An algorithm for measuring landslide deformation in terrestrial lidar point clouds using trees

Weidner, Luke; Veen, Megan van; Lato, Matthew J.; Walton, Gabriel

doi:10.1007/s10346-021-01723-4

Cited by 8 publications

(3 citation statements)

References 23 publications

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“…In recent years, optical cameras and laser sensors carried by UAVs are often used to obtain dense point clouds to characterize the 3D morphology of landslide surfaces [23], [24], [25], [26], [27]. The advantages of optical cameras, such as the ability to obtain high-precision, low-cost, and true-color point clouds, mean that UAV photogrammetry-derived data is more conducive to interpreting landslides and is therefore more widely used for landslide monitoring compared with light detection and ranging (LiDAR) data [28], [29], [30]. Therefore, in this study, we concentrate on estimating landslide displacement by using UAV photogrammetryderived data such as dense point clouds.…”

Section: Introductionmentioning

confidence: 99%

Robust Estimation of Landslide Displacement From Multitemporal UAV Photogrammetry-Derived Point Clouds

He,

Ming,

Zhang

et al. 2024

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

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Existing algorithms based on remote sensing for landslide displacement estimation, such as C2C, C2M, DOD, and M3C2, are sensitive to errors generated in data processing, and further improving their accuracy is difficult. To address this issue, given that redundant observations may occur in landslide monitoring, we proposed a robust estimation method of landslide displacement from multi-temporal unmanned aerial vehicle (UAV) photogrammetry-derived point clouds. The proposed method first establishes the relevant graph to manage the trajectory of error propagation for landslide displacement estimation for all possible paths. Two modules, namely, intraand inter-estimates, are explored to reduce the impact of outliers and high surface roughness in point clouds derived by UAV photogrammetry. Individually, the intra-estimate operation is used to calculate landslide displacement between two temporal point clouds by robust estimation considering outlier constraint, and the inter-estimate operation is used to obtain the optimal calculation of landslide displacement by minimizing a given objective function using IGG robust estimation proposed by the Institute of Geodesy and Geophysics at the Chinese Academy of Sciences. Experimental results show that the proposed method is significantly superior to conventional methods such as C2C, C2M, and M3C2, with an accuracy improvement of at least 8%.

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Section: Introductionmentioning

confidence: 99%

Robust Estimation of Landslide Displacement From Multitemporal UAV Photogrammetry-Derived Point Clouds

He,

Ming,

Zhang

et al. 2024

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

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“…Localized patterns can precede larger cliff failures [20], highlighting the importance of closely monitoring the cliff face for geomorphic changes without interference from any vegetation that may be present. Although tree trunks have been used to track landslide erosion [21], cliffs present a unique challenge as they may either lack distinct highlighting the importance of closely monitoring the cliff face for geomorphic changes without interference from any vegetation that may be present. Although tree trunks have been used to track landslide erosion [21], cliffs present a unique challenge as they may either lack distinct objects that can be tracked and used to measure change, or such objects may rest in a relatively stable position at the cliff base (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

“…Although tree trunks have been used to track landslide erosion [21], cliffs present a unique challenge as they may either lack distinct highlighting the importance of closely monitoring the cliff face for geomorphic changes without interference from any vegetation that may be present. Although tree trunks have been used to track landslide erosion [21], cliffs present a unique challenge as they may either lack distinct objects that can be tracked and used to measure change, or such objects may rest in a relatively stable position at the cliff base (Figure 1). In addition to obscuring our view of the cliff face, vegetation may also affect the coastal cliff stability [22], and it may be important to identify where vegetation is present and where it is absent.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Vegetation Filtering of Coastal Cliff and Bluff Point Clouds

Wernette

2024

Remote Sensing

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Coastal cliffs erode in response to short- and long-term environmental changes, but predicting these changes continues to be a challenge. In addition to a chronic lack of data on the cliff face, vegetation presence and growth can bias our erosion measurements and limit our ability to detect geomorphic erosion by obscuring the cliff face. This paper builds on past research segmenting vegetation in three-band red, green, blue (RGB) imagery and presents two approaches to segmenting and filtering vegetation from the bare cliff face in dense point clouds constructed from RGB images and structure-from-motion (SfM) software. Vegetation indices were computed from previously published research and their utility in segmenting vegetation from bare cliff face was compared against machine learning (ML) models for point cloud segmentation. Results demonstrate that, while existing vegetation indices and ML models are both capable of segmenting vegetation and bare cliff face sediments, ML models can be more efficient and robust across different growing seasons. ML model accuracy quickly reached an asymptote with only two layers and RGB images only (i.e., no vegetation indices), suggesting that these more parsimonious models may be more robust to a range of environmental conditions than existing vegetation indices which vary substantially from one growing season to another with changes in vegetation phenology.

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Using tree stems in multi-temporal terrestrial lidar scanning data to monitor landslides on vegetated slopes

et al. 2022

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An algorithm for measuring landslide deformation in terrestrial lidar point clouds using trees

Cited by 8 publications

References 23 publications

Robust Estimation of Landslide Displacement From Multitemporal UAV Photogrammetry-Derived Point Clouds

Robust Estimation of Landslide Displacement From Multitemporal UAV Photogrammetry-Derived Point Clouds

Machine Learning Vegetation Filtering of Coastal Cliff and Bluff Point Clouds

Using tree stems in multi-temporal terrestrial lidar scanning data to monitor landslides on vegetated slopes

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