Cletah Shoko

Water quality monitoring in sub-Saharan African lakes: a review of remote sensing applications

Water quality deterioration in sub-Saharan Africa has attained a scale that requires scientific intervention. It is therefore important to devise appropriate and reliable techniques to investigate the water quality of lakes and reservoirs for the development of water resource management strategies. Whilst conventional water quality monitoring methods have been widely used due to their accuracy, these methods are time-consuming, costly and practically impossible to use at broader scales. This paper reviews the literature on various remote sensing platforms and techniques used for assessing and monitoring water quality in sub-Saharan Africa, and highlights their strengths and weaknesses. The use of remote sensing technology could enhance water quality monitoring, since remotely sensed data offer timely, up-to-date and comparatively accurate information, which is necessary for water resource management and strategic decision making. However, the use of this technology in some parts of sub-Saharan Africa is still at its infancy because of its high cost and limited availability.

Applying the Surface Energy Balance System (SEBS) remote sensing model to estimate spatial variations in evapotranspiration in Southern Zimbabwe

Accurate, reliable and continuous understanding of water utilisation by different land cover types in arid environments is critical for water loss accounting to ensure sustainable water management in the face of the changing climate. Remote sensing provides a lucrative alternative for mapping and estimating the spatial and temporal distribution of water loss across the catchment. The results of this study have shown that evapotranspiration (ET) can be accurately estimated in arid environments from remotely sensed data, such as Moderate Resolution Imaging Spectroradiometer (MODIS) data, based on the Surface Energy Balance System (SEBS) algorithm. This study observed significant spatial and temporal variations in ET across the south western part of Zimbabwe. The findings from this study, therefore, underscore the importance of using cheap and readily available remotely sensed data for estimating and mapping the variations in ET in arid-environment areas found mainly in developing countries.

Estimating spatial variations of total evaporation using multispectral sensors within the uMngeni catchment, South Africa

Total evaporation is of importance in assessing and managing long-term water use, especially in water-limited environments. Therefore, there is need to account for water utilisation by different land uses for well-informed water resources management and future planning. This study investigated the feasibility of using multispectral Landsat 8 and moderate resolution imaging spectroradiometer (MODIS) remote sensing data to estimate total evaporation within the uMngeni catchment in South Africa, using surface energy balance system. The results indicated that Landsat 8 at 30 m resolution has a better spatial representation of total evaporation, when compared to the 1000 m MODIS. Specifically, Landsat 8 yielded significantly different mean total evaporation estimates for all land cover types (one-way ANOVA; F4.964 = 87.011, p < 0.05), whereas MODIS failed to differentiate (one-way ANOVA; F2.853 = 0.125, p = 0.998) mean total evaporation estimates for the different land cover types across the catchment. The findings of this study underscore the utility of the Landsat 8 spatial resolution and land cover characteristics in deriving accurate and reliable spatial variations of total evaporation at a catchment scale.

The utility of earth observation technologies in understanding impacts of land reform in the eastern region of Zimbabwe

ABSTRACT Land tenure and land policies influence the spatial variations of land use/cover (LULC) at any given time or place. Thus, it is important to evaluate the role of land tenure policies on land cover changes. In this study, we evaluate the utility of Landsat Thematic Mapper (TM) images in understanding the impacts of the 2000 fast track land reform (FTLR) policy on LULC in the eastern region, Zimbabwe. Landsat images for the year 1995, 2000, 2005 and 2011 were classified using traditional image classification techniques (i.e. the maximum likelihood (ML) classifier) in a geographic information system (GIS) environment. Results indicate that forested areas drastically decreased by approx. 30% between the year 2000 and 2005 (during and after the FTLR), while croplands marginally increased by (approx. 30%) the results further showed that slight increase in bare lands (degraded lands) and disturbed lands. The observed LULC changes after FTLR were mostly induced by human activities resulting from changes in land tenure. Overall, the findings of this study underscores the importance of remotely sensed data in assessing the impact of FTLR on forest resources for purposes of informed and sustainable forest management.

Understanding the spatial distribution of elephant (Loxodonta africana) poaching incidences in the mid-Zambezi Valley, Zimbabwe using Geographic Information Systems and remote sensing

Abstract The objective of this study was to understand the factors that explain the spatial distribution of elephant poaching activities in the areas of the mid-Zambezi Valley, Zimbabwe using geographic information system (GIS) and remotely sensed data integrated with spatial logistic regression. The results showed that significant (α = 0.05) elephant poaching hot spots are located closer to wildlife protected areas. Results further demonstrated that resource availability (water and forage) are the main factors explaining elephant poaching activities in the mid-Zambezi Valley. For example, the majority of poaching activities were found to occur in areas with high vegetation fractional cover (high forage) and close to waterholes. The results also showed that poaching incidences were more prevalent during the dry season. The findings of this study highlight the significance of integrating GIS, remotely sensed data and spatial logistic regression tools for understanding and monitoring elephant poaching activities. This information is critical if poaching activities are to be minimized and it is also important for planning, monitoring and mitigation of poaching activities in similar protected areas across the sub-Saharan Africa.

Effect of spatial resolution on remote sensing estimation of total evaporation in the uMngeni catchment, South Africa

Abstract. This study evaluated the effect of two readily available multispectral sensors: the newly launched 30 m spatial resolution Landsat 8 and the long-serving 1000 m moderate resolution imaging spectroradiometer (MODIS) datasets in the spatial representation of total evaporation in the heterogeneous uMngeni catchment, South Africa, using the surface energy balance system model. The results showed that sensor spatial resolution plays a critical role in the accurate estimation of energy fluxes and total evaporation across a heterogeneous catchment. Landsat 8 estimates showed better spatial representation of the biophysical parameters and total evaporation for different land cover types, due to the relatively higher spatial resolution compared to the coarse spatial resolution MODIS sensor. Moreover, MODIS failed to capture the spatial variations of total evaporation estimates across the catchment. Analysis of variance (ANOVA) results showed that MODIS-based total evaporation estimates did not show any significant differences across different land cover types (one-way ANOVA; F1.924=1.412, p=0.186). However, Landsat 8 images yielded significantly different estimates between different land cover types (one-way ANOVA; F1.993=5.185, p<0.001). The validation results showed that Landsat 8 estimates were more comparable to eddy covariance (EC) measurements than the MODIS-based total evaporation estimates. EC measurement on May 23, 2013, was 3.8  mm/day, whereas the Landsat 8 estimate on the same day was 3.6  mm/day, with MODIS showing significantly lower estimates of 2.3  mm/day. The findings of this study underscore the importance of spatial resolution in estimating spatial variations of total evaporation at the catchment scale, thus, they provide critical information on the relevance of the readily available remote sensing products in water resources management in data-scarce environments.

Estimating forest standing biomass in savanna woodlands as an indicator of forest productivity using the new generation WorldView-2 sensor

Abstract Accurate and up-to-date information on forest dendrometric traits, such as above ground biomass is important in understanding the contribution of terrestrial ecosystems to the regulation of atmsopheric carbon, especially in the face of global environmental change. Besides, dendrometric traits information is critical in assessing the healthy and the spatial planning of fragile ecosystems, such as the savanna dry forests. The aim of this work was to test whether red-edge spectral data derived from WorldView-2 multispectral imagery improve biomass estimation in savanna dry forests. The results of this study have shown that biomass estimation using all Worldview-2 raw spectral bands without the red-edge band yielded low estimation accuracies (R2 of 0.67 and a RMSE-CV of 2.2 t ha−1) when compared to when the red-edge band was included as a co-variate (R2 of 0.73 and a RMSE-CV of 2.04 t ha−1). Also, similar results were obseved when all WorldView-2 vegetation indices (without the red-edge computed ones), producing slightly low accuracies (R2 of about 0.67 and a RMSE-CV of 2.20 t ha−1), when compared to those obtained using all indices and RE-computed indices(R2 of 0.76 and a RMSE-CV of 1.88 t ha−1). Overall, the findings of this work have demontrated the potential and importance of strategically positioned bands, such as the red-edge band in the optimal estimation of indigeonus forest biomass. These results underscores the need to shift towards embracing sensors with unique and strategeically positioned bands, such as the forthcoming Sentinel 2 MSI and HysPIRI which have a global footprint.

Quantifying the spatial and temporal changes in forested landcover using landscape metrics derived from remotely sensed data in rural parts of Zimbabwe

This study examines the application of remotely sensed derived landscape metrics to understand the changes and dynamics in the form and morphology of forested areas in rural parts of Zimbabwe from 2002 to 2011. Specifically, the study determines the spatial and temporal changes in forest areas due to human activities, such as crop production, using landscape metrics derived from classified Landsat remote sensing images. The results from this study have shown that landscape metrics derived from the 30-m Landsat dataset have a great potential for understanding the patterns of change in forested areas in the developing world. For instance, in the year 2002, the majority of the land was occupied by forests, whereas in 2011, it was shown that non-forested areas became more dominant and scattered, and forested areas showed a decrease in spatial extent. Moreover, statistical results have shown that in 2002, the number of forest patches was higher and significantly different when compared to non-forested patches. However, in 2011, both land cover types showed an increase in the number of patches, although the number of non-forested patches was higher and indicated significant differences (p < 0.05) in terms of areal extent. Overall, the findings of this study provide a comprehensive understanding of the impacts of human activities on the natural ecosystem which leads to better and improved future land use planning and management strategies.

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 µm 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.

Total evaporation estimation for accurate water accounting purposes: an appraisal of various available estimation methods

Abstract The estimation of total evaporation is fundamental for water accounting, considering its influence on water availability. Moreover, the current increase in water consumption (e.g. in sub-Saharan Africa and the world over), land cover/use changes, deteriorating water quality and the climate change projections in most regions of the world underscore the need to understand water loss. So far, different approaches have been developed and implemented in estimating the variations of total evaporation, with varying accuracies. The aim of this work was therefore, to provide a review of these different approaches for estimating total evaporation, as well as a detailed discussion of their strengths and weaknesses. Findings from this review have shown that total evaporation estimates derived, using ground-based meteorological and micro-meteorological methods are inadequate for representing its large-scale spatial variations. On the other hand, remote sensing technology, which acquires data at different resolutions (i.e. radiometric, spectral, spatial and temporal), provides timely, up-to-date and relatively accurate spatial estimates of total evaporation over large geographic coverage, for sustainable and effective water accounting, which is key for well-informed and improved management of water resources at both catchment and regional scales. In this regard, more details on the remote sensing-based methods of estimating total evaporation are provided, especially considering the robust technological advancements and its potential in characterizing earth features over time and space. This work has also managed to identify research gaps and challenges in the accurate estimation of total evaporation, using remote sensing, especially with the emergence of more advanced sensors and the characteristics of the landscape.