Doctoral Degrees (Geography)

Permanent URI for this collectionhttps://hdl.handle.net/10413/6615

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Assessment of structural attributes of even-aged Eucalyptus grandis forest plantations using small-footprint discrete return lidar data.
(2009) Tesfamichael, Solomon Gebremariam.; van Aardt, Jan.; Ahmed, Fethi B.
Assessment of forest structural attributes has major implications in the management of forestry by providing information of ecological and economic importance. The traditional methods of assessment involve collecting data in the field and are regarded as labour-intensive and expensive. In plantation forestry, field campaigns are generally time consuming and costly, and may compromise profit maximisation. The introduction of lidar (light detection and ranging) remote sensing in forestry has shown promise to add value to the traditional field inventories mainly through large spatial coverages in a timely and cost-effective manner. Lidar remote sensing is an advanced system capable of acquiring information in both the vertical and horizontal dimensions at relatively high resolutions. Numerous studies have established that these qualities of lidar data are suited to estimating forest structural attributes at acceptably high accuracies. The generic approach in most studies is to use lidar data in combination with field data. Such an approach still warrants a high cost of inventory. It is therefore useful to explore alternative methods that rely primarily on lidar data by reducing the necessity for field-derived information. The aim of this study was to derive structural attributes of even-aged Eucalyptus grandis forest plantations using lidar data. The attributes are of significance to timber resource assessments and include plot-level tree height attributes, stems per hectare (SPHA), and volume. The surveyed field data included tree counting and measurement of tree height and diameter at breast height for sample plots. Volume was then calculated using standard allometric models. Small-footprint lidar data of the plantations were also acquired coincident with the field inventories. Mean tree height and dominant height were estimated at a range of simulated lidar point densities between 0.25 points/m2–6 points/m2. Various plot-level distributional metrics were extracted from height values of lidar non-ground points and related with field mean and dominant height values using stepwise regression analysis. The results showed that both attributes could be estimated at high accuracies with no significant differences arising from variations in lidar point density. Estimation of SPHA relied on the exploration of semi-variogram range as a mean window size for applying local maxima filtering to the lidar canopy height surface. A comparative approach of window size determination used pre-determined within-row tree spacing, based on planting information. Two secondary objectives were addressed: comparing spatial resolutions of canopy height surfaces interpolated from non-ground height values and comparison of lidar point densities simulated at three levels. Comparison of spatial resolutions of canopy height surfaces were performed at 0.2 m, 0.5 m, and 1 m using a lidar point density of 5 points/m2. The results indicated that 0.2 m is the most appropriate resolution for locating trees and consequently deriving SPHA. Canopy height surfaces of 0.2 m resolution were created at simulated densities of 1 point/m2, 3 points/m2, and 5 points/m2. While all estimates were negatively biased relative to field-observed SPHA, lidar densities of 3 points/m2 and 5 points/m2 returned similar accuracies, which were both superior to 1 point/m2. It was concluded that 3 points/m2 was sufficient to achieve the accuracy level obtained from higher lidar point densities. Plot-level mean height, dominant height, and volume of trees were estimated for trees located using local maxima filtering approaches at the three lidar point densities. Mean height and dominant height were both estimated at high accuracies for all local maxima filtering techniques and lidar point densities. The results were also comparable to the approach that employed regression analysis that related lidar-derived distributional metrics and field measurements. Estimated dominant height and SPHA, as well as age of trees, were used as independent variables in a function to estimate plot-level basal area. The basal area was then used to compute diameter of the tree with mean basal area, referred to as quadratic mean diameter at breast height (QDBH). Mean tree height and QDBH were used as independent variables in a standard equation to calculate mean tree volume, which was then scaled up to the plot-level. All estimates for the local maxima filtering approaches and lidar point densities returned negatively biased volume, when compared to field observations. This was due to the underestimation of SPHA, which was used as a conversion factor in scaling up from tree-level to plot-level. Volume estimates across lidar point densities exhibited similarities. This suggests that low lidar point densities (e.g., 1 point/m2) have potential for accurate volume estimation. It was concluded that multiple forest structural attributes can be assessed using lidar data only. The accuracy of height derivation meets the standards set by field inventories. The underestimation of SPHA may be comparable to other studies that applied different methods. However, improved estimation accuracy is needed in order to apply the approaches to commercial forestry scenarios. The significance of improving SPHA estimation extends to improved volume estimation. In addition, the potential improvement should also take into consideration the density of lidar points, as this will impact on the cost of acquisition. This research has taken a significant step towards determining if lidar data can be used as a stand-alone remote sensing data source for assessment of structural plantation parameters. Not only does such an approach seem viable, but the lower required point densities will help to reduce acquisition costs significantly.
The estimation of Eucalyptus plantation forest structural attributes using medium and high spatial resolution satellite imagery.
(2008) Gebreslasie, Michael Teweldemedhin.; Ahmed, Fethi B.; van Aardt, Jan.
Sustaining the socioeconomic and ecological benefits of South African plantation forests is challenging. A more systematic and rapid forest inventory system is required by forest managers. This study investigates the utility of medium (ASTER 15 m) and high (IKONOS 1-4 m) spatial resolution satellite imageries in an effort to improve the remote capture of structural attributes of even-aged Eucalyptus plantations grown in the warm temperate climatic zone of southern KwaZulu-Natal, South Africa. The conversion of image data to surface reflectance is a pre-requisite for the establishment of relationships between satellite remote sensing data and ground collected forest structural data. In this study image-based atmospheric correction methods applied on ASTER and IKONOS imagery were evaluated for the purpose of retrieving surface reflectance of plantation forests. Multiple linear regression and canonical correlation analyses were used to develop models for the prediction of plantation forest structural attributes from ASTER data. Artificial neural networks and multiple linear regression were also used to develop models for the assessment of plantation forests structural attributes from IKONOS data. The plantation forest structural attributes considered in this study included: stems per hectare, diameter at breast height, mean tree height, basal area, and volume. In addition, location based stems per hectare were determined using high spatial resolution panchromatic IKONOS data where variable and fixed window sizes of local maxima were employed. The image-based dark object subtraction (DOS) model was better suited for atmospheric correction of ASTER and IKONOS imagery of the study area. The medium spatial resolution data were not amenable to estimating even-aged Eucalyptus forest structural attributes. It is still encouraging that up to 64 % of variation could be explained by using medium spatial resolution data. The results from high spatial resolution data showed a promising result where the ARMSE% values obtained for stems per hectare, diameter at breast height, tree height, basal area and volume are 7.9, 5.1, 5.8, 8.7 and 8.7, respectively. Results such as these bode well for the application of high spatial resolution imagery to forest structural assessment. The results from the location based estimation of stems per hectare illustrated that a variable window size approach developed in this study is highly accurate. The overall accuracy using a variable window size was 85% (RMSE of 189 trees per hectare). The overall findings presented in this study are encouraging and show that high spatial resolution imagery was successful in predicting even-aged Eucalyptus forest structural attributes in the warm temperate climates of South Africa, with acceptable accuracy.
An evaluation of the periglacial morphology in the high Drakensberg and associated environmental implications.
(1997) Grab, Stefan Walter.; Hall, Kevin John.; Ahmed, Fethi B.
Although periglacial research in the high Drakensberg and Lesotho mountains has received growing interest amongst southern African geomorphologists, little detailed, quantitative information was available prior to this study. In an attempt to help overcome this deficit, a quantitative assessment on cryogenic landforms and processes operative in the high Drakensberg was undertaken. Morphological and sedimentological assessments of sorted patterned ground, non-sorted steps, thufur, blockstreams, stone-banked lobes, debris deposits and turf exfoliation landforms were undertaken. In addition, geomorphic process assessments in the field included the measurement of turf retreat at turf exfoliation sites, the determination of frost-heave mechanisms within wetlands and sediment mobilization along the Mashai Stream. Ground temperatures were recorded for thufur from 1993 to 1996. The environmental implications of some of the findings are discussed. Seasonal frost-induced sorted patterned ground emerges annually within a few weeks, demonstrating the effect of regular, diurnal freeze-thaw cycles during the winter months. It is found that the present climate is not conducive to maintaining or preserving miniature periglacial landforms below 3200m a.s.l. during the summer months. Large relict sorted circles, stone-banked lobes and blockstreams are the most conspicuous periglacial landforms in the high Drakensberg and are products of at least seasonally-frozen ground. It is suggested that debris deposits found within high Drakensberg cutbacks are possible indicators for marginal niche and cirque glaciation during the Late Pleistocene. It is demonstrated that in climatically marginal periglacial regions, the microtopographically controlled freezing processes may be of paramount importance in maintaining and modifying the cryogenic landforms that occur. Pronounced temperature differentials are found during the winter months, when thufur are frozen for several weeks and depressions remain predominantly unfrozen. It is suggested that such contemporary temperature differentials induce thermodynamic forces and ultimately ground heave at sites in the high Drakensberg. The pronounced seasonal weather patterns in the high Drakensberg have promoted a cycle of geomorphic process events that operate synergistically and initiate particular erosion landforms. However, cryogenic activity during the colder period is overwhelmed by water induced erosion processes during the summer months in the high Drakensberg. It is concluded that the high Drakensberg is currently a marginal periglacial region, but that periglacial conditions prevailed during both the Pleistocene and some Late Holocene Neoglacial events.
An investigation into using textural analysis and change detection techniques on medium and high spatial resolution imagery for monitoring plantation forestry operations.
(2006) Norris-Rogers, Mark.; Van Aardt, Jan.; Ahmed, Fethi B.; Coppin, P. R.
Plantation forestry involves the management of man-made industrial forests for the purpose of producing raw materials for the pulp and paper, saw milling and other related wood products industries. Management of these forests is based on the cycle of planting, tending and felling of forest stands such that a sustainable operation is maintained. The monitoring and reporting of these forestry operations is critical to the successful management of the forestry industry. The aim of this study was to test whether the forestry operations of clear-felling, re-establishment and weed control could be qualitatively and quantitatively monitored through the application of classification and change detection techniques to multi-temporal medium (15-30 m) and a combination of textural analysis and change detection techniques on high resolution (0.6-2.4 m) satellite imagery. For the medium resolution imagery, four Landsat 7 multi-spectral images covering the period from March 2002 to April 2003 were obtained over the midlands of KwaZulu-Natal, South Africa, and a supervised classification, based on the Maximum Likelihood classifier, as well as two unsupervised classification routines were applied to each of these images. The supervised classification routine used 12 classes identified from ground-truthing data, while the unsupervised classification was done using 10 and 4 classes. NDVI was also calculated and used to estimate vegetation status. Three change detection techniques were applied to the unsupervised classification images, in order to determine where clear-felling, planting and weed control operations had occurred. An Assisted "Classified" Image change detection technique was applied to the Ten-Class Unsupervised Classification images, while an Assisted "Quantified Classified" change detection technique was applied to the Four-Class Unsupervised Classification images. An Image differencing technique was applied to the NDVI images. For the high resolution imagery, a series of QuickBird images of a plantation forestry site were used and a combination of textural analysis and change detection techniques was tested to quantify weed development in replanted forest stands less than 24 months old. This was achieved by doing an unsupervised classification on the multi-spectral bands, and an edge-enhancement on the panchromatic band. Both the resultant datasets were then vectorised, unioned and a matrix derived to determine areas of high weed. It was found that clear-felling operations could be identified with accuracy in excess of 95%. However, using medium resolution imagery, newly planted areas and the weed status of forest stands were not definitively identified as the spatial resolution was too coarse to separate weed growth from tree stands. Planted stands younger than one year tended to be classified in the same class as bare ground or ground covered with dead branches and leaves, even if weeds were present. Stands older than one year tended to be classified together in the same class as weedy stands, even where weeds were not present. The NDVI results indicated that further research into this aspect could provide more useful information regarding the identification of weed status in forest stands. Using the multi-spectral bands of the high resolution imagery it was possible to identify areas of strong vegetation, while crop rows were identifiable on the panchromatic band. By combining these two attributes, areas of high weed growth could be identified. By applying a post-classification change detection technique on the high weed growth classes, it was possible to identify and quantify areas of weed increase or decrease between consecutive images. A theoretical canopy model was also derived to test whether it could identify thresholds from which weed infestations could be determined. The conclusions of this study indicated that medium resolution imagery was successful in accurately identifying clear-felled stands, but the high resolution imagery was required to identify replanted stands, and the weed status of those stands. However, in addition to identifying the status of these stands, it was also possible to quantify the level of weed infestation. Only wattle (Acacia mearnsii) stands were tested in this manner but it was recommended that in addition to applying these procedures to wattle stands, they also are tested in Eucalyptus and Pinus stands. The combination of textural analysis on the panchromatic band and classification of multi-spectral bands was found to be a suitable process to achieve the aims of this study, and as such were recommended as standard procedures that could be applied in an operational plantation forest monitoring environment.
The potential for using remote sensing to quantify stress in and predict yield of sugarcane (Saccharun spp. hybrid)
(2010) Abdel-Rahman, Elfatih Mohamed.; Ahmed, Fethi B.; Van den Berg, Maurits.
South Africa is the leading producer of sugarcane in Africa and one of the largest sugarcane producers in the world. Sugarcane is grown under a wide range of climatic, agronomic, and socio-economic conditions in the country. Stress factors such as water and nutrient deficiencies, and insect pests and diseases are among the most important factors affecting sugarcane production in the country. Monitoring of stress in sugarcane is therefore essential for assessing the consequences on yield and for taking action of their mitigation. The prediction of sugarcane yield, on the other hand is also a significant practice for making informed decisions for effective and sound crop planning and management efforts regarding e.g., milling schedules, marketing, pricing, and cash flows. In South Africa, the detection of stress factors such as nitrogen (N) deficiency and sugarcane thrips (Fulmekiola serrata Kobus) damage and infestation are made using traditional direct methods whereby leaf samples are collected from sugarcane fields and the appropriate laboratory analysis is then performed. These methods are regarded as being time-consuming, labour-intensive, costly, and can be biased as often they are not uniformly applied across sugarcane growing areas in the country. In this regard, the development of systematically organised geo-and time-referenced accurate methods that can detect sugarcane stress factors and predict yields are required. Remote sensing offers near-real-time, potentially inexpensive, quick and repetitive data that could be used for sugarcane monitoring. Processing techniques of such data have recently witnessed more development leading to more effective extraction of information. In this study the aim was to explore the potential use of remote sensing to quantify stress in and predict yield of sugarcane in South Africa. In the first part of this study, the potential use of hyperspectral remote sensing (i.e. with information on many, very fine, contiguous spectral bands) in estimating sugarcane leaf N concentration was examined. The results showed that sugarcane leaf N can be predicted at high accuracy using spectral data collected using a handheld spectroradiometer (ASD) under controlled laboratory and natural field conditions. These positive results prompted the need to test the use of canopy level hyperspectral data in predicting sugarcane leaf N concentration. Using narrow NDVI-based vegetation indices calculated from Hyperion data, sugarcane leaf N concentration could reliably be estimated. In the second part of this study, the focus was on whether leaf level hyperspectral data could detect sugarcane thrips damage and predict the incidence of the insect. The results indicated that specific wavelengths located in the visible region of the electromagnetic spectrum have the highest possibility of detecting sugarcane thrips damage. Thrips counts could also adequately be predicted for younger sugarcane crops (4–5 months). In the final part of this study, the ability of vegetation indices derived from multispectral data (Landsat TM and ETM+) in predicting sugarcane yield was investigated. The results demonstrated that sugarcane yield can be modelled with relatively small error, using a non-linear random forest regression algorithm. Overall, the study has demonstrated the potential of remote sensing techniques to quantify stress in and predict yield of sugarcane. However, it was found that models for detecting a stress factor or predicting yield in sugarcane vary depending on age group, variety, season of sampling, conditions at which spectral data are collected (controlled laboratory or natural field conditions), level at which remotely-sensed data are captured (leaf or canopy levels), and irrigation conditions. The study was conducted in only one study area (the Umfolozi mill supply area) and very few varieties (N12, N19, and NCo 376) were tested. For practical and operational use of remote sensing in sugarcane monitoring, the development of an optimum universal model for detecting factors of stress and predicting yield of sugarcane, therefore, still remains a challenging task. It is recommended that models developed in this study should be tested – or further elaborated – in other South African sugarcane producing areas with growing conditions similar to those under which the predictive models have been developed. Monitoring of sugarcane thrips should also be evaluated using remotely-sensed data at canopy level; and the ability of multispectral sensors other than Landsat TM and ETM+ should be tested for sugarcane yield prediction.
Unemployment in South Africa : in search of a spatial model.
(2015) Weir-Smith, Gina.; Ahmed, Fethi B.; Maharaj, Bridgemohan.
Consistent high unemployment perpetuates inequalities in the South African society. The 2014 growth expectation for the South African economy is 1.5 per cent and this will most certainly not be enough to reduce unemployment. This research aimed to create an understanding of the spatial intricacies related to unemployment and to create a longitudinal dataset since 1991. The challenge with such a dataset is that boundaries of the enumeration and administrative areas have changed continually in the past and makes it difficult to compare unemployment spatially over time. These particular problems were addressed by aggregating data for 1991 and 1996 census from magisterial districts to the 2005 municipal boundaries. Area based weighted areal interpolation was used and it assumes that data is distributed homogeneously across the area of each source unit. The 2001 census and 2007 community survey data was available at the 2005 municipal level, and therefore a longitudinal socio-economic dataset of four time points could be created. The results showed that unemployment has been spatially persistent in a number of areas. Furthermore, a spatial grouping of unemployment by municipality showed that metropolitan municipalities had unique unemployment characteristics whereas the remainder of the country could be clustered into five distinct groups. A spatial comparison between unemployment and poverty at municipality level revealed that people can be poor and unemployed, but also poor and employed. Finally, the longitudinal data was used to do spatial forecasting of future unemployment trends and these accounted for up to 60 per cent of change in unemployment. These national and provincial spatial unemployment models consisted of coefficients like the percentage of people employed in mining and agriculture. This research added new knowledge in terms of the spatio-temporal understanding of unemployment in South Africa. It created a methodology to overcome modifiable areal unit problems (MAUP) and a longitudinal dataset of unemployment and related socio-economic variables. Refined spatial data was this research’s main challenge and it recommends that unemployment data should be released at the most detailed spatial level possible - like sub-place or enumeration areas. The quality and timeliness of data remain obstacles for policy-making. Therefore, labour market data at a sub-place level would provide a more meaningful analysis. The results from census 2011 will allow the creation of longitudinal socio-economic trends at a spatially detailed level in South Africa in the future.
The use of spatial analysis and participatory approaches in strategic environmental assessment (SEA) : identifying and predicting the ecological impacts of development on the KwaZulu-Natal North Coast of South Africa.
(2010) Ahmed, Fathima.; Bob, Urmilla.; Ahmed, Fethi B.
The high pressures for coastal development, translated as prolific land cover transformation, coupled with the weaknesses of management to protect the environment has led to the gradual deterioration of environmental conditions in many coastal areas. Land use decisions in coastal areas are based on opportunities and constraints affected by both biophysical and socio-economic drivers, and hence present one of the main issues integrating the large debate on sustainable development in coastal zones (Lourenço and Machado, 2007: 1). The aim of this study is to investigate the effectiveness of the integration of spatial analysis and participatory approaches in SEA (particularly its ability to identify and predict ecological impacts) on the KwaZulu-Natal north coast of South Africa. The study adopted a conceptual framework based on landscape ecology, which was underpinned within the overarching political ecology framework. The former underscores the importance of integration, while the latter critiques the institutionalization of environmental concerns, which are characterized by inequalities in terms of social and political power and of how problems are defined, mediated and resolved. Hence this conceptual framework was considered appropriate to assess the strategic environmental issues pertaining to the coastal zone on the KwaZulu-Natal north coast. The researcher used participatory methods, primarily focus group discussions (which included venn diagramming, ranking exercises and participatory mapping) which were triangulated with both quantitative and qualitative methods as part of an integrated impact assessment. These relate to the use of semistructured questionnaires which were administered to a purposive sample of six key stakeholder interest groups within the study area. A spatial GIS time series analysis of land use and cover change was employed to determine baseline conditions, changes in the state of key ecosystems, key development drivers and emerging threats. Additionally, a policy and institutional review was undertaken. The analysis revealed that major natural land cover classes are in decline in the study area,within a time period of less than 10 years. The most sensitive ecosystems were found to be grasslands (-19.99%), coastal forest (-40%), wetlands (-37.49%) and secondary dunes (- 21.44%). Furthermore, agriculture and forestry are also indicating severe declines. The reasons attributed to this transformation of land cover are increasingly being linked with economic motives such as individual private land-owner dynamics, tourism growth and development in the area. Furthermore, the policy agendas are clearly economically motivated. These losses signify the cumulative decline in ecosystem goods and services, and could undermine pose risks to the society that relies on them either directly or indirectly. One of the main considerations in this research endeavor was to formulate a Strategic Environmental Assessment (SEA) Framework to inform future ICZM in the study area. SEA is planning with a long-term perspective, with a focus on a spatial rather than a project level, an element that is clearly lacking in the current development scenario of this coastline. It is critical that the SEA Framework advocated in this study include a range of variables that will permit short-term, medium-term and long-term monitoring and evaluation aimed at ensuring sustainable planning in the area.

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Browsing Doctoral Degrees (Geography) by Author "Ahmed, Fethi B."