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Article: A spatiotemporal probabilistic modelling of storm-induced shallow landsliding using aerial photographs and logistic regression

TitleA spatiotemporal probabilistic modelling of storm-induced shallow landsliding using aerial photographs and logistic regression
Authors
KeywordsGeographic information system (GIS)
Landslides
Logistic regression
Probability
Rainfall
Issue Date2003
PublisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www3.interscience.wiley.com/cgi-bin/jhome/2388
Citation
Earth Surface Processes And Landforms, 2003, v. 28 n. 5, p. 527-545 How to Cite?
AbstractLandslides constitute one of the major natural hazards that could cause significant losses of life and property. Mapping or delineating areas prone to landsliding is therefore essential for land-use activities and management decision making in hilly or mountainous regions. A landslide hazard map can be constructed by a qualitative combination of maps of site conditions, including geology, topography and geomorphology, by statistical methods through correlating landslide occurrence with geologic and geomorphic factors, or by using safety factors from stability analysis. A landslide hazard map should provide information on both the spatial and temporal probabilities of landsliding in a certain area. However, most previous studies have focused on susceptibility mapping, rather than on hazard mapping in a spatiotemporal context. This study aims at developing a predictive model, based on both quasi-static and dynamic variables, to determine the probability of landsliding in terms of space and time. The study area selected is about 13 km2 in North Lantau, Hong Kong. The source areas of the landslides caused by the rainstorms of 18 July 1992 and 4-5 November 1993 were interpreted from multi-temporal aerial photographs. Landslide data, lithology, digital elevation model data, land cover, and rainfall data were digitized into a geographic information system database. A logistic regression model was developed using lithology, slope gradient, slope aspect, elevation, slope shape, land cover, and rolling 24 h rainfall as independent variables, since the dependent variable could be expressed in a dichotomous way. This model achieved an overall accuracy of 87.2%, with 89.5% of landslide grid cells correctly classified and found to be performing satisfactorily. The model was then applied to rainfalls of a variety of periods of return, to predict the probability of landsliding on natural slopes in space and time. It is observed that the modelling techniques described here are useful for predicting the spatiotemporal probability of landsliding and can be used by land-use planners to develop effective management strategies. Copyright © 2003 John Wiley and Sons, Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/70743
ISSN
2015 Impact Factor: 3.505
2015 SCImago Journal Rankings: 1.574
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorDai, FCen_HK
dc.contributor.authorLee, CFen_HK
dc.date.accessioned2010-09-06T06:25:44Z-
dc.date.available2010-09-06T06:25:44Z-
dc.date.issued2003en_HK
dc.identifier.citationEarth Surface Processes And Landforms, 2003, v. 28 n. 5, p. 527-545en_HK
dc.identifier.issn0197-9337en_HK
dc.identifier.urihttp://hdl.handle.net/10722/70743-
dc.description.abstractLandslides constitute one of the major natural hazards that could cause significant losses of life and property. Mapping or delineating areas prone to landsliding is therefore essential for land-use activities and management decision making in hilly or mountainous regions. A landslide hazard map can be constructed by a qualitative combination of maps of site conditions, including geology, topography and geomorphology, by statistical methods through correlating landslide occurrence with geologic and geomorphic factors, or by using safety factors from stability analysis. A landslide hazard map should provide information on both the spatial and temporal probabilities of landsliding in a certain area. However, most previous studies have focused on susceptibility mapping, rather than on hazard mapping in a spatiotemporal context. This study aims at developing a predictive model, based on both quasi-static and dynamic variables, to determine the probability of landsliding in terms of space and time. The study area selected is about 13 km2 in North Lantau, Hong Kong. The source areas of the landslides caused by the rainstorms of 18 July 1992 and 4-5 November 1993 were interpreted from multi-temporal aerial photographs. Landslide data, lithology, digital elevation model data, land cover, and rainfall data were digitized into a geographic information system database. A logistic regression model was developed using lithology, slope gradient, slope aspect, elevation, slope shape, land cover, and rolling 24 h rainfall as independent variables, since the dependent variable could be expressed in a dichotomous way. This model achieved an overall accuracy of 87.2%, with 89.5% of landslide grid cells correctly classified and found to be performing satisfactorily. The model was then applied to rainfalls of a variety of periods of return, to predict the probability of landsliding on natural slopes in space and time. It is observed that the modelling techniques described here are useful for predicting the spatiotemporal probability of landsliding and can be used by land-use planners to develop effective management strategies. Copyright © 2003 John Wiley and Sons, Ltd.en_HK
dc.languageengen_HK
dc.publisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www3.interscience.wiley.com/cgi-bin/jhome/2388en_HK
dc.relation.ispartofEarth Surface Processes and Landformsen_HK
dc.rightsEarth Surface Processes and Landforms. Copyright © John Wiley & Sons Ltd.en_HK
dc.subjectGeographic information system (GIS)en_HK
dc.subjectLandslidesen_HK
dc.subjectLogistic regressionen_HK
dc.subjectProbabilityen_HK
dc.subjectRainfallen_HK
dc.titleA spatiotemporal probabilistic modelling of storm-induced shallow landsliding using aerial photographs and logistic regressionen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0197-9337&volume=28&spage=527&epage=545&date=2003&atitle=A+spatiotemporal+probabilistic+modelling+of+storm-induced+shallow+landsliding+using+aerial+photographs+and+logistic+regressionen_HK
dc.identifier.emailLee, CF: leecf@hkucc.hku.hken_HK
dc.identifier.authorityLee, CF=rp00139en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1002/esp.456en_HK
dc.identifier.scopuseid_2-s2.0-0038307228en_HK
dc.identifier.hkuros82461en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0038307228&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume28en_HK
dc.identifier.issue5en_HK
dc.identifier.spage527en_HK
dc.identifier.epage545en_HK
dc.identifier.isiWOS:000183353600006-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridDai, FC=7102055666en_HK
dc.identifier.scopusauthoridLee, CF=8068602600en_HK

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