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dc.contributor.advisorMutanga, Onisimo.
dc.contributor.advisorCho, Moses Azong.
dc.creatorMalahlela, Oupa.
dc.date.accessioned2014-04-04T11:47:43Z
dc.date.available2014-04-04T11:47:43Z
dc.date.created2013
dc.date.issued2013
dc.identifier.urihttp://hdl.handle.net/10413/10562
dc.descriptionThesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2013.en
dc.description.abstractSeveral alien plants are invading subtropical forest ecosystems through canopy gaps, resulting in the loss of native species biodiversity. The loss of native species in such habitats may result in reduced ecosystem functioning. The control and eradication of these invaders requires accurate mapping of the levels of invasion in canopy gaps. Our study tested (i) the utility of WorldView-2 imagery to map forest canopy gaps, and (ii) an integration of WorldView-2 data with environmental data to model the probability of occurrence of invasive Chromolaena odorata (triffid weed) in Dukuduku forest canopy gaps of KwaZulu- Natal, South Africa. Both pixel-based classification and object-based classification were explored for the delineation of forest canopy gaps. The overall classification accuracies increased by ± 12% from a spectrally resampled 4 band image similar to Landsat (74.64%) to an 8 band WorldView-2 imagery (86.90%). This indicates that the new bands of WorldView such as the red edge band can improve on the capability of common red, blue, green and near-infrared bands in delineating forest canopy gaps. The maximum likelihood classifier (MLC) in pixel-based classification yielded the overall classification accuracy of 86.90% on an 8 band WorldView-2 image, while the modified plant senescence reflectance index (mPSRI) in object-based classification yielded 93.69%. The McNemar’s test indicated that there was a statistical difference between the MLC and the mPSRI. The mPSRI is a vegetation index that incorporates the use of the red edge band, which solves a saturation problem common in sensors such as Landsat and SPOT. An integrated model (with both WorldView-2 data and environmental data) used to predict the occurrence of Chromolaena odorata in forest gaps yielded a deviance of about 42% (D2 = 0.42), compared to the model derived from environmental data only (D2 = 0.12) and WorldView-2 data only (D2 = 0.20). A D2 of 0.42 means that a model can explain about 42% of the variability of the presence/absence of Chromolaena odorata in forest gaps. The Distance to Stream and Aspect were the significant environmental variables (ρ < 0.05) which were positively correlated with presence/absence of Chromolaena in forest gaps. WorldView-2 bands such as the coastal band (λ425 nm) yellow band (λ605 nm) and the nearinfrared- 1 (λ833 nm) are positively and significantly related to the presence/absence of invasive species (ρ < 0.05). On the other hand, a significant negative correlation (ρ < 0.05) of near-infrared-2 band (λ950 nm) and the red edge normalized difference vegetation index (NDVI725) suggests that the probability of occurrence of invasive Chromolaena increases forest gaps with low vegetation density. This study highlights the importance of WorldView- 2 imagery and its application in subtropical indigenous coastal forest monitoring.en
dc.language.isoen_ZAen
dc.subjectForests and forestry--Remote sensing.en
dc.subjectForest canopies--Remote sensing.en
dc.subjectChromolaena odorata--KwaZulu-Natal--Dukuduku forest.en
dc.subjectTheses--Geography.en
dc.titleIntergrating environmental variables with worldview-2 data to model the probability of occurence of invasive chromolena odata in forest canopy gaps : Dukuduku forest in KwaZulu-Natal, South Africa.en
dc.typeThesisen


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