Browsing by Author "Singh, Rebekah Gereldene"

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    Change detection of bare areas in the Xolobeni region, South Africa using Landsat NDVI
    (CONSAS Conference, 2015-06) Kemp, Jaco; Singh, Rebekah Gereldene; Engelbrecht, Jeanine
    Identification and protection of areas that are vulnerable to erosion is essential for the conservation of the sensitive wetlands and estuarine ecosystems along the Xolobeni coastal strip. The forecasting of these erosion susceptible areas requires an understanding of the inter- relationships of the critical factors that have influenced erosion potential over time. Vegetation and bare areas are some of the contributing factors that have influenced erosion at Xolobeni. This study used remote sensing as a tool to provide some information on the inter-relationship between vegetated classes and bare areas. Normalised Difference Vegetation Index (NDVI) data derived from multi-temporal Landsat 5 imagery has formed the baseline information for this study. A density slicing approach was adopted to classify the region into four vegetation structure classes of predominant land cover types. Post classification change detection data has provided an understanding of the relative susceptibility of the different vegetated classes to being degraded to bare areas. The results suggest that poorly vegetated regions were most susceptible to further degradation and an elevated susceptibility to erosion. On the other hand, moderately and densely vegetated regions were less susceptible to land degradation. The information can be used to identify measures to mitigate the effects of land degradation in vulnerable areas.
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    A multi-scale study of wind erosion susceptibility along the South African Wild Coast
    (Stellenbosch : Stellenbosch University, 2024-03) Singh, Rebekah Gereldene; Kemp, Jaco; Botha, Greg; Stellenbosch University. Faculty of Arts and Social Sciences. Dept of Geography and Environmental Studies.
    ENGLISH ABSTRACT: Wind erosion is a significant driver of land degradation, affecting over a third of all land areas in recent centuries. Accelerated wind erosion in South Africa has caused severe localised land degradation, similar to that observed in parts of the ecologically important Wild Coast region. This erosion-induced degradation has led to localised desertification and poses risks to vulnerable wetland and river ecosystems. In data-sparse regions such as the Wild Coast, identifying highly susceptible areas becomes crucial to mitigate the detrimental effects of accelerated wind erosion. This study aims to determine the spatial distribution of wind erosion features along the Wild Coast, investigate factors influencing their occurrence and growth, and model the area's future susceptibility to wind erosion. Historical aerial photography, Google EarthTM imagery, and multi-temporal mapping spanning an 85-year period were utilised to create a wind erosion inventory map. It revealed an uneven spatial distribution of wind erosion sites, primarily clustered within a 2 km stretch along the coastal study area. These sites were concentrated in specific locations such as Xolobeni, Mkambati, Mngazi River Mouth - Noxova - Mbolompo Point, Wavecrest, and Kei River Mouth. Human activities in wind-exposed areas, such as disturbed agricultural fields, bare patches in grasslands, informal sand mines, and tracks, were identified as the key locations where these features initiated. Over the 85-year period, some erosion features expanded significantly, while others remained relatively stable due to the establishment of peripheral vegetation that acts as wind barriers. Long-term remote sensing analyses focused on the Xolobeni area, a representative subset of the broader Eastern Cape Wild Coast study region, aimed to comprehend the influence of long-term changes in land cover, vegetation status, soil texture, and soil moisture conditions on the occurrence and evolution of wind erosion features. This analysis utilised multi-temporal Landsat 5 Thematic Mapper (L5 TM) and Landsat 8 Operational Land Imager (OLI) imagery covering the period from 1987 to 2020, in conjunction with available topographical data from 1982, 1993, and 2004. The application of the Random Forest classifier successfully mapped land cover for the years 1987, 1991, 1999, 2004, 2010, 2015, and 2020, achieving overall accuracies exceeding 80.00% and Kappa indices surpassing 0.77 for each of these seven years. A primary finding of the land cover change assessment reveals the susceptibility of degraded grasslands to wind erosion and noted a rapid expansion of wind erosion features between 1987 and 1999, followed by a subsequent period of stability. The analysis of computed time-series Normalised Difference Vegetation Index (NDVI), Topsoil Grain Size Index (TGSI), and Normalised Difference Moisture Index (NDMI) data revealed that regions impacted by wind erosion consistently exhibited lower NDVI values, indicating reduced vegetation cover conditions, reaffirming the influence of vegetation on wind erosion development. Higher TGSI values denoted areas associated with higher wind erosion susceptibility and emphasised the significance of sandy soils with reduced clay content in erosion vulnerability. Lower NDMI values associated with affected regions highlighted that drier soil conditions promote wind erosion processes. The multi-temporal analyses of topographical data revealed that abandoned cultivated lands and zones with high track density were prone to erosion, highlighting a connection between human activities and wind erosion susceptibility in the study region. General concepts of the Wind Erosion Equation were adopted in this study to map the regional wind erosion susceptibility conditions. Two regional susceptibility methods were implemented and compared. Model 1 employed a geostatistical approach, based on erosion factor class frequency ratio data and Analytical Hierarchy Process importance weights. Model 2 utilised the data-driven Weights of Evidence modeling technique. Model 1 classified large areas of the study area as having low susceptibility (46%), while Model 2 classified more than 90% of the areas as very low susceptible zones. Both models show that less than 4% of the study region has a high to very high susceptibility to wind erosion. In general, areas associated with higher wind erosion susceptibility are poorly vegetated, wind-exposed coastal zones characterised by unconsolidated, erodible sandy soils. Model 1 and Model 2 are associated with area under the receiver operating characteristic curve values of 0.987 and 0.946, respectively, displaying satisfactory average performances. Recommendations for combating wind erosion along the ecologically sensitive Wild Coast include avoiding wind-aligned trackways, protecting existing vegetation, minimising bare soil patches in vulnerable areas, and establishing indigenous vegetation barriers. Utilisation of the developed wind erosion inventory and susceptibility maps will aid stakeholders in developing targeted conservation strategies required to shield vulnerable regions from further degradation.