Automated landslide mapping using spectral analysis and high-resolution topographic data: Puget Sound lowlands, Washington, and Portland Hills, Oregon

Booth, Adam M.; Roering, J. J.; Perron, J. Taylor

doi:10.1016/j.geomorph.2009.02.027

Cited by 258 publications

(201 citation statements)

References 36 publications

(64 reference statements)

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“…An extension of this method using Fourier analysis of the relief demonstrates that this method can classify more (Jaboyedoff et al 2004b). c 3D view of the volume of the faster area 3 (Data from swisstopo) than 80% of total area that has already suffered a landslide (or not) in western US (Booth et al 2009). Geomorphic criterion based on geostatistics (Glenn et al 2006) succeeds in differencing landslide activities.…”

Section: Short Historical Overviewmentioning

confidence: 99%

Use of LIDAR in landslide investigations: a review

et al. 2010

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This paper presents a short history of the appraisal of laser scanner technologies in geosciences used for imaging relief by high-resolution digital elevation models (HRDEMs) or 3D models. A general overview of light detection and ranging (LIDAR) techniques applied to landslides is given, followed by a review of different applications of LIDAR for landslide, rockfall and debris-flow. These applications are classified as: (1) Detection and characterization of mass movements; (2) Hazard assessment and susceptibility mapping; (3) Modelling; (4) Monitoring. This review emphasizes how LIDARderived HRDEMs can be used to investigate any type of landslides. It is clear that such HRDEMs are not yet a common tool for landslides investigations, but this technique has opened new domains of applications that still have to be developed.

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Section: Short Historical Overviewmentioning

confidence: 99%

Use of LIDAR in landslide investigations: a review

et al. 2010

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“…The automatic extraction of geomorphological features (Booth, Roering, & Perron, 2009;Dinesh, 2006;Euillades, Grosse, & Euillades, 2013;Iwahashi & Pike, 2007;Kweon & Kanade, 1994 ;Micheal & Vani, 2015;Miliaresis & Argialas, 1999;Obu & Podobnikar, 2013;Panagiotakis & Kokinou, 2014, 2015, 2017 using Digital Elevation Models (DEMs) belongs to the techniques of remote sensing data processing, that improve our understanding of regional geomorphology and geology prior to the fieldwork. The automatic extraction of geomorphological features highly contributes towards a rapid, objective and lower cost geological mapping.…”

Section: Introductionmentioning

confidence: 99%

The contribution of pattern recognition techniques in geomorphology and geology: the case study of Tinos Island (Cyclades, Aegean, Greece)

Toulia

Kokinou

Panagiotakis

2017

European Journal of Remote Sensing

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Scope of the present work is to apply modern methods of pattern recognition concerning the automatic detection of geomorphological features (curvilinear lineaments and topographic highs), with emphasis on geological faults and to compare the results of the automatic detection with aerial photographs and further geological data collected in Tinos Island. The contribution of this work in the geosciences is multidisciplinary because (a) it proposes to geoscientists a new tool of geomorphological analysis prior to the fieldwork and especially at the stage where published work and already available data are collected, evaluated and reprocessed, (b) it increases the detection accuracy of the geological features both prior and after the fieldwork, (c) it decreases the time of fieldwork and consequently the cost of the entire geological research and (d) it can be applied in digital elevation data. The automatically detected linear-curvilinear lineaments and topographic highs agree in location, shape and orientation with the ground truth data (geological maps, aerial photographs and field measurements). Furthermore, the shape and the orientation of the most prominent topographic high (Tsiknias) seems to be related to the tectonic regime in the wide area of study corresponding to the ENE-WSW, WSW-ESE, N-S and E-W trending normal faults. ARTICLE HISTORY

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“…In recent years, an increasing number of studies have been conducted worldwide regarding geohazard susceptibility mapping and risk assessment. Traditional landslide mapping methods are typically based on interpretations of aerial photography and field survey inspections, which are used to better understand the characteristics of landslides [11,16]. The photographic, or image interpretation, approach can be adapted and implemented manually, automatically, or semi-automatically [17,18].…”

Section: Introductionmentioning

confidence: 99%

“…Aside from being inaccessible and time-consuming, ground surveys and fieldwork are also subjective and prone to human and instrumental errors. Traditional strategies are unlikely to provide a satisfactory solution, especially for small-area investigations where mass movement rates are slower [16,22]. For these reasons, researchers have adopted remote sensing techniques in their research methodology [8,9,20,[23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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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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Automated landslide mapping using spectral analysis and high-resolution topographic data: Puget Sound lowlands, Washington, and Portland Hills, Oregon

Cited by 258 publications

References 36 publications

Use of LIDAR in landslide investigations: a review

Use of LIDAR in landslide investigations: a review

The contribution of pattern recognition techniques in geomorphology and geology: the case study of Tinos Island (Cyclades, Aegean, Greece)

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

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