Dissertations / Theses on the topic 'Remote sensing – South Africa – Eastern Cape'

Land degradation in most communal parts of the Keiskamma catchment has reached alarming proportions. The Keiskamma catchment is particularly predisposed to severe land degradation associated with soil erosion, thicket degradation and deteriorating riparian vegetation. There is a close coupling between land use/cover dynamics and degradation trends witnessed in the catchment. Soil erosion is prevalent in most of the communal areas in the catchment. The principal aim of this study was to investigate land use/cover trends, model the spatial patterns of soil loss and predict future land use/cover scenarios as a means of assessing land degradation in the Keiskamma catchment. Multi-temporal Landsat satellite imagery from 1972 to 2006 was used for land use/cover change analyses using object-oriented post-classification comparison. Fragmentation analysis was performed by computing and analyzing landscape metrics in the riparian and adjacent hillslope areas to determine the land cover structural changes that have occurred since 1972. The landscape function analysis was used to validate the current rangeland conditions in the communal areas and the former commercial farms. The current condition of the riparian zones and proximal hillslopes was assessed using the Rapid Appraisal of Riparian Condition and future land use/cover scenarios were simulated using the Markovcellular automata model. Spatial patterns of soil loss in the Keiskamma catchment were determined using the Sediment Assessment Tool for Effective Erosion Control (SATEEC), which is a GIS based RUSLE model that integrates sediment delivery ratios. Object oriented classification was used to map soil erosion surfaces and valley infill in ephemeral stream channels as a means of demonstrating the major sediment transfer processes operating in the Keiskamma catchment. The Mahalanobis distance method was used to compute the topographic thresholds for gully erosion. To understand the effect of soil characteristics in severe forms of erosion, laboratory analyses were undertaken to determine the physico-chemical soil properties. iv The temporal land use/cover analysis done using the post-classification change detection indicated that intact vegetation has undergone a significant decline from 1972 to 2006. The temporal changes within the intermediate years are characterized by cyclic transitions of decline and recovery of intact vegetation. An overall decline in intact vegetation cover, an increase in degraded vegetation and bare eroded soil was noted. Fragmentation analyses done in the communal villages of the central Keiskamma catchment indicated increasing vegetation fragmentation manifested by an increase in smaller and less connected vegetation patches, and a subsequent increase of bare and degraded soil patches which are much bigger and more connected. The Landscape Organisation Index revealed very low vegetation connectivity in the communal rangelands that have weak local traditional institutions. Fragmentation analyses in the riparian and proximal hillslopes revealed evidence of increasing vegetation fragmentation from 1972 to 2006. The Markov Cellular Automata simulation predicted a decline in intact vegetation and an increase in bare and degraded soil in 2019. The Keiskamma catchment was noted as experiencing high rates of soil loss that are above provincial and national averages. The classification of erosion features and valley infill showcased the vegetation enrichment in the ephemeral streams which is occurring at the expense of high soil losses from severe gully erosion on the hillslopes. This in turn has led to an inversion of grazing patterns within the catchment, such that grazing is now concentrated within the ephemeral stream channels. Soil chemical analyses revealed a high sodium content and low soluble salt concentration, which promote soil dispersion, piping and gully erosion. The presence of high amounts of illite-smectite in the catchment also accounts for the highly dispersive nature of the soil even at low SAR values. Significant amounts of swelling 2:1 silicate clays such as smectites cause cracking and contribute to the development of piping and gullying in the catchment. Given the worsening degradation trends in the communal areas, a systematic re-allocation of state land in sections of the catchment that belonged to the former commercial farms is recommended to alleviate anthropogenic pressure. Strengthening local institutions that effectively monitor and manage natural resources will be required in order to maintain v optimum flow regimes in rivers and curb thicket degradation. Measures to curb environmental degradation in the Keiskamma catchment should encompass suitable ecological interventions that are sensitive to the socio-economic challenges facing the people in communal areas.

Expansion of woody vegetation results in the transformation of a grass-dominated ecosystem to a tree-dominated ecosystem causing land degradation in most semi-arid areas. The imbalance in the natural ecosystem between herbaceous plants and woody vegetation poses a threat to the natural environment. Such changes alter the flow, availability and quality of nutrient resources in the biogeochemical cycle. Most of the dominating woody plants are often unpalatable to domestic livestock. Therefore, the objective is to assess the spatial extent of woody vegetation over time. Knowledge of the spatial and temporal characteristics of woody vegetation dynamics will enable the development of management plans. These characteristics can be derived using remote sensing techniques which have become efficient in such studies. This study aimed to characterize woody vegetation dynamics along the route between Alice and King Williams’s town in Eastern Cape Province South Africa using Landsat data. This aim was achieved by focussing on three specific objectives. The first objective was to compare the performance of multispectral data and Normalized Difference Vegetation Index (NDVI) data of Landsat imagery in mapping woody vegetation cover. The second objective was to investigate the effect of the spatial resolution of remotely-sensed data on discrimination of woody vegetation from other land cover types. The third objective characterised woody vegetation dynamics between 1986 and 2013/2014 using the results from the first objective. The study used Landsat imagery acquired in November or February of 1986, 1994/1995, 2002/2003 and 2013/2014. Due to lack of data which covered the study area two separate dates (November and February) where used for the study resulting in naming the study area western and eastern parts. Unsupervised classification was performed on the multispectral, NDVI and pan-sharpened images to generate four generic land cover classes, namely water, bare land, grassland and woodland. Accuracy assessments of the classified images was done using error matrix. The results showed that the classification based on NDVI images yielded a better overall accuracy than the classification based on multispectral images for the western (83 percent and 75 percent, respectively) and eastern (82 percent and 76 percent, respectively) parts of the study area. Similarly, pan-sharpening resulted in better overall classification accuracy than multispectral, but comparable to the classification of the NDVI images for both the western (82 percent) and eastern (83 percent) parts of the study area. Remote sensing is an effective tool in assessing changes in the physical environment. Landsat imagery is suitable in assessing land cover dynamics given the long-term and free availability of the image. In addition, the large spatial coverage it provides, enables Landsat data to be used on studies that have wide spatial coverage. Classification for the purpose of time-series analysis was then performed on the NDVI images of each date (1986, 1994/1995, 2002/2003 and 2013/2014). Both woody vegetation and grassland experienced changes from 1986 to 2013/2014 with grassland occupying (75 percent) compared to woodland (17 percent) in 1986. In the year 2013/14 grassland occupied 32 percent and woodland occupied 51 percent of the study area. The increase in woody vegetation in the study area can be attributed to livestock rearing and migration of people from the rural to urban areas post-Apartheid. The study output will aid in the development of a database on land cover distribution of the area between King William’s town and Alice town, providing useful information to decision-making and further studies on woody vegetation.

Vegetation stress as a form of degradation is a widespread problem in many catchments in the Eastern Cape province. The Keiskamma is one of the catchments where considerable parts of the thicket biome are stressed. This necessitates an assessment of the status of the thicket biome by way of detecting vegetation stress in the area. The underpinnings of vegetation stress are investigated in this study. As a basic method to evaluate the thicket condition, remotely sensed data were acquired. High resolution ASTER imagery for the Keiskamma area at two different dates (2001 and 2005) was used to compute SVI and NDVI as indicators of vegetation stress conditions. A Digital Elevation Model (DEM) was used to derive slope angle and aspect. By way of digitizing from ortho-photo maps, various land-use types were mapped using Arc View GIS. The relationship between land use, terrain, soil erosion and vegetation stress was established. Field based techniques comprising stomatal conductance measurements were used and compared to remotely sensed data. The SVI and NDVI resultant images expressed similarities in areas depicting vegetation stress conditions at both epochs. A strong linear regression between NDVI and stomatal conductance measurements (mmol/m²) serve to confirm that the NDVI is a reliable indicator of vegetation stress condition. Slope angle and aspect were found to have a significant influence on vegetation stress conditions. Similarly variations in soil moisture and soil surface condition have strong implications for vegetation stress. Amongst other land-use types, abandoned lands were found to have the lowest NDVI values implying an association with the worst vegetation stress scenarios. It was concluded that an element of persistent stress conditions exists amongst the thicket vegetation of the Keiskamma catchment. This was mainly due to land use activities in the area.

Land degradation is a global problem affecting many countries including South Africa. This study was conducted in order to assess and monitor the nature and extent of land degradation within Qoqodala in the Eastern Cape Province, of South Africa. The study used GIS and Remote Sensing techniques together with household interviews in determining extent, spatial characteristics and nature of land degradation within the study area. Vegetation cover and bare-ground change were the land degradation indicators assessed and monitored by this study. Through RGB band combination, Tasselled Cap Analysis and Unsupervised ISODATA classification techniques, Landsat images over the past eighteen years (1984, 1993, 1996, 2000 and 2002) have been analysed. The results showed that there is vegetation cover and bare-ground increase in the study area. The vegetation increase has been seen as a sign of land degradation increase due to the encroachment of indigenous vegetation by Euryops species (also known as Lapesi by the local community). The bare-ground land degradation indicator has also increased. The analyses of slope showed the spatial characteristics of bare-ground occurring on moderate to flat slopes while vegetation cover occurs on steep to very steep slopes. Furthermore the photographs captured during field visits show rills and gullies or dongas occurring on bare-ground. The interviewed respondents indicated that decline in food production, increase in dongas and vast increase in Euryops and a decline in grassland are the indicators of degradation that are observed in the study area. The occurrence of erosion features (rills and dongas) on bare-ground and the increase of vegetation shown by GIS and Remote Sensing techniques showed a positive correlation with field and household survey towards establishing the nature of land degradation. In this study Landsat images together with interviews proved to be a very useful tool for land degradation research. However the suggestion of a higher spatial resolution satellite image on small catchment studies is recommended

The subtropical thicket biome in the Eastern Cape Province of South Africa has been heavily degraded and transformed due overutilization during the last century. The highly degraded and transformed areas exhibit a significant loss of above ground carbon stocks (AGC) and loss of SOC content. Information about land use /cover change and fragmentation dynamics is a prerequisite for measuring carbon stock changes. The main aim of this study is to assess the trends of land use/cover change, fragmentation dynamics, model the temporal changes of AGC stocks in the Great Fish River Nature Reserve and its environs from 1972 to 2010, quantify and map the spatial distribution of SOC concentrations in the partial subtropical thicket cover in the Great Fish River Nature Reserve and environs (communal rangelands). Multi-temporal analyses based on 1972 Landsat MSS, 1982 and 1992 Landsat TM, 2002 Landsat ETM and 2010 SPOT 5 High Resolution images were used for land use/cover change detection and fragmentation analysis. Object oriented post-classification comparison was applied for land use/cover change detection analysis. Fragmentation dynamics analysis was carried out by computing and analyzing landscape metrics in land use/cover classes. Landscape fragmentation analyses revealed that thicket vegetation has increasingly become fragmented, characterized by smaller less linked patches of intact thicket cover. Landscape metrics for intact thicket and degraded thicket classes reflected fragmentation, as illustrated by the increase in the Number of Patches (NP), Patch Density (PD), Landscape Shape Index (LSI), and a decrease in Mean Patch Size (MPS). The use of remote sensing techniques and landscape metrics was vital for the understanding of the dynamics of land use/cover change and fragmentation. Baseline land use/cover maps produced for 1972, 1982, 1992 2002 and 2010 and fragmentation analyses were then used for analyzing carbon stock changes in the study area. To model the temporal changes of AGC stocks in the Great Fish River Nature Reserve and its environs from 1972 to 2010, a method based on the integration of RS and GIS was employed for the estimation of AGC stocks in a time series. A non-linear regression model was developed using NDVI values generated from SPOT 5 HRG satellite imagery of 2010 as the independent variable and AGC stock estimates from field plots as the dependent variable. The regression model was used to estimate AGC stocks for the entire study area on the 2010 SPOT 5 HRG and also extrapolated to the 1972 Landsat MSS, 1982 and 1992 Landsat TM, and 2002 Landsat ETM. The AGC stocks for the period 1972 -1982, 1982-1992, 1992-200) and 2002-2010 were compared by means of change detection analysis. The comparison of AGC stocks was carried out at subtropical thicket class level. The results showed a decline of AGC stocks in all the classes from 1972 to 2010. Degraded and transformed thicket classes had the highest AGC stock losses. The decline of AGC stocks was attributed to thicket transformation and degradation which were caused by anthropogenic activities. To map and quantify SOC concentration in partial (fractional) thicket vegetation cover, the spectral reflectance of both thicket vegetation and bare-soils was measured in situ. Soil samples were collected from the sampling sites and transported to the laboratory for spectral reflectance and SOC measurements. Thicket vegetation and bare soil reflectance were measured using spectroscopy both in situ and under laboratory conditions. Their respective endmembers were extracted from ASTER imagery using the Pixel Purity Index (PPI). The endmembers were validated with in situ and laboratory thicket and bare-soil reflectance signatures. The spectral unmixing technique was applied to ASTER imagery to discriminate pure pixels of thicket vegetation and bare-soils; a residual spectral image was produced. The Residual Spectral Unmixing (RSU) procedure was applied to the residual spectral image to produce an RSU soil spectrum image. Partial Least Squares Regression (PSLR) model was developed using spectral signatures of a residual soil spectrum image as the independent variable and SOC concentration measured from soil samples as the dependent variable. The PSLR prediction model was used to predict SOC concentration on the RSU soil spectral image. The predicted SOC concentration was then validated with SOC concentration measured from the field plots. A Strong correlation (R2 = 0.82) was obtained between the predicted SOC concentration and the SOC concentration measured from field samples. The PSLR was then used to generate a map of SOC concentration for the Great Fish River Nature Reserve and its environs. Areas with very low SOC concentrations were found in the degraded communal villages, as opposed to the higher SOC values in the protected area. The results confirmed that RS techniques are key to estimating and mapping the spatial distribution of SOC concentration in partial subtropical thicket vegetation. Partial thicket vegetation has a huge influence on the soil spectra; it can influence the prediction of SOC concentration. The use of the RSU approach eliminates partial thicket vegetation cover from bare soil spectra. The residual soil spectrum image contains enough information for the mapping of SOC concentration. The technique has the potential to augment the applicability of airborne imaging spectroscopy for soil studies in the sub-tropical thicket biome and similar environments.

This study originated from an evaluation of the performance of a commercially available high concentration point focus concentrator PV system. The effect of module design flaws was studied by using current-voltage (I-V) curves obtained from each module in the array. The position of reverse bias steps revealed the severity of mismatch in a string of series-connected cells. By understanding the effects of the various types of mismatch, power losses and damage to the solar cells resulting from hot spot formation can be minimized and several recommendations for improving the basic performance of similar systems were made. Concern over the extent and type of defect failure of the concentrator photovoltaic (CPV) cells prompted an investigation into the use of a light beam induced current (LBIC) technique to investigate the spatial distribution of defects. An overview of current and developing LBIC techniques revealed that the original standard LBIC techniques have found widespread application, and that far-reaching and important developments of the technique have taken place over the years. These developments are driven by natural progression as well as the availability of newly developed advanced measurement equipment. Several techniques such as Lock-in Thermography and the use of infrared cameras have developed as complementary techniques to advanced LBIC techniques. As an accurate contactless evaluation tool that is able to image spatially distributed defects in cell material, the basis of this method seemed promising for the evaluation of concentrator cells.

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.

Bibliography: pages 71-83.
A variety of studies have shown the problems of energy supply faced by low-income communities in southern Africa. Most of these communities are dependent upon indigenous fuelwood supplies. In addition, many of these communities use indigenous wood for construction. This largely uncontrolled utilisation imposes severe threats on woody vegetation communities. The Eastern Cape/Ciskei region is an area where energy supply problems are particularly severe and impacts on woody vegetation correspondingly severe. This study aimed to investigate the feasibility of using remote sensing techniques to monitor the the impact caused by collection of wood in the Eastern Cape/Ciskei forest and thicket communities. A variety of remote sensing techniques for landcover analysis were investigated. In all cases, visual interpretation was used because it is considerably cheaper and demands less technical expertise than would computer processing. In addition, many studies have shown visual interpretation to be superior. Maps were drawn from multitemporal aerial photograph sequences and from Landsat and SPOT satellite images. These maps showed that there has been relatively little change in area of woody vegetation in the study area since 1956. However, field studies showed that vegetation community structure had been degraded as a result of intense and sustained human impact. This qualitative decline also reflected a decline in usefulness of the woody vegetation of the area to local communities. This substantial degradation was not visible on any of the remote sensing imageries. This emphasises that field-based checks to monitor human impacts on forest and thicket formations are essential. Strategies for reducing the dependence of low-income communities on indigenous vegetation for energy supplies and constructional timber have been reviewed from the literature and these are descibed in Appendix 1. Most successful strategies in other parts of the world have been the result of a national commitment to tree planting, recognition of a multiplicity of constraints and the voluntary involvement of the communities the strategies are intended to assist.

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.

Using a range of satellite-derived indices I describe. monitor and predict vegetation conditions that exist in the Great Fish River Valley, Eastern Cape. The heterogeneous nature of the area necessitates that the mapping of vegetation classes be accomplished using a combination of a supervised approach, an unsupervised approach and the use of a Moving Standard Deviation Index (MSDI). Nine vegetation classes are identified and mapped at an accuracy of 84%. The vegetation classes are strongly related to land-use and the communal areas demonstrate a reduction in palatable species and a shift towards dominance by a single species. Nature reserves and commercial rangeland are by contrast dominated by good condition vegetation types. The Modified Soil Adjusted Vegetation Index (MSA VI) is used to map the vegetation production in the study area. The influence of soil reflectance is reduced using this index. The MSA VI proves to be a good predictor of vegetation condition in the higher rainfall areas but not in the more semi-arid regions. The MSA VI has a significant relationship to rainfall but no absolute relationship to biomass. However, a stratification approach (on the basis of vegetation type) reveals that the MSA VI exhibits relationships to biomass in vegetation types occurring in the higher rainfall areas and consisting of a large cover of shrubs. A technique based on an index which describes landscape spatial variability is presented to assist in the interpretation of landscape condition. The research outlines a method for degradation assessment which overcomes many of the problems associated with cost and repeatability. Indices that attempt to provide a correlation with net primary productivity, e.g. NDVI, do not consider changes in the quality of net primary productivity. Landscape variability represents a measure of ecosystem change in the landscape that underlies the degradation process. The hypothesis is that healthy/undisturbed/stable landscapes tend to be less variable and homogenous than their degraded heterogenous counterparts. The Moving Standard Deviation Index (MSDI) is calculated by performing a 3 x 3 moving standard deviation window across Landsat Thematic Mapper (TM) band 3. The result is a sensitive indicator of landscape condition which is not affected by moisture availability and vegetation type. The MSDI shows a significant negative relationship to NDVI confirming its relationship to condition. The cross-classification of MSDI with NDVI allows the identification of invasive woody weeds which exhibit strong photosynthetic signals and would therefore be categorised as good condition using NDVI. Other ecosystems are investigated to determine the relationship between NDVI and MSDI. Where increase in NDVI is disturbance-induced (such as the Kalahari Desert) the relationship is positive. Where high NDVI values are indicative of good condition rangeland (such as the Fish River Valley) the relationship is negative. The MSDI therefore always exhibits a significant positive relationship to degradation irrespective of the relationship of NDVI to condition in the ecosystem.

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 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.

Thesis (MSc)--Stellenbosch University, 2013.
Landslide susceptibility maps are important for development planning and disaster management. The current synthesis of landslide susceptibility maps largely applies GIS and remote sensing techniques. One of the most critical stages on landslide susceptibility mapping is the selection of landslide causative factors and weighting of the selected causative factors, in accordance to their influence to slope instability. GIS is ideal when deriving static factors i.e. slope and aspect and most importantly in the synthesis of landslide susceptibility maps. The integration of landslide causative thematic maps requires the selection of the weighting method; in order to weight the causative thematic maps in accordance to their influence to slope instability. Landslide susceptibility mapping is based on the assumption that future landslides will occur under similar circumstances as historic landslides. The weight of evidence method is ideal for landslide susceptibility mapping, as it calculates the weights of the causative thematic maps using known landslides points. This method was applied in an area within the Western Cape province of South Africa, the area is known to be highly susceptible to landslide occurrences. A prediction rate of 80.37% was achieved. The map combination approach was also applied and achieved a prediction rate of 50.98%. Satellite remote sensing techniques can be used to derive the thematic information needed to synthesize landslide susceptibility maps and to monitor the variable parameters influencing landslide susceptibility. Satellite remote sensing techniques can contribute to landslide investigation at three distinct phases namely: (1) detection and classification of landslides (2) monitoring landslide movement and identification of conditions leading up to an event (3) analysis and prediction of slope failures. Various sources of remote sensing data can contribute to these phases. Although the detection and classification of landslides through the remote sensing techniques is important to define landslide controlling parameters, the ideal is to use remote sensing data for monitoring of areas susceptible to landslide occurrence in an effort to provide an early warning. In this regard, optical remote sensing data was used successfully to monitor the variable conditions (vegetation health and productivity) that make an area susceptible to landslide occurrence.

Thesis (MSc)--Stellenbosch University, 2003.
ENGLISH ABSTRACT: The Cape Town metropolitan area has limited water supply due to rapid population and urban growth. In many instances, surface water is the only source in water supply schemes. There is a need for additional water supplies to supplement the existing water sources. Groundwater systems can be used as primary or supplemental water supply sources especially in areas where there is high demand for water resources. The aim of this study is to evaluate the groundwater potential within the Table Mountain Group (TMG) with the assistance of remote sensing and Geographical Information System (GIS). Previous hydrogeological studies have found the TMG to be the second largest hydrogeological unit in South Africa with extensively fractured and multi-porous rock. The study area is 5660 km2 with TMG covering 1336 km". In this study a Landsat Enhanced Thematic Mapper Plus (ETM+) image was used to identify lineaments. The identified lineaments were overlaid with vegetation, drainage patterns, faults and fractures digitized from 1:250 000 geological maps and borehole yields to show areas with promising groundwater resources. The results did not show correlation between vegetation and lineaments. Most of the lineaments intersected drainage lines at some points, and a few were parallel to the drainage lines. Forty five percent of the digitized faults and fractures overlap with the Landsat lineament. The most dominating lineaments are oriented in a NW-SE direction. High yielding boreholes with average yield of about 12 lIs were found within the distance of 150m from the lineaments. The lineaments were further analysed to locate areas that could be suitable for groundwater exploration. These areas were identified using Landsat lineaments, boreholes and a Digital Elevation Model (DEM). The results showed that the most favourable lineaments and geological features were oriented in a 135-180° and 0-45° direction and areas with slopes of less than 40% were found to be suitable for drilling boreholes. The amount of available groundwater within the TMG was also investigated by looking at both volume of recharge and amount that could be held in storage. Rainfall data was used to estimate recharge. Groundwater recharge was calculated to be 5% of the total precipitation that falls on this area. Based on the average rainfall of 600mm per annum, the results show that TMG has an average recharge value of 30mm per annum. The total recharge for the area covered by TMG, which has an area of 1336km2 , is 160 million nr'. Geological profiles and cross sections were drawn to determine the storage capacity of the TMG, which was estimated to be 525 million m.3 According to a study done by the Department of Water Affairs and Forestry (DW AF) in 1996, the anticipated water demand in the Cape Town metropolitan area will increase from 243 million m3 in 1990 to 560 million m3 in 2020. The estimated volume of water that can be stored within the TMG can meet the current demand for the next 10 years and supplement the existing surface water sources. Groundwater vulnerability of the TMG to contamination was assessed and mapped by using the DRASTIC index. The results demonstrate that the TMG area is at low risk to contamination.
AFRIKAANSE OPSOMMING: 'n Vinnig groeiende bevolking en stedelike uitbreiding plaas toenemende druk op Kaapstad se water voorraad. Addisionele waterbronne sal benodig word om bestaande bronne aan te vul. Oppervlakwater is in die meeste gevalle die enigste waterbron, maar grondwater het die potensiaal om te dien as 'n primêre of aanvullende voorsieningsbron, veral in areas waar groot water tekorte bestaan. Die doel van hierdie studie is om die grondwaterpotensiaal van gesteentes van die Tafelberg Groep (TBG) te evalueer deur van afstandswaarneming en geografiese inligtingstelsels gebruik te maak. Geohidrologiese studies het getoon dat die TBG gesteentes met sy veelvuldige nate en breuksones, die tweede grootste geohidrologiese eenheid in Suid Afrika is. Die studiegebied beslaan 5660 km", waarvan 1336 km2 deur Tafelberg Sandsteen beslaan word. Vir hierdie studie is 'n "Landsat Enhanced Thematic Mapper Plus (ETM+)" beeld gebruik in die identifisering van breuksones (lineamente). Verdere analises is uitgevoer om areas geskik vir grondwater ontginning te identifiseer. Geïdentifiseerde verskuiwings op Landsat beelde is met plantegroei, dreinerings patrone en bekende verskuiwings en fraktuur sones vanaf gelogiese kaarte vergelyk in 'n poging om areas met belowende grondwaterbronne uit te wys. Bekende boorgat posisies en lewerings volumes was 'n primêre databron vir die berekening van groundwater reserves. Die studie het egter geen korrelasie tussen plantegroei en die voorkoms van lineamente gevind nie. Die riviere in die studiegebied word op verskeie plekke deur verskuiwings gekruis. Slegs 'n paar van die verskuiwings lê parallel met die dreinering. Daar is gevind dat vyf-en-veertig persent van bekende verskuiwings en fraktuursones met die geïdentifiseerd op Landsat beelde oorvleuel. Die mees prominente lineamente het 'n NW-SO oriëntasie. Boorgate met lewerings van gemiddeld 12 lis is binne 'n 150m afstand van die verskuiwings gevind. Die verskuiwings is ook geanaliseer om die mees produktiewe areas vir grondwater ontginning te identifiseer. Landsat beelde, boorgate en 'n Digitale Elevasie Model (DEM) is gebruik om moontlike boorposisies te identifiseer. Die mees produktiewe verskuiwings en geologiese verskynsels het 'n N 135-180W en NO-450 oriëntasie, terwyl areas met 'n helling < 40% vir die boor van boorgate geskik is. Berekeninge oor die hoeveelheid water wat binne die TBG gesteentes beskikbaar is, is gemaak deur die hoeveelheid aanvulling en stoorkapasiteit van die TBG gesteentes te beraam. Grondwater aanvulling, soos bereken vanaf reënval data, is 5% van die totale presipitasie van 'n gegewe area. Met 'n gemiddelde jaarlikse reënval van 600mm in die studie gebied is die TBG se jaarlikse aanvulling ongeveer 30mm. Daar word beraam dat die totale aanvulling in die 1336km2 TBG area 160-miljoen m3 per jaar is. Geologiese profiele en dwarsnitte is gemaak om die stoorvermoë van die TBG te bepaal, en is beraam op 525-miljoen rrr'. 'n 1996 navorsing studie deur die Departement van Waterwese en Bosbou toon dat waterverbruik in die Kaapse Metropolitaanse gebied sal toeneem vanaf die 1990 vlak van 243-miljoen m3 tot 560 miljoen m3 teen 2020. Die berekende volume water wat binne die TBG gestoor word, kan die water aanvraag oor die volgende 10 jaar bevredig en as aanvulling dien vir oppervlak waterbronne. Die kwesbaarheid van die TBG akwifer vir besoedeling is met behulp van die DRASTIC indeks geëvalueer en gekarteer. Die resultate toon dat die TBG 'n lae risiko vir besoedeling het.

This paper examines the issues that arise in the use of visual interpretation of Landsat data during the analysis, classification and mapping of the natural vegetation of the semi-arid Northern Cape. Initial research involved the classifying and mapping of the vegetation using conventional methods. A vegetation map, accompanying legend and descriptive key were produced. The problems encountered during this process, and the constraints of manpower, time and funds, stimulated the investigation of Landsat imagery as a means of improving the speed and accuracy of vegetation classification and mapping. A study area comprising one Landsat scene and which met certain requirements was selected: a) The area had already been surveyed and mapped at a scale of 1:250 000. b) As many vegetation units as possible were included. c) There was maximum diversity, complexity and variability in terms of soil, geology and terrain morphology. Initially a suitable mapping scale was selected, viz. 1:250 000, as it met the requirements of nature conservation authorities and agricultural planners. The scales of survey and remote sensing were based on this. The basic unit of survey was the 1:50 000 topographical map and satellite imagery at a scale of 1:250 000 was found to meet the requirements of reconnaissance level mapping. The usefulness of Landsat imagery was markedly affected by the quality of image production and enhancement. Optimum image production was vitally important and to this end, interaction between the user and the operations engineer at the Satellite Applications Centre, Hartebeeshoek was essential. All images used, were edge-enhanced and systematically corrected. While these procedures were costly, they proved to be fundamental to the success of the investigation. Precision geometric correction was not required for reconnaissance level investigation. The manual superimposition of the UTM grid, using ground control points from 1:250 000 topographical maps, proved to be accurate and convenient. Pattern recognition on single-band, panchromatic imagery was difficult. The scene lacked crispness and contrast, and it was evident that black and white imagery did not satisfy the objectives of the study. Three-band false colour composite imagery was superior to single-band imagery in terms of clarity and number of cover classes. The addition of colour undoubtedly facilitated visual interpretation. False colour composite imagery was investigated further to establish which year, season and possibly time of season would best suit the objectives of the investigation. It was found that the environmental parameters affecting reflectance are relatively stable over time and it was not necessary to acquire imagery of the same year as field surveys. However, the year of imagery should be chosen so that similar climatic conditions prevail. While, in certain instances, imagery captured during winter had advantages in separating complex mosaics, summer imagery was superior in most respects. Furthermore, given "normal" climatic conditions, the ideal period during which there was maximum contrast between and within ground classes, and thus spectral classes, was narrowed to mid-January to mid-April. Units which were acceptably heterogeneous (relatively homogeneous) in terms of reflectance levels were delineated manually on the image. This delineation was done at three levels of complexity and the units were compared with the vegetation map. A series of field trips aided the interpretation of the images, especially where discrepancies occurred between the map and the image. In general, there was a close degree of correspondence between the prepared vegetation map and the delineated image. Field investigation revealed the image units to be more accurate than those on the vegetation map, and the image served to highlight the inadequacies inherent in classifying and mapping vegetation of extensive areas with limited resources.

>Magister Scientiae - MSc
The lack of monitoring of non-perennial rivers is a major problem for water resources management, despite their significance in satisfying agricultural, economic and recreational needs. Pools in non-perennial rivers are not monitored, due to their remoteness. Remote sensing offers a promising alternative for the monitoring of changes in water storage in these pools. This study aims to assess the extent to which remotely-sensed datasets can be used to monitor the spatio-temporal changes of water storage of pools along non-perennial rivers in the Western Cape. The objectives of this study are: (1) to determine a suitable image preprocessing and classification technique for detecting and monitoring surface water along nonperennial rivers, and (2) to describe the spatial and temporal changes of water availability of pools along non-perennial rivers, using remotely sensed datasets. The Normalised Difference Water Index (NDWI), Modified NDWI (MNDWI), Normalised Difference Vegetation Index (NDVI), Automated Water Extraction Index for shadowed (AWEIsh) and non-shadowed regions (AWEInsh) and the Multi-Band Water Index (MBWI) classification techniques were investigated in this study, using the Sentinel-2 and Landsat 8 datasets. In-situ measurements were used to validate the satellite-derived datasets, while the use of high resolution aerial photography and Digital-Globe WorldView imagery were further compared to the results. The results suggested that the NDWI is the most suitable classification technique for identifying water in pools along non-perennial rivers throughout the Western Cape. The NDWI applied to the Sentinel-2 Top-of-Atmosphere (TOA) reflectance dataset had the highest overall accuracy of 85%, when compared to the Sentinel-2 Dark Object Subtraction 1 (DOS1) atmospheric correction, Sentinel-2 Sen2Cor atmospheric correction, Landsat 8 TOA reflectance and Landsat 8 DOS1 atmospheric correction datasets. The incorporation of atmospheric correction was shown to eliminate surface water pixels in many of the smaller pools.

Thesis (MA.)--Stellenbosch University, 2001.
ENGLISH ABSTRACT: There are many reasons to conserve fynbos. Not only does fynbos form part of the Cape floral kingdom, one of the richest floral kingdoms in the world, but the contribution that it makes to the regional economy through utilisation, education, recreation and tourist opportunities is immeasurable. Fire plays an integral role in fynbos ecosystems. According to Van Wilgen, Richardson & Seydack (1994: 322) " ... managing fynbos equates to managing fire". Therefore managers need accurate fire information about a fynbos area to manage it properly. This is where satellite remote sensing can provide the manager with useful information about the fire regime. In other words, satellite remote sensing can help a manager establish where and when an area has burnt. Using readily available satellite data, this study attempts to establish (through comparison) what techniques would be most suitable and affordable to compile a fire information database. Landsat Thematic Mapper data from 1990 - 1996 of the southwestern Cape was used and compared with existing fire records of the area. The results show that techniques such as supervised and unsupervised classification are reliable in identifying burnt areas, but a major drawback of these techniques is that they require a large amount of user input and knowledge. They are thus not regarded as simple or easily repeatable. - The' more simple techniques like image differencing and image ratioing were also found to be reliable in identifying burnt areas. These techniques require less user input and in some instances less data (image bands) to produce similar (or better) results than supervised and unsupervised classification techniques. The results show that differencing temporally different Images, obtained from applying principle components analysis, produces reliable results with very little confusion and little user input. Using such a technique could enable users to procure only two bands of Landsat data and still produce reliable fire information for managing a fynbos ecosystem.
AFRIKAANSE OPSOMMING: Daar is verskeie redes waarom fynbos bewaar moet word. Nie net vorm dit deel van een van die rykste blommeryke in die wereld nie, maar die bydrae wat dit tot die streeksekonomie maak, deur die benutting van veldblomme en die geleenthede wat dit bied vir toerisme en ontspanning, is enorm. Vuur speel 'n belangrike rol in die bestuur van fynbos ekosisteme. Soos beklemtoon deur Van Wilgen, Richardson & Seydack (1994: 322) se stelling: " ... managing fynbos equates to managing fire". Om hierdie rede is dit belangrik dat 'n bestuurder akkurate inligting oor die verspreiding van veldbrande moet he. Satellietafstandwaarneming kan hier 'n belangrike rol speel deur sulke inligting te verskaf Deur gebruik te maak van maklik bekombare satellietdata, poog hierdie studie om te bepaal (d.m.v. vergelyking) watter tegnieke die mees geskikte is in terme van bekostigbaarheid en gebruikersvriendelikheid. Landsat Thematic Mapper data van 1990 tot 1996 van die suidwes-Kaap is gebruik en vergelyk met bestaande branddata van die studiegebied. Daar is gevind dat tegnieke soos gerigte en nie-gerigte klassifikasie in staat is om gebrande dele betroubaar uit te ken. Hierdie tegnieke verg egter baie insette en kennis van die gebruiker en is ook nie maklik om jaar na jaar te herhaal nie. Daarom word hierdie tegnieke nie aanbeveel nie. Daar is gevind dat die eenvoudiger tegnieke soos veranderingsanalise ook gebrande dele betroubaar kon uitken. Hierdie tegnieke het die voordeel dat die gebruiker nie baie' kennis van die gebied hoef te he nie en ook nie so baie insette hoef te lewer nie. Hierdie tegnieke word bo gerigte en nie-gerigte klassifikasie aanbeveel. - Die resultate dui daarop dat betroubare resultate verkry kan word deur tempo reel verskillende beeIde, verkry deur hoofkomponentanalise, van mekaar af te trek. Hierdie tegniek vereis relatief min gebruikersinsette en daar kan selfs met slegs twee Landsat bande gewerk word. So 'n tegniek kan beslis 'n bekostigbare en effektiewe manier wees om nodige inligting vir die bestuur van 'n fynbos ekosisteem te bekom.

Invasive Prosopis trees pose significant threats to biodiversity and ecosystem services in the Northern Cape Province of South Africa. Several estimates have been made of the spatial extent of alien plant invasion in South Africa. The South African Plant Invaders Atlas (SAPIA) suggested that about 10 million hectares of South Africa has been invaded. However, the rate and spatial extent of Prosopis invasion has never been accurately quantified. The objective of the study is to use Remote Sensing and Geographic Information System (GIS) techniques to: (i) reveal areas susceptible to future invasion, (ii) describe the current extent and densities of Prosopis, (iii) to reveal the spatial dynamics and (iv) establish the extent of fragmentation of the natural vegetation in the Northern Cape Province. Image classification products were generated using spectral analysis of seasonal profiles, various resolution image inputs, spectral indices and ancillary data. Classification approaches varied by scene and spatial resolution as well as application of the data. Coarse resolution imagery and field data were used to create a probability map estimating the area vulnerable to Prosopis invasion using relationships between actual Prosopis occurrence, spectral response, soils and terrain unit. Multi-temporal Landsat images and a 500m x 500m point grid enabled vector analysis and statistical data to quantify the change in distribution and density as well as the spatial dynamics of Prosopis since 1974. Fragmentation and change of natural vegetation was quantified using a combined cover density class, calculating patch density per unit (ha) for each biome The extent of Prosopis cover in the Northern Cape Province reached 1.473 million hectare or 4% of the total land area during 2007. The ability of the above mentioned Remote Sensing and GIS techniques to map the extent and densities of Prosopis in the Northern Cape Province of South Africa demonstrated a high degree of accuracy (72%). While neither the image classification nor the probability map can be considered as 100% accurate representations of Prosopis density and distribution, the products provide use full information on Prosopis distribution and are a first step towards generating more accurate products. For primary invasion management, these products and the association of a small area on a map with Prosopis plants and patches, mean that the management effort and resources are efficiently focused. Further studies using hyper-spectral image analysis are recommended to improve the classification accuracy of the spatial extent and density classes obtained in this study.
Thesis (M. Environmental Science)--North-West University, Potchefstroom Campus, 2010.

Thesis (MSc)--Stellenbosch University, 2015.
ENGLISH ABSTRACT: Urban planning and management require up-to-date information about urban land cover. Producing such geospatial information is time consuming as it is usually done manually. The classification of such information from satellite imagery is challenging owing to the difficulties associated with distinguishing urban features having similar spectral properties. Therefore, this study evaluates the combination of a digital surface model (DSM) derived from LiDAR data and very high-resolution GeoEye-1 satellite imagery for classifying urban land cover in Cape Town. The value of the DSM was assessed by comparing a land cover product obtained from the GeoEye-1 image to a map produced using both the GeoEye-1 image and the DSM. A systematic segmentation procedure for the two classifications scenarios preceded a supervised (using a support vector machine, K nearest neighbour and classification and regression algorithm tree classifiers) and rule-based classification. The various approaches were evaluated using a combination of methods. When including the DSM in the supervised and rule-based classifications, the overall accuracy and kappa vary between 80% to 83% and 0.74 to 0.77 respectively. When the DSM is excluded, the overall accuracy ranges between 49 to 64% whereas kappa ranges between 0.32 to 0.53 for the two classification approaches. The accuracies obtained are always about 20% higher when the DSM is included. The normalised DSM (nDSM) enabled accurate discrimination of elevated (e.g. buildings) and non-elevated (e.g. paved surfaces) urban features having similar spectral characteristics. The nDSM of at least one-metre resolution and one metre vertical accuracy influenced the accuracy of the results by correctly differentiating elevated from non-elevated. The rule-based approach was more effective than the supervised classification, particularly for extracting water bodies (dams and swimming pools) and bridges. Consequently, a rule-based approach using very high spatial resolution (EHSR) satellite imagery and a LiDAR-derived DSM is recommended for mapping urban land cover.
AFRIKAANSE OPSOMMING: Stedelike beplanning- en bestuur vereis dat inligting oor grondbedekking (land cover) op datum moet wees. Die vervaardiging van hierdie georuimtelike inligting is tydrowend omdat dit gewoonlik met die hand gedoen word. Die onttrekking van sulke inligting vanuit satellietbeelde bied ʼn groot uitdaging omdat stedelike voorwerpe met soortgelyke spektrale eienskappe moeilik is om van mekaar te onderskei. Hierdie studie evalueer die kombinasie van ʼn digitale oppervlak model (DOM) afkomstig van LiDAR-data en ʼn baie hoë resolusie GeoEye-1-satellietbeeld om stedelike grondbedekking in Kaapstad te klassifiseer. Die waarde van die DOM word bepaal deur ʼn grondbesettingsproduk wat vanuit ʼn GeoEye-1-beeld verkry is te vergelyk met ʼn grondbesettingsproduk wat verkry is deur beide die GeoEye-1-beeld en die DOM te gebruik. Sistematiese segmentasie word op die twee benaderings uitgeoefen en dit word gevolg deur ʼn gekontroleerde klassifikasie (steunvektormasjiene, k-naaste aangrensende waarde en klassifikasie en regressie algoritme) en ʼn reël-gebaseerde algoritme. Hierdie verskeie benaderings is geëvalueer met behulp van ʼn kombinasie van kwalitatiewe en kwantitatiewe metodes. Toe die DOM in die gekontroleerde en reël-gebaseerde klassifikasie ingesluit is, het die algehele akkuraatheid en kappa tussen 80% en 83%, en 74% en 77% gewissel. Toe die DOM uitgesluit is, het die algehele akkuraatheid en kappa tussen 49% en 64%, en 32% en 53% vir die twee klassifikasiebenaderings gewissel. Die behaalde akkurraatheidswaardes is altyd 20% hoër as die DOM ingesluit word. Dit is hoofsaaklik omdat die DOM akkurate onderskeiding tussen hoë (bv. geboue) en plat (bv. geplaveide oppervlaktes) stedelike bakens met gelyksoortige spektrale eienskappe in staat stel. Die kwaliteit van die DOM beïnvloed die akkuraatheid van die resultate. ʼn DOM van ten minste een meter resolusie, met een meter of beter vertikale akkuraatheid, word benodig om te verseker dat geboue en ander beboude bakens korrek van mekaar onderskei kan word. Die reël-gebaseerde benadering was meer effektief as die gekontroleerde klassifikasie, veral om waterliggame (damme en swembaddens) en brûe te identifiseer. Gevolglik word ʼn reël-gebaseerde benadering met die hoë resolusie satellietbeelde en ʼn LiDAR-afgeleide DOM aanbeveel om stedelike grondbesetting te karteer.

Climate change and variability have great impact on the hydrological cycle and consequently on the availability of water resources. Variations in temperature and precipitation trends that are occurring are a consequent of the increase in the concentrations of greenhouse gases, which are subsequently affecting the hydrological cycle. This in turn affects water quantity and quality, which is essential for agriculture, domestic and industrial uses. This study, done in Elliot Town and the surrounding areas of Sakhisizwe Municipality in the Eastern Cape Province of South Africa, evaluates how climate change and variability is affecting water availability and its quality in the town. The impact climate change and variability on agricultural production is also assessed. Remote Sensing, Geographic Information Systems (GIS), databases and some statistical packages have been used to collect, analyse and create spatial maps used to derive concrete conclusions. The methods used aided in spatially analysing the changes in temperature and rainfall along the years and make a comparative analysis. The study has shown that the spatial changes in the amount, intensity and frequency of rainfall affects the magnitude and frequency of stream flows; consequently, increasing the intensity of floods and droughts that have been recurring in the last few decades. The municipality is more affected by climate variability than change, and the resultant extreme climate events are affecting the water resources resulting in domestic water cuts, poor water quality and low agriculture productivity. This study recommends the importance of an awareness campaigns on climate change and variability and their effect directed towards the community, especially on novel water harvesting technologies. The study also highlights the importance of a robust early warning system to prepare the community in case of a climate shock, which is an area that needs further research.
Environmental Sciences

A research report submitted in partial partial compliance compliance with the requirements for the Master's Diploma in Technology: Electrical Engineering, M.L.Sultan Technikon, 1995.
The purpose of this study is to investigate the most cost effective way of electrifying rural areas in the Transkei concentrating in the district of Lady Frere. One Administrative Area (A.A) was used for research. Questionnaires were send to people of this area where a like rat format was followed. For the purpose of this study, 20 families were randomly selected for investigation.
M