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2017 | OriginalPaper | Buchkapitel

12. Slope Vulnerability and Risk Assessment Using High-Resolution Airborne Laser Scanning Data

verfasst von : Biswajeet Pradhan, Norbazlan Mohd Yusof

Erschienen in: Laser Scanning Applications in Landslide Assessment

Verlag: Springer International Publishing

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Abstract

Natural hazards, such as landslides, earthquakes, and floods, result in considerable losses of lives and properties. Natural disasters are in fact the main cause of irrecoverable damages worldwide.

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Vorheriges Kapitel Performance Evaluation and Sensitivity Analysis of Expert-Based, Statistical, Machine Learning, and Hybrid Models for Producing Landslide Susceptibility Maps

Nächstes Kapitel Landslide Risk Assessment Using Multi-hazard Scenario Produced by Logistic Regression and LiDAR-Based DEM

Akgun, A. (2012). A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: A case study at İzmir, Turkey. Landslides, 9(1), 93–106.CrossRef

Alimohammadlou, Y., Najafi, A., & Yalcin, A. (2013). Landslide process and impacts: A proposed classification method. Catena, 104, 219–232.CrossRef

Alonso, E. E., & Pinyol, N. M. (2010). Criteria for rapid sliding I. A review of Vaiont case. Engineering Geology, 114(3), 198–210.CrossRef

Althuwaynee, O. F., Pradhan, B., & Ahmad, N. (2014a). Estimation of rainfall threshold and its use in landslide hazard mapping of Kuala Lumpur metropolitan and surrounding areas. Landslides, 12, 1–15.

Althuwaynee, O. F., Pradhan, B., & Lee, S. (2012). Application of an evidential belief function model in landslide susceptibility mapping. Computers & Geosciences, 44, 120–135.CrossRef

Althuwaynee, O. F., Pradhan, B., Park, H. J., & Lee, J. H. (2014b). A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena, 114, 21–36.CrossRef

Ayalew, L., & Yamagishi, H. (2005). The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology, 65(1), 15–31.CrossRef

Bell, R., & Glade, T. (2004). Quantitative risk analysis for landslides? Examples from Bíldudalur, NW-Iceland. Natural Hazard Earth System, 4(1), 117–131.CrossRef

Budimir, M., Atkinson, P., & Lewis, H. (2015). A systematic review of landslide probability mapping using logistic regression. Landslides, 12(3), 1–18.CrossRef

Bulut, F., Boynukalin, S., Tarhan, F., & Ataoglu, E. (2000). Reliability of landslide isopleth maps. Bulletin of Engineering Geology and the Environment, 58(2), 95–98.CrossRef

Calil, J., Beck, M. W., Gleason, M., Merrifield, M., Klausmeyer, K., & Newkirk, S. (2015). Aligning natural resource conservation and flood hazard mitigation in California. PLoS ONE, 10(7), e0132651. doi:10.1371/journal.pone.0132651 CrossRef

Calvo, B., & Savi, F. (2009). A real-world application of Monte Carlo procedure for debris flow risk assessment. Computers & Geosciences, 35(5), 967–977.CrossRef

Chau, K., Sze, Y., Fung, M., Wong, W., Fong, E., & Chan, L. (2004). Landslide hazard analysis for Hong Kong using landslide inventory and GIS. Computers & Geosciences, 30(4), 429–443.CrossRef

Conforti, M., Pascale, S., Robustelli, G., & Sdao, F. (2014). Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy). Catena, 113, 236–250.CrossRef

Corominas, J., Van Westen, C., Frattini, P., Cascini, L., Malet, J. P., Fotopoulou, S., et al. (2014). Recommendations for the quantitative analysis of landslide risk. Bulletin of Engineering Geology and the Environment, 73(2), 209–263.

Crovelli, R. A. (2000). Probability models for estimation of number and costs of landslides. Reston, VA: US Geological Survey.

Crozier, M. J., & Glade, T. (2006). Landslide hazard and risk: Issues, concepts and approach. Landslide Hazard and Risk (Wiley, West Sussex), 14, 1–40.

Cui, P., Zhu, Y. Y., Han, Y. S., Chen, X. Q., & Zhuang, J. Q. (2009). The 12 May Wenchuan earthquake-induced landslide lakes: Distribution and preliminary risk evaluation. Landslides, 6(3), 209–223.CrossRef

Dai, F., Lee, C., & Ngai, Y. Y. (2002). Landslide risk assessment and management: An overview. Engineering Geology, 64(1), 65–87.CrossRef

Dang, N. M., Babel, M. S., & Luong, H. T. (2011). Evaluation of food risk parameters in the Day River flood diversion area, Red River delta, Vietnam. Natural Hazards, 56(1), 169–194.CrossRef

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(4), 627–637.CrossRef

Demir, G., Aytekin, M., & Akgun, A. (2015). Landslide susceptibility mapping by frequency ratio and logistic regression methods: An example from Niksar-Resadiye (Tokat, Turkey). Arabian Journal of Geosciences, 8(3), 1801–1812.CrossRef

Devkota, K. C., Regmi, A. D., Pourghasemi, H. R., Yoshida, K., Pradhan, B., Ryu, I. C., et al. (2013). Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya. Natural Hazards, 65(1), 135–165.CrossRef

Dou, J., Bui, D. T., Yunus, A. P., Jia, K., Song, X., Revhaug, I., et al. (2015). Optimization of causative factors for landslide susceptibility evaluation using remote sensing and GIS data in parts of Niigata, Japan. PloS One, 10(7), e0133262. doi:10.1371/journal.pone.0133262 CrossRef

Dou, J., Oguchi, T., Hayakawa, Y. S., Uchiyama, S., Saito, H., & Paudel, U. (2014). GIS-based landslide susceptibility mapping using a certainty factor model and its validation in the Chuetsu Area, Central Japan. Landslide Science for a Safer Geoenvironment (pp. 419–424). Berlin: Springer.

Dragićević, S., Lai, T., & Balram, S. (2015). GIS-based multicriteria evaluation with multiscale analysis to characterize urban landslide susceptibility in data-scarce environments. Habitat International, 45, 114–125.CrossRef

Feizizadeh, B., & Blaschke, T. (2013). GIS-multicriteria decision analysis for landslide susceptibility mapping: Comparing three methods for the Urmia lake basin, Iran. Natural Hazards, 65(3), 2105–2128.CrossRef

Fuchs, S., Heiss, K., & Hübl, J. (2007). Towards an empirical vulnerability function for use in debris flow risk assessment. Natural Hazard Earth Systems, 7(5), 495–506.CrossRef

Galli, M., & Guzzetti, F. (2007). Landslide vulnerability criteria: A case study from Umbria, Central Italy. Environmental Management, 40(4), 649–665.CrossRef

Glade, T., Crozier, M., & Smith, P. (2000). Applying probability determination to refine landslide-triggering rainfall thresholds using an empirical “Antecedent Daily Rainfall Model”. Pure and Applied Geophysics, 157(6–8), 1059–1079.CrossRef

Guzzetti, F., Carrara, A., Cardinali, M., & Reichenbach, P. (1999). Landslide hazard evaluation: A review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology, 31(1), 181–216.CrossRef

Guzzetti, F., Peruccacci, S., Rossi, M., & Stark, C. P. (2008). The rainfall intensity–duration control of shallow landslides and debris flows: An update. Landslides, 5(1), 3–17.CrossRef

Haneberg, W. C. (2004). A rational probabilistic method for spatially distributed landslide hazard assessment. Environmental and Engineering Geoscience, 10(1), 27–43.CrossRef

He, X., Hong, Y., Yu, X., Cerato, A. B., Zhang, X., & Komac, M. (2014). Landslides susceptibility mapping in Oklahoma state using GIS-based weighted linear combination method. In Landslide science for a safer geoenvironment (pp. 371–377).

Huang, J., Lyamin, A., Griffiths, D., Krabbenhoft, K., & Sloan, S. (2013). Quantitative risk assessment of landslide by limit analysis and random fields. Computers and Geotechnics, 53, 60–67.CrossRef

Jaiswal, P., van Westen, C. J., & Jetten, V. (2010). Quantitative landslide hazard assessment along a transportation corridor in southern India. Engineering Geology, 116(3), 236–250.CrossRef

Jaiswal, P., van Westen, C. J., & Jetten, V. (2011). Quantitative assessment of landslide hazard along transportation lines using historical records. Landslides, 8(3), 279–291.CrossRef

Jakob, M., Stein, D., & Ulmi, M. (2012). Vulnerability of buildings to debris flow impact. Natural Hazards, 60(2), 241–261.CrossRef

Jebur, M. N., Pradhan, B., & Tehrany, M. S. (2014). Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale. Remote Sensing of Environment, 152, 150–165.CrossRef

Jebur, M. N., Pradhan, B., & Tehrany, M. S. (2015). Manifestation of LiDAR-derived parameters in the spatial prediction of landslides using novel ensemble evidential belief functions and support vector machine models in GIS. IEEE J-STARS, 8(2), 674–690.

Joshi, C., de Leeuw, J., & van Duren, I. C. (Eds.) (2004). Remote sensing and GIS applications for mapping and spatial modelling of invasive species. In Proceedings of ISPRS.

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(4), 407–416.CrossRef

Kappes, M. S., Keiler, M., von Elverfeldt, K., & Glade, T. (2012). Challenges of analyzing multi-hazard risk: A review. Natural Hazards, 64(2), 1925–1958.CrossRef

Kavzoglu, T., Sahin, E. K., & Colkesen, I. (2014). Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides, 11(3), 425–439.CrossRef

Kia, M. B., Pirasteh, S., Pradhan, B., Mahmud, A. R., Sulaiman, W. N. A., & Moradi, A. (2012). An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environmental Earth Science, 67(1), 251–264.CrossRef

Kobal, M., Bertoncelj, I., Pirotti, F., Dakskobler, I., & Kutnar, L. (2015). Using LiDAR data to analyse Sinkhole characteristics relevant for understory vegetation under forest cover-case study of a high Karst Area in the Dinaric Mountains. PLoS ONE, 10(3), e0122070. doi:10.1371/journal.pone.0122070 CrossRef

Kovač, B., & Žalik, B. (2010). Visualization of LiDAR datasets using point-based rendering technique. Computers & Geosciences, 36(11), 1443–1450.CrossRef

Kritikos, T., & Davies, T. (2014). Assessment of rainfall-generated shallow landslide/debris-flow susceptibility and runout using a GIS-based approach: application to western Southern Alps of New Zealand. Landslides, 15, 1–25.

Lee, M. L., Ng, K. Y., Huang, Y. F., & Li, W. C. (2014). Rainfall-induced landslides in Hulu Kelang area, Malaysia. Natural Hazards, 70(1), 353–375.CrossRef

Leone, F., Asté, J., & Leroi, E. (1996). Vulnerability assessment of elements exposed to mass-movement: Working toward a better risk perception. Landslides-Glissements de Terrain Balkema, Rotterdam, 7, 263–270.

Ma, R. (2004). Building model reconstruction from LiDAR data and aerial photographs. The Ohio State University.

Martha, T. R., van Westen, C. J., Kerle, N., Jetten, V., & Kumar, K. V. (2013). Landslide hazard and risk assessment using semi-automatically created landslide inventories. Geomorphology, 184, 139–150.CrossRef

Moonjun, R. (2007). Application of artificial neural network and decision tree in a GIS–based predictive soil mapping for landslide vulnerability study: A case study of Hoi Num Rin sub–Watershed, Thailand, ITC. Enschede, 104, 16–32.

Moreiras, S. M. (2015). Landslides as climate indicators in Argentinean Central Andes (32° S). In Engineering geology for society and territory (Vol. 1, pp. 443–447).

Muhammad, R. F. (2010). The geomorphology and origin of Gua Tempurung, Perak, West Malaysia. Bulletin of the Geological Society of Malaysia, 56, 127–132.

Murillo-García, F., Rossi, M., Fiorucci, F., & Alcántara-Ayala, I. (2015). Population Landslide vulnerability evaluation: The case of the indigenous population of Pahuatlán-Puebla, Mexico. In Engineering geology for society and territory (Vol. 2, pp. 1793–1797).

Muthukumar, M. (2013). GIS based geosystem response modelling for landslide vulnerability mapping parts of Nilgiris, South India. Disaster Advances, 6(7), 58–66.

Nandi, A., & Shakoor, A. (2010). A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Engineering Geology, 110(1), 11–20.CrossRef

Ohlmacher, G. C., & Davis, J. C. (2003). Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Engineering Geology, 69(3), 331–343.CrossRef

Opolot, E. (2013). Application of remote sensing and geographical information systems in flood management: A review. Research Journal of Applied Sciences Engineering and Technology, 6, 1884–1894.

Pan, W. S., Lu, Y. D., & Guo, J. Y. (2014). Risk assessment and management of geological disaster based on risk period analysis and GIS in Loess Areas. In Applied mechanics and materials (Vol. 675, pp. 1184–1191).

Papathoma-Köhle, M., Zischg, A., Fuchs, S., Glade, T., & Keiler, M. (2015). Loss estimation for landslides in mountain areas–An integrated toolbox for vulnerability assessment and damage documentation. Environment Modelling and Software, 63, 156–169.CrossRef

Park, S., Choi, C., Kim, B., & Kim, J. (2013). Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environmental Earth Sciences, 68(5), 1443–1464.CrossRef

Pedrazzini, A., Humair, F., Jaboyedoff, M., & Tonini, M. (2015). Characterisation and spatial distribution of gravitational slope deformation in the Upper Rhone catchment (Western Swiss Alps). Landslides, 8, 1–19.

Pradhan, B. (2010). Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. Journal of the Indian Society of Remote Sensing, 38(2), 301–320.CrossRef

Pradhan, B. (2011). Manifestation of an advanced fuzzy logic model coupled with Geo-information techniques to landslide susceptibility mapping and their comparison with logistic regression modelling. Environmental and Ecological Statistics, 18(3), 471–493.CrossRef

Pradhan, B., Hagemann, U., Tehrany, M. S., & Prechtel, N. (2014). An easy to use ArcMap based texture analysis program for extraction of flooded areas from TerraSAR-X satellite image. Computers & Geosciences, 63, 34–43.CrossRef

Pradhan, B., & Lee, S. (2009). Landslide risk analysis using artificial neural network model focusing on different training sites. International Journal of Physical Sciences, 3(11), 1–15.

Pradhan, B., & Lee, S. (2010). Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environmental Earth Sciences, 60(5), 1037–1054.CrossRef

Regmi, N. R., Giardino, J. R., McDonald, E. V., & Vitek, J. D. (2014). A comparison of logistic regression-based models of susceptibility to landslides in western Colorado, USA. Landslides, 11(2), 247–262.CrossRef

Romeo, R. W., Mari, M., & Pappafico, G. (2013). A performance-based approach to landslide risk analysis and management. In C. Margottini, P. Canuti, & K. Sassa (Eds.), Landslide science and practice (pp. 91–95). Berlin: Springer.CrossRef

Saito, H., Nakayama, D., & Matsuyama, H. (2009). Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: The Akaishi Mountains, Japan. Geomorphology, 109(3), 108–121.CrossRef

Sarkar, S., Kanungo, D. P., Patra, A., & Kumar, P. (2008). GIS based spatial data analysis for landslide susceptibility mapping. Journal of Mountain Science-England, 5(1), 52–62.CrossRef

Sattenpalli, S., & Parkash, S. (2013). Landslides, land use systems and food security. In C. Margottini, P. Canuti, & K. Sassa (Eds.), Landslide science and practice (pp. 151–161). Berlin: Springer.CrossRef

Shaharom, S., Huat, L. T., & Othman, M. A. (2014). Area based landslide hazard and risk assessment for Penang Island Malaysia. In K. Sassa, P. Canuti, & Y. Yin (Eds.), Landslide Science for a Safer Geoenvironment (pp. 513–519).

Stoffel, M. (2010). Magnitude–frequency relationships of debris flows—A case study based on field surveys and tree-ring records. Geomorphology, 116(1), 67–76.CrossRef

Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2013). Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. Journal of Hydrology, 504, 69–79.CrossRef

Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2014). Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. Journal of Hydrology, 512, 332–343.CrossRef

Tierney, K. J., Lindell, M. K., & Perry, R. W. (2001). Facing the unexpected: Disaster preparedness and response in the United States. USA: Joseph Henry Press.

Tournadour, E., Mulder, T., Borgomano, J., Hanquiez, V., Ducassou, E., & Gillet, H. (2015). Origin and architecture of a mass transport complex on the northwest slope of Little Bahama Bank (Bahamas): Relations between off-bank transport, bottom current sedimentation and submarine landslides. Sediment Geology, 317, 9–26.CrossRef

Umar, Z., Pradhan, B., Ahmad, A., Jebur, M. N., & Tehrany, M. S. (2014). Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. Catena, 118, 124–135.CrossRef

Uzielli, M., Nadim, F., Lacasse, S., & Kaynia, A. M. (2008). A conceptual framework for quantitative estimation of physical vulnerability to landslides. Engineering Geology, 102(3), 251–256.CrossRef

Van Westen, C. J., Castellanos, E., & Kuriakose, S. L. (2008). Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview. Engineering Geology, 102(3), 112–131.CrossRef

Varoonchotikul, P. (2003). Flood forecasting using artificial neural networks. The Netherlands: CRC Press.

Wang, L. J., Sawada, K., & Moriguchi, S. (2013). Landslide susceptibility analysis with logistic regression model based on FCM sampling strategy. Computers & Geosciences, 57, 81–92.CrossRef

Winter, M., Smith, J., Fotopoulou, S., Pitilakis, K., Mavrouli, O., Corominas, J., et al. (2014). An expert judgement approach to determining the physical vulnerability of roads to debris flow. Bulletin of Engineering Geology and the Environment, 73(2), 291–305.CrossRef

Xu, C., Dai, F., Xu, X., & Lee, Y. H. (2012). GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology, 145, 70–80.CrossRef

Xu, C., Xu, X., Yao, X., & Dai, F. (2014). Three (nearly) complete inventories of landslides triggered by the May 12, 2008 Wenchuan Mw 7.9 earthquake of China and their spatial distribution statistical analysis. Landslides, 11(3), 441–461.CrossRef

Yalcin, A., Reis, S., Aydinoglu, A., & 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(3), 274–287.CrossRef

Yesilnacar, E., & Topal, T. (2005). Landslide susceptibility mapping: A comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Engineering Geology, 79(3), 251–266.CrossRef

Yilmaz, I. (2009). Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat-Turkey). Computers & Geosciences, 35(6), 1125–1138.CrossRef

Yuan, R. M., Tang, C. L., & Deng, Q. H. (2015). Effect of the acceleration component normal to the sliding surface on earthquake-induced landslide triggering. Landslides, 12(2), 335–344.CrossRef

Yusof, N. M., Pradhan, B., Shafri, H. Z. M., Jebur, M. N., & Yusoff, Z. (2015). Spatial landslide hazard assessment along the Jelapang Corridor of the North–South expressway in Malaysia using high resolution airborne LiDAR data. Arabian Journal of Geoscience, 8, 1–12.CrossRef

Zare, M., Pourghasemi, H. R., Vafakhah, M., & Pradhan, B. (2013). Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: A comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms. Arabian Journal of Geoscience, 6(8), 2873–2888.CrossRef

Titel: Slope Vulnerability and Risk Assessment Using High-Resolution Airborne Laser Scanning Data
verfasst von: Biswajeet Pradhan
Norbazlan Mohd Yusof
Verlag: Springer International Publishing
Buch: Laser Scanning Applications in Landslide Assessment
Print ISBN: 978-3-319-55341-2

Electronic ISBN: 978-3-319-55342-9

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-55342-9_12