Application of UAV Photogrammetry to Slope-Displacement Measurement

Cho, Jung Min; Lee, Jongseok; Lee, Byoung Kil

doi:10.1007/s12205-021-1374-1

Cited by 12 publications

(7 citation statements)

References 32 publications

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“…Moreover, obtaining spatially accurate 3D data without RTK poses a significant challenge [52,53]. A method for determining the number of GCPs to be used in areas < 1 ha and on steep slopes has not yet been defined, even in recent GCP geo-referencing studies [29,[54][55][56][57]. Installations of GCPs over stumps enabled an accurate scale of the PCD and geo-referencing [54,56,[58][59][60], thereby facilitating the generation of precise 3D data using the GCP coordinates.…”

Section: Gcp Registration-based Geo-referencing and Distortion Occurr...mentioning

confidence: 99%

UAV Photogrammetry for Soil Surface Deformation Detection in a Timber Harvesting Area, South Korea

Kim

et al. 2023

Forests

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During forest operations, canopy removal results in the soil surface being vulnerable to deformation, negatively impacting soil fertility and water quality. This study utilized unmanned aerial vehicle (UAV) photogrammetry to accurately detect soil surface deformation (SSD). Two-dimensional images were safely collected on a steep slope without real-time kinematics by conducting vertically parallel flights (VPFs). A high-resolution digital surface model (DSM) with a <3 cm resolution was acquired for precise SSD detection. Using DSM of difference (DoD), SSDs were calculated from DSMs acquired in June, July, September, and October 2022. By checking spatial distances at ground control points, errors of DSM alignments were confirmed as only 3 cm, 11.1 cm, and 4 cm from July to June, September to June, and October to June, respectively. From the first month of monitoring, erosion and deposition of approximately 7 cm and 9 cm, respectively, were detected at validation points (VPs). However, from total monitoring, cumulative SSD was assessed as having deposition tendencies at all VPs, even compared to ground truths. Although UAV photogrammetry can detect SSDs, spatial distortion may occur during UAV surveys. For vegetation growth issues, UAV photogrammetry may be unable to capture data on the soil surface itself.

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Section: Gcp Registration-based Geo-referencing and Distortion Occurr...mentioning

confidence: 99%

UAV Photogrammetry for Soil Surface Deformation Detection in a Timber Harvesting Area, South Korea

Kim

et al. 2023

Forests

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“…The optimal hyperplane is then built based on the highdimensional feature space, as in Equation (5). N i refers to any set of sample data values, as expressed in Equation (6). By constructing and using different kernel functions, the SVM algorithm builds the discriminant function by finding the dot product of the vectors and performing a non-linear transformation to the inner product in the high-dimensional feature space for the solution, instead of by mapping the sample data set directly into the high-dimensional feature space for the solution set:…”

Section: Uav Technical Framework For Quantification Of Repeated Obser...mentioning

confidence: 99%

Repeated UAV Observations and Digital Modeling for Surface Change Detection in Ring Structure Crater Margin in Plateau

Luo

Gan

Yuan

et al. 2023

Drones

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As UAV technology has been leaping forward, small consumer-grade UAVs equipped with optical sensors are capable of easily acquiring high-resolution images, which show bright prospects in a wide variety of terrains and different fields. First, the crater rim landscape of the Dinosaur Valley ring formation located on the central Yunnan Plateau served as the object of the surface change detection experiment, and two repetitive UAV ground observations of the study area were performed at the same altitude of 180 m with DJI Phantom 4 RTK in the rainy season (P1) and the dry season (P2). Subsequently, the UAV-SfM digital three-dimensional (3D) modeling method was adopted to build digital models of the study area at two points in time, which comprised the Digital Surface Model (DSM), Digital Orthomosaic Model (DOM), and Dense Image Matching (DIM) point cloud. Lastly, a quantitative analysis of the surface changes at the pit edge was performed using the point-surface-body surface morphological characterization method based on the digital model. As indicated by the results, (1) the elevation detection of the corresponding check points of the two DSM periods yielded a maximum positive difference of 0.2650 m and a maximum negative value of −0.2279 m in the first period, as well as a maximum positive difference of 0.2470 m and a maximum negative value of −0.2589 m in the second period. (2) In the change detection of the two DOM periods, the vegetation was 9.99% higher in the wet season than in the dry season in terms of coverage, whereas the bare soil was 10.54% more covered than the wet season. (3) In general, the M3C2-PM distances of the P1 point cloud and the P2 point cloud were concentrated in the interval (−0.2,0.2), whereas the percentage of the interval (−0.1,0) accounted for 26.69% of all intervals. The numerical model of UAV-SfM was employed for comprehensive change detection analysis. As revealed by the result of the point elevation difference in the constant area, the technique can conform to the requirements of earth observation with certain accuracy. The change area suggested that the test area can be affected by natural conditions to a certain extent, such that the multi-source data can be integrated to conduct more comprehensive detection analysis.

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“…Presently, the frequently used targets in UAV surveys include X-shaped, L-shaped, O-shaped targets, etc. [32], [33], and the corner points of ground objects with obvious features are also widely used as GCPs [34]. Notably, the accuracy of UAV survey projects depends on many factors, including typical parameters of the camera, RTK and other measuring equipment, route planning, environmental conditions, spatial distribution and absolute accuracy of GCPs, accuracy of pinpointing the exact target center, among others.…”

Section: Introductionmentioning

confidence: 99%

An Automatic and Accurate Method for Marking Ground Control Points in Unmanned Aerial Vehicle Photogrammetry

Kong

Chen

Kang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Owing to the rapid development of unmanned aerial vehicle (UAV) technology and various photogrammetric software, UAV photogrammetry projects are becoming increasingly automated. However, marking ground control points (GCPs) in current UAV surveys still generally needs to be manually completed, which brings the problem of inefficiency and human error. Based on the characteristics of UAV photogrammetry, a novel type of circular coded target with its identification and decoding algorithm is proposed to realize an automatic and accurate approach for marking GCPs. UAV survey experiments validate the feasibility of the proposed method, which has comparative advantages in efficiency, robustness, and accuracy over traditional targets. Additionally, we conducted experiments to discuss the effects of projection size and viewing angle, number of coded bits, and environmental conditions on the proposed method. The results show that it can achieve robust identification and accurate positioning even under challenging conditions, and a smaller number of coded bits is recommended for better robustness.

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Application of UAV Photogrammetry to Slope-Displacement Measurement

Cited by 12 publications

References 32 publications

UAV Photogrammetry for Soil Surface Deformation Detection in a Timber Harvesting Area, South Korea

UAV Photogrammetry for Soil Surface Deformation Detection in a Timber Harvesting Area, South Korea

Repeated UAV Observations and Digital Modeling for Surface Change Detection in Ring Structure Crater Margin in Plateau

An Automatic and Accurate Method for Marking Ground Control Points in Unmanned Aerial Vehicle Photogrammetry

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