Intelligent site selection method for UAV-dropped GNSS landslide monitoring equipment

doi:10.11947/j.AGCS.2024.20230358

Abstract

Abstract:

The GNSS technology is widely used in landslide deformation monitoring. However, in complex and hazardous landslide 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 digital 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 deployment of GNSS monitoring devices and recommend the coordinates of the target delivery locations. The site selection experiments 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 comprehensively 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.

Key words: GNSS, UAV dropping, landslide monitoring, monitoring station site selection, analytic hierarchy process

CLC Number:

P227

Hao XU, Qin ZHANG, Li WANG, Bao SHU, Yuan DU, Guanwen HUANG. Intelligent site selection method for UAV-dropped GNSS landslide monitoring equipment[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(6): 1140-1153.

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P_ij	含义
1	选址因子i和选址因子j同样重要
3	选址因子i比选址因子j稍微重要
5	选址因子i比选址因子j明显重要
7	选址因子i比选址因子j强烈重要
9	选址因子i比选址因子j极端重要
2、4、6、8	上述重要性的中间值

站点名	历史形变	裂缝分布	坡度	地表粗糙度	植被指数	坡向	适宜性值
P₁	0.47	0.97	0.57	0.66	0.71	0.8	0.69
P₂	0.39	0.82	0.24	0.70	0.64	0.8	0.56