GIS-Based Landslide Susceptibility Mapping of the Circum-Baikal Railway in Russia Using UAV Data

The investigation of hard-to-reach areas that are prone to landslides is challenging. The research of landslide hazards can be significantly advanced by using remote sensing data obtained from an unmanned aerial vehicle (UAV). Operational acquisition and high detail are the advantages of UAV data. T...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2021-09, Vol.13 (18), p.3629
Main Authors: Gantimurova, Svetlana, Parshin, Alexander, Erofeev, Vladimir
Format: Article
Language:English
Subjects:
Aerial surveys
AHP
Algorithms
Analytic hierarchy process
Cameras
Decision making
Geographic information systems
Geological hazards
GIS
Hazardous areas
Heuristic methods
landslide susceptibility mapping
Landslides
Landslides & mudslides
Photogrammetry
Remote sensing
Satellites
Spatial data
UAV
Unmanned aerial vehicles
Vegetation
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Summary:The investigation of hard-to-reach areas that are prone to landslides is challenging. The research of landslide hazards can be significantly advanced by using remote sensing data obtained from an unmanned aerial vehicle (UAV). Operational acquisition and high detail are the advantages of UAV data. The development of appropriate automated algorithms and software solutions is necessary for quick decision-making based on the received heterogeneous spatial data characterising various aspects of the environment. This article introduces the first phase of a long-term study about landslide detection and prediction that aims to develop an automatic algorithm for detecting potentially hazardous landslide areas, using data obtained by UAV surveys. As a part of the project, the selection of appropriate techniques was implemented and a landslide susceptibility (LS) map of the study site was developed. This paper presents the outcomes of the applied indirect heuristic approach of landslide susceptibility assessment using an analytical hierarchy process (AHP) in a GIS environment, based on UAV data. The results obtained have been tested on a real-world entity.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs13183629