Super-resolution mapping is becoming an increasing important technique in remote sensing for land cover mapping at a sub-pixel scale from coarse spatial resolution imagery. The potential of this technique could increase the value of the low cost coarse spatial resolution imagery. Among many types of land cover patches that can be represented by the super-resolution mapping, the prediction of patches smaller than an image pixel is one of the most difficult. This is because of the lack of information on the existence and spatial extend of the small land cover patches. Another difficult problem is to represent the location of small patches accurately. This thesis focuses on the potential of super-resolution mapping for accurate land cover mapping, with particular emphasis on the mapping of small patches. Popular super-resolution mapping techniques such as pixel swapping and the Hopfield neural network are used as well as a new method proposed. Using a Hopfield neural network (HNN) for super-resolution mapping, the best parameters and configuration to represent land cover patches of different sizes, shapes and mosaics are investigated. In addition, it also shown how a fusion of time series coarse spatial resolution imagery, such as daily MODIS 250 m images, can aid the determination of small land cover patch locations, thus reducing the spatial variability of the representation of such patches. Results of the improved HNN using a time series images are evaluated in a series of assessments, and demonstrated to be superior in terms of mapping accuracy than that of the standard techniques. A novel super-resolution mapping technique based on halftoning concept is presented as an alternative solution for the super-resolution mapping. This new technique is able to represent more land cover patches than the standard techniques.

The insurance industry is becoming increasingly exposed to the adverse impacts of climate variability and climate change. In developing policies and adapting strategies to better manage climate risk, insurers and reinsurers are therefore engaging directly with the climate modelling community to further understand the predictive capabilities of climate models and to develop techniques to utilise climate model output. With an inherent interest in the present and future frequency and magnitude of extreme climate-related loss events, insurers rely on the climate modelling community to provide informative model projections at the relevant spatial and temporal scales for insurance decisions. Furthermore, given the high economic stakes associated with enacting strategies to address climate change, it is essential that climate model experiments are designed to thoroughly explore the multiple sources of uncertainty. Determining the reliability of model based projections is a precursor to examining their relevance to the insurance industry and more widely to the climate change adaptation community. Designing experiments which adequately account for uncertainty therefore requires careful consideration of the nonlinear and chaotic properties of the climate system. Using the well developed concepts of dynamical systems theory, simple nonlinear chaotic systems are investigated to further understand what is meant by climate under climate change. The thesis questions the conventional paradigm in which long-term climate prediction is treated purely as a boundary value problem (predictability of the second kind). Using simple climate-like models to draw analogies to the climate system, results are presented which support the emerging view that climate prediction ought to be treated as both an initial value problem and a boundary condition problem on all time scales. The research also examines the application of the ergodic assumption in climate modelling and climate change adaptation decisions. By using idealised model experiments, situations in which the ergodic assumption breaks down are illustrated. Consideration is given to alternative model experimental designs which do not rely on the assumption of ergodicity. Experimental results are presented which support the view that large initial condition ensembles are required to detail the changing distribution of climate under altered forcing conditions. It is argued that the role of chaos and nonlinear dynamic behaviour ought to have more prominence in the discussion of the forecasting capabilities in climate prediction.

The thesis sets out to examine the utility of remote sensing techniques in helping to define recommendation domains relatively homogeneous agricultural areas - to act as foci for agricultural development planning in lower Meru, Kenya. Recommendation domains are used in farming systems research (FSR) for agricultural research and development initiatives enabling greater participation from rural producers within the development process. Recommendation domains are defined by agricultural potential (agro-ecological zones) and farming systems (agro-economic groupings). A multilevel approach incorporating Landsat MSS data, 1:50,000 stereo panchromatic air photography, large scale aerial colour slide photography and ground surveys is used to collect data on the farming systems of the study area. Relatively homogeneous farming patterns are identified and mapped using a number of different computer software packages. These patterns are related to previously identified zones of agricultural potential (agro-ecological zones) to define recommendation domains for new agricultural development initiatives in the area. Several domains are identified for specific attention. Recommendations are made which are relevant to both national and district level agricultural planning in Kenya. It is suggested that future development programmes should focus on areas undergoing population movement and cultivation change since without careful planning these changes are likely to detrimentally affect the local farming systems and natural environment.

This thesis proposes a construct to measure the competitiveness of Brazilian tourist destinations and orient the actions of public and private tourism managers. The model of this study is based on a relevant literature review, a panel with specialists that have expertise in Brazilian tourism and field research. The investigation was carried out in 15 Brazilian tourist cities, representative of the socioeconomic diversity that characterises the heterogeneity of a developing country. The results of the study show the level of competitiveness of these 15 destinations according to the 13 dimensions proposed in the model, identifying the strong and weak points of local tourism. An assessment was carried out in detail that should serve to assist public managers in the strategic planning of these cities now and in the future. As a complementary result, the study may be successfully replicated in other destinations, thus initiating a historical series for monitoring the competitiveness of Brazilian tourist destinations.

Urban morphology has been a longstanding field of interest for geographers but without adequate focus on its economic significance. From an economic perspective, urban morphology appears to be a fundamental determinant of house prices since morphology influences accessibility. This PhD thesis investigates the question of how the housing market values urban morphology. Specifically, it investigates people’s revealed preferences for street patterns. The research looks at two distinct types of housing market, one in the UK and the other in China, exploring both static and dynamic relationships between urban morphology and house price. A network analysis method known as space syntax is employed to quantify urban morphology features by computing systemic spatial accessibility indices from a model of a city’s street network. Three research questions are empirically tested. Firstly, does urban configuration influence property value, measured at either individual or aggregate (census output area) level, using the Cardiff housing market as a case study? The second empirical study investigates whether urban configurational features can be used to better delineate housing submarkets. Cardiff is again used as the case study. Thirdly, the research aims to find out how continuous change to the urban street network influences house price volatility at a micro-level. Data from Nanjing, China,is used to investigate this dynamic relationship. The results show that urban morphology does, in fact, have a statistically significant impact on housing price in these two distinctly different housing markets. I find that urban network morphology features can have both positive and negative impacts on housing price. By measuring different types of connectivity in a street network it is possible to identify which parts of the network are likely to have negative accessibility premiums (locations likely to be congested) and which parts are likely to have positive premiums (locations highly connected to destination opportunities). In the China case study, I find that this relationship holds dynamically as well as statically, showing evidence that price change is correlated with some aspects of network change.

This thesis examines how new types of water governance institutions, water partnerships, emerged and performed in Korea, a centralised state-driven society. Beyond conventional water management by either government or market, new forms of governance have been sought to address problems such as under-provision, pollution and water conflict. This study investigates voluntary water partnerships as a leading example of new water governance in East Asia. Conceptually, it uses a modified institutional approach, the Institutional Analysis and Development (IAD) framework, to examine how global water reform discourses informed social actors in the design and implementation of regional and urban water partnerships in Korea. A comparative case analysis of six water partnerships finds that co-governance institutions emerged and operated in a complex linkage with existing water governance systems. For the three urban water partnerships, local actors actively seized opportunities to rehabilitate long abandoned urban streams. For the three regional water partnerships, public and private sector actors successfully negotiated partnership agreements, focusing on the restoration of polluted water sources. A modified IAD framework captured these complicated interactions among stakeholders within multi-layered water governance structures. An attitudinal survey of partnership members complemented the comparative case studies by assessing how the partnerships performed according to selected evaluation criteria. A multi-criteria assessment of the data reveals three key findings. First, the partnerships achieved mainly positive procedural and socio-economic outcomes in water management. Second, observed lower environmental outcomes result mainly from the interlinked features of water resources management and the partnerships’ relatively brief history. Third, the overall findings indicate that the outcomes of co-governance institutions tended to be contextual. The scale of organisations and of the water resources concerned did not determine the outcomes of the water partnerships. Thus, this finding challenges the claim that ‘smaller is better’ in collaborative governance. This study concludes that the voluntary co-management of shared water resources by the six partnerships have simultaneously brought some solutions as well as costs to water governance in Korea. The design and development of co-management institutions for water governance requires a greater understanding of local and national settings, as well as the facilitative role of national government. Co-operation between new co-governance institutions and existing water institutions is vital to long-term, effective water management.

Detection of leaking gas pipelines is important for safety, economic and environmental reasons. Remote sensing of vegetation offers the potential to identify gas leakage. The research aim was to determine the effects of elevated soil concentrations of natural gas on overlying vegetation. Pot-scale investigations were carried out to determine whether changes in spectral characteristics were specific to natural gas or were a generic response to soil-oxygen displacement. Natural gas, argon, nitrogen and waterlogging were used to displace soil-oxygen. Leaf response to soil oxygen displacement was increased reflectance in the visible wavelengths and changes in the position and shape of the red-edge, which shifted towards longer wavelengths as the control plant matured, while the red-edge of the treated plant remained stationary indicating an inhibition of maturing. The shape of the red-edge differed in bean and barley with bean exhibiting a single peak in the first derivative that moved with plant maturity; barley exhibited a peak at 704 nm with a shoulder at 722 nm that shifted to shorter wavelengths during plant stress. Argon and waterlogging exhibited a greater response than natural gas, which had been administered noncontinuously. These experiments suggest the response to natural gas was generic to soil-oxygen deficiency. Field studies were conducted to determine whether spectral changes in leaves identified in pot trials were observable in crop canopies under field conditions. Reflectance of barley growing above a leaking gas pipeline was increased in the visible wavelengths and the red-edge was at a shorter wavelength. When the majority of the crop was fully developed, the barley above the gas leak was greener, suggesting that development was inhibited by soil-oxygen displacement. It might be possible to detect leaking gas by remote sensing of vegetation in conjunction with pipeline maps, but limitations in the spatial resolution of current satellite sensors and the infrequency of cloud free skies in the UK suggest that further work is needed before an operational system could be available.

In today's world of ever advancing technology the time is precisely right for investment in the development and implementation of a national spatial data infrastructure. This implies that all spatial data presently scattered in different departments and organisations are coordinated and shared. In the Kingdom of Saudi Arabia there are a number of different mapping and Geographic Information System (GIS) activities being implemented within various government organisations, each with its own merits. Certain research and pilot projects have also been carried out aiming to provide help and recommendations with regard to spatial data sharing and to promote awareness of the importance of spatial data to the Kingdom's development. However, there is an urgent need for a consolidation of effort to avoid the costly mistake of duplication of work; hence the need for a unified national spatial data infrastructure. This research aims to develop a conceptual framework for a strategy for a national spatial data infrastructure (SNSDI) including its main components. A proposal is presented for a Saudi national spatial data infrastructure (which happens to have the same abbreviation - SNSDI) to consolidate isolated mapping and spatial data efforts in the Kingdom of Saudi Arabia in place of the current practice of each agency acting independently. This research project will hopefully provide a leadership role in developing a Kingdom-wide spatial data infrastructure.

Artificial neural networks (ANNs) have attracted the attention of researchers in many fields, and have been used to solve a wide range of problems. In the field of remote sensing they have been used in a variety of applications, including land cover mapping, image compression, geological mapping and meteorological image classification, and have generally proved to be more powerful than conventional statistical classifiers, especially when training data are limited and the data in each class are not normally distributed. The use of ANNs requires some critical decisions on the part of the user. These decisions, which are mainly concerned with the determinations of the components of the network structure and the parameters defined for the learning algorithm, can significantly affect the accuracy of the resulting classification. Although there are some discussions in the literature regarding the issues that affect network performance, there is no standard method or approach that is universally accepted to determine the optimum values of these parameters for a particular problem. In this thesis, a feed-forward network structure that learns the characteristics of the training data through the backpropagation learning algorithm is employed to classify land cover features using multispectral, multitemporal, and multisensory image data. The thesis starts with a review and discussion of general principles of classification and the use of artificial neural networks. Special emphasis is put on the issue of feature selection, due to the availability of hyperspectral image data from recent sensors. The primary aims of this research are to comprehensively investigate the impact of the choice of network architecture and initial parameter estimates, and to compare a number of heuristics developed by researchers. The most effective heuristics are identified on the basis of a large number of experiments employing two real-world datasets, and the superiority of the optimum settings using the 'best' heuristics is then validated using an independent dataset. The results are found to be promising in terms of ease of design and use of ANNs, and in producing considerably higher classification accuracies than either the maximum likelihood or neural network classifiers constructed using ad hoc design and implementation strategies. A number of conclusions are drawn and later used to generate a comprehensive set of guidelines that will facilitate the process of design and use of artificial neural networks in remote sensing image classification. This study also explores the use of visualisation techniques in understanding the behaviour of artificial neural networks and the results produced by them. A number of visual analysis techniques are employed to examine the internal characteristics of the training data. For this purpose, a toolkit allowing the analyst to perform a variety of visualisation and analysis procedures was created using the MATLAB software package, and is available in the accompanying CD-ROM. This package was developed during the course of this research, and contains the tools used during the investigations reported in this thesis. The contribution to knowledge of the research work reported in this thesis lies in the identification of optimal strategies for the use of ANNs in land cover classifications based on remotely sensed data. Further contributions include an indepth analysis of feature selection methods for use with high-dimensional datasets, and the production of a MATLAB toolkit that implements the methods used in this study.

An artificial satellite orbit determination (OD) computer program is the most essential tool in satellite geodesy. Such a program has been developed at Nottingham as part of this research and was tested with Satellite Laser Ranging (SLR) observations of the Laser Geodynamics Satellite (LAGEOS). This thesis describes the basic theory behind orbit determination and the software development at Nottingham. It includes details of the adopted force model, coordinate reference frames, and numerical integration and interpolation techniques. It is also explained how several geodetic parameters can be determined. The thesis discusses the results of two separate determinations of the LAGEOS orbit with an emphasis on the solutions for station coordinates and for earth rotation and polar motion. The NAVSTAR Global Positioning System (GPS) is on schedule to replace Transit as the most important satellite navigation system. When fully operational, in 1988, it will consist of 18 satellites which will provide continuous global coverage. This thesis describes the Global Positioning System and outlines the theory behind the most accurate techniques of adjustment of the CPS observables. It derives the equations for interferometric techniques and shows that, by differencing the observations, several undesirable unknowns can be eliminated. GPS data from the NAVSTAR Geodetic Receiver System (NGRS) have been provided for Nottingham by the US Defence Mapping Agency (DMA). The thesis describes the software development to analyse these data and gives the results of several solution schemes to derive the absolute coordinates of the NGRS antenna. It is also shown how the software can be modified to incorporate interferometric techniques. Significant improvements over the NGRS solutions can be expected when GPS is fully operational, with refinements in both receiver hardware and software.