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dc.contributor.authorMiller, Servel*
dc.contributor.authorLeszczyńska, Małgorzata*
dc.date.accessioned2016-03-09T17:53:58Zen
dc.date.available2016-03-09T17:53:58Zen
dc.date.issued2014-12-31en
dc.identifier.citationMiller, S. & Leszczynska, M. (2014). Evaluation of the use high resolution satellite imagery to map slope instability in a tropical environment: St. Thomas, Jamaica. Selected papers_The 9th International Conference Environmental Engineering. Vilnius, Lithuania.en
dc.identifier.isbn9786094576409en
dc.identifier.doi10.3846/enviro.2014.229
dc.identifier.urihttp://hdl.handle.net/10034/601056en
dc.description.abstractLandslides are a major natural hazard in Jamaica, and have resulted in loss of life, major economic losses, social disruption and damage to public and private properties. There is a need to delineate areas that are prone to slope instability in order to mitigate their effects. The first and most important stage for the creation of a landslide risk maps is the collection of accurate landslide data in a timely manner. However the type of terrain makes landslide mapping particularly difficult. Aerial Photographs have proven to be an effective way of mapping landslides but acquiring new photographs to map recent landslides is very expensive. High resolution satellite imagery were evaluated for their effectiveness in delineating landslides. The landslides on a whole had no distinctive spectral property; hence no one classification technique could be used to identify them. This research developed integrative methods utilising a combination of: edge enhancement to delineate the scarps area; Wetness Index to identify back titling blocks and debris flow lobes where moisture is higher; shape classification (to distinguish from e.g. ground cleared for agriculture); and slope curvature to map scarps. The information from the image classification was combined in a GIS and automated to determine the probability of the presence and or absence of a landslides. Data derived was validated against detailed field mapping at a scale of 1:5000. For more recent landslides, the modelling proved to be effective, accurately identifying 91% of the landslide both in terms of the location and extent. For the older landslides Pre 2000) the mapping was less effective, with misclassification as high as 24% particularly for smaller landslides. However, the use of these imagery does have great potential as they prove useful for mapping new landslides quickly and efficiently after landslide disaster and are much cheaper and quicker to acquire.
dc.description.sponsorshipUniversity of Chester, KT research grant, and as an outcome of statutory research no. 528-0302-0828 Faculty of Geodesy and Land Management, Institute of Geodesy, bUniversity of Warmia and Mazury in Olsztynen
dc.language.isoenen
dc.publisherVGTU Pressen
dc.relation.urlhttp://leidykla.vgtu.lt/conferences/ENVIRO_2014/Articles/5/229_Miller.pdfen
dc.subjectLandslideen
dc.subjectGISen
dc.subjectRemore sensingen
dc.subjectGeomaticsen
dc.subjectHazardsen
dc.subjectRisken
dc.titleEvaluation of the use high resolution satellite Imagery to map slope instability in a tropical environment: St. Thomas, Jamaicaen
dc.typeMeetings and Proceedingsen
dc.contributor.departmentUniversity of Chesteren
rioxxterms.versionofrecordhttps://doi.org/10.3846/enviro.2014.229
html.description.abstractLandslides are a major natural hazard in Jamaica, and have resulted in loss of life, major economic losses, social disruption and damage to public and private properties. There is a need to delineate areas that are prone to slope instability in order to mitigate their effects. The first and most important stage for the creation of a landslide risk maps is the collection of accurate landslide data in a timely manner. However the type of terrain makes landslide mapping particularly difficult. Aerial Photographs have proven to be an effective way of mapping landslides but acquiring new photographs to map recent landslides is very expensive. High resolution satellite imagery were evaluated for their effectiveness in delineating landslides. The landslides on a whole had no distinctive spectral property; hence no one classification technique could be used to identify them. This research developed integrative methods utilising a combination of: edge enhancement to delineate the scarps area; Wetness Index to identify back titling blocks and debris flow lobes where moisture is higher; shape classification (to distinguish from e.g. ground cleared for agriculture); and slope curvature to map scarps. The information from the image classification was combined in a GIS and automated to determine the probability of the presence and or absence of a landslides. Data derived was validated against detailed field mapping at a scale of 1:5000. For more recent landslides, the modelling proved to be effective, accurately identifying 91% of the landslide both in terms of the location and extent. For the older landslides Pre 2000) the mapping was less effective, with misclassification as high as 24% particularly for smaller landslides. However, the use of these imagery does have great potential as they prove useful for mapping new landslides quickly and efficiently after landslide disaster and are much cheaper and quicker to acquire.
rioxxterms.publicationdate2014-12-31
dc.date.deposited2016-03-09


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