The use of earth observation multi-sensor systems to monitor and model Pastures: a case of Savannah Grasslands in Hluvukani Village, Bushbuckridge Local Municipality, Mpumalanga Province, South Africa
- Nduku, Lwandile https://orcid.org/0000-0001-9168-4548
- Authors: Nduku, Lwandile https://orcid.org/0000-0001-9168-4548
- Date: 2022-01
- Subjects: Climatic changes , Grassland conservation
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/22578 , vital:52470
- Description: Grassland degradation associated with climate change and inappropriate grassland management has been characterized as a global environmental concern driving decreased grassland ecosystem's ecological functioning. More than 60% of South African grassland is degraded or permanently transformed to other land uses and nearly 2% properly conserved. Yet, grasslands are a major source of food for livestock grazing and provide material and non-material benefits to many livelihoods. Therefore, grassland above-ground biomass (AGB) estimation is crucial in planning and managing pastoral agriculture and the benefits derived from it. However, current grassland monitoring techniques used in rural smallholder livestock farms rely on conventional methods, which are destructive, labour-intensive, costly, and restricted to small areas. This study investigated the monitoring and modelling of protected grasslands biomass using current Earth observation systems (EOS), an approach, which is non-destructive, cost-effective, cover larger areas and is a time-saving alternative to conventional methods. Hence, the research objectives were: (i) to map the trends and advances in data and models used in the monitoring of grassland (pastures) with Earth observation systems, and (ii) to assess above-ground biomass estimation in semi-arid savannah grassland integrating Sentinel-1 and Sentinel-2 data with Machine-Learning. This goal was to assess if this approach could provide the requisite information, which could contribute to the long-term goal of developing a semi-automated system for data processing, and mapping grassland biomass to benefit local communities. For this investigation, it was crucial to understanding what research had achieved so far in this area of pasture management. An assessment of the Scopus database showed the recent developments in European Union (EU) programs and Sentinel missions, including statistical models and machine learning for monitoring grassland changes at multiple scales. However, Sentinel-1 and Sentinel-2 data, machine learning models, and variable importance techniques were applied for grassland AGB estimation. These techniques have been used in similar studies to determine optimum machine learning models, influential variables, and the capability of integrated Sentinel datasets for mapping grassland AGB, spatial distribution, and abundance. Results showed improved performance with the Random forest regression (RFR) model (R² of 34.7%, RMSE of 9.47 Mg and MAE of 7.68 Mg ). The study also observed optimum sensitivity of Difference Vegetation Index (DVI) and Enhanced Vegetation Index (EVI) in all three machine learning models for modelling grassland AGB estimation in the study area. A further, statistical comparison of all three machine learning models showed an insignificant difference in the predictive capacity for AGB in the study area with Gradient Boosting regression (GBR) model (R² of 27.7, RMSE of 9.97 Mg and MAE of 8.03 Mg ) and Extreme Gradient Boost Regression (XGBR) model (R² of 17.3%, RMSE of 10.66 Mg and MAE of 8.83 Mg ). The study revealed that an integration of Sentinel-1 and Sentinel-2 has improved capabilities for monitoring grassland AGB estimation. This research sheds light on the timely and cost-effective techniques for grassland management strategies to enhance or restore the ecological functioning of grassland ecosystems and promote community sustainability. , Thesis (MSc) -- Faculty of Science and Agriculture, 2022
- Full Text:
- Authors: Nduku, Lwandile https://orcid.org/0000-0001-9168-4548
- Date: 2022-01
- Subjects: Climatic changes , Grassland conservation
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/22578 , vital:52470
- Description: Grassland degradation associated with climate change and inappropriate grassland management has been characterized as a global environmental concern driving decreased grassland ecosystem's ecological functioning. More than 60% of South African grassland is degraded or permanently transformed to other land uses and nearly 2% properly conserved. Yet, grasslands are a major source of food for livestock grazing and provide material and non-material benefits to many livelihoods. Therefore, grassland above-ground biomass (AGB) estimation is crucial in planning and managing pastoral agriculture and the benefits derived from it. However, current grassland monitoring techniques used in rural smallholder livestock farms rely on conventional methods, which are destructive, labour-intensive, costly, and restricted to small areas. This study investigated the monitoring and modelling of protected grasslands biomass using current Earth observation systems (EOS), an approach, which is non-destructive, cost-effective, cover larger areas and is a time-saving alternative to conventional methods. Hence, the research objectives were: (i) to map the trends and advances in data and models used in the monitoring of grassland (pastures) with Earth observation systems, and (ii) to assess above-ground biomass estimation in semi-arid savannah grassland integrating Sentinel-1 and Sentinel-2 data with Machine-Learning. This goal was to assess if this approach could provide the requisite information, which could contribute to the long-term goal of developing a semi-automated system for data processing, and mapping grassland biomass to benefit local communities. For this investigation, it was crucial to understanding what research had achieved so far in this area of pasture management. An assessment of the Scopus database showed the recent developments in European Union (EU) programs and Sentinel missions, including statistical models and machine learning for monitoring grassland changes at multiple scales. However, Sentinel-1 and Sentinel-2 data, machine learning models, and variable importance techniques were applied for grassland AGB estimation. These techniques have been used in similar studies to determine optimum machine learning models, influential variables, and the capability of integrated Sentinel datasets for mapping grassland AGB, spatial distribution, and abundance. Results showed improved performance with the Random forest regression (RFR) model (R² of 34.7%, RMSE of 9.47 Mg and MAE of 7.68 Mg ). The study also observed optimum sensitivity of Difference Vegetation Index (DVI) and Enhanced Vegetation Index (EVI) in all three machine learning models for modelling grassland AGB estimation in the study area. A further, statistical comparison of all three machine learning models showed an insignificant difference in the predictive capacity for AGB in the study area with Gradient Boosting regression (GBR) model (R² of 27.7, RMSE of 9.97 Mg and MAE of 8.03 Mg ) and Extreme Gradient Boost Regression (XGBR) model (R² of 17.3%, RMSE of 10.66 Mg and MAE of 8.83 Mg ). The study revealed that an integration of Sentinel-1 and Sentinel-2 has improved capabilities for monitoring grassland AGB estimation. This research sheds light on the timely and cost-effective techniques for grassland management strategies to enhance or restore the ecological functioning of grassland ecosystems and promote community sustainability. , Thesis (MSc) -- Faculty of Science and Agriculture, 2022
- Full Text:
Coastal urban climate change adaptation and disaster risk reduction assessment: the case of East London city, South Africa
- Busayo, Emmanuel Tolulope https://orcid.org/ 0000-0002-9274-2145
- Authors: Busayo, Emmanuel Tolulope https://orcid.org/ 0000-0002-9274-2145
- Date: 2021-05
- Subjects: Climate change mitigation , Climatic changes , Emergency management
- Language: English
- Type: Doctoral theses , text
- Identifier: http://hdl.handle.net/10353/20938 , vital:46756
- Description: The increasing incidences of climate change and its registered negative effects have disturbed the entire world, with the coastal areas being the worst hit. Given the fact that coastal areas are becoming centres of global population settlement. An attempt to explore climate change-related disasters and risks is an important aspect in building communities' adaptation and resilience, especially for the most vulnerable global south. Consequently, climate change adaptation (CCA) and disaster risk reduction (DRR) have become fundamentally linked to offering sustainable solutions to address climate change and related disaster risk problems witnessed frequently in recent years. However, the assessment of synergy between CCA and DRR for coastal areas remains fragmented, vague and limited, especially for Sub-Saharan Africa and thus the need for exploration. Furthermore, the urban populace and planning stakeholders are grappling with the challenges of seeking ways to integrate adaptation measures into human livelihoods and planning systems. Also, considering complex issues inhibiting sustainable planning, for example, poor communication of climate risks affecting coastal areas, little records of hazards disclosure and disaster history, inundation and/or sea level rise etc warranted further investigation. Accordingly, the synergies between CCA and DRR in addressing various climate change-related disaster risks, especially for the coastal areas and cities was explored in this study. To this end, given the complexity of CCA and DRR, trio-theories were adopted, which included Resilience Theory (RT), Social Vulnerability Theory (SVT) and Protective Motivation Theory (PMT) as the study’s theoretical underpinnings using East London Coastal City as a case study. Consequently, a multi-method approach was employed using a review of literature, bibliometric analysis, field survey, geographic information system (GIS), and remote sensing. The first objective reveals that there is a need for convergence and harmonisation of CCA and DRR policy, programme, and practice to improve sustainable planning outcomes. Accordingly, the study proposed the adoption of a problem analysis model (PAM) for place function sustainability and local or community level resilience building. The second objective revealed that the Sendai framework for disaster risk reduction has not been fully operationalised at the local and global scales. However, in South Africa, there are efforts to streamline DRR across manifold sectors through the Integrated Urban Development Framework (IUDF). Therefore, disaster risk managers and climate change adaptation stakeholders at the local level need to embrace the position of the SFDRR to possibly offer sound and sustainable results to the most vulnerable. In addition, a bibliometric analysis on climate change adaptation from 1996 – 2019 highlights the need for more African countries' engagement and cross-collaboration between developing and developed countries in CCA research to advance sustainable solutions and improve resilience. The third objective revealed the need for more awareness, flexibility, and adaptability among stakeholders at various levels as fundamental ingredients for CCA and DRR sustainable planning outcomes. The fourth objective highlighted that floods were recorded as the most predominant hydro-meteorological hazard (n=118, 81.9percent) in the East London, coastal city. Finally, the fifth objective portrayed that many communities, populace, buildings (types), and areas are exposed to flood disaster risks, especially, communities such as Nahoon Park Valley, Sunrise on Sea, Beacon Bay, Buffalo, Gonubie, and East London are among the most vulnerable. The study recommends that early action and warning systems should be adopted, and allocation proper building codes to boost awareness to reduce the potential flood disaster risks. Moreover, the study reveals the significance of local flood disaster risk mapping in advancing CCA and DRR to ensure the implementation of coherent spatial planning for sustainable planning outcomes. The overall lessons learnt from this study are vital in contributing to the attainment of the sustainable development goals (SDGs) such as goal 11: sustainable cities and communities, and goal 13: climate action, including the seven targets and four priorities for action of the Sendai framework at a local level. The study results are deemed critical in guiding city planners, decision-makers, disaster risk managers, local communities among others towards the development of a more resilient coastal community. In general, the study calls for the integration of CCA and DRR initiatives to be premised on PAM for sustainable planning outcomes to achieve sustainable development goals and reduction of fatalities from climate-related disasters. , Thesis (PhD) -- Faculty of Science and Agriculture, 2021
- Full Text:
- Authors: Busayo, Emmanuel Tolulope https://orcid.org/ 0000-0002-9274-2145
- Date: 2021-05
- Subjects: Climate change mitigation , Climatic changes , Emergency management
- Language: English
- Type: Doctoral theses , text
- Identifier: http://hdl.handle.net/10353/20938 , vital:46756
- Description: The increasing incidences of climate change and its registered negative effects have disturbed the entire world, with the coastal areas being the worst hit. Given the fact that coastal areas are becoming centres of global population settlement. An attempt to explore climate change-related disasters and risks is an important aspect in building communities' adaptation and resilience, especially for the most vulnerable global south. Consequently, climate change adaptation (CCA) and disaster risk reduction (DRR) have become fundamentally linked to offering sustainable solutions to address climate change and related disaster risk problems witnessed frequently in recent years. However, the assessment of synergy between CCA and DRR for coastal areas remains fragmented, vague and limited, especially for Sub-Saharan Africa and thus the need for exploration. Furthermore, the urban populace and planning stakeholders are grappling with the challenges of seeking ways to integrate adaptation measures into human livelihoods and planning systems. Also, considering complex issues inhibiting sustainable planning, for example, poor communication of climate risks affecting coastal areas, little records of hazards disclosure and disaster history, inundation and/or sea level rise etc warranted further investigation. Accordingly, the synergies between CCA and DRR in addressing various climate change-related disaster risks, especially for the coastal areas and cities was explored in this study. To this end, given the complexity of CCA and DRR, trio-theories were adopted, which included Resilience Theory (RT), Social Vulnerability Theory (SVT) and Protective Motivation Theory (PMT) as the study’s theoretical underpinnings using East London Coastal City as a case study. Consequently, a multi-method approach was employed using a review of literature, bibliometric analysis, field survey, geographic information system (GIS), and remote sensing. The first objective reveals that there is a need for convergence and harmonisation of CCA and DRR policy, programme, and practice to improve sustainable planning outcomes. Accordingly, the study proposed the adoption of a problem analysis model (PAM) for place function sustainability and local or community level resilience building. The second objective revealed that the Sendai framework for disaster risk reduction has not been fully operationalised at the local and global scales. However, in South Africa, there are efforts to streamline DRR across manifold sectors through the Integrated Urban Development Framework (IUDF). Therefore, disaster risk managers and climate change adaptation stakeholders at the local level need to embrace the position of the SFDRR to possibly offer sound and sustainable results to the most vulnerable. In addition, a bibliometric analysis on climate change adaptation from 1996 – 2019 highlights the need for more African countries' engagement and cross-collaboration between developing and developed countries in CCA research to advance sustainable solutions and improve resilience. The third objective revealed the need for more awareness, flexibility, and adaptability among stakeholders at various levels as fundamental ingredients for CCA and DRR sustainable planning outcomes. The fourth objective highlighted that floods were recorded as the most predominant hydro-meteorological hazard (n=118, 81.9percent) in the East London, coastal city. Finally, the fifth objective portrayed that many communities, populace, buildings (types), and areas are exposed to flood disaster risks, especially, communities such as Nahoon Park Valley, Sunrise on Sea, Beacon Bay, Buffalo, Gonubie, and East London are among the most vulnerable. The study recommends that early action and warning systems should be adopted, and allocation proper building codes to boost awareness to reduce the potential flood disaster risks. Moreover, the study reveals the significance of local flood disaster risk mapping in advancing CCA and DRR to ensure the implementation of coherent spatial planning for sustainable planning outcomes. The overall lessons learnt from this study are vital in contributing to the attainment of the sustainable development goals (SDGs) such as goal 11: sustainable cities and communities, and goal 13: climate action, including the seven targets and four priorities for action of the Sendai framework at a local level. The study results are deemed critical in guiding city planners, decision-makers, disaster risk managers, local communities among others towards the development of a more resilient coastal community. In general, the study calls for the integration of CCA and DRR initiatives to be premised on PAM for sustainable planning outcomes to achieve sustainable development goals and reduction of fatalities from climate-related disasters. , Thesis (PhD) -- Faculty of Science and Agriculture, 2021
- Full Text:
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