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Modelling biomass of the rehabilitation forest around the Buffelsdraai landfill site using remote sensing data, Durban, South Africa.

dc.contributor.advisorGebreslasie, Michael Teweldemedhin.
dc.contributor.authorMkhabela, Nozipho Nokubongwa.
dc.date.accessioned2020-03-31T09:31:41Z
dc.date.available2020-03-31T09:31:41Z
dc.date.created2017
dc.date.issued2017
dc.descriptionMasters Degree. University of KwaZulu-Natal, Durban.en_US
dc.description.abstractForests have important roles in ecosystem service provisions and maintenance of the global carbon cycle hence they are one of the main subjects of the Intergovernmental Panel on Climate Change which recommends strategies to stabilize greenhouse gas emissions. Remote sensing is an advancing science whose data products keep improving spectrally and spatially with time which makes them worth exploitation for broad scientific uses including forest-related studies such as biomass estimations. These are important for understanding of carbon sequestration potential of trees which informs monitoring and forest cover enhancement strategies across various environments. This study investigated the potential of optical data, Landsat 8 Operational Land Imager (OLI) to achieve biomass estimation in a secondary indigenous forest that buffers the Buffelsdraai landfill site. Image processing types used included extraction of spectral reflectance bands, vegetation indices and texture parameters. A Partial Least Squares analysis was performed to determine a significant set of independent variables that could predict aboveground biomass of the Buffelsdraai rehabilitation forest. The findings indicated that the Partial Least Squares models of bands and vegetation indices were rather weak in biomass prediction as only 11.22% and 30.88% biomass variation was explained, respectively. Models inclusive of texture extractions, however, performed much better and demonstrated an improved 77.33% variation explanation of above-ground biomass. Overall, the results indicate that texture parameters derived from Landsat 8 OLI optical data are effective to achieve improved biomass estimation. The development of allometric equations built directly from the species found in the rehabilitation zone and national instilment of environmental responsibility within society for improved local waste management were the major recommendations provided which would assist in the stabilization of greenhouse gas emissions in Buffelsdraai and South Africa.en_US
dc.identifier.urihttps://researchspace.ukzn.ac.za/handle/10413/17263
dc.language.isoenen_US
dc.subject.otherForests.en_US
dc.subject.otherRemote sensing.en_US
dc.subject.otherBiomass estimations.en_US
dc.subject.otherOptical data.en_US
dc.subject.otherSecondary indigenous forest.en_US
dc.subject.otherRehabilitation.en_US
dc.titleModelling biomass of the rehabilitation forest around the Buffelsdraai landfill site using remote sensing data, Durban, South Africa.en_US
dc.typeThesisen_US

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