The effect of spatial resolution in remote sensing estimates of total evaporation in the uMgeni catchment.

M. Sc. University of KwaZulu-Natal, Durban 2014.The estimation of total evaporation plays a vital role in water resources monitoring and management, especially in water-limited environments. In South Africa, the increasing water demand, due to population growth and economic development, threatens the long-term water supply. This, therefore, underscores the need to account for water by different consumers, for well-informed management, allocation and future planning. Currently, there are different methods (i.e. ground-based and remote sensing-based methods), which have been developed and implemented to quantify total evaporation at different spatial and temporal scales. However, previous studies have shown that ground-based methods are inadequate for understanding the spatial variations of total evaporation, within a heterogeneous landscape; they only represent a small area, when compared to remotely sensed methods. The advent of remote sensing therefore provides an invaluable opportunity for the spatial characterization of total evaporation at different spatial scales. This study is primarily aimed at estimating variations of total evaporation across a heterogeneous catchment in KwaZulu-Natal, South Africa, using remote sensing data. The first part provides an overview of total evaporation, its importance within the water balance and consequently in the management of water resources. It also covers various methods developed to estimate total evaporation, highlighting their applications, limitations, and finally, the need for further research. Secondly, the study determines the effect of sensor spatial resolution in estimating variations of total evaporation within a heterogeneous uMngeni Catchment. Total evaporation estimates were derived, using multispectral 30 m Landsat 8 and 1000 m MODIS, based on the Surface Energy Balance (SEBS) model. The results have shown that different sensors, with varying spatial resolutions, have different abilities in representing variations of total evaporation at catchment scale. It was found that Landsat-based estimates were significantly different (p < 0.05) from MODIS. The study finally estimates spatial variations of total evaporation from Landsat 8 and MODIS datasets for the uMngeni Catchment. It was found that the Landsat 8 dataset has greater potential for the detection of spatial variations of total evaporation, when compared to the MODIS dataset. For instance, MODIS-based daily total evaporation estimates did not show any significant difference across different land cover types (One way ANOVA; F1.924 = 1.412, p= 0.186), when compared to the 30 m Landsat 8, which yielded significantly different estimates between different land cover types (One way ANOVA; F1.993= 5.185, p < 0.001). The validation results further indicate that Landsat-based estimates were more comparable to ground-based eddy covariance measurements (R2 = 0.72, with a RMSE of 32.34 mm per month (30.30% of the mean)). In contrast, MODIS performed poorly (R2 = 0.44), with a RMSE of 93.63 mm per month (87.74% of the mean). In addition, land cover-based estimates have shown that, not only does the land cover type have an effect on total evaporation, but also the land cover characteristics, such as areal extent and patchiness. Overall, findings from this study underscore the importance of the sensor type, especially spatial resolution, and land cover type characteristics, such as areal extent and patchiness, in accurately and reliably estimating total evaporation at a catchment scale. It is also evident from the study that the spatial and temporal variations in SEBS inputs (e.g., LAI, NDVI and FVC) and energy fluxes (e.g., Rn) calculated by SEBS for the two sensors can affect the spatial and temporal variations in total evaporation estimates. For instance, spatial variations in total evaporation reflected similar spatial variations in Rn. Areas with high NDVI, FVC and LAI (which denotes dense vegetation cover) tend to have higher total evaporation estimates, compared to areas with lower vegetation cover. In addition, the MODIS sensor at 1000 m spatial resolution showed lower estimates of SEBS inputs with less variability across the catchment. This resulted in lower total evaporation estimates, with less variability, compared to the 30 m Landsat 8. In addition, with regard to inputs derived from remote sensing, it was found that the spatial variations in total evaporation are not determined by individual variables (e.g., LST), but are influenced by a combination of many biophysical variables, such as LAI, FVC and NDVI. These findings lay a foundation for a better approach to estimate total evaporation using remote sensing for use in the management and allocation of water

Discrimination and biomass estimation of co-existing C3 and C4 grass functional types over time : a view from space.

Doctor of Philosophy in Environmental Sciences. University of KwaZulu-Natal, Pietermaritzburg, 2018.The co-existence of C3 and C4 grass species significantly influence their spatio-temporal variations of biochemical cycling, productivity (i.e. biomass) and role in provision of ecosystem goods and services. Consequently, the discrimination of the two species is critical in understanding their spatial distribution and productivity. Such discrimination is particularly valuable for accounting for their socio-economic and environmental contributions, as well as decisions related to climate change mitigation. Due to the growing popularity of remotely sensed approaches, this study sought to discriminate the two grass species and determine their AGB using new generation sensors. Specifically, the potential of Landsat 8, Sentinel 2 and Worldview 2, with improved sensing characteristics were tested in achieving the above objectives. Generally, the results demonstrate the suitability of the adopted sensors in the discrimination and determination of C3 and C4 AGB using Discriminant Analysis and Sparse Partial Least Squares Regression models. Using multi-date Sentinel 2 data, the study established that winter period (May) was the most suitable for discriminating the two grass species. On the other hand, the winter fall (August) was found to be the least optimal period for the two grass species discrimination. The study also established that the amount of AGB for C3 and C4 were higher in winter and summer, respectively; a variability attributed to elevation and rainfall. The study concludes that Sentinel 2 dataset, although had weaker performance than Worldview 2; it offers a valuable opportunity in understanding the C3 and C4 spatial distribution within a landscape; hence useful in understanding both temporal and multi-temporal distribution of the two grass species. Successful seasonal characterization of C3 and C4 AGB allows for inferences on their contribution to forage availability and fire regimes; therefore, this contributes to the development of well-informed conservation strategies, which can lead to sustainable utilization of rangelands, especially in relation to the changing climate

Impacts of groundwater and climate variability on terrestrial groundwater dependent ecosystems: A review of geospatial assessment approaches and challenges and possible future research directions

Terrestrial groundwater dependent vegetation (TGDV) are crucialecosystems which provide important goods and services such ascarbon sequestration, habitat, water purification and aestheticbenefits in semi-arid environments. Global climate change andanthropogenic impacts on surface water resources have led toincreased competing claims on groundwater resources to meetan exponential water demand for environmental needs, agricul-tural and developmental needs. This has led to the unsustainableexploitation of groundwater resources, resulting in groundwatertable declines, threatening the sustainability of TGDV. It is on thispremise that the review aims to provide a detailed overview onthe progress in remote sensing of TGDV

Multispectral remote sensing of potential groundwater dependent vegetation in the greater Floristic region of the Western Cape, South Africa

Groundwater dependent vegetation (GDV) is increasingly threa-tened by the transformation of the natural environment. An under-standing of the nature of GDV at the appropriate scale helps environmental managers make suitable decisions. This study assesses the potential for mapping the distribution of GDV within the Heuningnes Catchment using multispectral remotely sensed data (i.e., Landsat 8 (L8) and Sentinel 2 (S2)), the derived vegetation indices (Normalised Difference Vegetation Index (NDVI) and the Soil-Adjusted Vegetation Index (SAVI)) and in-situ data. The GDV distribution maps were produced by integrating vegetation pro-ductivity, landcover, and topographic layers as GDV indicators

Remotely sensed data for estimating chlorophyll-a concentration in wetlands located in the Limpopo Transboundary River Basin, South Africa

Wetlands in semi-arid regions are highly productive and biologically diverse ecosystems that contribute significantly to livelihood and economic development and play a substantial role in sustaining rural livelihoods. These ecosystems are not only rich in biodiversity, but also predominantly valuable in terms of the services they provide to people, including water security, hydrological regulation, and other services. Chlorophyll-a concentrations and associated dynamics in two tropical wetland systems were estimated in the Makuleke and Nylsvlei wetlands. The Makuleke and Nylsvlei wetlands are in the Limpopo Transboundary River Basin, South Africa. Moderate-resolution Landsat 8 images for September 2018 and June 2019 and in situ field measurements were used to estimate and map chlorophyll-a concentration from the two wetlands. Landsat-derived chlorophyll-a concentrations were validated using field-derived chlorophyll-a measurements. Validation was implemented to assess the consistency of the remotely sensed chlorophyll-a estimates

Remote sensing of land use-land cover change and climate variability on hydrological processes in Sub-Saharan Africa: Key scientific strides and challenges

The impact of land use land cover (LULC) change and climatevariability on water resources poses as a major threat in semi-aridenvironments, especially in the sub-Saharan Africa. Countries insub-Saharan Africa are vulnerable to water scarcity. Hence, thereis an urgent need for understanding the various methods forLULC change and climate variability assessment, to aid in waterresources management at various scales. Various studies havemodelled and assessed the effect of LULC change and climatevariability on hydrological responses, using different approaches.In this regard, this paper provides a detailed review on the pro-gress of various remote sensing techniques in modelling andassessing the effect of LULC change and climate variability onhydrological processes

Advances in satellite remote sensing of the wetland ecosystems in Sub-Saharan Africa

Wetlands are highly productive systems that act as habitats for avariety of fauna andflora. Despite their ecohydrological signifi-cance, wetland ecosystems are severely under threat from globalenvironmental changes as well as pressure from anthropogenicactivities. Such changes results in severe disturbances of plantspecies composition, spatial distribution, productivity, diversity,and their ability to offer critical ecosystem goods and services .However, wetland degradation varies considerably from place toplace with severe degradation in developing countries, especiallyin sub-Saharan Africa due to poor management practices thatleads to underutilization and over reliance on them for liveli-hoods. The lack of monitoring and assessment in this region hastherefore led to the lack of consolidated detailed understandingon the rate of wetland loss

Determining optimal new generation satellite derived metrics for accurate C3 and C4 grass species aboveground biomass estimation in South Africa

While satellite data has proved to be a powerful tool in estimating C3 and C4 grass species Aboveground Biomass (AGB), finding an appropriate sensor that can accurately characterize the inherent variations remains a challenge. This limitation has hampered the remote sensing community from continuously and precisely monitoring their productivity. This study assessed the potential of a Sentinel 2 MultiSpectral Instrument, Landsat 8 Operational Land Imager, and WorldView-2 sensors, with improved earth imaging characteristics, in estimating C3 and C4 grasses AGB in the Cathedral Peak, South Africa. Overall, all sensors have shown considerable potential in estimating species AGB; with the use of different combinations of the derived spectral bands and vegetation indices producing better accuracies. However,WorldView-2 derived variables yielded better predictive accuracies (R2 ranging between 0.71 and 0.83; RMSEs between 6.92% and 9.84%), followed by Sentinel 2, with R2 between 0.60 and 0.79; and an RMSE 7.66% and 14.66%. Comparatively, Landsat 8 yielded weaker estimates, with R2 ranging between 0.52 and 0.71 and high RMSEs ranging between 9.07% and 19.88%. In addition, spectral bands located within the red edge (e.g., centered at 0.705 and 0.745 m for Sentinel 2), SWIR, and NIR, as well as the derived indices, were found to be very important in predicting C3 and C4 AGB from the three sensors. The competence of these bands, especially of the free-available Landsat 8 and Sentinel 2 dataset, was also confirmed from the fusion of the datasets. Most importantly, the three sensors managed to capture and show the spatial variations in AGB for the target C3 and C4 grassland area. This work therefore provides a new horizon and a fundamental step towards C3 and C4 grass productivity monitoring for carbon accounting, forage mapping, and modelling the influence of environmental changes on their productivity

Advancements in the satellite sensing of the impacts of climate and variability on bush encroachment in savannah rangelands

An increase in shrubs or woody species is likely, directly or indirectly, to significantly affect rural livelihoods, wildlife/livestock productivity and conservation efforts. Poor and inappropriate land use management practices have resulted in rangeland degradation, particularly in semi-arid regions, and this has amplified the bush encroachment rate in many African countries, particularly in key savannah rangelands. The rate of encroachment is also perceived to be connected to other environmental factors, such as climate change, fire and rainfall variability, which may influence the structure and density of the shrubs (woody plants), when compared to uncontrolled grazing. Remote sensing has provided robust data for global studies on both bush encroachment and climate variability over multiple decades, and these data have complemented the local and regional evidence and process studies. This paper thus provides a detailed review of the advancements in the use of remote sensing for the monitoring of bush encroachment on the African continent, which is fuelled by climate variability in the rangeland areas

Evaluating the impact of land use and land cover change on unprotected wetland ecosystems in the arid-tropical areas of South Africa using the Landsat dataset and support vector machine

The study explored the impact of Land Use and Land Cover (LULC) change dynamics in relation to the condition and status of an unprotected wetland located in the arid-tropical parts of the Limpopo Province, South Africa. The long-term Landsat archival data series was used to map and quantify the impacts of LULC change on the wetland over a period of 36 years (1983–2019). A multi-source satellite image analysis was performed, using the support vector machine (SVM) algorithm and advanced spatially- explicit geographic information system tools. Landsat data series covering the entire study area was used to assess, map and monitor LULC change that occurred over-time. Post-classification maps for the Maungani wetland area were analysed to provide a quantitative assessment and a detailed overview of the rate of change. The generated wetland detection maps for four temporal phases (i.e., 1983–1992, 1992–2001, 2002–2010) were analysed