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Discriminating wetland vegetation species in an African savanna using hyperspectral data.

dc.contributor.advisorMutanga, Onisimo.
dc.contributor.authorMafuratidze, Pride.
dc.descriptionThesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2010.en_US
dc.description.abstractWetland vegetation is of fundamental ecological importance and is used as one of the vital bio-indicators for early signs of physical or chemical degradation in wetland systems. Wetland vegetation is being threatened by expansion of extensive lowland areas of agriculture, natural resource exploitation, etc. These threats are increasing the demand for detailed information on vegetation status, up-to-date maps as well as accurate information for mitigation and adaptive management to preserve wetland vegetation. All these requirements are difficult to produce at species or community level, due to the fact that some parts of the wetlands are inaccessible. Remote sensing offers nondestructive and real time information for sustainable and effective management of wetland vegetation. The application of remote sensing in wetland mapping has been done extensively, but unfortunately the uses of narrowband hyperspectral data remain unexplored at an advanced level. The aim of this study is to explore the potential of hyperspectral remote sensing for wetland vegetation discrimination at species level. In particular, the study concentrates on enhancing or improving class separability among wetland vegetation species. Therefore, the study relies on the following two factors; a) the use of narrowband hyperspectral remote sensing, and b) the integration of vegetation properties and vegetation indices to improve accuracy. The potential of vegetation indices and red edge position were evaluated for vegetation species discrimination. Oneway ANOVA and Canonical variate analysis were used to statistically test if the species were significantly different and to discriminate among them. The canonical structure matrix revealed that hyperspectral data transforms can discriminate vegetation species with an overall accuracy around 87%. The addition of biomass and water content variables improved the accuracy to 95.5%. Overall, the study demonstrated that hyperspectral data and vegetation properties improve wetland vegetation separability at species level.en_US
dc.subjectRemote sensing.en_US
dc.subjectMultispectral photography.en_US
dc.subjectWetlands--KwaZulu-Natal--iSimangaliso Wetland Park--Remote sensing.en_US
dc.subjectRemote sensing--KwaZulu-Natal--iSimangaliso Wetland Park.en_US
dc.titleDiscriminating wetland vegetation species in an African savanna using hyperspectral data.en_US


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