A GIS-based automated procedure for landslide susceptibility mapping by the Conditional Analysis method: the Baganza valley case study (Italian Northern Apennines)

Clerici, A.; Perego, Susanna; Tellini, Claudio; Vescovi, Paolo

doi:10.1007/s00254-006-0264-7

Cited by 175 publications

(95 citation statements)

References 48 publications

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“…At the same time, GIS and RS technologies have comprehensively been used in the landslide susceptibility study Lan et al 2002Lan et al , 2004Kouli et al 2010;Niu et al 2012). All landslide susceptibility assessment methods, despite methodological and operational differences, are based on the common assumption that the future landslides will be likely to occur under the same conditions that induce past landslides (Clerici et al 2006;Aleotti and Chowdhury 1999;Guzzetti et al 1999).…”

Section: Introductionmentioning

confidence: 99%

Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China

et al. 2014

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The Lushan earthquake with magnitude M s 7.0 (M w 6.6, USGS) in Sichuan Province, China, triggered a large number of landslides, which seriously aggravated the earthquake's destructive consequences. This paper mainly focuses on the methodology of the urgent landslide susceptibility assessment right after the earthquake. The detailed landslide inventory (including 5,688 landslides) is prepared by means of urgent postearthquake landslide field survey, landslide remote sensing interpretation of multi-source remote sensing images including high-resolution unmanned aerial vehicle images and historical landslide archives. Ten remarkable causative factors for landslide occurrence have been selected to conduct the landslide susceptibility assessment, including earthquake intensity, landslide density and slope gradient. An integration assessment approach is developed to facilitate the effective urgent post-earthquake landslide susceptibility assessment using three methods: factor sensitivity analysis, analytical hierarchy process and factor-weighted overlay. Such integration can effectively reduce the subjectivity and uncertainty resulting from using single method. The validation evaluation using the area under curve suggests the landslide susceptibility assessment results have satisfactory accuracy, and the suggested methodology is effective for the urgent post-earthquake landslide susceptibility assessment. The study results reveal that earthquake intensity and slope gradient are the two most important causative factors for post-earthquake landslide occurrence in the Lushan earthquake-impacted area. The dominant slope gradient and slope aspect with relatively higher landslide frequency are 45°-50°and south-east direction, respectively. The intense earthquake impact increased the dominant slope gradient of 123Nat Hazards (2015) 75:2467-2487 DOI 10.1007/s11069-014-1441 landslide spatial distribution, and the thrust campaign of seismogenic fault with strike NE-SW made south-east direction as the dominant slope aspect of the landslide spatial distribution. The locations with very high and high landslide susceptibility are mainly distributed in the regions with higher earthquake intensity and adverse terrain conditions, such as Shuangshi town and Longmen town of Lushan county and Muping town of Baoxing county. The study results are expected to provide a beneficial reference for the landslide prevention and infrastructure reconstruction after the Lushan earthquake.

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Section: Introductionmentioning

confidence: 99%

Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China

et al. 2014

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“…Val i da tion of the model was per formed by ap ply ing a com bina tion of the meth ods used by Clerici et al (2006). At first, the land slide da ta base was ran domly split into two datasets.…”

Section: Methodsmentioning

confidence: 99%

Landslide susceptibility mapping in the Beskid Niski Mts., Western Carpathians (Dukla commune, Poland)

Bronowski

Chybiorz

Jura

2016

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. Land slide sus cep ti bil ity map ping in the Beskid Niski Mts., West ern Carpathians (Dukla com mune, Po land). Geo log i cal Quar terly, 60 (3): 586-596, doi: 10.7306/gq.1275 Land slides are one of the most prom i nent pro cesses de grad ing the Beskidy Mts. slopes. Sus cep ti bil ity as sess ment is based on the de tailed map ping of the Dukla com mune (235 km 2 ) at the scale 1:10,000 in frames of the pro ject Land slide Coun teract ing Sys tem. The ter rain af fected by land slides is 19.59 km 2 , which is 8.34% of the re searched area. The larg est num ber of land slides can be found in the Beskid Dukielski Mts. area, where nearly 78% of all land slide ar eas are con cen trated. Anal y sis of land slide-caus ing fac tors was per formed us ing the in dex of en tropy method. The fol low ing five fac tors were con sid ered: lithol ogy, slope in cli na tion, slope as pect, dis tance to faults, and dis tance to drain age. The most im por tant role in the de vel opment of mass move ments in the study area is played by slope in cli na tion (0.0079) and li thol ogy of siliciclastic flysch (0.0066). The method ap plied also con trib uted to de vis ing a land slide sus cep ti bil ity map of con sid er able ac cu racy at 90.5%. The analy ses of the land slide fac tors have proven that un der stand ing of ge ol ogy and land forms is not only of cog ni tive sig nif i cance, but adds to prac ti cal ap pli ca tions in spa tial man age ment. The stud ies con firmed use ful ness of large-scale map ping of landslides and ap ply ing sta tis ti cal meth ods and GIS tech niques in the spa tial anal y sis of landform trans for ma tions. The ob tained land slide sus cep ti bil ity map can be a ba sis for as sess ing land slide risk. High and very high land slide sus cep ti bil ity classes cover nearly 50% of the study area.

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“…Recently, there have been studies on landslidehazard evaluation using GIS, and many of these studies have applied probabilistic models (Akgun et al 2007;Dahal et al 2007;Clerici et al 2006;Cevik and Topal 2003;Rowbotham and Dudycha 1998;Jibson et al 2000;Luzi et al 2000;Parise and Jibson 2000;Baeza and Corominas 2001;Clerici et al 2002;Donati and Turrini 2002;Zhou et al 2002;Lee et al 2004b). One of the statistical models available, the logistic regression models, has also been applied to landslide-hazard mapping (Tunusluoglu et al 2007;Lamelas et al 2008;Wang and Sassa 2005;Süzen and Doyuran 2004;Dai and Lee 2002;Ohlmacher and Davis 2003;Mansor et al 2007;Pradhan et al 2006, as has the geotechnical model and the safety factor model (Gokceoglu et al 2000;Romeo 2000;Refice and Capolongo 2002;Carro et al 2003;Shou and Wang 2003;Zhou et al 2003, Lee 2007.…”

Section: Introductionmentioning

confidence: 99%

Use of geospatial data and fuzzy algebraic operators to landslide-hazard mapping

2009

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Geospatial data base creation for landslidehazard mapping is often an almost inhibitive activity. This has been the reason that for quite some time landslidehazard analysis was modeled on the basis of spatially related factors. This paper presents the use of fuzzy logic to landslide-hazard analysis in the Penang Island, Malaysia, using remote sensing data and a geographic information system (GIS). To achieve the goal, a data-derived model (frequency ratio) and a knowledge-derived model (fuzzy operator) were combined for landslide-hazard analysis. Landslide locations within the study areas were identified by interpreting aerial photographs, satellite images and field surveys. The nine factors that influence landslide occurrence were extracted from the database and the frequency ratio coefficient for each factor was computed. Using the factors and the identified landslide, the fuzzy membership values were calculated. Then fuzzy algebraic operators were applied to the fuzzy membership values for landslidehazard mapping. Finally, the produced map was verified by comparing with existing landslide locations for calculating prediction accuracy. Among the fuzzy operators, in the case in which the gamma operator (λ=0.8) showed the best accuracy (80%) while the case in which the fuzzy or operator was applied showed the worst accuracy (56%).

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A GIS-based automated procedure for landslide susceptibility mapping by the Conditional Analysis method: the Baganza valley case study (Italian Northern Apennines)

Cited by 175 publications

References 48 publications

Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China

Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China

Landslide susceptibility mapping in the Beskid Niski Mts., Western Carpathians (Dukla commune, Poland)

Use of geospatial data and fuzzy algebraic operators to landslide-hazard mapping

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