ETD Collection
Permanent URI for this collectionhttps://wiredspace.wits.ac.za/handle/10539/104
Please note: Digitised content is made available at the best possible quality range, taking into consideration file size and the condition of the original item. These restrictions may sometimes affect the quality of the final published item. For queries regarding content of ETD collection please contact IR specialists by email : IR specialists or Tel : 011 717 4652 / 1954
Follow the link below for important information about Electronic Theses and Dissertations (ETD)
Library Guide about ETD
Browse
8 results
Search Results
Item The remote sensing of forest canopy gaps in a selectively logged submontane tropical forest reserve in Kenya(2022) Jackson, Colbert MutisoForests constitute 31% (about 4 billion ha) of the land area of the earth, and tropical forests cover is about 2 billion ha. Tropical forests play a significant role in supporting the earth's life and natural ecosystems. But many conservation and protection efforts have not been effective, as they are being cleared in many countries for timber and expansion of agricultural land. Few undisturbed tropical forests remain today, and unsustainable selective logging (SL) is probably the single biggest factor contributing to the global degradation of tropical forests. The amount of forest degradation that is not detected using currently available remote sensing (RS) techniques is unknown. Many methods used to map SL in tropical forests using low/medium spatial resolution datasets have a high rate of false detections. As a result, reliable and operational methods for monitoring SL in tropical forests ought to be utilized. Recently, very high resolution (VHR) RS datasets have caught the interest of researchers studying SL in tropical forests. Therefore, this study was aimed to apply spectral-texture analysis approach to detect canopy gaps caused by illegal logging of Ocotea usambarensis (East African camphor) in Mt. Kenya Forest Reserve (MKFR) in Chuka, Tharaka Nithi County, Kenya using the VHR WorldView-3 (WV-3) satellite data. Several features were derived from the WV-3 data— however, a large number of features results to longer computing time, and might result in reduced classification accuracy. Therefore, feature ranking measures—the mean decrease accuracy, the mean decrease Gini, and the pairwise feature correlation were used. The JeffriesMatusita distance, the transformed divergence index, the G-statistic, and the Euclidean distance were used to calculate the separability of the forest landscape classes. First, the study reviewed and discussed RS techniques used to map SL in the tropical forests. Second, the threatened trees species (TS) in the selectively logged MKFR were mapped. Third, gaps in the forest canopy were detected and quantified using two approaches—initially, only spectral features were used to detect gaps in the forest canopy. A total of 55 spectral features were extracted from the WV-3 dataset—23 means (of 15 vegetation indices–VIs and 8 visible-near-infrared– VNIR bands), and 23 standard deviations–SDs (of 15 VIs and 8 VNIR bands). Also extracted were 8 ratios (of 8 VNIR bands), and 1 brightness feature (average of the means of bands 1 to 8). The study also explored the potential of rich textural features combined with color to model canopy gaps using GLCM-, LBP-, and MLBP-based rotation-invariant feature descriptors derived from WV-3 imagery. Due to their excellent performance and clear logic, two advanced machine learning (ML) classification models—the random forest (RF) and support vector machine (SVM) models were used to identify and classify canopy gaps in the spectral and texture domains of the WV-3 data. During the training process the learning parameters of RF (mtry and ntree) and SVM (γ and C) algorithms were optimised to obtain the best possible settings. Finally, the study reviewed Kenya’s forest policy and law on participatory forest management (PFM). The best tree species classification results reported F1-scores of 68.56 ± 2.6% and 64.64 ± 3.4% for RF and SVM, respectively. The RF and SVM models used to map canopy gaps using the spectral features reported average overall accuracies (OAs) of 92.3 ± 2.6% and 94.0 ± 2.1%, respectively. Average kappa coefficients (ĸ) were 0.88 ± 0.03 for RF and 0.90 ± 0.02 for SVM. The user’s accuracy (UA) and producer’s accuracy (PA) were in the range of 84– 100%. The OA for the classification of canopy gaps using textural/spectral features reported values between 80 (RF, block F’s MLBP/ASM) and 95.1% (SVM, block E’s MLBP/CON). The average OA scores were 84.68 ±3.1, 84.54 ±2.5, 84.86 ±3.0, 86.46 ±3.9, 87 ±4.0, and 85.44 ±3.7 for image blocks A, B, C, D, E, and F, respectively, for the RF classifier, and 85.44 ±3.6, 87.2 ±1.8, 86.3 ±4.3, 89.84 ±2.8, 87.28 ±4.5 and 86.12 ±3.6 for the SVM classifier. The iii UA and PA were in the range of 67-75% and 77-100% for the univariate LBP and MLBP models, respectively. Texture fused with colour resulted to higher classification accuracies. Overall, the approach used in this study demonstrated improved ability of VHR satellite data and ML classification models to accurately map fine canopy gaps resulting from SL. Knowledge about where canopy gaps are located and how they are distributed is critical in accurate estimation of carbon densities of forests, and also for managing the proliferation of invasive species, among other applications. LiDAR datasets acquire the three-dimensional (3- D) structure of forest vegetation—repeat surveys can thus detect the removal of individual trees. The integration of optical images and LiDAR data may boost canopy gap classification.Item Mapping the spatiotemporal distribution of the exotic Tamarix species in riparian ecosystem using Multi-temporal remote sensing data(2019) Kekana, Thabiso.Tamarix spp, commonly known as tamarisk or salt cedar, belong to the family of Tamaricaceae. It is a phreaphytic halophyte with 55 species in the genus Tamarix. South Africa has one indigenous (Tamarix usneoides) and two exotic (T. ramosissima and T.chinensis). Not only are the exotic Tamarix species becoming infamous invaders, but their hybridisation with the indigenous T. usneoides is also complicating morphological discrimination between the different species, and the prospect of potential use of bio-control agents to curb invasion. Thus, lack of spatial information about the current and the past distribution of tamarisk have hampered the effort to control its invasion. This study aimed at investigating the use of multi-temporal remotely sensed data to map the exotic Tamarix invasion in the riparian ecosystem of the Western Cape Province of South Africa, where it predominantly occurs. Random Forest (RF) and Support Vector Machine (SVM) were tested to classify Tamarix and other land-cover types. Sentinel 2 data and Landsat OLI earth observation data were used to map the current and the temporal exotic Tamarix distribution between 2007 and 2018, respectively. This included mapping the current and the multi-temporal Tamarix extent of invasion using the multi-spectral sensors Sentinel 2 and Landsat 5 and 8, respectively. Sentinel 2 was able to detect and discriminate the exotic Tamarix spp invasion using RF and SVM algorithms. The Random Forest classification achieved an overall accuracy of 87.83% and kappa of 0.85, while SVM achieved an overall accuracy of 86.31% and kappa of 0.83. Multi-temporal Landsat data was able to map the current and previous extent of exotic Tamarix invasion for the period between 2007 and 2018. Six land-cover types were classified using SVM. The overall accuracies achieved for 2007, 2014 and 2018 were 87.66%, 91.10%, and 90.62% respectively, and the kappa were 0.85, 0.89, and 0.88, respectively. It was found that the exotic Tamarix invasion increased from 284.67 ha to 647.10 ha in De Rust area, 74.70 ha to 97.29 ha in Leeu Gamka and 215.01 ha to 544.41 ha in Prince Albert region in a period of 11 years. Sentinel 2 and Landsat data have shown the potential to be used in Tamarix mapping. The results obtained in this study would help in implementation of conservation and rehabilitation plans.Item Assessing the impacts of flooding on vegetation cover in the Shashe-Limpopo confluence area using earth observation data(2018) Gangashe, Andani TheopheniaThe extensive and frequent flood events in the Shashe-Limpopo confluence area provide an opportunity to investigate the impacts of such an extreme event in terms of vegetation cover. Extreme flooding events are expected to occur more frequently as a consequence of climate change. Understanding the impacts of flood events on vegetation dynamic would be very useful to develop a dynamic simulation model that can predict the woody species composition of water retention areas or restored floodplains on the basis of flooding characteristics and therefore proposed riverine forest and landscape planning and management. This study used Landsat 7 data to quantify the response of vegetation to flood events in the Shashe-Limpopo confluence area. Two flood events that occurred in 2000 and 2013 respectively were analysed to evaluate the patterns of vegetation response in the area of study. Different indices such as NDVI, NDWI and Modified NDWI (MNDWI) were tested in mapping vegetation patterns and the flood extent. The MNDWI was shown to be more effective in extracting water information than the NDWI. NDVI results and change detection statistical change showed efficiency in indicating vegetation response to floods. The results show that using MNDWI and a 0.2 threshold value, water delineation is possible. Vegetation shows that it flourishes after the floods however; there is some degree of change. The results derived from this can be able to help with landscape planning and management.Item Reconstructing the history of urban development in the mining town of Virginia, Free State between 1940 and 2015(2017) Ajayi, Paul OluwanifemiThe nature of urban development experienced by mining towns across the world has been a subject of concern among urban planners because of its transitory nature. Most times mining towns develop gloriously into booming urban centres that create employment, generate wealth and satisfaction. All these fades into oblivion as soon as the mines get depleted. Mining towns often go through a number of urban processes which have been considered an expression of ‘infrastructural violence’ especially in the earlier stage of urban growth, and continually persists throughout the town’s life span. This research sought to reconstruct the history of urban development in the mining town of Virginia, Free State, and to quantify the manifestations of infrastructural violence throughout its timeline using GIS and remote sensing. Hence, land use and land cover maps were produced from aerial photographs, topographical maps and Landsat images through manual on-screen digitizing and classification using supervised support vector machine algorithms. Land use change detection analysis was conducted on the produced images using the cross classification and tabulation tool of QGIS 2.18.4 and the post classification tool of ENVI 5.3. Landscape metrics were employed to calculate the dimensions of growth and change experienced by all the land use classes during the timeline under study. Results obtained from this study confirmed the thoughts and findings of several theories vis a vis the nature of mining towns. Results reveal a rapid growth in the urban formal land use class up until 1995 with urban expansion and sprawl happening in the years between 1986 and 1995 with metrics of CA, NP and ED multiplying to twice their initial values ten years earlier. The urban informal land use class also experienced its subtle growth throughout the timeline of the study with its own urban expansion also happening between 1986 and 1995 with double increase in CA, NP and ED metric values. However, unlike the formal class that experienced decline after this period of urban expansion, the informal class continued to experience growth up until the end of the study period. Infrastructural violence was measured using the fractal dimension index (AWMPFD) of the landscape metrics for the formal and informal LU class. The results reveal continuous fragmentation throughout the period of study but with higher values in the years in which urban development started.Item Mapping illegal dumping using a high resolution remote sensing image case study: Soweto township in South Africa(2017) Selani, LungileAlthough a vast number of illegal dumping investigations have been conducted in the City of Johannesburg by City of Johannesburg Municipality, Government, Corporates as well as NGOs previously, there has been a limited attempt to integrate available datasets from the different methods of illegal dumping monitoring (satellite, spatial data collection and ground-based observations) and GIS modelling. Most South African municipal administrations have had to acknowledge their incapability to cope with the difficulty of illegal dumping monitoring. Illegal dumping challenges often emanate from the incapacity of municipality administrations to meet the required assemblage and removal of wastes. Vacant or unoccupied land is the target of illegal dumping in most areas. This study compares modelled, satellite and collected data using GIS methods to determine the most accurate estimate of detecting illegal dumping. A comparison between Random Forest (RF) and Support Vector Machine (SVM) in mapping illegal dumping and to quantity the significance of Worldview-2 band in detecting and mapping illegal dumping was pursued. Two results were generated: multispectral imagery sorting production using machine-learning RF and SVM algorithms in a comparable land and definition of the significance of unrelated WorldView bands on sorting production. Precision of the derivative thematic maps was evaluated by calculating mix-up milieus of the classifiers’ land use/ land cover maps with separate autonomous justification data sets. A complete classification accurateness of 84.07 % with a kappa value of 0.8116, and 85.16% with a kappa value of 0.8238 was attained using RF and SVM, respectively. An assessment of diverse WorldView-2 bands using the two classifiers indicated that the blend of the red-edge band had a vital consequence on the overall classification accurateness in mapping of illegal dumping. Keywords: Illegal dumping, remote sensing, monitoring, vegetation, spatial datasets, image processing, image classification.Item Goldmine tailings : a remote sensing survey(2004) Khumalo, Bheki, RomeoPollution originating from mine tailings is currently one of the environmental problems South Africa has to deal with. Because of the large number of tailings impoundments and their changing status, authorities are battling to keep their records and controls up to date. This project is aimed at investigating the use of remote sensing as a way of conducting surveys of mine tailings efficiently, regularly and at a low cost. Mine tailings impoundments of the Witwatersrand in Gauteng provide an ideal study area because of the large number of tailings dams of different sizes and conditions and the availability of satellite images and aerial photographs covering the area. Tailings impoundments conditions are analysed through satellite images, airborne multi-spectral data and aerial photographs captured during the Safari 2000 dry season campaign. Remote sensing interpretation of colour composites of multi-spectral bands, Principal Components and supervised and unsupervised classifications are the methods of analysis used. The overall goal of the project has been achieved through the production of a comprehensive database of tailings impoundments and their rehabilitation status, in an accessible format, containing identity, coordinates, area, rehabilitation status and owner of each tailings impoundment, map them and end up with a comprehensive database of tailings impoundment on the Witwatersrand.Item Detecting ash middens using remote sensing techniques: a comparative study in Southern Gauteng, South Africa(2016) Siteleki, Mncedisi JabulaniThe Iron Age is a very critical aspect of South Africa’s history. It represents a technology that laid a solid foundation for the development of South Africa in terms of its economy, politics and society. It is therefore imperative to study Iron Age, or rather its remnants such as stone-walled structures and ash middens because these give insight into this critical time period’s technology and those responsible for it. Remote sensing spatial technology provides the opportunity not only to study these Iron Age remnants but to save time and resources while doing so through satellite imagery. This study employs remote sensing by comparing different multispectral satellite images ̶ GeoEye 1 and SPOT 5 ̶ to find the optimum platform to detect key archaeological remnants ash middens from the Iron Age period in the Suikerbosrand Nature Reserve located in Southern Gauteng, South Africa. The performance of GeoEye 1 and SPOT 5 in detecting ash middens was compared through supervised classification techniques, Support Vector Machine and Maximum Likelihood Classification, on different band combinations of the two images. Overall, the band combination of Green, Red and NIR is the best performing on both SPOT 5 and GeoEye 1 compared to Green, Red, and Mid IR on SPOT 5 and Green, Red, and Blue on GeoEye 1. However, higher accuracy of results for the detection of ash middens were obtained on the GeoEye 1 platform. The GeoEye platform performed better than the SPOT platform in the detection and analysis of ash middens. Key Words: Ash Middens, GeoEye, Remote Sensing, Satellite Imagery, SPOTItem Locating the rock art of the Maloti-Drakensberg: identifying areas of higher likelihood using remote sensing(2016) Pugin, James MalcolmThis dissertation examines the role of remote sensing on rock art survey and is motivated by two key objectives: to determine if remote sensing has any value to rock art survey, furthermore if remote sensing is successful to determine if these individual remote sensing components can contribute to a predictive (site locating) model for rock art survey. Previous research effectively applied remote sensing techniques to alternate environmental studies which could be replicated in such a study. The successful application of google earth imagery to rock art survey (Pugin 2012) demonstrated the potential for a more expansive automated procedure and this dissertation looks to build on that success. The key objectives were tested using three different research areas to determine remote sensing potential across different terrain. Owing to the nature of the study, the initial predictions were formulated using the MARA database – a database of known rock art sites in the surrounds of Matatiele, Eastern Cape – and were then applied to surrounding areas to expand this database further. Upon adding more sites to this database, the predictions were applied to Sehlabathebe National Park, Lesotho and then 31 rock art sites in the areas adjacent to Underberg. The findings of this research support the use of predictive models provided that the predictive model is formulated and tested using a substantial dataset. In conclusion, remote sensing is capable of contributing to rock art surveys and to the production of successful predictive models for rock art survey or alternate archaeological procedures focusing on specific environmental features.