV photogrammetry

Rajat Kumar Pattanaik () and Yengkhom Kesorjit Singh ()
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Rajat Kumar Pattanaik: Indian Institute of Technology Bhubaneswar
Yengkhom Kesorjit Singh: Indian Institute of Technology Bhubaneswar

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2024, vol. 120, issue 7, No 20, 6417-6435

Abstract: Abstract Landslide is one of the natural calamities that severely affect society and economy, specifically in the mountainous regions. Monitoring of the possible landslide areas is essential to minimize the effect. Remote sensing and geographical information system are useful tools for landslide monitoring in order to prevent the loss associated with landslide. In recent years, the development of unmanned aerial vehicle (UAV) photogrammetry opened a new window in the field of geosciences for reconnaissance survey, mapping and hazard monitoring. In the present study, high-resolution aerial images are collected by DJI phantom 4 drone, with 24 megapixel camera, at an elevation of 70 m from the ground. From the aerial images, 3D point clouds, orthomosaic and DEM are generated, using Agisoft Photoscan software. The collected ground control points are used for georeferencing the model and for error estimation. The slope, slope direction and drainage pattern maps are prepared using ArcGIS. Landslide material spread over 0.365 m2 area with more than 1 million m3 of debris transported. However, the debris material is not completely washed out of the slope and stuck at the upper part of the village that has a chance of further slide. In such a devastating situation, the southern part of the Kangpokpi area could be affected in the future. With the help of this mapping and photogrammetric technique, the landslide can be monitored constantly so that future disasters can be prevented.

Keywords: Landslide; DEM; UAV; Structure from Motion; Photogrammetry (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11069-024-06484-6

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