Abstract
The purpose of this study is to detect landslide locations from satellite images and use them for landslide susceptibility mapping in the Sagimakri area, Korea using a geographic information system and a data-driven weight of evidence model. The landslide location areas were identified from Korea multipurpose satellite images by means of change detection technique and further verified by extensive field survey. Subsequently, landslide locations were randomly selected in a 70:30 ratio for training and validation of the model, respectively. A spatial database was constructed, which is composed of topography, forest, soil, and land cover, and 14 landslide-related factors were extracted from the database. The relationships between the detected landslide locations and the factors were identified and quantified by weights of evidence model. Tests of conditional independence were performed for the selection of factors, allowing five different combinations of factors to be analyzed. The relationships were used as the contrast values, W + and W − of factor ratings in the overlay analysis to create landslide susceptibility indexes and maps. The results of the analysis were validated by comparison with known landslide locations that were not used directly in the analysis.
Similar content being viewed by others
References
Abdallah C, Chorowicz J, Bou kheir R, Khawlie M (2005) Detecting major terrain parameters relating to mass movements’ occurrence using GIS, remote sensing and statistical correlations, case study Lebanon. Remote Sens Environ 99:448–461
Akgun A, Türk N (2010) Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multicriteria decision analysis. Environ Earth Sci 61:595–611
Baeza C, Lantada N, Moya J (2010) Validation and evaluation of two multivariate statistical models for predictive shallow landslide susceptibility mapping of the Eastern Pyrenees (Spain). Environ Earth Sci 61:507–523
Bai SB, Wang J, Lü GN, Zhou PG, Hou SS, Xu SN (2010) GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China. Geomorphology 115:23–31
Bai S, Lü G, Wang J, Zhou P, Ding L (2011) GIS-based rare events logistic regression for landslide-susceptibility mapping of Lianyungang, China. Environ Earth Sci 62:139–149
Bonham-Carter GF (1994) Geographic information systems for geoscientists, modeling with GIS. Pergamon, Oxford
Bonham-Carter GF, Agterberg FP, Wright DF (1989) Weights of evidence modeling: a new approach to mapping mineral potential. In: Agterberg FP, Bonham-Carter GF (Eds) Statistical applications in the earth sciences: geological survey Canada paper 89-9, pp 171–183
Borgogno Mondino E, Giardino M, Perotti L (2009) A neural network method for analysis of hyperspectral imagery with application to the Cassas landslide (Susa Valley, NW-Italy). Geomorphology 110:20–27
Brabb EE (1984) Innovative Approaches to Landslide Hazard and Risk Mapping. Proc Fourth Int Symp Landslides Can Geotech Soc, Toronto, Canada 1:307–324
Carranza EJM, Hale M (2000) Geologically constrained probabilistic mapping of gold potential, Baguio district, Philippines. Nat Resour Res 9:237–253
Chang KT, Chiang SH (2009) An integrated model for predicting rainfall-induced landslides. Geomorphology 105:366–373
Chauhan S, Sharma M, Arora M (2010) Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model. Landslides 7:411–423
Chen CH, KE CC, Wang CL (2009a) A back-propagation network for the assessment of susceptibility to rock slope failure in the eastern portion of the Southern Cross-Island Highway in Taiwan. Environ Geol 57:723–733
Chen YR, Ni PN, Chen JW, Hsieh SC (2009b) The application of remote sensing technology to the interpretation of land use for rainfall-induced landslides based on genetic algorithms and artificial neural networks. IEEE J Sel Top Applied Earth Observ Rem Sens 2:87–95
Choi J, Oh HJ, Won JS, Lee S (2010) Validation of an artificial neural network model for landslide susceptibility mapping. Environ Earth Sci 60:473–483
Chung CJF, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping. Photogramm Eng Rem S 65:1389–1399
Dahal R, Hasegawa S, Nonomura A, Yamanaka M, Masuda T, Nishino K (2008) GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping. Environ Geol 54:311–324
Das I, Sahoo S, Van Westen C, Stein A, Hack R (2010) Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas (India). Geomorphology 114:627–637
Dominguez-Cuesta M, Jimenez-Sánchez M, Colubi A, González-Rodríguez G (2010) Modelling shallow landslide susceptibility: a new approach in logistic regression by using favourability assessment. Int J Earth Sci 99:661–674
Dong JJ, Lee CT, Tung YH, Liu CN, Lin KP, Lee JF (2009) The role of the sediment budget in understanding debris flow susceptibility. Earth Surf Proc Land 34:1612–1624
Ercanoglu M, Temiz FA (2011) Application of logistic regression and fuzzy operators to landslide susceptibility assessment in Azdavay (Kastamonu, Turkey). Environ Earth Sci 64:949–964
Erener A, Düzgün HSB (2012) Landslide susceptibility assessment: what are the effects of mapping unit and mapping method? Environ Earth Sci 66:859–877
Falaschi F, Giacomelli F, Federici P, Puccinelli A, D’amato Avanzi G, Pochini A, Ribolini A (2009) Logistic regression versus artificial neural networks: landslide susceptibility evaluation in a sample area of the Serchio River valley, Italy. Nat Hazards 50:551–569
Gao J, Maro J (2010) Topographic controls on evolution of shallow landslides in pastoral Wairarapa, New Zealand, 1979–2003. Geomorphology 114:373–381
Gorsevski PV, Jankowski P (2010) An optimized solution of multi-criteria evaluation an analysis of landslide susceptibility using fuzzy sets and Kalman filter. Com Geo 36:1005–1020
Guzzetti F, Reichenbach P, Cardinali M, Galli M, Ardizzone F (2005) Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72:272–299
He YR, Fu WJ (2009) Application of fuzzy support vector machine to landslide risk assessment. J Nat Disasters 18:107–112
Hong Y, Adler RF, Huffman G (2007) An experimental global prediction system for Rainfall-Triggered landslides using satellite remote sensing and geospatial datasets. IEEE T Geosci Remote 45:1671–1680
Intarawichian N, Dasananda S (2011) Frequency ratio model based landslide susceptibility mapping in lower Mae Chaem watershed, Northern Thailand. Environ Earth Sci 64:2271–2285
Kanungo D, Arora M, Gupta R, Sarkar S (2008) Landslide risk assessment using concepts of danger pixels and fuzzy set theory in Darjeeling Himalayas. Landslides 5:407–416
Kanungo DP, Arora MK, Sarkar S, Gupta RP (2009) A fuzzy set based approach for integration of thematic maps for landslide susceptibility zonation. Georisk 3:30–43
Kawabata D, Bandibas J (2009) Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN). Geomorphology 113:97–109
Kincal C, Akgun A, Koca M (2009) Landslide susceptibility assessment in the İzmir (West Anatolia, Turkey) city center and its near vicinity by the logistic regression method. Environ Earth Sci 59:745–756
Korea Forest Research Institute (1997) Forest map, 1:25,000
Korea Institute of Geoscience and Mineral Resources (2001) Geological map, 1:250,000
Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int J Remote Sens 26:1477–1491
Lee S (2007a) Application and verification of fuzzy algebraic operators to landslide susceptibility mapping. Environ Geol 52:615–623
Lee S (2007b) Landslide susceptibility mapping using an artificial neural network in the Gangneung area, Korea. Int J Remote Sens 28:4763–4783
Lee S, Choi J (2004) Landslide susceptibility mapping Using GIS and the weight-of-evidence model. Int J Geogr Inf Sci 18:789–814
Lee S, Lee MJ (2006) Detecting landslide location using KOMPSAT 1 and its application to landslide-susceptibility mapping at the Gangneung area, Korea. Adv Space Res 38:2261–2271
Lee S, Ryu JH, Lee MJ, Won JS (2006) The application of artificial neural networks to landslide susceptibility mapping at Janghung, Korea. Math Geol 38:199–220
Lee S, Ryu JH, Kim IS (2007) Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea. Landslides 4:327–338
Legorreta Paulin G, Bursik M (2009) Logisnet: a tool for multimethod, multiple soil layers slope stability analysis. Com Geo 35:1007–1016
Lepore C, Kamal SA, Shanahan P, Bras RL (2012) Rainfall-induced landslide susceptibility zonation of Puerto Rico. Environ Earth Sci 66:1667–1681
Liu CN, Dong JJ, Peng YF, Huang HF (2009) Effects of strong ground motion on the susceptibility of gully type debris flows. Eng Geol 104:241–253
Lyon JG, Yuan D, Lunetta RS, Elvidge CD (1998) A change detection experiment using vegetation indices. Photogramm Rem Sens 64:143–150
Mathew J, Jha VK, Rawat GS (2007) Application of binary logistic regression analysis and its validation for landslide susceptibility mapping in part of Garhwal Himalaya, India. Int J Remote Sens 28:2257–2275
Melchiorre C, Matteucci M, Azzoni A, Zanchi A (2008) Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology 94:379–400
Metternicht G, Hurnl L, Gogu R (2005) Remote sensing of landslides: an analysis of the potential contribution to geo-spatial systems for hazard assessment in mountainous environments. Remote Sens Environ 98:284–303
Miles SB, Keefer DK (2009) Evaluation of CAMEL—comprehensive areal model of earthquake-induced landslides. Eng Geol 104:1–15
Muthu K, Petrou M, Tarantino C, Blonda P (2008) Landslide possibility mapping using fuzzy approaches. IEEE T Geosci Remote 46:1253–1265
National Geographic Information Institute (2005) Topography map, 1:5,000
National Institute of Agricultural Science and Technology (1983) Detailed soil map, 1:25,000
Neuhäuser B, Terhorst B (2007) Landslide susceptibility assessment using “weights-of-evidence” applied to a study area at the Jurassic Escarpment (Sw-Germany). Geomorphology 86:12–24
Oh HJ, Lee S (2010) Cross-validation of logistic regression model for landslide susceptibility mapping at Geneoung areas, Korea. Disaster Advances 3:44–55
Oh HJ, Lee S (2011a) Landslide susceptibility mapping on Panaon Island, Philippines using a geographic information system. Environ Earth Sci 62:935–951
Oh HJ, Lee S (2011b) Cross-application used to validate landslide susceptibility maps using a probabilistic model from Korea. Environ Earth Sci 64:395–409
Oh HJ, Pradhan B (2011) Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Com Geo 37:1264–1276
Oh HJ, Lee S, Soedradjat GM (2010) Quantitative landslide susceptibility mapping at Pemalang area, Indonesia. Environ Earth Sci 60:1317–1328
Ozdemir A (2009) Landslide susceptibility mapping of vicinity of Yaka Landslide (Gelendost, Turkey) using conditional probability approach in GIS. Environ Geol 57:1675–1686
Park NW (2010) Application of Dempster-Shafer theory of evidence to GIS-based landslide susceptibility analysis. Environ Earth Sci 62:367–376
Poudyal CP, Chang C, Oh HJ, Lee S (2010) Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya. Environ Earth Sci 61:1049–1064
Prabu S, Ramakrishnan S (2009) Combined use of socio economic analysis, remote sensing and GIS data for landslide hazard mapping using ANN. J Indian Soc Rem Sens 37:409–421
Pradhan B (2010) Remote sensing and GIS-based landslide hazard analysis and cross validation using multivariate logistic regression model on three test areas in Malaysia. Adv Space Res 45:1244–1256
Pradhan B (2011) Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia. Environ Earth Sci 63:329–349
Pradhan B, Buchroithner MF (2010) Comparison and validation of landslide susceptibility maps using an artificial neural network model for three test areas in Malaysia. Environ Eng Geosci 16:107–126
Pradhan B, Lee S (2007) Utilization of optical remote sensing data and GIS tools for regional landslide hazard analysis using an artificial neural network model. Earth Sci Front 14:143–151
Pradhan B, Lee S (2009) Landslide risk analysis using artificial neural network model focussing on different training sites. Int J Physic Sci 4:01–015
Pradhan B, Lee S (2010) Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides 7:13–30
Pradhan B, Lee S, Mansor S, Buchroithner M, Jamaluddin N, Khujaimah Z (2008) Utilization of optical remote sensing data and geographic information system tools for regional landslide hazard analysis by using binomial logistic regression model. J App Rem Sens 2:023542
Pradhan B, Lee S, Buchroithner MF (2010) A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses. Comput Environ Urban 34:216–235
Qi S, Xu Q, Lan H, Zhang B, Liu J (2010) Spatial distribution analysis of landslides triggered by 2008.5.12 Wenchuan Earthquake. China. Eng Geol 116:95–108
Rapolla A, Paoletti V, Secomandi M (2010) Seismically-induced landslide susceptibility evaluation: application of a new procedure to the island of Ischia, Campania Region, Southern Italy. Eng Geol 114:10–25
Regmi NR, Giardino JR, Vitek JD (2010) Modeling susceptibility to landslides using the weight of evidence approach: western Colorado, USA. Geomorphology 115:172–187
Shafri HZM, Zahidi IM, Bakar SA (2010) Development of landslide susceptibility map utilizing remote sensing and Geographic Information Systems (GIS). Disaster Prev Manag 19:59–69
Singh A (1989) Digital change detection techniques using remotely-sensed data. Int J Remote Sens 10:989–1003
Sterlacchini S, Ballabio C, Blahut J, Masetti M, Sorichetta A (2011) Spatial Agreement of Predicted Patterns in Landslide Susceptibility Maps. Geomorphology 125:51–61
Suh J, Choi Y, Roh TD, Lee HJ, Park HD (2011) National-scale assessment of landslide susceptibility to rank the vulnerability to failure of rock-cut slopes along expressways in Korea. Environ Earth Sci 63:619–632
Tangestani MH (2009) A comparative study of Dempster-Shafer and fuzzy models for landslide susceptibility mapping using a GIS: an experience from Zagros Mountains, SW Iran. J Asian Earth Sci 35:66–73
Thiery Y, Malet JP, Sterlacchini S, Puissant A, Maquaire O (2007) Landslide susceptibility assessment by bivariate methods at large scales: application to a complex mountainous environment. Geomorphology 92:38–59
Vahidnia MH, Alesheikh AA, Alimohammadi A, Hosseinali F (2009) Landslide hazard zonation using quantitative methods in GIS. Int J Civil Eng 7:176–189
Van Westen CJ, Rengers N, Soeters R (2003) Use of geomorphological information in indirect landslide susceptibility assessment. Nat Hazards 30:399–419
Wang WD, Guo J, Fang LG, Chang XS (2012) A subjective and objective integrated weighting method for landslides susceptibility mapping based on GIS. Environ Earth Sci 65:1705–1714
Winterkorn HF, Fang HY (1991) Soil technology and engineering properties of soils. In: Foundation engineering handbook, 2nd edn. Van Nostrand Reinhold, New York, pp 88–143
Wu CH, Chen SC (2009) Determining landslide susceptibility in Central Taiwan from rainfall and six site factors using the analytical hierarchy process method. Geomorphology 112:190–204
Yalcin A, Reis S, Aydinoglu AC, Yomralioglu T (2011) A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena 85:274–287
Yilmaz I (2009a) A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by artificial neural networks. Bull Eng Geol Environ 68:297–306
Yilmaz I (2009b) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat-Turkey). Com Geo 35:1125–1138
Yilmaz I (2010) The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability and artificial neural networks. Environ Earth Sci 60:505–519
Yilmaz C, Topal T, Suzen ML (2011) GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak-Turkey). Environ Earth Sci. doi:10.1007/s12665-011-1196-4
Acknowledgments
This research was supported by the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources (KIGAM) funded by the Ministry of Knowledge and Economy of Korea.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lee, S. Landslide detection and susceptibility mapping in the Sagimakri area, Korea using KOMPSAT-1 and weight of evidence technique. Environ Earth Sci 70, 3197–3215 (2013). https://doi.org/10.1007/s12665-013-2385-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12665-013-2385-0