Intelligent site selection method for UAV-dropped GNSS landslide moni-toring equipment
The GNSS technology is widely used in landslide deformation monitoring.However,in complex and hazardous land-slide areas,it is difficult for personnel to access,and the installation of GNSS monitoring devices faces challenges.The UAV-dropped deployment technology offers a potential solution to this problem,but it requires appropriate target delivery locations for the UAVs.Traditional site selection methods mainly rely on expert field surveys,which fail to meet the requirements of such scenarios.To address this,this study first utilizes drone aerial photography and InSAR-Stacking technology to obtain dig-ital surface models(DSM),digital orthophoto maps(DOM),and surface deformation rate maps of the target site.Then,based on deep learning,terrain analysis,and other methods,the key site selection factors such as historical deformation,crack distribution,slope,surface roughness,vegetation index,and slope direction are extracted.Finally,an analytic hierarchy process is employed to intelligently evaluate the suitability of different locations within the landslide area for UAV-dropped de-ployment of GNSS monitoring devices and recommend the coordinates of the target delivery locations.The site selection experi-ments were conducted in the Heifangtai landslide area in Gansu province,China.The suitability of the selected locations within this area was assessed,and four airdrop positions for GNSS monitoring devices were recommended.The effectiveness of the proposed method was validated through on-site observations and historical station deformation sequences.This method com-prehensively considers the demands of deformation monitoring,deployment difficulty,observation conditions,and continuous operation,enabling efficient evaluation of the suitability of equipment deployment in the site selection area.It holds significant reference value for the unmanned and intelligent deployment of GNSS monitoring devices.
GNSSUAV droppinglandslide monitoringmonitoring station site selectionanalytic hierarchy process
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NSTL
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