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dc.contributor.authorMondini AC
dc.contributor.authorChang KT
dc.contributor.authorChiang SH
dc.contributor.authorSchlögel R
dc.contributor.authorNotarnicola C
dc.contributor.authorSaito H
dc.date.accessioned2018-09-24T17:21:51Z
dc.date.available2018-09-24T17:21:51Z
dc.date.issued2017
dc.identifier.issn0303-2434
dc.identifier.urihttp://dx.doi.org/10.1016/j.jag.2017.07.016
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S0303243417301629
dc.identifier.urihttp://hdl.handle.net/10863/6095
dc.description.abstractWe propose a framework to systematically generate event landslide inventory maps from satellite images in southern Taiwan, where landslides are frequent and abundant. The spectral information is used to assess the pixel land cover class membership probability through a Maximum Likelihood classifier trained with randomly generated synthetic land cover spectral fingerprints, which are obtained from an independent training images dataset. Pixels are classified as landslides when the calculated landslide class membership probability, weighted by a susceptibility model, is higher than membership probabilities of other classes. We generated synthetic fingerprints from two FORMOSAT-2 images acquired in 2009 and tested the procedure on two other images, one in 2005 and the other in 2009. We also obtained two landslide maps through manual interpretation. The agreement between the two sets of inventories is given by the Cohen’s k coefficients of 0.62 and 0.64, respectively. This procedure can now classify a new FORMOSAT-2 image automatically facilitating the production of landslide inventory maps.en_US
dc.language.isoenen_US
dc.rights
dc.titleAutomatic mapping of event landslides at basin scale in Taiwan using a Montecarlo: approach and synthetic land cover fingerprintsen_US
dc.typeArticleen_US
dc.date.updated2018-09-24T17:20:37Z
dc.language.isiEN-GB
dc.journal.titleInternational Journal of Applied Earth Observation and Geoinformation
dc.description.fulltextnoneen_US


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