Remote sensing of landslides: An analysis of the potential contribution to geo-spatial systems for hazard assessment in mountainous environments

Metternicht, Graciela; Hurni, Lorenz; Gogu, Radu Constantin

doi:10.1016/j.rse.2005.08.004

Cited by 421 publications

(244 citation statements)

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“…Since 10 years, SAR interferograms are used for landslide detection, mapping and monitoring (Canuti et al, 2004;Colesanti and Wasowski, 2006;Lu et al, 2012;Metternicht et al, 2005). For the Ubaye valley, only ascending ALOS/PALSAR images are available, limiting the coverage of the territory to 60 % because of layover and slope portions hidden by the relief ( Fig.…”

Section: Datamentioning

confidence: 99%

Analysis of a landslide multi-date inventory in a complex mountain landscape: the Ubaye valley case study

Schlögel

Malet

Reichenbach

et al. 2015

Nat. Hazards Earth Syst. Sci.

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Abstract. In the paper we analyse a multi-date landslide inventory prepared for a mountainous area affected by several landslide types with different degrees of activity, we attempt to quantify the uncertainties associated to the mapping, we measure the evolution of morphological indicators and estimate landslide activity and temporal occurrence. The inventory, covering the period 1956-2010, is prepared for the middle section of the Ubaye valley (southern French Alps) based on the analysis of multi-source documents (geomorphological maps, historical reports of landslide events, field surveys, orthophotographs and SAR (synthetic aperture radar) satellite images). The uncertainties derived from the expert interpretation of different sources of information, the landslide morphological features and the affected land covers are taken into account in relation to the source documents.Morphological indicators are calculated to describe quantitatively the evolution of the landslides (length, area, relative elevation, runout distance). Frequency-area density functions are computed to estimate the changes in the landslide distributions and a Poisson model is used to estimate the probability of reactivation of the observed landslides and the occurrence of new failures. The proposed multi-date inventory and the associated statistics provide additional information to the event catalogue managed by the local policy makers.

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Section: Datamentioning

confidence: 99%

Analysis of a landslide multi-date inventory in a complex mountain landscape: the Ubaye valley case study

Schlögel

Malet

Reichenbach

et al. 2015

Nat. Hazards Earth Syst. Sci.

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“…The use of LiDAR technology for landslide identification has become increasingly popular in digital terrain modelling [12,[34][35][36][37][38][39][40]. LiDAR is a valuable tool in geology [41], geomorphology [12], and hazard reduction efforts. Only a few studies have attempted to develop computer-assisted methods for Remote Sens.…”

Section: Introductionmentioning

confidence: 99%

“…TLS has attracted interest for use in landslide studies, including (1) landslide detection and characterization [46,47]; (2) hazard assessment and susceptibility mapping [41]; and (3) modelling and monitoring [40,[48][49][50][51][52]. A comprehensive review of laser scanning technology and its applications in landslide investigations can be found in the work of Jaboyedoff et al [53].…”

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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“…Many different approaches and methodologies have been proposed and remote sensing can be an essential tool for landslide classification and mapping because it allows the observation of wide areas and is time-saving when compared to conventional methodologies (Mantovani, Soeters, & Van Westen, 1996;Metternicht, Hurni, & Gogu, 2005).…”

Section: Introductionmentioning

confidence: 99%

Integrated geomorphological mapping in the north-western sector of Agrigento (Italy)

et al. 2012

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The geomorphological map is an essential tool to perform a proper urban planning in mountainous or hilly areas. In this paper a multidisciplinary approach to derive a 1:2000 geomorphological map is described. The proposed methodology consists of the integration between aerial photographs, acquired in 2003, and four datasets of Persistent Scatterer Interferometry (PSI) measures to update a pre-existing landslide inventory. The integrated data were used to achieve a validated geomorphological map by means of a geomorphological survey. The study area is located in southern Italy (Agrigento, Sicily). The city of Agrigento, included in the World Heritage List of UNESCO in 1997, is located on the Girgenti hill which is exposed on its northern side, to several landslide phenomena. The top of the hill is characterized by the presence of part of the cultural heritage of the city and is affected by rockfalls, rock topples and shallow-seated landslides, representing a serious risk for important historical buildings. The results demonstrate the validity of this method to achieve a suitable tool in landscape and cultural heritage management.

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Remote sensing of landslides: An analysis of the potential contribution to geo-spatial systems for hazard assessment in mountainous environments

Cited by 421 publications

References 50 publications

Analysis of a landslide multi-date inventory in a complex mountain landscape: the Ubaye valley case study

Analysis of a landslide multi-date inventory in a complex mountain landscape: the Ubaye valley case study

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

Integrated geomorphological mapping in the north-western sector of Agrigento (Italy)

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