Prosopis invasion in the Northern Cape: remote sensing analysis of management action effectiveness

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dc.contributor.advisorDe Klerk, Helen Margareten_ZA
dc.contributor.advisorVan Wilgen, Brian W.en_ZA
dc.contributor.advisorEckert, Sandraen_ZA
dc.contributor.authorBarnard, Johannes Jacobusen_ZA
dc.contributor.otherStellenbosch University. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies.en_ZA
dc.date.accessioned2021-11-09T16:44:09Z
dc.date.accessioned2021-12-22T14:24:58Z
dc.date.available2021-11-09T16:44:09Z
dc.date.available2021-12-22T14:24:58Z
dc.date.issued2021-12
dc.descriptionThesis (MSc)--Stellenbosch University, 2021.en_ZA
dc.description.abstractENGLISH ABSTRACT: Large-scale land acquisitions (LSLAs) for agricultural sector have grown significantly in the past decade, and are mostly prevalent in developing countries. Because LSLAs are not without negative effects on the environment and local communities, and because information about them is scarce and difficult to obtain, systems allowing LSLAs detection, characterization and monitoring in space and time are needed. With the increasing availability of global satellite data products, technological development in cloud computing, image and data mining analysis, remote sensing has evolved to an interesting tool for the detection and characterization of changes in land use systems. This study presents a novel approach to generically detect and characterize LSLAs at regional spatial extents. In order to capture and analyse the full range of land use spectral and spatial signatures related to agricultural LSLAs, this study is based on a 2-level data driven approach (Self-Organizing Maps followed by a clustering algorithm), consisting of two phases: 1) land use/land cover change detection at regional scale within dense temporal stacks of vegetation indices (MODIS-NDVI, 250m) and 2), discrimination of different land use/land cover classes using a set of spectral vegetation indices, textural features and shape metrics computed from landscape-extracted objects (Landsat-8, 30m). Evaluation of the methodology is performed against a ground truth database on LSLAs in Senegal. Results obtained during this exploratory research, are promising and provide some insights in agricultural LSLAs in the northern half of Senegal. With a very limited number of discriminative features (consisting of two Vegetation Indices and two textural features), detection of agricultural LSLAs is possible. Recommendations are given for enhancement of the generalization performance of the unsupervised classifier.en_ZA
dc.description.abstractAFRIKAANSE OPSOMMING: Geen Afrikaanse opsomming beskikbaar.af_ZA
dc.description.versionMastersen_ZA
dc.format.extent89 pages : illustrationsen_ZA
dc.identifier.urihttp://hdl.handle.net/10019.1/123849
dc.language.isoen_ZAen_ZA
dc.publisherStellenbosch : Stellenbosch Universityen_ZA
dc.rights.holderStellenbosch Universityen_ZA
dc.subjectMesquiteen_ZA
dc.subjectProsopisen_ZA
dc.subjectInvasive plantsen_ZA
dc.subjectRemote sensingen_ZA
dc.subjectGoogle Earthen_ZA
dc.subjectLegumesen_ZA
dc.subjectPlant introduction -- South Africa -- Northern Capeen_ZA
dc.subjectMultispectral imagingen_ZA
dc.subjectGeometry -- Data processingen_ZA
dc.subjectMachine learningaf_ZA
dc.subjectAlgorithmsen_ZA
dc.subjectUCTD
dc.titleProsopis invasion in the Northern Cape: remote sensing analysis of management action effectivenessen_ZA
dc.typeThesisen_ZA
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