Using an Unmanned Aerial Vehicle-Based Digital Imaging System to Derive a 3D Point Cloud for Landslide Scarp Recognition

被引:97
作者
Al-Rawabdeh, Abdulla [1 ]
He, Fangning [2 ]
Moussa, Adel [1 ,3 ]
El-Sheimy, Naser [1 ]
Habib, Ayman [2 ]
机构
[1] Univ Calgary, Dept Geomat Engn, Calgary, AB T2N 1N4, Canada
[2] Purdue Univ, Lyles Sch Civil Engn, W Lafayette, IN 47907 USA
[3] Port Said Univ, Dept Elect Engn, Port Said 42523, Egypt
基金
加拿大自然科学与工程研究理事会;
关键词
landslides scarps; geomorphology; slope; surface roughness; Semi-Global dense matching (SGM); unmanned aerial vehicles (UAVs); REMOTE-SENSING DATA; STABILITY ANALYSIS; MOUNTAIN BELT; DEBRIS FLOWS; LOW-COST; HAZARD; GIS; SUSCEPTIBILITY; PHOTOGRAMMETRY; SEDIMENT;
D O I
10.3390/rs8020095
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Landslides often cause economic losses, property damage, and loss of lives. Monitoring landslides using high spatial and temporal resolution imagery and the ability to quickly identify landslide regions are the basis for emergency disaster management. This study presents a comprehensive system that uses unmanned aerial vehicles (UAVs) and Semi-Global dense Matching (SGM) techniques to identify and extract landslide scarp data. The selected study area is located along a major highway in a mountainous region in Jordan, and contains creeping landslides induced by heavy rainfall. Field observations across the slope body and a deformation analysis along the highway and existing gabions indicate that the slope is active and that scarp features across the slope will continue to open and develop new tension crack features, leading to the downward movement of rocks. The identification of landslide scarps in this study was performed via a dense 3D point cloud of topographic information generated from high-resolution images captured using a low-cost UAV and a target-based camera calibration procedure for a low-cost large-field-of-view camera. An automated approach was used to accurately detect and extract the landslide head scarps based on geomorphological factors: the ratio of normalized Eigenvalues (i.e., 1/2 3) derived using principal component analysis, topographic surface roughness index values, and local-neighborhood slope measurements from the 3D image-based point cloud. Validation of the results was performed using root mean square error analysis and a confusion (error) matrix between manually digitized landslide scarps and the automated approaches. The experimental results using the fully automated 3D point-based analysis algorithms show that these approaches can effectively distinguish landslide scarps. The proposed algorithms can accurately identify and extract landslide scarps with centimeter-scale accuracy. In addition, the combination of UAV-based imagery, 3D scene reconstruction, and landslide scarp recognition/extraction algorithms can provide flexible and effective tool for monitoring landslide scarps and is acceptable for landslide mapping purposes.
引用
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页数:32
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