Assessment of shallow landslide susceptibility by means of multivariate statistical techniques

Baeza, Cristina; Corominas, Jordi

doi:10.1002/esp.263

Cited by 366 publications

(244 citation statements)

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“…LP finally joins the first cluster (NB, Q). Hence, two different groups of LSMs appear in the same way as Landslide density was also analyzed by the ''relative landslide density index R'' (Baeza and Corominas, 2001) defined as follows:…”

Section: Fig3b Results Of the Hierarchical Cluster Analysis For Faimentioning

confidence: 99%

“…The contribution of water through catchment areas exceeded 1000m3 with a mean angle ranging between 25º and 30º helped to generate many landslides in the study area. The significance of these parameters with respect to slope stability in the study area is discussed in Baeza and Corominas (2001) and Santacana et al (2003). The failures considered in this study are shallow landslides with small mobilized volumes (less than 10.000m3) and do not exceed two meters in depth.…”

Section: Study Areamentioning

confidence: 99%

“…They were subsequently digitized and then converted into raster format for the analysis. The method of selecting these variables and their significance are discussed in earlier works by Baeza and Corominas (2001) and Hürlimann and Baeza (2002). The diagnosis, validation and model evaluation of the discriminant function obtained in the study area, are extensively described and analyzed in Baeza et al (2010).…”

Section: Data Source and Landslide Susceptibility Mapmentioning

confidence: 99%

“…Hence, in order to shed light on this issue, a set of Landslide Susceptibility Maps (LSMs) developed from the same dataset by discriminant analysis (Baeza and Corominas, 2001) using different classification systems were built. These LSMs were compared and their similarities, differences, efficacy and consistency were assessed.…”

Section: Introductionmentioning

confidence: 99%

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Statistical and spatial analysis of landslide susceptibility maps with different classification systems

Baeza¹,

Lantada

Amorim

2016

Environ Earth Sci

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A landslide susceptibility map is an essential tool for land-use spatial planning and management in mountain areas. However, a classification system used for readability determines the final appearance of the map and may therefore influence the decision-making tasks adopted. The present paper addresses the spatial comparison and the accuracy assessment of some well-known classification methods applied to a susceptibility map that was based on a discriminant statistical model in an area in the Eastern Pyrenees. A number of statistical approaches (Spearman's correlation, Kappa index, Factorial and Cluster analyses and Landslide density index) for map comparison were performed to quantify the information provided by the usual image analysis.The results showed the reliability and consistency of the Kappa index against Spearman's correlation as accuracy measures to assess the spatial agreement between maps. Inferential tests between unweighted and linear weighted Kappa results showed that all the maps were more reliable in classifying areas of highest susceptibility and less reliable in classifying areas of low to moderate susceptibility. The spatial variability detected and quantified by Factorial and Cluster analyses showed that the maps classified by Quantile and Natural Break methods were the closest whereas those classified by Landslide Percentage and Equal Interval methods displayed the greatest differences. The difference image analysis showed that the five classified maps only matched 9% of the area. This area corresponded to the steeper slopes and the steeper watershed angle with forestless and sunny slopes at low altitudes. This means that the five maps coincide in identifying and classifying the most dangerous areas. The Equal Interval map overestimated the susceptibility of the study area and the Landslide Percentage map was considered to be a very optimistic model. The spatial pattern of the Quantile and Natural Break maps was very similar but the latter was more consistent and predicted potential landslides more efficiently and reliably in the study area.

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Section: Fig3b Results Of the Hierarchical Cluster Analysis For Faimentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

Section: Data Source and Landslide Susceptibility Mapmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Statistical and spatial analysis of landslide susceptibility maps with different classification systems

Baeza¹,

Lantada

Amorim

2016

Environ Earth Sci

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“…So far, a number of methods has been proposed for quantitative landslide spatial probability mapping, e.g. discriminant analysis (Baeza and Corominas 2001), likelihood ratio (Chung and Fabbri 2003), ANN (Kanungo et al 2006) and logistic regression (Das et al 2010;Lee and Pradhan 2007). However, actual methodological developments for quantitative hazard analysis have been scarce, particularly in medium scales (Guzzetti et al 1999;Van Westen et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic landslide hazard assessment using homogeneous susceptible units (HSU) along a national highway corridor in the northern Himalayas, India

et al. 2011

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Probabilistic landslide hazard assessment using homogeneous susceptible units (HSU) along a national highway corridor in the northern Himalayas, IndiaAbstract The increased socio-economic significance of landslides has resulted in the application of statistical methods to assess their hazard, particularly at medium scales. These models evaluate where, when and what size landslides are expected. The method presented in this study evaluates the landslide hazard on the basis of homogenous susceptible units (HSU). HSU are derived from a landslide susceptibility map that is a combination of landslide occurrences and geo-environmental factors, using an automated segmentation procedure. To divide the landslide susceptibility map into HSU, we apply a region-growing segmentation algorithm that results in segments with statistically independent spatial probability values. Independence is tested using Moran's I and a weighted variance method. For each HSU, we obtain the landslide frequency from the multi-temporal data. Temporal and size probabilities are calculated using a Poisson model and an inverse-gamma model, respectively. The methodology is tested in a landslide-prone national highway corridor in the northern Himalayas, India. Our study demonstrates that HSU can replace the commonly used terrain mapping units for combining three probabilities for landslide hazard assessment. A quantitative estimate of landslide hazard is obtained as a joint probability of landslide size, of landslide temporal occurrence for each HSU for different time periods and for different sizes.

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A Review of Scale Dependency in Landslide Hazard and Risk Analysis

Glade¹,

Crozier²

2005

Landslide Hazard and Risk

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Landslides are part of the natural environment and affect economy and communities worldwide at various scales. Consequently, methods of assessing landslide risk depend heavily on specific demands. Within this frame, spatial landslide hazard and risk assessments are an important tool to support local and regional communities. These assessments include hazard and risk analysis, risk perception and a sustainable risk management. In Bíldudalur, Iceland, hazard analysis was performed at 1:5,000 scale and is build on landslide hazard determined by a combination of heuristic analysis, in particular geomorphological assessment, and process modelling. The investigation was focussed on debris flows and rock falls only. Resulting landslide maps give run-out distances of different sized landslide events. This information is assigned to hazard classes expressing through its magnitude the probability of occurrence of a given magnitude within a given region. Risk analysis in Rheinhessen, Germany is based on landslide hazard derived from multivariate statistical analysis at scale 1:25,000. Landslide types were not differentiated specifically, however, most landslides in this region are shallow, translational earth and debris slides and flows. Risk elements were mapped and categorized from aerial photographs and land use plans. Potential damage was calculated as average values based on official German statistics sources. Due to missing information, vulnerability was assigned as 1 (highly vulnerable) -thus analysis returns a worst case scenario. Resulting landslide risk map identifies high risk areas in urban settings. Within their scale of investigation, both approaches demonstrate their importance for prevention strategies, in particular for planning and mitigation purposes. The examples are linked to an overall scheme of landslide risk assessment, which has the potential to include other natural hazards and consequently might lead to a mutli-natural risk assessment.

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Assessment of shallow landslide susceptibility by means of multivariate statistical techniques

Cited by 366 publications

References 14 publications

Statistical and spatial analysis of landslide susceptibility maps with different classification systems

Statistical and spatial analysis of landslide susceptibility maps with different classification systems

Probabilistic landslide hazard assessment using homogeneous susceptible units (HSU) along a national highway corridor in the northern Himalayas, India

A Review of Scale Dependency in Landslide Hazard and Risk Analysis

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