Theses And Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/20.500.11837/218

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    Assessing the vulnerability of resource-poor households to disasters associated with climate variability using remote sensing and GIS techniques in the Nkonkobe Local Municipality, Eastern Cape Province, South Africa.
    (University of Fort Hare, 2016) Chari, Martin M; Hamandawana, H.
    The main objective of the study was to assess the extent to which resource-poor households in selected villages of Nkonkobe Local Municipality in the Eastern Cape Province of South Africa are vulnerable to drought by using an improvised remote sensing and Geographic Information System (GIS)-based mapping approach. The research methodology was comprised of 1) assessment of vulnerability levels and 2) the calculation of established drought assessment indices comprising the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI) from wet-season Landsat images covering a period of 29 years from 1985 to 2014 in order to objectively determine the temporal recurrence of drought in Nkonkobe Local Municipality. Vulnerability of households to drought was determined by using a multi-step GIS-based mapping approach in which 3 components comprising exposure, sensitivity and adaptive capacity were simultaneously analysed and averaged to determine the magnitude of vulnerability. Thereafter, the Analytical Hierarchy Process (AHP) was used to establish weighted contributions of these components to vulnerability. The weights applied to the AHP were obtained from the 2012 - 2017 Nkonkobe Integrated Development Plan (IDP) and perceptions that were solicited from key informants who were judged to be knowledgeable about the subject. A Kruskal-Wallis H test on demographic data for water access revealed that the demographic results are independent of choice of data acquired from different data providers (χ2(2) = 1.26, p = 0.533, with a mean ranked population scores of 7.4 for ECSECC, 6.8 for Quantec and 9.8 for StatsSA). Simple linear regression analysis revealed strong positive correlations between NDWI and NDVI ((r = 0.99609375, R2 = 1, for 1985), 1995 (r = 0.99609375, R2 = 1 for 1995), (r = 0.99609375, R2 = 1 for 2005) and (r = 0.99609375, R2 = 1 for 2014). The regression analysis proved that vegetation condition depends on surface water arising from rainfall. The results indicate that the whole of Nkonkobe Local Municipality is susceptible to drought with villages in south eastern part being most vulnerable to droughts due to high sensitivity and low adaptive capacity.
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    GIS and remote sensing as a potential tool to support digital soil mapping in the Eastern Cape Province in South Africa
    (University of Fort Hare, 2014) Mathe, Tumelo
    This study is based on assessing the potential use of GIS and Remote Sensing in trying to fill the various soil maps of selected regions at different scales with spatial soil data. A variety of processes are available for use. These include band ratios, principal component analysis as well as use of a digital elevation model (DEM). With the advent of GIS and Remote Sensing, these principles in the new niche of study are investigated to check if they can be used to augment the current processes available in soil mapping techniques. Such processes as band ratioing, principal component analysis and use of Digital Elevation Models (DEMs) are investigated to check if they can be used in soil mapping techniques. From the results produced it is evident that these processes have the potential to be used in the Digital Soil Mapping process. Despite the limitation of remote sensing to a few centimetres of the topsoil these processes can be used together with the soil mapping techniques currently being used to come up with soil maps.
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    Modelling and mapping the suitability of land for crop production using a combination of GIS and remote sensing in the Eastern Cape: a case study of Mbashe and Mquma local municipalities-South Africa
    (University of Fort Hare, 2012) Vuso, Solly
    In order to achieve sustainable agriculture, decision makers require appropriate and fully detailed spatial information on land resources. Crop-land suitability analysis is a prerequisite to achieve optimum utilization of the available land resources for sustainable agriculture rural production (T.R. Nisar Ahamed et al., 2000). It is indeed of paramount importance to identify suitable land for cropping while causing minimum impact to the environment. The Food and Agriculture Organization (FAO, 1976), recommended an approach of land suitability evaluation for crops in terms of suitable land based on climatic and terrain data and soil properties. In this study, an attempt was made to identify suitable areas for massive crop production using remote sensing and GIS methodologies and the knowledge from extension officers. The primary method aims at generating land cover data using SPOT 5 satellite imagery and modeling with the existing land capability. The research purpose was to map the map the number hectares suitable areas for crop production number hectares suitable areas for crop production number hectares suitable areas for crop productionnumber hectares suitable areas for crop production number hectares suitable areas for crop production number hectares suitable areas for crop production number hectares suitable areas for crop production number hectares suitable areas for crop productionnumber hectares suitable areas for crop productionnumber hectares suitable areas for crop production number hectares suitable areas for crop productionnumber hectares suitable areas for crop production number hectares suitable areas for crop production number hectares suitable areas for crop production number hectares suitable areas for crop production number hectares suitable areas for crop production number hectares suitable areas for crop production number hectares suitable areas for crop production . Spatial modeling techniques were utilized to model land suitability model in an effective and efficiently way. The spatial modeling extension from ESRI product was used to model the crop suitability areas. The model run on ArcGIS platform and due to the fact that modeling only uses raster formats, all the data sets were projected and converted to raster format. The weighted overlay model was used to create land suitability map. The model results revealed that 4046251.79 hectares were suitable for cropping in the study area. The final outputs of suitable areas were calculated and each ward was given a value of suitable area as well as unsuitable area. The validation of the final maps compliments the 500 000 hectares that were mapped by Dept of Agriculture EC using 8% slope as the best potential areas. The method provides a cheap, effective and efficient way to map suitable areas over a large area and it also uses remote sensing data. It is hoped that decision makers will make use of the information produced in this paper as the whole world is in crisis of food security.