Landslide inventory maps: New tools for an old problem

Guzzetti, Fausto; Mondini, Alessandro Cesare; Cardinali, Mauro; Fiorucci, Federica; Santangelo, Michele; Chang, Kai H.

doi:10.1016/j.earscirev.2012.02.001

Cited by 1,519 publications

(1,209 citation statements)

References 143 publications

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“…Preparing an inventory map is a time-consuming and not straightforward procedure which requires experienced geomorphologists trained in the recognition of slope features and processes (Wills and McCrink, 2002;van den Eeckhaut et al, 2005;Guzzetti et al, 2012). In addition, the quality of the final map depends on the spatial resolution of the airborne photographs, the scale of the topographic maps, and the complexity of the landscape (Carrara et al, 1992;Ardizzone et al, 2002;Galli et al, 2008).…”

Section: Published By Copernicus Publications On Behalf Of the Europementioning

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: Published By Copernicus Publications On Behalf Of the Europementioning

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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“…A set of characteristics (shape, size, colour, topography) were used to detect and map landslides. Among these characteristics shape was the most useful for the detection of landslides in combination with the vertical exaggeration of the stereoscopic vision (Guzzetti et al, 2012).…”

Section: Photo-interpretationmentioning

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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“…Despite their lower resolutions compared to UAS imagery, satellite and aerial images as data sources have also been studied for fissure extraction by various researchers [23,[34][35][36][37][38]. Youssef et al [34] demonstrated the use of high-resolution satellite images using QuickBird imagery, acquired on 2 June 2007 (0.61 m spatial resolution), for detailed mapping of recent developments and slope instability hazard zones.…”

Section: Introductionmentioning

confidence: 99%

“…Hugenholtz et al [21] also explained the use of a UAS-based photogrammetric process for a digital terrain model and some basic landform feature extractions, using the maximum likelihood supervised classification. Cell-based or raster-based geomorphic feature extraction was applied, not only in landforms, but also in landslides to discover significant surface shapes [22,23].…”

Section: Introductionmentioning

confidence: 99%

Landslide Fissure Inference Assessment by ANFIS and Logistic Regression Using UAS-Based Photogrammetry

Akçay

2015

IJGI

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Unmanned Aerial Systems (UAS) are now capable of gathering high-resolution data, therefore, landslides can be explored in detail at larger scales. In this research, 132 aerial photographs were captured, and 85,456 features were detected and matched automatically using UAS photogrammetry. The root mean square (RMS) values of the image coordinates of the Ground Control Points (GPCs) varied from 0.521 to 2.293 pixels, whereas maximum RMS values of automatically matched features was calculated as 2.921 pixels. Using the 3D point cloud, which was acquired by aerial photogrammetry, the raster datasets of the aspect, slope, and maximally stable extremal regions (MSER) detecting visual uniformity, were defined as three variables, in order to reason fissure structures on the landslide surface. In this research, an Adaptive Neuro Fuzzy Inference System (ANFIS) and a Logistic Regression (LR) were implemented using training datasets to infer fissure data appropriately. The accuracy of the predictive models was evaluated by drawing receiver operating characteristic (ROC) curves and by calculating the area under the ROC curve (AUC). The experiments exposed that high-resolution imagery is an indispensable data source to model and validate landslide fissures appropriately.

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Landslide inventory maps: New tools for an old problem

Cited by 1,519 publications

References 143 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

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

Landslide Fissure Inference Assessment by ANFIS and Logistic Regression Using UAS-Based Photogrammetry

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