An approach for GIS-based statistical landslide susceptibility zonation?with a case study in the Himalayas

Saha, Aniruddha; Gupta, Ravi P.; Sarkar, Irene; Arora, Manoj K.; Csaplovics, Elmar

doi:10.1007/s10346-004-0039-8

Cited by 353 publications

(170 citation statements)

References 16 publications

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“…In the Himalayan region, landslides are recurring annually and are prominent during the summer months between June and October when the seasonal monsoon occurs. Landslides in this area are the result of a combination of geotectonics, adverse natural topography, such as steep slopes, weathered rocks and soils, human influences on the topography and high rainfall (Choubey and Ramola 1997;Saha et al 2005).…”

Section: Landslides 8 and (2011) 295mentioning

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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Section: Landslides 8 and (2011) 295mentioning

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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“…The frequent occurrence of landslides is a major threat for the economy in the area (NRSA 2001). Landslides in this area are the result of a combination of an intrinsic geology, adverse natural topography like steep slopes, weathered rocks and soils, human influences on the topography, and high rainfall (Saha et al 2005;Choubey and Ramola 1997). The landslides considered in this study are mainly shallow translational rock slides that are prominent in this area.…”

Section: Site Characteristics and Data Collectionmentioning

confidence: 99%

Stochastic landslide vulnerability modeling in space and time in a part of the northern Himalayas, India

Das

Gaurav

Stein³

et al. 2010

Environ Monit Assess

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Little is known about the quantitative vulnerability analysis to landslides as not many attempts have been made to assess it comprehensively. This study assesses the spatio-temporal vulnerability of elements at risk to landslides in a stochastic framework. The study includes buildings, persons inside buildings, and traffic as elements at risk to landslides. Building vulnerability is the expected damage and depends on the position of a building with respect to the landslide hazard at a given time. Population and vehicle vulnerability are the expected death toll in a building and vehicle damage in space and time respectively. The study was carried out in a road

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“…Table 1 shows the percentage of the total study area covered by each class of the four parameters considered in the analysis. Saha et al (2005) propose the Information Value (InfoVal) method developed by Yin and Yan (1988) for terrain failure susceptibility assessment. In this procedure combining the failure map with each thematic map in the training zone allows us to determine the weight of influence on terrain instability for each parameter class with the following equation W i (Eq.…”

Section: Selection Extraction and Discretisation Of Instability Factmentioning

confidence: 99%

GIS-based debris flow source and runout susceptibility assessment from DEM data – a case study in NW Nicaragua

Guinau

Vilajosana

Vilaplana

2007

Nat. Hazards Earth Syst. Sci.

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Abstract. In October 1998, Hurricane Mitch triggered numerous landslides (mainly debris flows) in Honduras and Nicaragua, resulting in a high death toll and in considerable damage to property. The potential application of relatively simple and affordable spatial prediction models for landslide hazard mapping in developing countries was studied. Our attention was focused on a region in NW Nicaragua, one of the most severely hit places during the Mitch event.A landslide map was obtained at 1:10 000 scale in a Geographic Information System (GIS) environment from the interpretation of aerial photographs and detailed field work. In this map the terrain failure zones were distinguished from the areas within the reach of the mobilized materials. A Digital Elevation Model (DEM) with 20 m×20 m of pixel size was also employed in the study area.A comparative analysis of the terrain failures caused by Hurricane Mitch and a selection of 4 terrain factors extracted from the DEM which, contributed to the terrain instability, was carried out. Land propensity to failure was determined with the aid of a bivariate analysis and GIS tools in a terrain failure susceptibility map. In order to estimate the areas that could be affected by the path or deposition of the mobilized materials, we considered the fact that under intense rainfall events debris flows tend to travel long distances following the maximum slope and merging with the drainage network. Using the TauDEM extension for ArcGIS software we generated automatically flow lines following the maximum slope in the DEM starting from the areas prone to failure in the terrain failure susceptibility map. The areas crossed by the flow lines from each terrain failure susceptibility class correspond to the runout susceptibility classes represented in a runout susceptibility map.Correspondence to: M. Guinau (mguinau@ub.edu)The study of terrain failure and runout susceptibility enabled us to obtain a spatial prediction for landslides, which could contribute to landslide risk mitigation.

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An approach for GIS-based statistical landslide susceptibility zonation?with a case study in the Himalayas

Cited by 353 publications

References 16 publications

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

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

Stochastic landslide vulnerability modeling in space and time in a part of the northern Himalayas, India

GIS-based debris flow source and runout susceptibility assessment from DEM data – a case study in NW Nicaragua

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