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dc.contributorFaculty of Construction and Environmenten_US
dc.contributorDepartment of Land Surveying and Geo-Informaticsen_US
dc.contributor.advisorWong, Man-sing Charles (LSGI)-
dc.creatorHo, Jessica Evelyn-
dc.publisherHong Kong Polytechnic University-
dc.rightsAll rights reserveden_US
dc.titleMapping mikania micrantha using vegetation indices of rapideye imagery on Lamma Island, Hong Kongen_US
dcterms.abstractMikania micrantha is an invasive plant species to Hong Kong due to its rapid growing speed and its virtuous climbing ability, which outcompetes local vegetation species for sunlight, killing native plants and causing destruction to local habitats and ecosystems. Current approach to monitor and control the spread of mikania micrantha relies on inspection works by relevant government departments and report by volunteers. Limited information about their actual distribution was available. The advancement in remote sensing and image processing technology offers an alternative to identify potential areas invaded by mikania micrantha. The aim of this study was to design an approach to map mikania micrantha by RapidEye satellite imagery. Using maximum likelihood classification, the potential use of four vegetation indices including normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), blue normalized difference vegetation index (BNDVI) and normalized difference red edge index (NDRE) computed from the original spectral bands of RapidEye imagery to map mikania on Lamma Island, seriously invaded by the weed, were investigated. Field data was used to validate classification results by confusion matrices. This study demonstrated an increase of number of vegetation indices incorporated in the analysis has improved classification accuracy of the species. An integration of all four vegetation indices in analysis yielded best results with an overall accuracy of 78%. Moreover, it was found that normalized difference red edge index (NDRE) had more contribution than other indices on accuracy to classify mikania micrantha. This added a strong advantage to RapidEye for mikania micrantha mapping since it has red-edge and near infra-red bands for computing NDRE. As satisfactory results were obtained by the proposed method in this study, further research could be conducted to evaluate the method for mapping the species in areas beyond Lamma Island in Hong Kong or in other areas affected by the species.en_US
dcterms.extentxv, 86 pages : color illustrationsen_US
dcterms.educationalLevelAll Masteren_US
dcterms.LCSHInvasive plants -- China - Hong Kongen_US
dcterms.LCSHVegetation monitoring -- Remote sensingen_US
dcterms.LCSHLamma Island (Hong Kong, China)en_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertationsen_US
dcterms.accessRightsrestricted accessen_US

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