The remote sensing of papyrus vegetation (Cyperus papyrus L.) in swamp wetlands of South Africa.

Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2010.Papyrus (Cyperus papyrus .L) swamp is the most species rich habitat that play vital hydrological, ecological, and economic roles in central tropical and western African wetlands. However, the existence of papyrus vegetation is endangered due to intensification of agricultural use and human encroachment. Techniques for modelling the distribution of papyrus swamps, quantity and quality are therefore critical for the rapid assessment and proactive management of papyrus vegetation. In this regard, remote sensing techniques provide rapid, potentially cheap, and relatively accurate strategies to accomplish this task. This study advocates the development of techniques based on hyperspectral remote sensing technology to accurately map and predict biomass of papyrus vegetation in a high mixed species environment of St Lucia- South Africa which has been overlooked in scientific research. Our approach was to investigate the potential of hyperspectral remote sensing at two levels of investigation: field level and airborne platform level. First, the study provides an overview of the current use of both multispectral and hyperspectral remote sensing techniques in mapping the quantity and the quality of wetland vegetation as well as the challenges and the need for further research. Second, the study explores whether papyrus can be discriminated from each one of its coexistence species (binary class). Our results showed that, at full canopy cover, papyrus vegetation can be accurately discriminated from its entire co-existing species using a new hierarchical method based on three integrated analysis levels and field spectrometry under natural field conditions. These positive results prompted the need to test the use of canopy hyperspectral data resampled to HYMAP resolution and two machine learning algorithms in identifying key spectral bands that allowed for better discrimination among papyrus and other co-existing species (n = 3) (multi-class classification). Results showed that the random forest algorithm (RF) simplified the process by identifying the minimum number of spectral bands that provided the best overall accuracies. Narrow band NDVI and SR-based vegetation indices calculated from hyperspectral data as well as some vegetation indices published in literature were investigated to test their potential in improving the classification accuracy of wetland plant species. The study also evaluated the robustness and reliability of RF as a variables selection method and as a classification algorithm in identifying key spectral bands that allowed for the successful classification of wetland species. Third, the focus was to upscale the results of field spectroscopy analysis to airborne hyperspectral sensor (AISA eagle) to discriminate papyrus and it co-existing species. The results indicated that specific wavelengths located in the visible, red-edge, and near-infrared region of the electromagnetic spectrum have the highest potential of discriminating papyrus from the other species. Finally, the study explored the ability of narrow NDVI-based vegetation indices calculated from hyperspectral data in predicting the green above ground biomass of papyrus. The results demonstrated that papyrus biomass can be modelled with relatively low error of estimates using a non-linear RF regression algorithm. This provided a basis for the algorithm to be used in mapping wetland biomass in highly complex environments. Overall, the study has demonstrated the potential of remote sensing techniques in discriminating papyrus swamps and its co-existing species as well as in predicting biomass. Compared to previous studies, the RF model applied in this study has proved to be a robust, accurate, and simple new method for variables selection, classification, and modelling of hyperspectral data. The results are important for establishing a baseline of the species distributions in South African swamp wetlands for future monitoring and control efforts

Bulk heterojunction organic solar cells and thin film electrode buffer layers : synthesis, preparation and characterization.

Doctor of Philosophy in Physics. University of KwaZulu-Natal, Pietermaritzburg 2016.Abstract available in PDF file

Exploring the utility of the additional WorldView-2 bands and support vector machines in mapping land use/land cover in a fragmented ecosystem, South Africa

Land use/land cover (LULC) classification is a key research field in environmental applications of remote  sensing on the earthfs surface. The advent of new high resolution multispectral sensors with unique bands has  provided an opportunity to map the spatial distribution of detailed LULC classes over a large fragmented area. The objectives of the present study were: (1) to map LULC classes using multispectral WorldView-2 (WV-2) data and SVM in a fragmented ecosystem; and (2) to compare the accuracy of three WV-2 spectral data sets in distinguishing amongst various LULC classes in a fragmented ecosystem. WV-2 image was spectrally  resized to its four standard bands (SB: blue, green, red and near infrared-1) and four strategically located  bands (AB: coastal blue, yellow, red edge and near infrared-2). WV-2 image (8bands: 8B) together with SB and AB subsets were used to classify LULC using support vector machines. Overall classification accuracies of 78.0% (total disagreement = 22.0%) for 8B, 51.0% (total disagreement = 49.0%) for SB, and 64.0% (total disagreement = 36.0%) for AB were achieved. There were significant differences between the performance of all WV-2 subset pair comparisons (8B versus SB, 8B versus AB and SB versus AB) as demonstrated by the results of McNemarfs test (Z score .1.96). This study concludes that WV-2 multispectral data and the SVM classifier have the potential to map LULC classes in a fragmented ecosystem. The study also offers relatively accurate information that is important for the indigenous forest managers in KwaZulu-Natal, South Africa for making informed decisions regarding conservation and management of LULC patterns.Keywords: land use/cover classification, fragmented ecosystem, WorldView-2, support vector  machines

Testing the spectral resolutions of the new multispectral sensors for detecting Phaeosphaeria leaf spot (PLS) infestations in maize crop

Maize is one of the most important subsistence and commercial crops in the world. In Africa, it is regarded as one of the most popular food crops. Recently however, significant losses due to Phaeosphaeria leaf spot (PLS) infestation have been reported. Therefore, techniques for early detection of PLS infestation are valuable for mitigating maize yield losses. Recently, remotely sensed datasets have become valuable in crop assessment. In this study, we sought to detect early PLS infestation by comparing the performance of commonly used higher spatial resolution sensors (WorldView, Quickbird, Sentinel series 2, RapidEye and SPOT 6) based on their spectrally resampled field spectra. Canopy training spectra were collected on leaves with signs of early infestation and healthy leaves spectral characteristics used for comparison. Training data was collected in 2013 growing season while test data was collected under similar conditions in 2014. The Random Forest algorithm was used to establish the Kappa and overall, user and producer's accuracies. Results showed that the RapidEye sensor with an overall classification accuracy of 86.96% and Kappa value of 0.76 performed better than the rest of the sensors while the Red, Yellow and Red-Edge bands were most useful for detecting early PLS infestation. The value of the RapidEye sensor in detecting early PLS infestation can be attributed to the optimally centred Red Red-Edge bands sensitive to changes in chlorophyll content, a consequent of PLS infestation on maize leaves. The study provides valuable insight on the value of existing sensors, based on their sensor characteristics in detecting early PLS infestation.Keywords:  Phaeosphaeria leaf spot, Remote Sensing, sensors Random Forest, Variable importanc

A critical review of the impact of South Africa’s mine closure policy and the winding-up process of mining companies

Abstract: Background: Most mining operations are viable for a period of 30 years, depending on the mineral extracted and the available reserves. Whilst the expectation is that mines will continue uninterrupted until the planned period is complete, unscheduled closure can occur. Sudden and unplanned mine closure can result in immediate environmental and social impacts. In South Africa, the challenges of mine closure are exacerbated by unexpected sudden closures owing to winding-up and business rescue processes. The literature is inconclusive regarding these issues and there is poor integration of affected communities by mining operations. Aim: We reviewed South Africa’s legal frameworks relating to mine closure, the winding- up of gold mining companies and the impact of sudden closure on the environment and communities. Method: This review built on and extended previous systematic reviews. We focused on the regulation for fi provisioning for prospecting, mining, exploration and rehabilitation. Two examples of gold mining companies that were closed prematurely were examined. We also reviewed the mine closure and environmental policies of other countries, notably Australia and Canada and noticed similarities to South African policies. Results: Differences are evident in the enforcement of compliance in Australia and Canada, which are more proactive in dealing with the challenges of winding-up and its impacts. Conclusion: South Africa could adopt these countries’ models to enforce compliance and proactivity regarding sudden mine closure. One recommendation is to establish a fund for immediate rehabilitation in such cases as part of the temporary mine closure framework

Dust deposition impacts at a liquidated gold mine Village : Gauteng province in South Africa

Abstract: The windy season brings numerous community complaints for gold mining companies situated in theWitwatersrand due to windblown dust from partially rehabilitated tailings storage facilities (TSFs). For communities encroaching onto TSFs, windblown dust is perceived as a health hazard and an environmental challenge. In a study conducted in 2017 by the Lawyers for Human Rights, the community of a gold mine village perceived tailings storage facility 6 (TSF6) and other surrounding tailings storage facilities which are partially rehabilitated to be a health and socio-economic threat. Since 2013, when a nearby gold mining company was liquidated, this community has been complaining about dust fallout. To validate the claims made by the community this paper reports on the dust deposition impacts, and respiratory illnesses risk posed by wind-blown generated dust. The study conducts an air quality assessment using dispersion modelling of windblown dust. Surface material from the TSFs was sampled, analysed for silica and heavy metal content using X-ray fluorescence (XRF) and inductively coupled plasma-mass spectrometry (ICP-MS) respectively. This study finds that PM10 dust fallout, high in silica and uranium content, could potentially pose health threats to the surrounding community. The study further shows that dust deposition is the highest in July–October, with TSF6 posing a nuisance while TSF1 represents a potential health threat owing to its particle size distribution for the surrounding gold mine village community. Potential receptors of the air pollution by dust in this study area include neighbouring property owners, business owners of the nearby shopping centre, the school and the clinic. This study further finds that sudden mine closure due to mine liquidation results in unrehabilitated tailings storage facilities which exacerbates dust deposition

Exploring the Potential of Feature Selection Methods in the Classification of Urban Trees Using Field Spectroscopy Data

Mapping of vegetation at the species level using hyperspectral satellite data can be effective and accurate because of its high spectral and spatial resolutions that can detect detailed information of a target object. Its wide application, however, not only is restricted by its high cost and large data storage requirements, but its processing is also complicated by challenges of what is known as the Hughes effect. The Hughes effect is where classification accuracy decreases once the number of features or wavelengths passes a certain limit. This study aimed to explore the potential of feature selection methods in the classification of urban trees using field hyperspectral data. We identified the best feature selection method of key wavelengths that respond to the target urban tree species for effective and accurate classification. The study compared the effectiveness of Principal Component Analysis Discriminant Analysis (PCA-DA), Partial Least Squares Discriminant Analysis (PLS-DA) and Guided Regularized Random Forest (GRRF) in feature selection of the key wavelengths for classification of urban trees. The classification performance of Random Forest (RF) and Support Vector Machines (SVM) algorithms were also compared to determine the importance of the key wavelengths selected for the detection of the target urban trees. The feature selection methods managed to reduce the high dimensionality of the hyperspectral data. Both the PCA-DA and PLS-DA selected 10 wavelengths and the GRRF algorithm selected 13 wavelengths from the entire dataset (n = 1523). Most of the key wavelengths were from the short-wave infrared region (1300-2500 nm). SVM outperformed RF in classifying the key wavelengths selected by the feature selection methods. The SVM classifier produced overall accuracy values of 95.3%, 93.3% and 86% using the GRRF, PLS-DA and PCA-DA techniques, respectively, whereas those for the RF classifier were 88.7%, 72% and 56.8%, respectively

Assessing the Effects of Land Use on Surface Water Quality in the Lower uMfolozi Floodplain System, South Africa

This study investigated the impacts of cultivation on water and soil quality in the lower uMfolozi floodplain system in KwaZulu-Natal province, South Africa. We did this by assessing seasonal variations in purposefully selected water and soil properties in these two land-use systems. The observed values were statistically analysed by performing Student’s paired t-tests to determine seasonal trends in these variables. Results revealed significant seasonal differences in chloride and sodium concentrations and electrical conductivity (EC) and the sodium adsorption ratio (SAR) with cultivated sites exhibiting higher values. Most of the analyzed chemical parameters were within acceptable limits specified by the South African agricultural-water-quality (SAWQ) water quality guidelines for irrigation except for sodium adsorption ratio (SAR), chloride, sodium and EC. EC, pH and nitrate content which were higher than the specified SAWQ limits in cultivated sites. Quantities of glyphosate, ametryn and imidacloprid could not be measured because they were below detectable limits. The study concludes that most water quality parameters met SAWQ’s standards. These results argue for concerted efforts to systematically monitor water and soil quality characteristics in this environment to enhance sustainability by providing timely information for management purposes

A remote sensing-based approach to investigate changes in land use and land cover in the lower uMfolozi floodplain system, South Africa

The goal of this study was to understand land use and land cover (LULC) changes within the lower uMfolozi floodplain system, South Africa, and relate those changes to wetland loss. Changes in LULC were assessed using a geographic object-based image analysis (GEOBIA) algorithm to classify multi-date Landsat images into eight cover types over a period of 20 years, between 1997 and 2017. Post-classification accuracy assessment of all map-outputs was conducted by compiling confusion matrixes and calculating producer, user, and global accuracies and kappa coefficients (K) for each map-output. Levels of accuracy for all map-outputs were within acceptable limits, ranging between 79% and 88% (K = 0.76 and 0.86, respectively). Thereafter, paired t-tests were applied to determine whether the changes in LULC over the study period were significant. Results of this investigation showed a significant (p-value, < 0.01) conversion of wetland to cultivation, by 14%. This finding is important because it demonstrates that in this environment, human agency is one of the major drivers of a persistent decrease in the wetland ecosystem. The major insight from this observation is that there is an urgent need to formulate and implement objectively informed interventions to enhance the sustainability of the uMfolozi floodplain system and that of others elsewhere.https://www.tandfonline.com/loi/ttrs20hj2022Geography, Geoinformatics and Meteorolog

Direct Extraction and Assessment of Genomic DNA of Mycetoma Fungi from Black-grains Specimen

Background: Direct isolation of genomic DNA of mycetoma fungi from black-grains achieve rapid diagnosis and may overcome culture disadvantages. Objectives: This study aimed to isolate and assess the DNA of mycetoma fungi using black-grains and to apply amplification of ITS region and nucleotide sequences. Methods: CTAB method was followed by manual homogenization alternatively to liquid nitrogen and glass beads disruption to obtain the genomic DNA. Results: Yielded DNA concentrations vary from 1.50 to 47.97 μg/ml (mean 10.09 μg/ml) while the optimum DNA purity recorded with 75.8% of specimens (n=69/91).Successful amplification of ITS region was done using pan-fungal primers (ITS4/5) with 90.1 (n=82/91)percentage. Species nucleotide sequences were detected with 67 (94.4%) amplicons from a total of 71.Conclusion: The study recommended using of black-grain specimens for DNA extraction of mycetoma fungi parallel with culture to insure rabid diagnosis and identification