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eISSN 2029-7092 online
eISBN 978-609-457-640-9
ISBN 978-609-457-690-4 CD
Technologies of Geodesy and Cadastre

Servel Miller, Malgorzata Leszczynska

Evaluation of the use high resolution satellite Imagery to map slope instability in a tropical environment: St. Thomas, Jamaica

Conference Information: 9th International Conference “Environmental Engineering”, 22–23 May 2014, Vilnius, LITHUANIA
Source: ICEE-2014 - International Conference on Environmental Engineering
Book Series: International Conference on Environmental Engineering (ICEE) Selected papers
ISSN: eISSN 2029-7092 online
ISBN: eISBN 978-609-457-640-9
ISBN: ISBN 978-609-457-690-4 CD
Year: 2014
Publisher: Vilnius Gediminas Technical University Press Technika

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Landslides 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.

Keywords: natural hazard; landslide risk maps; landslide data; Geographical Information System.

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