Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs)

Pajares, Gonzalo

doi:10.14358/pers.81.4.281

Cited by 655 publications

(419 citation statements)

References 431 publications

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“…This landslide affected approximately 250 m of the highway, destroyed two houses, caused the evacuation of 10 others, and delayed the opening of the highway for several months [71]. as, soil erosion [59,60], landslides or rocky surfaces inventory [56,61], landslide detection [62], landslide dynamics and monitoring [57,[63][64][65][66][67], and natural disaster monitoring and evaluation [68,69]. A comprehensive review of remote sensing applications based on UAVs equipped with specific sensor-based technologies has been accomplished to varying degrees across a range of applications.…”

Section: Location Of the Study Areamentioning

confidence: 99%

“…A comprehensive review of remote sensing applications based on UAVs equipped with specific sensor-based technologies has been accomplished to varying degrees across a range of applications. A detailed summary, specifically focused on the progress made in environmental science applications, is provided in Pajares [56] and Colomina et al [58]. The purpose of this research is to present a comprehensive system that uses a low-cost UAV, Semi-Global dense Matching (SGM) techniques, and automated approaches to detect and extract the geomorphological features of landslide scarps.…”

Section: Location Of the Study Areamentioning

confidence: 99%

“…UAVs combine the advantages of vertical aerial photogrammetry and the high resolution of ground-based images. UAVs have the ability to fly at low altitudes and slow speeds and to reach areas that are not accessible from the ground or with manned aircrafts [56]. Moreover, these systems can be stored and deployed at a minimal expense [56][57][58].…”

Section: Introductionmentioning

confidence: 99%

“…UAVs have the ability to fly at low altitudes and slow speeds and to reach areas that are not accessible from the ground or with manned aircrafts [56]. Moreover, these systems can be stored and deployed at a minimal expense [56][57][58]. These characteristics make UAVs an optimal platform for affordable rapid responses in mapping applications; consequently there is great interest in utilizing them in natural hazard applications.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Al‐Rawabdeh

Moussa

et al. 2016

Remote Sensing

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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., λ1/λ2 ě λ3) 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.

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