An assessment on the use of Terra ASTER L3A data in landslide susceptibility mapping

Nefeslioğlu, Hakan A.; San, Bekir Taner; Gökçeoğlu, Candan; Duman, Tamer Y.

doi:10.1016/j.jag.2011.08.005

Cited by 52 publications

(25 citation statements)

References 70 publications

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“…Landslide densities computed for factors classes a) Slope angle (degree), b) slope aspect (degree), c) plan curvature, d) profile curvature. Conforti et al, 2014;Lineback Gritzner et al, 2001;Nefeslioglu et al, 2012;Ozdemir and Altural, 2013;Peng et al, 2014;Schicker and Moon, 2012;Yao et al, 2008). Since the degree and type of erosion and the concentration of human activities are affected by altitude, it is considered to be an effective factor in landslide hazard assessment .…”

Section: Altitudementioning

confidence: 99%

A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran

Dehnavi

Aghdam

Pradhan

et al. 2015

CATENA

244

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Keywords: Landslide susceptibilityStep-wise weight assessment ratio analysis (SWARA) method Adaptive neuro-fuzzy inference system (ANFIS) Geographical information system (GIS) Remote sensing Iran In recent years, Iran has experienced many landslides due to high tectonic activity, and a variety of geological and climatic conditions. This paper proposes a novel hybrid model based on step-wise weight assessment ratio analysis (SWARA) method and adaptive neuro-fuzzy inference system (ANFIS) to evaluate landslide susceptible areas using geographical information system (GIS). At first, based on an inventory map, landslide locations were randomly divided into two parts, 70% of which were used for generating the landslide hazard map and 30% of which were used for the validation of the model. Then, twelve landslide predisposing factors, such as lithology, slope angle, slope aspect, plan curvature, profile curvature, altitude, distance to streams, distance to faults, distance to roads, land use, seismicity, and rainfall were considered for the analysis. All the factors were then weighted by the SWARA method. Considering the nature of predisposing factors, they were split into two groups, factors with discrete data and factors with continuous data. For factors with discrete data, the SWARA method was used for final weight of each class, and for factors with continuous data, results related to the center of each class were obtained from the SWARA method. Subsequently, AFNIS was used to obtain weight of each value. All the values obtained from the model were then used to generate the landslide hazard map of the study area. Finally, the landslide hazard map was validated by receiver operating characteristics (ROC) using both success rate curve and prediction rate curve. 70% of observed landslides were used for the former while the remaining was used for the latter. The validation results showed that the area under the success rate curve and prediction rate curve (AUC) are 0.84 and 0.80 respectively. Additionally, the prediction performance of the SWARA method for landslide hazard mapping was investigated and the results were compared with those obtained from the proposed model. The comparison revealed that the developed model has better prediction ability for landslide hazard assessment. The results also indicated that the proposed model used in this study produced satisfactory and reliable landslide hazard map, which can be used for preliminary land use and infrastructure planning in Iran.

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Section: Altitudementioning

confidence: 99%

A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran

Dehnavi

Aghdam

Pradhan

et al. 2015

CATENA

244

View full text Add to dashboard Cite

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“…Remote sensing data is becoming a significant source to calculate landslide susceptibility maps. ASTER image has been proved to be highly applicable in landslide susceptibility mapping in the Kelemen catchment area [3] and it has been applied to calculate landslide susceptibility maps for the Candir catchment area [2]. Aerial photographs are another significant data source and have been widely used in many regions [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A new technique for landslide mapping from a large-scale remote sensed image: A case study of Central Nepal

Chen

2017

Computers & Geosciences

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This paper presents a new technique for landslide mapping from large-scale Landsat8 images. The method introduces saliency enhancement to enhance the landslide regions, making the landslides salient objects in the image. Morphological operations are applied to the enhanced image to remove most background objects. Afterwards, digital elevation model is applied to further remove the ground objects of plain areas according to the height of landscape, since most landslides occur in mountainous areas. Final landslides are extracted by the proposal regions from selective search. The study area covers 2°x2°, making it more similar with practical cases, such as emergency response and landslide inventory mappings. The proposed method performs satisfactorily by detecting 99.1% of the landslides in the image, and obtains an overall accuracy of 99.8% in the landslides/background classification problem, which gets further validated in another Landsat8 image of a different site.The experiment shows that the proposed method is feasible for landslide detection from large-scale area, which may contribute to the further landslide-related research.

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“…Numerous studies indicate that landslides are greatly conditioned by the lithological properties of the land surface (e.g., Carrara et al, 1991;Anbalagan, 1992;Mejia-Navarro and Wohl, 1994;Mejia-Navarro and Garcia, 1996;Pachauri et al, 1998;Luzi and Pergalani, 1999;Dai and Lee, 2001;Duman et al, 2006;Hammond, 2007;Yalcin, 2008;Boboc et al, 2012;Ercanoğlu and Temiz, 2011;Nefeslioglu et al, 2012;Das et al, 2013). As an accepted rule, the geologic/lithologic theme is one of the landslide predisposing factors considered in any landslide susceptibility assessment using data-driven methods (e.g., Guzzetti et al, 1996;Van Westen et al, 2006;Blahut et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

The role of the lithological setting on the landslide pattern and distribution

Henriques

Zêzere

Marques

2015

Engineering Geology

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An assessment on the use of Terra ASTER L3A data in landslide susceptibility mapping

Cited by 52 publications

References 70 publications

A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran

A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran

A new technique for landslide mapping from a large-scale remote sensed image: A case study of Central Nepal

The role of the lithological setting on the landslide pattern and distribution

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