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dc.contributor.advisorMutanga, Onisimo.
dc.creatorSibandze, Phila.
dc.date.accessioned2013-10-31T13:23:43Z
dc.date.available2013-10-31T13:23:43Z
dc.date.created2010
dc.date.issued2013-10-31
dc.identifier.urihttp://hdl.handle.net/10413/9848
dc.descriptionThesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2010.en
dc.description.abstractCannabis sativa L. is a drug producing crop that is illegally cultivated in South Africa. The South African Police Service (SAPS) use aerial spotters on low flying fixed wing aircrafts to identify cannabis from other land cover. Cannabis is usually intercropped with maize to conceal it from law enforcement officers. Therefore the use of remote sensing in identifying and monitoring cannabis when intercropped with maize and other crops is imperative. This study aimed to investigate the potential of hyper spectral indices to discriminate cannabis from maize under different cropping methods, namely, monocropped and intercropped. Cannabis and maize were grown in a greenhouse. The spectral signatures were measured in a dark room environment. Green pigments (chlorophyll and carotenoid) from the treatments were also measured. These pigments were then compared with their respective indices. Photosynthetic reflective index (PRI) and Carotenoid Reflective Index (CRI) were two of the indices used to discriminate cannabis from maize using carotenoid content while the Red Edge Position (REP) and the narrow band Normalized Difference Vegetation Index (NDVI) used chlorophyll content and morphological differences respectively to discriminate the two plant species. CRI and NDVI proved to be capable of identifying cannabis under the two cropping conditions. NDVI showed a 25% spectral over lap for the monocropped treatments and 60% over lap for the intercropped treatments. CRI displayed 18% and 58% over lap for the monocropped and intercropped treatments, respectively. As a result CRI emerged as the most suitable index for discriminating cannabis from maize. With proper calibration of airborne or space borne imagery, the study offers potential to detect cannabis using remote sensing technology.en
dc.language.isoen_ZAen
dc.subjectCannabis.en
dc.subjectCannabis--Remote sensing.en
dc.subjectCorn--Remote sensing.en
dc.subjectCrops--Remote sensing.en
dc.subjectTheses--Geography.en
dc.titleSpectral differentiation of Cannabis sativa L from maize using hyperspectral indices.en
dc.typeThesisen


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