Browsing by Author "Sibanda, Mbulisi."
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Item Assessing the utility of unmanned aerial vehicle remotely sensed data for estimating maize leaf area index (LAI) and yield across the growing season.(2021) Buthelezi, Siphiwokuhle.; Mutanga, Onisimo.; Sibanda, Mbulisi.Abstract available in PDF.Item Assessment of maize crop health and water stress based on multispectral and thermal infrared unmanned aerial vehicle phenotyping in smallholder farms.(2021) Brewer, Kiara Raynise.; Clulow, Alistair David.; Sibanda, Mbulisi.; Mabhaudhi, Tafadzwanashe.Abstract available in PDF.Item The characterisation of the yellowwoods’ leaf area index, within a southern African mistbelt forest, using geographic information systems and remote sensing.(2020) Gumede, Nokwanda.; Mutanga, Onisimo.; Sibanda, Mbulisi.Abstract available in pdf.Item Estimating critical grassland vegetation moisture parameters using topoclimatic variables and remotely sensed data in relation to fire occurrence.(2021) Shinga, Wenzile.; Mutanga, Onisimo.; Sibanda, Mbulisi.Abstract available in pdf.Item Remote sensing drought variability across different selected biomes of South Africa.(2022) Diza, Duduzile.; Mutanga, Onisimo.; Sibanda, Mbulisi.Abstract available in PDF.Item Remote sensing grass quantity under different grassland management treatments practised in the Southern African rangelands.(2016) Sibanda, Mbulisi.; Mutanga, Onisimo.; Rouget, Mathieu.Abstract available in PDF file.Item The utility of Sentinel-2 MSI to assess wetland vegetation chlorophyll content and leaf area index in wetland areas in Pietermaritzburg, KwaZulu-Natal.(2020) Tshabalala, Nonjabulo Neliswa.; Mutanga, Onisimo.; Sibanda, Mbulisi.Wetland ecosystems are being modified and threatened due to anthropogenic activities and climate change, hence the urgent need for wetland restoration. Wetland rehabilitation is important in the reversal of these dire conditions, through restoring damaged wetland ecosystems and recovering wetland vegetation. Wetland biophysical properties such as leaf area index and chlorophyll content are important indicators of vegetation productivity and stress. Therefore, the overall aim of this study was to assess the variations in wetland vegetation productivity between wetlands under different management regimes in Pietermaritzburg, South Africa using Sentinel-2 MSI data. Chlorophyll and leaf area index were used as proxies of wetland Cyperus dives and Typha capensis productivity in this study. The first objective was to test the ability of Sentinel-2 MSI data and vegetation indices in estimating leaf area index of wetland vegetation across natural and rehabilitated wetlands. The second objective was to assess the utility of the high-spatial resolution Sentinel-2 MSI data in the estimation of chlorophyll content of Cyperus dives and Typha capensis species across natural and rehabilitated wetlands. Results showed that vegetation indices derived from red-edge bands produced better LAI estimation accuracies for both wetlands with a root mean square error (RMSE) of 0.32 m2/m2 and 0.51 m2/m2 as well as R2 ‘s of 0.61 and 0.75 for the natural and rehabilitated wetlands, respectively. The optimal model for predicting LAI across natural and rehabilitated wetlands was attained based on red-edge bands centered at 705 nm (Band 5), 740 nm (Band 6), 783 nm (Band 7) as well as 865 nm (Band 8a) yielding a RMSE of 0.51 m2/m2 and R2 of 0.75. In addition, the combination of all spectral variables in estimating chlorophyll across different wetland management regimes and species exhibited a relatively low RMSE of 9.11 μg cm2 (12%) and R2 value of 0.88 based on red-edge bands centered at 705 nm (Band 5), 740 nm (Band 6), 783 nm (Band 7) as well as 865 nm (Band 8a). The findings of this study indicate that Sentinel-2 MSI data can be optimally used to estimate productivity (chlorophyll content and LAI) of wetland plant species such Cyperus dives and Typha capensis growing under different management regimes, with the rehabilitated wetland exhibiting improved productivity. Results of this study underscores the unique potential of new generation earth observation sensors in wetland vegetation monitoring and management, this has implications on other ecosystem processes such as wetland water use and carbon sequestration.Item The utility of very-high resolution unmanned aerial vehicles (UAV) imagery in monitoring the spatial and temporal variations in leaf moisture content of smallholder maize farming systems.(2021) Ndlovu, Helen Snethemba.; Odindi, John Odhiambo.; Sibanda, Mbulisi.; Mutanga, Onisimo.Maize moisture stress, resulting from rainfall variability, is a primary challenge in the production of rain-fed maize farming, especially in water-scarce regions such as southern Africa. Quantifying maize moisture variations throughout the growing season can support agricultural decision-making and prompt the rapid and robust detection of smallholder maize moisture stress. Unmanned Aerial Vehicles (UAVs), equipped with light-weight multispectral sensors, provide spatially explicit near real-time information for determining maize moisture content at farm scale. Therefore, this study evaluated the utility of UAV derived multispectral imagery in estimating maize leaf moisture content indicators on smallholder farming systems throughout the maize growing season. The first objective of the study was to conduct a comparative analysis in order to evaluate the performance of five regression techniques (support vector regression, random forest regression, decision trees regression, artificial neural network regression and the partial least squares regression) in predicting maize water content indicators (i.e. equivalent water thickness (EWT), fuel moisture content (FMC) and specific leaf area (SLA)), and determine the most suitable indicator of smallholder maize water content variability based on multispectral UAV data. The results illustrated that both NIR and red-edge derived spectral variables were critical in characterising maize moisture indicators on smallholder farms. Furthermore, the best models for estimating EWT, FMC and SLA were derived from the random forest regression algorithm with a relative root mean square error (rRMSE) of 3.13%, 1% and 3.48 %, respectively. Additionally, EWT and FMC yielded the highest predictive performance of maize leaf moisture and demonstrated the best correlation with remotely sensed data. The study’s second objective was to evaluate the utility of UAVderived multispectral imagery in estimating the temporal variability of smallholder maize moisture content across the maize growing season using the optimal maize moisture indicators. The findings illustrated that the NIR and red-edge wavelengths were influential in characterising maize moisture variability with the best models for estimating maize EWT and FMC resulting in a rRMSE of 2.27 % and 1%, respectively. Furthermore, the early reproductive stage was the most optimal for accurately estimating maize EWT and FMC using UAVproximal remote sensing. The findings of this study demonstrate the prospects of UAV- derived multispectral data for deriving insightful information on maize moisture availability and overall health conditions. This study serves as fundamental step towards the creation of an early maize moisture stress detection and warning systems, and contributes towards climate change adaptation and resilience of smallholder maize farming.