Estimating woody vegetation cover in an African Savanna using remote sensing and geostatistics.

Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2008.A major challenge in savanna rangeland studies is estimating woody vegetation cover and densities over large areas where field based census alone is impractical. It is therefore crucial that the management and conservation oriented research in savannas identify data sources that provides quick, timely and economical means to obtain information on vegetation cover. Satellite remote sensing can provide such information. Remote sensing investigations, however, require establishing statistical relationships between field and remotely sensed data. Usually regression is the empirical method applied to field and remotely sensed data for the spatial estimation of woody vegetation variables. Geostatistical techniques, which take spatial autocorrelation of variables into consideration, have rarely been used for this purpose. We investigated the possibility of improving woody biomass predictions in tropical savannas using cokriging. Cokriging was used to evaluate the cross-correlated information between SPOT (Satellites Pour l’Observation de la Terre or Earth-observing Satellites)-derived vegetation variables and field sampled woody vegetation percentage canopy cover and density. The main focus was to estimate woody density and map the distribution of woody cover in an African savanna environment. In order to select the best SPOT-derived vegetation variable that best correlate with field sampled woody variables, several spectral vegetation and texture indices were evaluated. Next, variogram models were developed: one for woody canopy cover and density, one for the best SPOT-derived vegetation variable, and a crossvariogram between woody variables and best SPOT-derived data. These variograms were then used in cokriging to estimate woody density and map its spatial distribution. Results obtained indicate that through cokriging, the estimation accuracy can be improved compared to ordinary kriging and stepwise linear regression. Cokriging therefore provided a method to combine field and remotely sensed data to accurately estimate woody cover variables

Remote sensing of the distribution and quality of subtropical C3 and C4 grasses.

Thesis (Ph.D.)-University of KwaZulu-Natal, Durban, 2013.Global climate change is expected to be accompanied by changes in the composition of plant functional types. Such changes are predicted to follow shifts in the percentage cover and abundance of grass species, following the C3 and C4 photosynthetic pathways. These two groups differ in a number of physiological, structural and biochemical aspects. It is important to measure these characteristic properties because they affect ecosystem processes, such as nutrient cycling. High spectral and spatial resolution remote sensing systems have been proven to offer data, which can be used to accurately detect, classify and map plant species. The major challenge, however, is that the spectral reflectance data obtained over many narrow contiguous channels (i.e. hyperspectral data) represent multiple classes that are often mixed for a limited training-sample size. This is commonly referred to as the Hughes phenomenon or “the curse of dimensionality”. In the context of hyperspectral data analysis, the Hughes phenomenon often introduces a high degree of multicollinearity, which is caused by the use of highly-correlated spectral predictors. Multicollinearity is a prominent problem in processing hyperspectral data for vegetation applications, due to similarities in the spectral reflectance properties of biophysical and biochemical attributes. This study explored an innovative method to solve the problems associated with spectral dimensionality and the related multicollinearity, by developing a user-defined inter-band correlation filter function to resample hyperspectral data. The proposed resampling technique convolves the spectral dependence information between a chosen band-centre and its shorter and longer wavelength neighbours. The utility of the new resampling technique was assessed for discriminating C3 (Festuca costata) and C4 (Themeda triandra and Rendlia altera) grasses and for predicting their nutrient content (nitrogen, protein, moisture, and fibre), using partial least squares and random forest regressions. In general, results obtained showed that the user-defined inter-band correlation filter technique can mitigate the problem of multicollinearity in both classification and regression analyses. Wavebands in the shortwave infrared region were found to be very important in regression and classification analyses, using field spectra-only datasets. Next, the analyses were up-scaled from field spectra to the new generation multispectral satellite, WorldView-2 imagery, which was acquired for the Cathedral Peak region of the Drakensberg Mountains. The results obtained, showed that the WV2 image data contain useful information for classifying the C3 and C4 grasses and for predicting variability in their nitrogen and fibre concentrations. This study makes a contribution by developing a user-defined inter-band correlation filter to resample hyperspectral data, and thereby mitigating the high dimensionality and multicollinearity problems, in remote sensing applications involving C3 and C4 grass species or communities

Exploring Transferable Techniques to Retrieve Crop Biophysical and Biochemical Variables Using Sentinel-2 Data

The current study aimed to determine the spatial transferability of eXtreme Gradient Boosting (XGBoost) models for estimating biophysical and biochemical variables (BVs), using Sentinel-2 data. The specific objectives were to: (1) assess the effect of different proportions of training samples (i.e., 25%, 50%, and 75%) available at the Target site (DT) on the spatial transferability of the XGBoost models and (2) evaluate the effect of the Source site (DS) (i.e., trained) model accuracy on the Target site (i.e., unseen) retrieval uncertainty. The results showed that the Bothaville (DS) → Harrismith (DT) Leaf Area Index (LAI) models required only fewer proportions, i.e., 25% or 50%, of the training samples to make optimal retrievals in the DT (i.e., RMSE: 0.61 m2 m−2; R2: 59%), while Harrismith (DS) →Bothaville (DT) LAI models required up to 75% of training samples in the DT to obtain optimal LAI retrievals (i.e., RMSE = 0.63 m2 m−2; R2 = 67%). In contrast, the chlorophyll content models for Bothaville (DS) → Harrismith (DT) required significant proportions of samples (i.e., 75%) from the DT to make optimal retrievals of Leaf Chlorophyll Content (LCab) (i.e., RMSE: 7.09 µg cm−2; R2: 58%) and Canopy Chlorophyll Content (CCC) (i.e., RMSE: 36.3 µg cm−2; R2: 61%), while Harrismith (DS) →Bothaville (DT) models required only 25% of the samples to achieve RMSEs of 8.16 µg cm−2 (R2: 83%) and 40.25 µg cm−2 (R2: 77%), for LCab and CCC, respectively. The results also showed that the source site model accuracy led to better transferability for LAI retrievals. In contrast, the accuracy of LCab and CCC source site models did not necessarily improve their transferability. Overall, the results elucidate the potential of transferable Machine Learning Regression Algorithms and are significant for the rapid retrieval of important crop BVs in data-scarce areas, thus facilitating spatially-explicit information for site-specific farm management

Exploring Transferable Techniques to Retrieve Crop Biophysical and Biochemical Variables Using Sentinel-2 Data

The current study aimed to determine the spatial transferability of eXtreme Gradient Boosting (XGBoost) models for estimating biophysical and biochemical variables (BVs), using Sentinel-2 data. The specific objectives were to: (1) assess the effect of different proportions of training samples (i.e., 25%, 50%, and 75%) available at the Target site (DT) on the spatial transferability of the XGBoost models and (2) evaluate the effect of the Source site (DS) (i.e., trained) model accuracy on the Target site (i.e., unseen) retrieval uncertainty. The results showed that the Bothaville (DS) → Harrismith (DT) Leaf Area Index (LAI) models required only fewer proportions, i.e., 25% or 50%, of the training samples to make optimal retrievals in the DT (i.e., RMSE: 0.61 m2 m−2; R2: 59%), while Harrismith (DS) →Bothaville (DT) LAI models required up to 75% of training samples in the DT to obtain optimal LAI retrievals (i.e., RMSE = 0.63 m2 m−2; R2 = 67%). In contrast, the chlorophyll content models for Bothaville (DS) → Harrismith (DT) required significant proportions of samples (i.e., 75%) from the DT to make optimal retrievals of Leaf Chlorophyll Content (LCab) (i.e., RMSE: 7.09 µg cm−2; R2: 58%) and Canopy Chlorophyll Content (CCC) (i.e., RMSE: 36.3 µg cm−2; R2: 61%), while Harrismith (DS) →Bothaville (DT) models required only 25% of the samples to achieve RMSEs of 8.16 µg cm−2 (R2: 83%) and 40.25 µg cm−2 (R2: 77%), for LCab and CCC, respectively. The results also showed that the source site model accuracy led to better transferability for LAI retrievals. In contrast, the accuracy of LCab and CCC source site models did not necessarily improve their transferability. Overall, the results elucidate the potential of transferable Machine Learning Regression Algorithms and are significant for the rapid retrieval of important crop BVs in data-scarce areas, thus facilitating spatially-explicit information for site-specific farm management

Assessing the built-up footprint in an agricultural system using multi-temporal remotely sensed data

The advent of the new political dispensation in South Africa has seen an exponential growth in the rate of land transformation and encroachment by other land uses into valuable agro-ecological zones. Due to the socio-economic value of the often limited high-potential agricultural land in the country, a reliable determination of encroachment and transformation is necessary for effective monitoring and management of such agro-ecological resources. Using the robust support vector machine classification algorithm, this study adopted multi-temporal, remotely sensed datasets to assess the extent to which the physical development footprint in the uMngeni Local Municipality affected the existing agro-ecological zones from 1993 to 2003 and from 2003 to 2013. The results show a steady increase in built-up areas during the period under investigation. The study demonstrates the value of multi-temporal, remotely sensed datasets and techniques in mapping the vulnerability of existing agricultural land to urbanisation in the study area

Evaluating efficacy of landsat-derived environmental covariates for predicting malaria distribution in rural villages of Vhembe District, South Africa

Malaria in South Africa is still a problem despite existing efforts to eradicate the disease. In the Vhembe District Municipality, malaria prevalence is still high, with a mean incidence rate of 328.2 per 100,0000 persons/year. This study aimed at evaluating environmental covariates, such as vegetation moisture and vegetation greenness, associated with malaria vector distribution for better predictability towards rapid and efficient disease management and control. The 2005 malaria incidence data combined with Landsat 5 ETM were used in this study. A total of nine remotely sensed covariates were derived, while pseudo-absences in the ratio of 1:2 (presence/absence) were generated at buffer distances of 0.5–20 km from known presence locations. A stepwise logistic regression model was applied to analyse the spatial distribution of malaria in the area. A buffer distance of 10 km yielded the highest classification accuracy of 82% at a threshold of 0.9. This model was significant (ρ < 0.05) and yielded a deviance (D2) of 36%. The significantly positive relationship (ρ < 0.05) between the soil-adjusted vegetation index and malaria distribution at all buffer distances suggests that malaria vector (Anopheles arabiensis) prefer productive and greener vegetation. The significant negative relationship between water/moisture index (a1 index) and malaria distribution in buffer distances of 0.5, 10, and 20 km suggest that malaria distribution increases with a decrease in shortwave reflectance signal. The study has shown that suitable habitats of malaria vectors are generally found within a radius of 10 km in semi-arid environments, and this insight can be useful to aid efforts aimed at putting in place evidence-based preventative measures against malaria infections. Furthermore, this result is important in understanding malaria dynamics under the current climate and environmental changes. The study has also demonstrated the use of Landsat data and the ability to extract environmental conditions which favour the distribution of malaria vector (An. arabiensis) such as the canopy moisture content in vegetation, which serves as a surrogate for rainfall.The South African National Space Agency under the Human Capital Development.http://link.springer.com/journal/103932019-03-01hj2018Geography, Geoinformatics and Meteorolog

Evaluating the potential of the red edge channel for C3 (Festuca spp.) grass discrimination using Sentinel-2 and Rapid Eye satellite image data

Integrating the Red Edge channel in satellite sensors is valuable for plant species discrimination. Sentinel-2 MSI and Rapid Eye are some of the new generation satellite sensors that are characterized by finer spatial and spectral resolution, including the red edge band. The aim of this study was to evaluate the potential of the red edge band of Sentinel-2 and Rapid Eye, for mapping festuca C3 grass using discriminant analysis and maximum likelihood classification algorithms. Spectral bands, vegetation indices and spectral bands plus vegetation indices were analysed. Results show that the integration of the red edge band improved the festuca C3 grass mapping accuracy by 5.95 and 4.76% for Sentinel-2 and Rapid Eye when the red edge bands were included and excluded in the analysis, respectively. The results demonstrate that the use of sensors with strategically positioned red edge bands, could offer information that is critical for the sustainable rangeland management

Predicting the distribution of C3 (Festuca spp.) grass species using topographic variables and binary logistic regression model

The spatial distribution of different C3 and C4 grass species in tropical montane areas is commonly influenced by a number of factors that include site-specific topography. Hence, the distribution of these grasses across topographic gradients can vary significantly. In this study, we investigate the influence of topographic factors (elevation, slope and aspect) on the spatial distribution of Festuca grass species in a commonage area, comprising agro-biodiversity conservation land use. Integration of the topographic variables using GIS and binary logistic regression (LR) modelling showed that C3, Festuca grass species distribution can be predicted or mapped with an accuracy of 80% in the landscape under study. The study contributes to understanding the spatial distribution of C3 grass species and provides valuable information for designing and optimizing rangeland conservation in the subtropical montane landscapes

Testing Sentinel-2 spectral configurations for estimating relevant crop biophysical and biochemical parameters for precision agriculture using tree-based and kernel-based algorithms

Sentinel-2 spectral configurations, S2-10m and S2-20m, were evaluated for retrieving essential crop biophysical and biochemical parameters and their effect on the performance of three machine learning regression algorithms (MLRAs) in two African semi-arid sites. The results were benchmarked against all spectral bands (S2-All). The results show that the S2-20m was more robust in retrieving Leaf Area Index (LAI) (RMSEcv: 0.58 m2 m−2, 0.47 m2 m−2), while the S2-10m provided optimal retrievals Leaf Chlorophyll a + b (LCab) (RMSEcv: 6.89 µg cm−2, 7.02 µg cm−2) for the two sites, respectively. In contrast, S2-20m performed better in retrieving Canopy Chlorophyll Content (CCC) in Bothaville to an RMSEcv of 35.65 µg cm−2, while S2-10m yielded relatively lower uncertainties (RMSEcv of 26.84 µg cm−2) in Harrismith. Moreover, various MLRAs were sensitive to the various spectral configurations, and performance varied by site. GPR and XGBoost were more robust, and thus have the most potential for crop biophysical and biochemical parameter retrieval in both sites. Based on the benchmark results, the two configurations can be used independently. The results obtained here are relevant for the rapid development of essential crop biophysical and biochemical parameters for precision agriculture using Sentinel-2’s 10 m or 20 m bands, without the need for resampling

Sugarcane: a way out of energy poverty

Universal access to modern energy is required if the world is to reduce poverty and enable sustainable development. Energy poverty is the lack of access to safe and efficient energy carriers. It affects more than 1.3 billion people without electricity and 2.7 billion without appropriate cooking facilities and fuels, particularly in sub-Saharan Africa (SSA). Under these conditions, people rely on traditional biomass to fulfil basic needs. Traditional biomass use causes death and disease associated with indoor air pollution, high labor demands to obtain biomass, and environmental damage due to deforestation. This paper analyzes the potential for modern energy production based on sugarcane in SSA. A standard, business-as-usual (BAU), sugar-based project is explored vis-a-vis more aggressive scenarios for producing modern energy, including electricity, ethanol, and solid fuel pellets. All scenarios considered are based on a single sugarcane mill processing one million tonnes of cane per year, grown in an area of 15 000 hectares. Our simulations show that over 210 000 households could be served with electricity and other 31 000 with modern cooking fuels under the scenarios examined. Less dependence on traditional biomass may also spare wooded environments from deforestation. However, harnessing modern energy from sugarcane does not come without challenges. Economic pitfalls (e.g. investment costs and affordability) coupled with poor political environments are among the main obstacles. Nevertheless, encouraging local and regional trends proved bioenergy a feasible way out of energy poverty and an alternative to sustainable development.104393408FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP2012/00282-