{"title":"Site selection for landslide GNSS monitoring stations using InSAR and UAV photogrammetry with analytical hierarchy process","authors":"","doi":"10.1007/s10346-023-02188-3","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>Site selection for global navigation satellite system (GNSS) monitoring stations is the primary task in landslide GNSS monitoring work, and the selected site directly determines the monitoring effect of landslide deformation. Currently, the method for selecting site locations predominantly relies on the manual judgment of geological disaster experts by site inspections, which is experimental and labor-intensive. In this study, we propose an alternative site selection method on GNSS landslide monitoring station aided by interferometric synthetic aperture radar (InSAR) and unmanned aerial vehicle (UAV) photogrammetry methodology. Firstly, InSAR technology is used to obtain historical deformation data of the landslide area as prior information. Then, high-resolution digital surface model (DSM) and digital orthophoto map (DOM) are obtained by using UAV photogrammetric methodology, and relevant site selection criteria such as slope, aspect, surface roughness index, and vegetation index are extracted. Finally, the analytic hierarchy process (AHP) method is applied to evaluate and quantify the suitability of GNSS station site selection at various positions in the landslide area. The suitability of GNSS monitoring station site selection was evaluated in an experimental area located in the Heifangtai landslide in Northwest China’s Gansu province. Seven evenly and reasonably distributed locations with suitability values exceeding 0.67 were recommended as potential sites for the construction of GNSS monitoring stations, which can meet the requirements for their establishment. With less manual intervention, this method provides quantitative and intuitive representation of suitability distribution, which make GNSS site selection more intelligent.</p>","PeriodicalId":17938,"journal":{"name":"Landslides","volume":"46 1","pages":""},"PeriodicalIF":5.8000,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Landslides","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s10346-023-02188-3","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, GEOLOGICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Site selection for global navigation satellite system (GNSS) monitoring stations is the primary task in landslide GNSS monitoring work, and the selected site directly determines the monitoring effect of landslide deformation. Currently, the method for selecting site locations predominantly relies on the manual judgment of geological disaster experts by site inspections, which is experimental and labor-intensive. In this study, we propose an alternative site selection method on GNSS landslide monitoring station aided by interferometric synthetic aperture radar (InSAR) and unmanned aerial vehicle (UAV) photogrammetry methodology. Firstly, InSAR technology is used to obtain historical deformation data of the landslide area as prior information. Then, high-resolution digital surface model (DSM) and digital orthophoto map (DOM) are obtained by using UAV photogrammetric methodology, and relevant site selection criteria such as slope, aspect, surface roughness index, and vegetation index are extracted. Finally, the analytic hierarchy process (AHP) method is applied to evaluate and quantify the suitability of GNSS station site selection at various positions in the landslide area. The suitability of GNSS monitoring station site selection was evaluated in an experimental area located in the Heifangtai landslide in Northwest China’s Gansu province. Seven evenly and reasonably distributed locations with suitability values exceeding 0.67 were recommended as potential sites for the construction of GNSS monitoring stations, which can meet the requirements for their establishment. With less manual intervention, this method provides quantitative and intuitive representation of suitability distribution, which make GNSS site selection more intelligent.
期刊介绍:
Landslides are gravitational mass movements of rock, debris or earth. They may occur in conjunction with other major natural disasters such as floods, earthquakes and volcanic eruptions. Expanding urbanization and changing land-use practices have increased the incidence of landslide disasters. Landslides as catastrophic events include human injury, loss of life and economic devastation and are studied as part of the fields of earth, water and engineering sciences. The aim of the journal Landslides is to be the common platform for the publication of integrated research on landslide processes, hazards, risk analysis, mitigation, and the protection of our cultural heritage and the environment. The journal publishes research papers, news of recent landslide events and information on the activities of the International Consortium on Landslides.
- Landslide dynamics, mechanisms and processes
- Landslide risk evaluation: hazard assessment, hazard mapping, and vulnerability assessment
- Geological, Geotechnical, Hydrological and Geophysical modeling
- Effects of meteorological, hydrological and global climatic change factors
- Monitoring including remote sensing and other non-invasive systems
- New technology, expert and intelligent systems
- Application of GIS techniques
- Rock slides, rock falls, debris flows, earth flows, and lateral spreads
- Large-scale landslides, lahars and pyroclastic flows in volcanic zones
- Marine and reservoir related landslides
- Landslide related tsunamis and seiches
- Landslide disasters in urban areas and along critical infrastructure
- Landslides and natural resources
- Land development and land-use practices
- Landslide remedial measures / prevention works
- Temporal and spatial prediction of landslides
- Early warning and evacuation
- Global landslide database