In this thesis the mathematics of the potential field gradient tensor is thoroughly reviewed and the inter - relationships between the various components and quantities are established. Numerical forward modelling of 3D regolith scenarios is undertaken, showing that current instrumentation for measuring magnetic gradients is useful for near - surface exploration. Gravity gradiometry is only able to detect relatively large contrasts in density, and does not offer the same small - scale resolution as magnetic gradiometry. I examine the magnetic case further by incorporating surface measurements of magnetic susceptibility. These data are highly variable, and forward modelling shows while gradiometry is useful for regolith exploration, interpretation is difficult due to the high level of geological noise. I have tested new and standard filters as tools for extracting information from gradient tensor data. This includes boundaries between geological units and possible mineralisation positions. New techniques developed involve calculating an Analytic Signal for the entire gradient tensor, and forming multiplicative combinations of the individual gradient tensor components. The determinant and inverse of the gradient tensor 3 x 3 matrix can also yield useful information pertaining to source position and geological boundaries. These are illustrated by means of synthetic data examples. Examination of inversion techniques on gradient tensor data illustrates that single component inversion is comparable in the quality of the final result to a multi - component inversion, albeit much faster. Eigenvalues and eigenvectors of the full tensor produce information about depth and direction, although interpretation must be taken with care. I have developed an inversion routine to locate magnetic dipole sources, and illustrate how it produces multiple solutions to a problem, and how it is not possible to unambiguously determine which of these is correct. A new inversion routine was therefore developed. This new hybrid global inversion routine is an automated technique to locate a generalised magnetic multipole. It can locate dipole and quadrupole ( and some octupole ) sources successfully, and be used either in real time as data is being collected, or as post - processing on an entire data set. / Philip Heath / Thesis (Ph.D.)--School of Chemistry and Physics, 2007.

One of the most popular challenges facing academia and industry is the development of effective techniques and tools for maximizing the availability of data as the most valuable source of knowledge. The internet has dominated as the core for maximizing data availability and XML (eXtensible Markup Language) has emerged and is being gradually accepted as the universal standard format for platform independent publishing and exchanging data over the Internet. On the other hand, there remain large amount of data held in structured databases and database management systems have been traditionally used for the effective storage and manipulation of large volumes of data. This raised the need for effective methodologies capable of smoothly transforming data between different formats in general and between XML and structured databases in particular. This dissertation addresses the issue by proposing a two-way mapping approach between XML and object-oriented databases. The basic steps of the proposed approach are applied in a systematic way to produce a graph from the source and then transform the graph into the destination format. In other words, the derived graph summarizes characteristics of the source whether XML (elements and attributes) or object-oriented database (classes, inheritance and nesting hierarchies). Then, the developed methodology classifies nodes and links from the graph into the basic constructs of the destination, i.e., elements and attributes for XML or classes, inheritance and nesting hierarchies for object-oriented databases. The methodology has been successfully implemented and illustrative case studies are presented in this document.

Thesis (MSc)--Stellenbosch University, 2005. / ENGLISH ABSTRACT: Remote sensing and image processing tools provide speedy and up-to-date information on land resources. Although remote sensing is the most effective means of land cover and land use mapping, it is not without limitations. The accuracy of image analysis depends on a number of factors, of which the image classifier used is probably the most significant. It is noted that there is no perfect classifier, but some robust classifiers achieve higher accuracy results than others. For certain land cover/uses, discrimination based only on spectral properties is extremely difficult and often produces poor results. The use of ancillary data can improve the classification process. Some classifiers incorporate ancillary data before or after the classification process, which limits the full utilization of the information contained in the ancillary data. Expert classification, on the other hand, makes better use of ancillary data by incorporating data directly into the classification process. In this study an expert classification model was developed based on spatial operations designed to identify a specific land cover/use, by integrating both spectral and available ancillary data. Ancillary data were derived either from the spectral channels or from other spatial data sources such as DEM (Digital Elevation Model) and topographical maps. The model was developed in ERDAS Imagine image-processing software, using the expert engineer as a final integrator of the different constituent spatial operations. An attempt was made to identify the Level I land cover classes in the South African National Land Cover classification scheme hierarchy. Rules were determined on the basis of expert knowledge or statistical calculations of mean and variance on training samples. Although rules could be determined by using statistical applications, such as the classification analysis regression tree (CART), the absence of adequate and accurate training data for all land cover classes and the fact that all land cover classes do not require the same predictor variables makes this option less desirable. The result of the accuracy assessment showed that the overall classification accuracy was 84.3% and kappa statistics 0.829. Although this level of accuracy might be suitable for most applications, the model is flexible enough to be improved further. / AFRIKAANSE OPSOMMING: Afstandswaameming-en beeldverwerkingstegnieke kan akkurate informasie oorbodemhulpbronne weergee. Alhoewel afstandswaameming die mees effektiewe manier van grondbedekking en grondgebruikkartering is, is dit nie sonder beperkinge nie. Die akkuraatheid van beeldverwerking is afhanklik van verskeie faktore, waarvan die beeld klassifiseerder wat gebruik word, waarskynlik die belangrikste faktor is. Dit is welbekend dat daar geen perfekte klassifiseerder is nie, alhoewel sekere kragtige klassifiseerders hoër akkuraatheid as ander behaal. Vir sekere grondbedekking en -gebruike is uitkenning gebaseer op spektrale eienskappe uiters moeilik en dikwels word swak resultate behaal. Die gebruik van aanvullende data, kan die klassifikasieproses verbeter. Sommige klassifiseerders inkorporeer aanvullende data voor of na die klassifikasieproses, wat die volle aanwending van die informasie in die aanvullende data beperk. Deskundige klassifikasie, aan die ander kant, maak beter gebruik van aanvullende data deurdat dit data direk in die klassifikasieproses inkorporeer. Tydens hierdie studie is 'n deskundige klassifikasiemodel ontwikkel gebaseer op ruimtelike verwerkings, wat ontwerp is om spesifieke grondbedekking en -gebruike te identifiseer. Laasgenoemde is behaal deur beide spektrale en beskikbare aanvullende data te integreer. Aanvullende data is afgelei van, óf spektrale eienskappe, óf ander ruimtelike bronne soos 'n DEM (Digitale Elevasie Model) en topografiese kaarte. Die model is ontwikkel in ERDAS Imagine beeldverwerking sagteware, waar die 'expert engineer' as finale integreerder van die verskillende samestellende ruimtelike verwerkings gebruik is. 'n Poging is aangewend om die Klas I grondbedekkingklasse, in die Suid-Afrikaanse Nasionale Grondbedekking klassifikasiesisteem te identifiseer. Reëls is vasgestel aan die hand van deskundige begrippe of eenvoudige statistiese berekeninge van die gemiddelde en variansie van opleidingsdata. Alhoewel reëls met behulp van statistiese toepassings, soos die 'classification analysis regression tree (CART)' vasgestel kon word, maak die afwesigheid van genoegsame en akkurate opleidingsdata vir al die grondbedekkingsklasse hierdie opsie minder aantreklik. Bykomend tot laasgenoemde, vereis alle grondbedekkingsklasse nie dieselfde voorspellingsveranderlikes nie. Die resultaat van hierdie akkuraatheidsskatting toon dat die algehele klassifikasie-akkuraatheid 84.3% was en die kappa statistieke 0.829. Alhoewel hierdie vlak van akkuraatheid vir die meeste toepassings geskik is, is die model aanpasbaar genoeg om verder te verbeter.

Pollution of surface and groundwater, due to improper land management, has become a major problem worldwide. Integrated watershed modelling provides a tool for the understanding of the processes governing water and matter transport at different scales within the watershed. The Soil Water Assessment Tool (SWAT) has been successfully utilized for the combined modelling of water fluxes and quality within a large range of scales and environmental conditions across the world. For suitable assessments integrated watershed models require large data sets of measured information for both model parameterization as for model calibration and validation. Data scarcity represents a serious limitation to the use of hydrologic models for supporting decision making processes, and may lead unsupported statements, poor statistics, misrepresentations, and, ultimately, to inappropriate measures for integrated water resources management efforts. In particular, the importance of spatially distributed soil information is often overlooked. In this thesis the eco-hydrological SWAT model was been applied to assess the water balance and diffuse pollution loadings of two rivers within a rural context at the mesoscale watershed level: 1) the Western Bug River, Ukraine, 2) the Águeda River, Portugal. Both watersheds in focus serve as examples for areas where the amount and quality of the measured data hinders a strait forward hydrologic modelling assessment. The Dobrotvir watershed (Western Bug River, Ukriane) is an example of such a region. In the former Soviet Union, soil classification primarily focused on soils of agricultural importance, whereas, forested, urban, industrial, and shallow soil territories were left underrepresented in the classification systems and resulting soil maps. Similarly the forest-dominated Águeda watershed in North-Central Portugal is a second example of a region with serious soil data availability limitations. Through the use of pedotransfer functions (PTFs) and the construction of soil-landscape models the data gaps could be successfully diminished, allowing a subsequent integrated watershed modelling approach. A valuable tool for the data gap closure was the fuzzy logic Soil Land Inference Model (SoLIM) which, combined with information from several soil surveys, was used to create improved maps. In the Dobrotvir watershed the fuzzy approach was used to close the gaps of the existing soil map, while in the Águeda watershed a new soil properties map, based upon the effective soil depths of the landscape, was constructed. While the water balance simulation in both study areas was successful, a calibration parameter ensemble approach was tested for the Águeda watershed. In the common modelling practice the individual best simulation and best parameter set is considered, the tested approach involved merging individual model outputs from numerous acceptable parameter sets, tackling the problematic of parameter equifinality. This procedure was tested for both original soil map and the newly derived soil map with differentiation of soil properties. It was noticeable that a better model set-up, with a better representation of the soil spatial distribution, was reflected in tighter model output spreads and narrower parameter distances. A further challenge was the calibration of water quality parameters, namely nitrate-N in the Dobrotvir watershed and sediment loads in the Águeda watershed. The limited amount of water quality observations were handled by assessing and by process verification at the smallest modelling unit, the hydrological response unit (HRU). The ruling hydrological processes could be depicted by combining own measured data and modelling outputs. The management scenario simulations showed the anticipated response to changes in management and reflected the rational spatial variation within the watershed reasonably well. The impacts of the different intervention options were evaluated on water balance, nitrate-N export and sediment yield at the watershed, sub-watershed and, when feasible, HRU level. This thesis covers two regional case studies with particular data limitations and specific processes of water and matter fluxes. Still, data reliability is a problem across the globe. This thesis demonstrates how relevant it is to tackle shortages of spatially differentiated soil information. The considered approaches contribute toward more reliable model predictions. Furthermore, the tested methods are transferable to other regions with differing landscape and climate conditions with similar problems of data scarcity, particularly soil spatially differentiated information.