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Detecting and predicting land use and land cover change in the cross-sanaga-bioko coastal forest region for sustainable forest management
This study assesses forest, agriculture and built-up areas change in the Cross-Sanaka-Bioko (CSB) region from 2000 to 2021, aiming to provide reliable data for sustainable forest management practices. This analysis will be accomplished with the aid of GIS tools (Google Earth Engine and ArcGIS Pro) a...
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| Format: | Thesis |
| Language: | English English |
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School of Architecture, Planning and Geomatics
2025
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| _version_ | 1867614004171505664 |
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| access_status_str | Open Access |
| author | Njume, Epie Wesner |
| author2 | Hull, Simon |
| author_browse | Hull, Simon Njume, Epie Wesner |
| author_facet | Hull, Simon Njume, Epie Wesner |
| author_sort | Njume, Epie Wesner |
| collection | Thesis |
| description | This study assesses forest, agriculture and built-up areas change in the Cross-Sanaka-Bioko (CSB) region from 2000 to 2021, aiming to provide reliable data for sustainable forest management practices. This analysis will be accomplished with the aid of GIS tools (Google Earth Engine and ArcGIS Pro) and remote sensing data (LULC maps and digital elevation models) in the CSB region. Land use and land cover (LULC) changes in forested regions are critical indicators of environmental transformation, contributing to deforestation, forest degradation, and biodiversity loss, with significant impacts on the environment and human well-being. Sustainable forest management is essential for maintaining ecological balance and ensuring forest resources for future generations. A supervised LULC classification map was created for 2000, 2007, 2014, and 2021 using a decision tree-based machine learning algorithm. Loss, gain and post-classification change detection analysis were used to pinpoint significant LULC changes in the region. Identifying the potential impacts of LULC changes to the environment, air pollutants (CO, NO2, SO2, and PM2.5) were used to first evaluate the variation of emission of the pollutants over the years using a descriptive statistic. Furthermore, a point biserial correlation analysis was used to test the strength of association between the supervised LULC classes with the identified pollutants. Lastly the Multi-Layer Perceptron and Cellular Automata-Markov chain models were used to predict land cover change in the region in the year 2063 and validated by comparing the predicted 2063 map with the 2000 and 2021 classified maps in the CSB region. The study revealed a significant reduction in forested areas (35.55% loss), with the most substantial decline (14.69%) between 2007 and 2014. Agricultural and built-up areas increased by 28.05% and 13.73%, respectively. The primary LULC transition was from forests to agricultural areas, followed by built-up areas. Pollutant emissions, except for NO2, exceeded WHO-recommended values in the region. The results from the correlation analysis showed positive and negative correlations between the LULC changes and air pollutants. For example, agriculture had a moderate positive correlation with NO2 and a moderate negative correlation with CO. There is a projected 21.03% loss in forested areas by 2063, with agricultural lands expanding by 19.69% and built-up areas by 10.88%. These findings highlight the urgent need for sustainable development practices to balance forest conservation, agricultural growth, and urban expansion, aligning with Goal 7 of the African Union Agenda to promote environmental sustainability, and Goal 15, Target 15.2 of the United Nations Sustainable Development Goals. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/41137 |
| institution | University of Cape Town (South Africa) |
| language | English eng |
| last_indexed | 2026-06-10T12:45:08.739Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | School of Architecture, Planning and Geomatics |
| publisherStr | School of Architecture, Planning and Geomatics |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/41137 Detecting and predicting land use and land cover change in the cross-sanaga-bioko coastal forest region for sustainable forest management Njume, Epie Wesner Hull, Simon sustainable development LULC correlation analysis change detection descriptive statistics machine learning land cover forecasting air pollution This study assesses forest, agriculture and built-up areas change in the Cross-Sanaka-Bioko (CSB) region from 2000 to 2021, aiming to provide reliable data for sustainable forest management practices. This analysis will be accomplished with the aid of GIS tools (Google Earth Engine and ArcGIS Pro) and remote sensing data (LULC maps and digital elevation models) in the CSB region. Land use and land cover (LULC) changes in forested regions are critical indicators of environmental transformation, contributing to deforestation, forest degradation, and biodiversity loss, with significant impacts on the environment and human well-being. Sustainable forest management is essential for maintaining ecological balance and ensuring forest resources for future generations. A supervised LULC classification map was created for 2000, 2007, 2014, and 2021 using a decision tree-based machine learning algorithm. Loss, gain and post-classification change detection analysis were used to pinpoint significant LULC changes in the region. Identifying the potential impacts of LULC changes to the environment, air pollutants (CO, NO2, SO2, and PM2.5) were used to first evaluate the variation of emission of the pollutants over the years using a descriptive statistic. Furthermore, a point biserial correlation analysis was used to test the strength of association between the supervised LULC classes with the identified pollutants. Lastly the Multi-Layer Perceptron and Cellular Automata-Markov chain models were used to predict land cover change in the region in the year 2063 and validated by comparing the predicted 2063 map with the 2000 and 2021 classified maps in the CSB region. The study revealed a significant reduction in forested areas (35.55% loss), with the most substantial decline (14.69%) between 2007 and 2014. Agricultural and built-up areas increased by 28.05% and 13.73%, respectively. The primary LULC transition was from forests to agricultural areas, followed by built-up areas. Pollutant emissions, except for NO2, exceeded WHO-recommended values in the region. The results from the correlation analysis showed positive and negative correlations between the LULC changes and air pollutants. For example, agriculture had a moderate positive correlation with NO2 and a moderate negative correlation with CO. There is a projected 21.03% loss in forested areas by 2063, with agricultural lands expanding by 19.69% and built-up areas by 10.88%. These findings highlight the urgent need for sustainable development practices to balance forest conservation, agricultural growth, and urban expansion, aligning with Goal 7 of the African Union Agenda to promote environmental sustainability, and Goal 15, Target 15.2 of the United Nations Sustainable Development Goals. 2025-03-10T15:49:25Z 2025-03-10T15:49:25Z 2024 2025-03-07T13:39:25Z Thesis / Dissertation Masters MSc http://hdl.handle.net/11427/41137 en eng application/pdf School of Architecture, Planning and Geomatics Faculty of Engineering and the Built Environment University of Cape Town |
| spellingShingle | sustainable development LULC correlation analysis change detection descriptive statistics machine learning land cover forecasting air pollution Njume, Epie Wesner Detecting and predicting land use and land cover change in the cross-sanaga-bioko coastal forest region for sustainable forest management |
| thesis_degree_str | Master's |
| title | Detecting and predicting land use and land cover change in the cross-sanaga-bioko coastal forest region for sustainable forest management |
| title_full | Detecting and predicting land use and land cover change in the cross-sanaga-bioko coastal forest region for sustainable forest management |
| title_fullStr | Detecting and predicting land use and land cover change in the cross-sanaga-bioko coastal forest region for sustainable forest management |
| title_full_unstemmed | Detecting and predicting land use and land cover change in the cross-sanaga-bioko coastal forest region for sustainable forest management |
| title_short | Detecting and predicting land use and land cover change in the cross-sanaga-bioko coastal forest region for sustainable forest management |
| title_sort | detecting and predicting land use and land cover change in the cross sanaga bioko coastal forest region for sustainable forest management |
| topic | sustainable development LULC correlation analysis change detection descriptive statistics machine learning land cover forecasting air pollution |
| url | http://hdl.handle.net/11427/41137 |
| work_keys_str_mv | AT njumeepiewesner detectingandpredictinglanduseandlandcoverchangeinthecrosssanagabiokocoastalforestregionforsustainableforestmanagement |