Contour Connection Method for automated identification and classification of landslide deposits

Leshchinsky, Ben; Olsen, Michael J.; Tanyu, Burak F.

doi:10.1016/j.cageo.2014.10.007

Cited by 37 publications

(35 citation statements)

References 16 publications

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“…In the case of SVM classification and the same DTM resolution and features, the kappa index is 0.55 and the OA is 81.4%. Similar results have also been demonstrated by other authors (Leshchinsky et al 2015;Mezaal et al 2017). Lower kappa values are likely related to under-prediction of landslides and over-prediction of non-landslide areas.…”

Section: Performance Of Classification Methodssupporting

confidence: 78%

“…In the last few years, several studies have investigated this issue to develop more or less automated remote sensing techniques for ALM (Stumpf et al 2017). Automatic techniques include analysis of RS data, such as optical images (Chen et al 2017;Dou et al 2015;Kurtz et al 2014), synthetic aperture radar (SAR) data (Del Ventisette et al 2014;Wasowski and Bovenga 2014), and light detection and ranging (LiDAR) digital terrain models (DTMs) (Leshchinsky et al 2015;Lin et al 2013b;Tarolli et al 2012;Van Den Eeckhaut et al 2012). The availability of high-resolution (HS) optical images (spaceborne, airborne, and terrestrial) affords more accurate and efficient landslide mapping than ever before (Li et al 2016).…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity analysis of automatic landslide mapping: numerical experiments towards the best solution

2018

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The automatic detection of landslides after major events is a crucial issue for public agencies to support disaster response. Pixel-based approaches (PBAs) are widely used in the literature for various applications. However, the accuracy of PBAs in the case of automatic landslide mapping (ALM) is affected by several issues. In this study, we investigated the sensitivity of ALM using PBA through digital terrain models (DTMs). The analysis, carried out in a study area of Poland, consisted of the following steps: (1) testing the influence of selected DTM resolutions for ALM, (2) assessing the relevance of diverse landslide morphological indicators for ALM, and (3) assessing the sensitivity to landslide features for a selected size of moving window (kernel) calculations for ALM. Ultimately, we assessed the performance of three classification methods: maximum likelihood (ML), feed-forward neural network (FFNN), and support vector machine (SVM). This broad analysis, as combination of grid cell resolution, surface derivatives calculation, and performance classification methods, is the challenging aspect of the research. The results of almost 500 experimental tests provide valuable guidelines for experts performing ALM. The most important findings indicate that feature sensitivity in the case of kernel size increases with coarser DTM resolution; however, the peak of the optimal feature performance for the selected study area and landslide type was demonstrated for a resolution of 20 m. Another finding indicated that in combining a set of topographic variables, the optimal performance was acquired for a DTM resolution of 30 m and the support vector machine classification. Moreover, the best performance of the identification is represented for SVM classification.

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Section: Performance Of Classification Methodssupporting

confidence: 78%

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis of automatic landslide mapping: numerical experiments towards the best solution

2018

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“…A landslide is defined as a mass movement of rock, debris or earth down a slope (Cruden, 1991) and is a common natural hazard that has an effect on economic, environmental and social issues (Leshchinsky et al, 2015). Therefore, mapping of landslides and producing landslide inventory maps are of interest to a wide range of specialists (Moosavi et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Towards the Optimal Pixel Size of Dem for Automatic Mapping of Landslide Areas

Pawłuszek

Borkowski

Tarolli

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Determining appropriate spatial resolution of digital elevation model (DEM) is a key step for effective landslide analysis based on remote sensing data. Several studies demonstrated that choosing the finest DEM resolution is not always the best solution. Various DEM resolutions can be applicable for diverse landslide applications. Thus, this study aims to assess the influence of special resolution on automatic landslide mapping. Pixel-based approach using parametric and non-parametric classification methods, namely feed forward neural network (FFNN) and maximum likelihood classification (ML), were applied in this study. Additionally, this allowed to determine the impact of used classification method for selection of DEM resolution. Landslide affected areas were mapped based on four DEMs generated at 1m, 2m, 5m and 10m spatial resolution from airborne laser scanning (ALS) data. The performance of the landslide mapping was then evaluated by applying landslide inventory map and computation of confusion matrix. The results of this study suggests that the finest scale of DEM is not always the best fit, however working at 1m DEM resolution on micro-topography scale, can show different results. The best performance was found at 5m DEM-resolution for FFNN and 1m DEM resolution for results. The best performance was found to be using 5m DEM-resolution for FFNN and 1m DEM resolution for ML classification.

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“…However, the extremely high cost associated with the use of aircraft and its time-consuming nature makes this strategy an impractical solution, especially for investigations of small areas. Leshchinsky et al [45] used LiDAR DEMs and head scarps to perform a semi-automated landslide inventory comparing three different study areas. They noted an increase in computational cost associated with post-processing data, which became especially prominent when one or more of the following parameters increased: study area size, input parameters, and resolution of the datasets.…”

Section: Introductionmentioning

confidence: 99%

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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Contour Connection Method for automated identification and classification of landslide deposits

Cited by 37 publications

References 16 publications

Sensitivity analysis of automatic landslide mapping: numerical experiments towards the best solution

Sensitivity analysis of automatic landslide mapping: numerical experiments towards the best solution

Towards the Optimal Pixel Size of Dem for Automatic Mapping of Landslide Areas

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

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