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Prosopis invasion in the Northern Cape: remote sensing analysis of management action effectiveness
Thesis (MSc)--Stellenbosch University, 2021.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2021
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| _version_ | 1867614079610257408 |
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| access_status_str | Open Access |
| author | Barnard, Johannes Jacobus |
| author2 | De Klerk, Helen Margaret |
| author_browse | Barnard, Johannes Jacobus De Klerk, Helen Margaret |
| author_facet | De Klerk, Helen Margaret Barnard, Johannes Jacobus |
| author_sort | Barnard, Johannes Jacobus |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MSc)--Stellenbosch University, 2021. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/123849 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:46:20.037Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/123849 Prosopis invasion in the Northern Cape: remote sensing analysis of management action effectiveness Barnard, Johannes Jacobus De Klerk, Helen Margaret Van Wilgen, Brian W. Eckert, Sandra Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies. Mesquite Prosopis Invasive plants Remote sensing Google Earth Legumes Plant introduction -- South Africa -- Northern Cape Multispectral imaging Geometry -- Data processing Algorithms Machine learning UCTD Thesis (MSc)--Stellenbosch University, 2021. ENGLISH 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. AFRIKAANSE OPSOMMING: Geen Afrikaanse opsomming beskikbaar. Masters 2021-11-09T16:44:09Z 2021-12-22T14:24:58Z 2021-11-09T16:44:09Z 2021-12-22T14:24:58Z 2021-12 Thesis http://hdl.handle.net/10019.1/123849 en_ZA Stellenbosch University 89 pages : illustrations application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Mesquite Prosopis Invasive plants Remote sensing Google Earth Legumes Plant introduction -- South Africa -- Northern Cape Multispectral imaging Geometry -- Data processing Algorithms Machine learning UCTD Barnard, Johannes Jacobus Prosopis invasion in the Northern Cape: remote sensing analysis of management action effectiveness |
| title | Prosopis invasion in the Northern Cape: remote sensing analysis of management action effectiveness |
| title_full | Prosopis invasion in the Northern Cape: remote sensing analysis of management action effectiveness |
| title_fullStr | Prosopis invasion in the Northern Cape: remote sensing analysis of management action effectiveness |
| title_full_unstemmed | Prosopis invasion in the Northern Cape: remote sensing analysis of management action effectiveness |
| title_short | Prosopis invasion in the Northern Cape: remote sensing analysis of management action effectiveness |
| title_sort | prosopis invasion in the northern cape remote sensing analysis of management action effectiveness |
| topic | Mesquite Prosopis Invasive plants Remote sensing Google Earth Legumes Plant introduction -- South Africa -- Northern Cape Multispectral imaging Geometry -- Data processing Algorithms Machine learning UCTD |
| url | http://hdl.handle.net/10019.1/123849 |
| work_keys_str_mv | AT barnardjohannesjacobus prosopisinvasioninthenortherncaperemotesensinganalysisofmanagementactioneffectiveness |