5 research outputs found
Multitemporal analysis of tropical cyclone impacts on the iSimangaliso Wetland Park sea turtle nesting beach using geospatial technologies.
No full textMasters Degree. University of KwaZulu-Natal, Durban.Increase in the intensity and frequency of tropical cyclones due to changing climatic conditions, poses a threat to sea turtle nesting beaches. In general, tropical cyclones increase the rate of coastal erosion along sandy beaches, potentially disrupting Sea turtle nesting when tropical cyclone seasons coincide with Sea turtle nesting seasons. The iSimangaliso Wetland Park Sea turtle nesting beach is situated along the coast in north-eastern KwaZulu-Natal, South Africa. Its unique location adjacent to the southwest Indian Ocean means that it experiences seasonal flooding due to tropical cyclones. Nevertheless, the impact of these tropical cyclones on the iSimangaliso Wetland Park sea turtle nesting beach remains uncertain. There is, therefore, a need to examine the intensity and frequency of tropical cyclones in this Ocean basin in order to understand their potential impact on the adjacent sea turtle nesting beaches. In this study, tropical cyclone Track Archive data was downloaded from the National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Centre for the years 1980 to 2020. Space-time pattern mining tools were then used to analyse the tropical cyclone data in ArcGIS 10.6. Medium resolution multi-spectral Landsat 7 and 8 satellite images were also collected from the USGS and were used in the Digital Shoreline Analysis System to calculate tropical cyclone induced changes in the position of the shoreline along the iSimangaliso Wetland Park sea turtle nesting beach. The results indicate that: (1) the intensity of tropical cyclones within 1000 km off the coast of the iSimangaliso Wetland Park sea turtle nesting beach have increased by 13.80% from 1980 to 2020. (2) Between the year 1980 and 2020 the frequency of tropical cyclones exhibited a spatiotemporal trend that is not statistically significant (z = 0.56 and p = 0.58 (>.05), suggesting that there is no noticeable increase or decrease in the frequency of tropical cyclones within 1000 km off the coast of the iSimangaliso Wetland Park sea turtle nesting beach despite rising sea surface temperatures. (3) The iSimangaliso Wetland Park sea turtle nesting beach is situated along an oscillating tropical cyclone Cold Spot, suggesting that tropical cyclones rarely reach the study area and generally dissipate further north towards southern Mozambique. (4) From 1999 to 2020, the iSimangaliso Wetland Park sea turtle nesting beach has experienced an average seaward shoreline movement by 68.73 m, averaging 0.76 m/year despite the increase in the intensity of tropical cyclones. (5) The results of this study suggest that the frequency of tropical cyclones have a significantly negative relationship (p < 0.01; r2 = -0.69) with the rate of shoreline movement along the iSimangaliso Wetland Park sea turtle nesting beach. (6) The intensity of tropical cyclone has a
moderate correlation (p < 0.076, r2 = 0.39) with the rate of shoreline change along the iSimangaliso Wetland Park sea turtle nesting beach. (7) Distance of tropical cyclones to the study area has a very low negative relationship with the rate of shoreline change (p < 0.2, r2 = -0.2). Generally, these results suggest that unlike other sea turtle nesting beaches, the iSimangaliso Wetland Park sea turtle nesting beach is relatively safe from tropical cyclones activity despite the rapid increase in sea surface temperatures. The impact of tropical cyclones on the study area is attenuated by the presence of steep dunes, coastal vegetation, mangroves, and flood induced sediment deposition from the Mfolozi River and the St. Lucia estuary. A limitation to this study was inaccessibility to high spatial resolution satellite images due to cost. As a result, Landsat images with a medium spatial resolution of 30 m were used as these are freely available. Future research should consider the use of higher resolution satellite or drone and lidar images to study shoreline changes in relation to tropical cyclone activity and possible sea-level rise along the iSimangaliso Wetland Park sea turtle nesting beach.Author's publications are listed on page vi
Dike-induced aquifer models derived from high-resolution multi-spectral satellite imagery
No full textAbstract The Main Karoo Basin in South Africa is a typical example of an expanding arid region dependent on groundwater resources. Dolerite dikes in the region, analogous to dolerite dikes worldwide, are known to influence subsurface groundwater flow and spatially relate to high-yielding boreholes. Here, the effect of dolerite dikes on groundwater flow is remotely assessed using the Modified Soil Adjusted Vegetation Index derived from high-resolution multi-spectral satellite imagery. From imagery collected during the wet and dry seasons of 2018 and 2021, two aquifer models relating to 505 dikes were identified; (1) barrier-controlled aquifers are induced by ~ 56% of dikes, (2) fractured aquifers are induced by ~ 35% of dikes. Surficial areas overlying aquifers are also shown to sustain vegetation growth through dry seasons. This research demonstrates the efficacy of vegetation indices to rapidly characterise dike-related aquifer models and their seasonal sustainability, critical for effective groundwater exploration and management
Modified Soil Adjusted Vegetation Index (MSAVI) in proximity to dolerite dikes in the Main Karoo Basin, South Africa during wet (January-March) and dry seasons (June-August) of 2018 and 2021. MSAVI scenes are combined with graphs of MSAVI response as a function of lateral distance to dolerite dikes. This dataset is Appendix A to Radebe and Clark (submitted).
No full textModified Soil Adjusted Vegetation Index was calculated using Planet Labs 3-meter resolution multispectral imagery in proximity to 505 dolerite dikes in the Main Karoo Basin. The imagery was collected during wet (January-March) and dry (June-August) seasons of 2018 and 2021.
MSAVI pixel values were visualized graphically against the lateral distance from dolerite dikes to a 300m extent. Green points represent MSAVI pixel values during the wet season and brown points represent MSAVI pixel values during the dry season.
The MSAVI imagery & associated graphs illustrate the spatiotemporal variability of vegetation in relation to dolerite dikes.</p
A near-surface groundwater prospectivity model for the Main Karoo Basin of South Africa derived from multivariate machine learning
No full textAbstract Climate change affecting arid and semi-arid regions increases the periodicity and intensity of droughts resulting in a need for the development of effective groundwater exploration techniques. Here, the availability of near-surface groundwater in the Main Karoo Basin (MKB) is evaluated using multivariate machine learning models. These models integrate 21 conditioning factors ranging from spectral indices, topographical features, geological formations, and hydrological parameters. Among the five machine learning (ML) models tested, the Fast Tree Decision Learning models achieved the highest classification accuracy (81.4%) and a robust Receiver Operating Characteristic (ROC) area curve of 0.87. The resultant near-surface groundwater prospectivity model showed a statistically significant (p < 0.00001) alignment with the spatial locations of high-yielding boreholes, springs, and groundwater-dependent vegetation. Areas with a high potential for near-surface groundwater were identified along the Drakensberg Escarpment, the Cape Fold Belt, and along the eastern MKB adjacent to the Indian Ocean. In the arid western MKB, localized zones identified to be highly prospective for near-surface groundwater coincide with the intersections of drainage networks and major geological structures. Geo-hydrologically, these areas are characterized by borehole yields exceeding 9 L/s. This study illustrates the effectiveness the ML models that harness regional datasets in characterizing prospective areas for near-surface groundwater in data-scarce, arid environments
Combined Application of Filter Cake and Macadamia Husk Compost Affects Soil Fertility and Plant Mineral Content of Orange-Fleshed Sweet Potatoes
No full textA greenhouse pot experiment was conducted to investigate the influence of the combined application of filter cake and macadamia husk compost (FC+MHC) on the soil fertility and dry matter partitioning of Beauregard and 199062.1 cultivars of orange-fleshed sweet potato. The effects of the two organic wastes on the mineral nutrients in the leaves and the storage roots of the 199062.1 cultivar were also investigated. In addition to FC+MHC, four other treatments—filter cake only (FC), macadamia husk compost only (MHC), inorganic fertilizer only (IF), a combination of filter cake and inorganic fertilizer (FC+IF), a combination of macadamia husk compost and inorganic fertilizer (MHC+IF), and a control (CONT)—were included in the investigation for the purpose of comparison. To achieve this, 1 kg of compost was homogenized with 20 kg of soil and filled into graduated 25 L buckets. The experimental design was completely randomized. The plants were grown for 4 months. The results indicated that all treatments altered the soil fertility positively. There were indications that both filter cake and macadamia husk compost inhibited the absorption of iron (Fe), copper (Cu), and aluminum (Al). Also, zinc (Zn) and phosphorus (P) deficiencies in the initial soil were corrected after the application of the organic wastes. In terms of yield, FC+MHC was better than all other treatments. The outcome of this study will no doubt greatly benefit the resource-poor farmers of Northern KwaZulu-Natal who are involved in the production of orange-fleshed sweet potatoes
