Detecting and predicting land use and land cover change in the cross-sanaga-bioko coastal forest region for sustainable forest management

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dc.contributor.advisorHull, Simon
dc.contributor.authorNjume, Epie Wesner
dc.date.accessioned2025-03-10T15:49:25Z
dc.date.available2025-03-10T15:49:25Z
dc.date.issued2024
dc.date.updated2025-03-07T13:39:25Z
dc.description.abstractThis 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.
dc.identifier.apacitationNjume, E. W. (2024). <i>Detecting and predicting land use and land cover change in the cross-sanaga-bioko coastal forest region for sustainable forest management</i>. (). University of Cape Town ,Faculty of Engineering and the Built Environment ,School of Architecture, Planning and Geomatics. Retrieved from http://hdl.handle.net/11427/41137en_ZA
dc.identifier.chicagocitationNjume, Epie Wesner. <i>"Detecting and predicting land use and land cover change in the cross-sanaga-bioko coastal forest region for sustainable forest management."</i> ., University of Cape Town ,Faculty of Engineering and the Built Environment ,School of Architecture, Planning and Geomatics, 2024. http://hdl.handle.net/11427/41137en_ZA
dc.identifier.citationNjume, E.W. 2024. Detecting and predicting land use and land cover change in the cross-sanaga-bioko coastal forest region for sustainable forest management. . University of Cape Town ,Faculty of Engineering and the Built Environment ,School of Architecture, Planning and Geomatics. http://hdl.handle.net/11427/41137en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Njume, Epie Wesner AB - 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. DA - 2024 DB - OpenUCT DP - University of Cape Town KW - sustainable development KW - LULC KW - correlation analysis KW - change detection KW - descriptive statistics KW - machine learning KW - land cover forecasting KW - air pollution LK - https://open.uct.ac.za PB - University of Cape Town PY - 2024 T1 - Detecting and predicting land use and land cover change in the cross-sanaga-bioko coastal forest region for sustainable forest management TI - Detecting and predicting land use and land cover change in the cross-sanaga-bioko coastal forest region for sustainable forest management UR - http://hdl.handle.net/11427/41137 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/41137
dc.identifier.vancouvercitationNjume EW. Detecting and predicting land use and land cover change in the cross-sanaga-bioko coastal forest region for sustainable forest management. []. University of Cape Town ,Faculty of Engineering and the Built Environment ,School of Architecture, Planning and Geomatics, 2024 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/41137en_ZA
dc.language.isoen
dc.language.rfc3066eng
dc.publisher.departmentSchool of Architecture, Planning and Geomatics
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.publisher.institutionUniversity of Cape Town
dc.subjectsustainable development
dc.subjectLULC
dc.subjectcorrelation analysis
dc.subjectchange detection
dc.subjectdescriptive statistics
dc.subjectmachine learning
dc.subjectland cover forecasting
dc.subjectair pollution
dc.titleDetecting and predicting land use and land cover change in the cross-sanaga-bioko coastal forest region for sustainable forest management
dc.typeThesis / Dissertation
dc.type.qualificationlevelMasters
dc.type.qualificationlevelMSc
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