Mapping and monitoring land transformation of Boane district, Mozambique (1980 – 2020), using remote sensing
| dc.contributor.author | Dengo, Claudio Antonio | |
| dc.contributor.co-supervisor | Atif, Iqra | |
| dc.contributor.supervisor | Adam, Elhadi | |
| dc.date.accessioned | 2024-11-14T15:00:30Z | |
| dc.date.available | 2024-11-14T15:00:30Z | |
| dc.date.issued | 2023 | |
| dc.description | A research report submitted in fulfilment of the requirements for the degree of Masters of Science (in Geographical Information Systems and Remote Sensing), to the Faculty of Science, at the School of Geography, Archaeology & Environmental Studies, University of the Witwatersrand, Johannesburg, 2023. | |
| dc.description.abstract | Although natural and environmental factors play a significant role in land transformation, human actions dominate. Therefore, to better understand the present land uses and predict the future, accurate information describing the nature and extent of changes over time is necessary and critical, especially for developing countries. It is estimated that these countries will account for 50% of the world's population growth in the next few years. Hence, this research was an attempt to assess and monitor land cover changes in Boane, Mozambique, over the past 40 years and predict what to expect in the next 30 years. This district has been challenged by a fast-growing population and land use dynamic, with quantitative information, driving forces and impacts remaining unknown. Through a supervised process in a cloud base Google Earth Engine platform, a set of five Landsat images at ten-year intervals were classified using a random forest algorithm. Seven land classes, i.e., agriculture, forest, built-up, barren, rock, wetland and water bodies, were extracted and compared through a pixel-by-pixel process as one of the most precise and accurate methods in remote sensing and geographic information system applications. The results indicate an active alternate between all land classes, with significant changes observed within agriculture, forest and build-up classes. As it is, while agriculture (-26.1%) and forest (-21.4%) showed a continuously decreasing pattern, build-up class (45.8%) increased tremendously. Consequently, over 69% of the forest area and 59% of the agricultural area shifted into build-up, i.e., was degraded or destroyed. Similarly, the conversion of barren land area (57.2%) and rock area (47.3%) into build-up indicates that those areas were cleaned. The overall classification accuracy averaged 90% and a kappa coefficient of 0.8779 were obtained. The CA-Markov model, used to assess future land uses, indicates that build-up will continue to increase significantly, covering 60% of the total area. From this finding, the land cover situation in the next 30 years will be critical if no action is taken to stop this uncontrolled urban sprawl. An adequate land use plan must be drawn, clearly indicating the locations for different activities and actions for implementation. | |
| dc.description.sponsorship | National Statistical Institute of Mozambique. | |
| dc.description.submitter | MMM2024 | |
| dc.faculty | Faculty of Science | |
| dc.identifier.citation | Dengo, Claudio Antonio. (2023). Mapping and monitoring land transformation of Boane district, Mozambique (1980 – 2020), using remote sensing. [Master's dissertation, University of the Witwatersrand, Johannesburg]. https://hdl.handle.net/10539/42552 | |
| dc.identifier.uri | https://hdl.handle.net/10539/42552 | |
| dc.language.iso | en | |
| dc.publisher | University of the Witwatersrand, Johannesburg | |
| dc.rights | ©2023 University of the Witwatersrand, Johannesburg. All rights reserved. The copyright in this work vests in the University of the Witwatersrand, Johannesburg. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of University of the Witwatersrand, Johannesburg. | |
| dc.rights.holder | University of the Witwatersrand, Johannesburg | |
| dc.school | School of Geography, Archaeology and Environmental Studies | |
| dc.subject | Boane | |
| dc.subject | Land use-land cover | |
| dc.subject | Change detection | |
| dc.subject | Land transformation | |
| dc.subject | Google Earth Engine | |
| dc.subject | Random forest | |
| dc.subject | UCTD | |
| dc.subject.other | SDG-13: Climate action | |
| dc.title | Mapping and monitoring land transformation of Boane district, Mozambique (1980 – 2020), using remote sensing | |
| dc.type | Dissertation |