A fragmented ecosystem contains communities of organisms that live in fragmented habitats. Understanding the way biological processes such as reproduction and dispersal over the fragmented habitats take place constitutes a major challenge in spatial ecology. In this thesis we discuss a number of mathematical models of density-dependent populations in inhomogeneous environments presenting growth, decay and diffusion amongst woodland patches of variable potential for reproductive success. These models include one- and two-dimensional analyses of single population systems in fragmented environments. We investigate and compute effective properties for single patch systems in one dimension, linking ecological features with landscape structure and size. A mathematical analysis of potential impacts on spread rates due to the behaviour of individuals in the population is then developed. For the analysis of the population dispersal between areas of plentiful resources and areas of scarce resources, we introduce a novel development that models individuals hazard sensitivity when outside plentiful regions. This sensitivity is modelled by introducing a term called endrotaxis that generates a dispersal gradient, resulting in realistically low migration between regions of plentiful resources. Numerical methods and semi-analytic results yield maximum patch separations for one and two dimensional systems and show that the velocity of spread depends on inter-patch distances and patch geometries. By introducing Allee effects (i.e., inverse density-dependent responses to the difficulty of finding mates at low density) over the population growth function, we find that dispersal is slowed down when combined with hazard sensitivity. In the final Chapter we sumarise the results of the previous chapters, concluding that the work performed in this thesis complements and enriches the current mathematical models of movement behaviour.

Accurate, instantaneous and high resolution spatial air-quality information can better inform the public and regulatory agencies of the air pollution levels that could cause adverse health effects. The most direct way to obtain accurate air quality information is from measurements made at surface monitoring stations across a study region of interest. Typically, however, air monitoring sites are sparsely and irregularly spaced over large areas. That is why, it is now very important to develop space-time models for air pollution which can produce accurate spatial predictions and temporal forecasts. This thesis focuses on developing spatio-temporal models for interpolating and forecasting ground level ozone concentration levels over a vast study region in the eastern United States. These models incorporate output from a computer simulation model known as the Community Multi-scale Air Quality (Eta-CMAQ) forecast model that can forecast up to 24 hours in advance. However, these forecasts are known to be biased. The models proposed here are shown to improve upon these forecasts for a two-week study period during August 2005. The forecasting problems in both hourly and daily time units are investigated in detail. A fast method, based on Gaussian models is constructed for instantaneous interpolation and forecasts of hourly data. A more complex dynamic model, requiring the use of Markov chain Monte Carlo (MCMC) techniques, is developed for forecasting daily ozone concentration levels. A set of model validation analyses shows that the prediction maps that are generated by the aforementioned models are more accurate than the maps based solely on the Eta-CMAQ forecast data. A non-Gaussian measurement error model is also considered when forecasting the extreme levels of ozone concentration. All of the methods presented are based on Bayesian methods and MCMC sampling techniques are used in exploring posterior and predictive distributions.

The establishment of an accurate global carbon budget and the consequent ability to understand and predict future environmental change is dependent on knowing the strength of terrestrial sinks and sources of carbon. Regenerating tropical forests are one of the major terrestrial carbon sinks as they are found growing quickly and are sequestering carbon from the atmosphere. Total forest biomass (which includes above and below ground living mass of plants and litter) is a measure of terrestrial vegetation carbon content. It follows that to determine the strength of terrestrial carbon sinks we require information on the location, extent, biomass and biomass change of regenerating tropical forests. Near-constant cloud cover over the tropics and an insensitivity to biomass change at relatively low levels of biomass has limited the use of optical imagery but not Synthetic Aperture Radar (SAR) imagery for the provision of such information. The biophysical properties of regenerating tropical forests are related to the temporal, spatial, spectral and polarisation characteristics of SAR backscatter (a°) and this formed the framework for this thesis. The objectives were to (i) detect biomass accumulation using the temporal characteristics of 0°, (ii) use the spatial characteristics of a° (texture) to increase the strength of the a7biomass relationship and (ill) use the spectral and polarisation characteristics of 0° to classify a surrogate for biomass in regenerating tropical forests (optical Landsat TM data were also included to widen the spectral analysis). Although no biomass change was detectable using temporal 0°, a seasonal pattern in 0° for young regenerating forest was detected, as a result of changing water content in both vegetation and soil. The influence of recent rainfall was confirmed to be an important source of variation in a°, suggesting the use of SAR data from the dry season only. Using simulated data, seven texture measures showed potential for strengthening the a7biomass relationship. However, when applied to real SAR data only GLCM (Grey Level Co-occurrence Matrix) derived contrast strengthened the a7biomass relationship. The addition of GLCM-derived contrast to a° potentially increases the accuracy of biomass estimation and mapping. Neural networks can be used for the classification of land cover in tropical forest regions. Classification accuracy of around 80% was achieved using combined multiwavelength and multipolarisation SAR and Landsat TM bands for 4 land cover classes (pasture, mature forest, 0-5 years old regenerating forests and 6-18 years old regenerating forest). These results demonstrated that multiwavelength and multipolarisation SAR data could provide information on the location, and extent of regenerating tropical forests. However an increase in the accuracy of biomass estimation relies on the optimal use of additional information that resides within the spatial, spectral and polarisation domains of SAR data.

Spaceborne Synthetic Aperture Radar (SAR) sensors obtain regular and frequent radar images from which ground motion can be precisely detected using a variety of different techniques. The ability to remotely measure slope displacements over large regions has many uses and advantages, although the limitations of an increasingly common technique, Differential SAR Interferometry (D-InSAR), must be considered to avoid the misinterpretation of results. Areas of low coherence and the geometrical effects of mountainous terrain in SAR imagery are known to hinder the exploitation of D-InSAR results. A further major limitation for landslide studies is the assumption that variable rates of movement over a given distance cannot exceed a threshold value, dependent upon the SAR image pixel spacing, the radar sensor wavelength and satellite revisit frequency. This study evaluates the use of three SAR image modes from TerraSAR-X and ENVISAT satellites for monitoring slow-moving landslides in the densely vegetated Three Gorges region, China. Low coherence and episodically fast movements are shown to exceed the measureable limit for regular D-InSAR analysis even for the highest resolution, 11-day interferograms. Subsequently, sub-pixel offset time-series techniques applied to corner reflectors and natural targets are developed as a robust method of resolving time-variable displacements. Verifiable offsets are generated with the TerraSAR-X imagery and the precise movement history of landslides is obtained over a period of up to four years. The capability to derive two-dimensional movements from sub-pixel offsets is used to infer a rotational failure mechanism for the most active landslide detected, and a greater understanding of the landslide behaviour is achieved through comparisons with likely triggering factors and 2D limit equilibrium slope stability analysis.

Maintaining river health is of vital importance to the human populations that depend on them for drinking water, and for the income generated from industry and leisure activities. The key to a clear understanding of the current state of the river environment lies in assimilating the various data that are available for a particular river catchment. As a result of the large expense involved in extensive data collection programmes, measurements are often only taken at a handful of monitoring locations, resulting in large portions of a river network remaining unmonitored and rendering it difficult to assess the health of the river as a whole. Interpreting observations associated with a particular response variable pivots on understanding many other variables whose underlying relationships are often highly complex and which may not be routinely measured. Cutting-edge statistical methods can play a crucial role in the interpretation of such data, particularly when faced with small sample sizes and the presence of latent processes. In particular, developing models for environmental data that relax the assumption of simple linear dependencies between response and covariate is a core theme of this thesis, which can enable powerful descriptions of such complex systems. This approach adopts and promotes modern flexible regression techniques based on penalised splines, which are motivated and summarised in Chapter 2; these permit regression relationships to assume a wide variety of non-linear shapes, without requiring the modeller to impose a priori structure. This thesis aims to address two related, but distinct regression problems for data collected within a river catchment. Firstly, the relationship between rainfall data collected at a rain gauge and subsequent river flow rates collected at a point downstream is tackled in Chapter 3. In this application, it is of particular interest to understand the degree, duration and time-lag of the influence of a rainfall event on a measurable increase in river flow rates at a downstream location. This relationship is complex because it is governed by attributes of the surrounding river environment that may not be readily available, such as soil composition, land use and ground strata. However, rainfall and flow data are frequently collected at a high temporal resolution, and Chapter 3 develops models that exploits this feature that are able to express complex lagged dependence structures between a sequence of flow rates and a rainfall time series. The chapter illustrates how the resulting model enables insight into the sensitivity of the river to additional rainfall, and provides a mechanism for obtaining predictions of future flow rates, without recourse to traditional computationally intensive deterministic modelling. This thesis also tackles the problem of constructing appropriate models for the spatial structure of variables that are carried by water along the channels of the river network. This problem cannot be approached using traditional spatial modelling tools due to the presence of the different volumes of water that mix at confluence points, often causing sudden changes in the levels of the measured variable. Very little literature is available for this type of spatial problem, and none has been developed that is appropriate for the large data sets that are becoming increasingly common in many environmental settings. Chapters 4 and 5 develop new regression models that can incorporate spatial variation on a stream network that respects the presence of confluences, flow rates and direction, while including non-linear functional representations for the influence of covariates. These different model components are constructed using the same modern flexible regression framework as used in Chapter 3, and the computational benefits of adopting this approach are highlighted. Chapter 4 illustrates the utility of the new models by applying them to a large set of dissolved nitrate concentrations collected over a Scottish river network. The application reveals strong trends in both space and time, and evidence of a subtle interaction between temporal trend and the location in space; both conclusions would have been difficult to reach using other techniques.

The strand of Nineteenth-Century Anarchism known as Anarchist-Communism conceived of the abolition of both state and market, and their replacement by a system of free distribution of goods organized through federated communes. While briefly this was the most developed and sophisticated strand of anarchism, it suffered an eclipse in the face of both the failure of the Russian Revolution, and the rise of the essentially a-theoretical industrial syndicalism that blossomed in many countries during the early decades of the twentieth century. With the expansion of the state and capitalism after WWII new forms of contestation appeared, most notably, in terms of Anarchist Communist theory, in the United States. In the 1960s and 1970s two currents emerged which represented the first significant development in anarchist communist theory for fifty years. These were the Social Ecology of Murray Bookchin, and a current which grew up around the Detroit underground paper Fifth Estate, later known as "Anarchist Primitivism". It is these two strands that are the subject of this research. Not surprisingly these two perspectives, appearing around a decade apart, and both in the same country, dealt with many of the same issues. What is more surprising is that in virtually every area, the conclusions they arrive at are completely different. In this research I locate these two strands historically as developments of Anarchist Communist theory, and examine their theories in four key areas: The Primitive, History, Reason and Rationality, and Technology. Examination of these areas serves to define the projects themselves, as well as highlighting how they disagree. To explain why they disagree, this work uses a methodological approach suggested Quentin Skinner. Skinner argued that in order to fully understand a text in the history of ideas, it is necessary to understand the author's intention in writing it. The study therefore examines not only the texts, but also the backgrounds of the writers concerned, their aims in producing it, and their approaches to debate with other theorists and perspectives. Through a combination of textual analysis and recovering the intentions of the writers, the high levels of disagreement can be accounted for.

This Thesis is concerned with multilevel and multi-sector forest governance in the Malaysian State of Sabah. It begins by amending the theory of vertical institutional interplay by applying constructivist and historical new institutional theoretical approaches, which contrasts with the more static theoretical foundations that have characterised study of this area to date. It then develops an analytical framework that uses policy frame analysis. This is designed to apply this new theoretical approach to the empirical context of Sabah. This framework analyses empirical subject through three stages. The first stage investigates the development of forest governance institutions at the global level and the state level within Sabah. The second stage then considers how the intersection of these developments, specifically focusing on role of ideas, discourse and agency, created the impetus for new policy initiatives in two local-level empirical examples. The third stage then considers the extent to which these initiatives were successful in institutionalising new forest conservation practices, or conversely how they were impeded by state level historical institutional continuities. The findings of this Thesis differentiate two forms of vertical institutional interplay. The first is the way global institutions affect state level ones where key actors mobilise ideas and discourses to in order to shift the direction of policy and initiate institutional change. The second is where the influence of global institutions is blocked by barriers created by long term historic institutional legacies that have shaped state level institutions. These findings show that vertical institutional interplay has initiated a partial shift in forest institutions and policy in Sabah. This shift varies between different locations according to the relative influence of these two forms of institutional interplay, and has created more dynamism and uncertainty in Sabah’s forest governance institutions. This Thesis contributes to existing literature through its ability to better conceptualise the role of vertical institutional interplay in a way that can account for the tension between the fixed and dynamic aspects of institutions. This contrasts to older approaches that have focused largely on the fixed aspects of institutions. The contribution is also demonstrated in the way this theoretical approach is able to better conceptualise fine grain variations in these dynamics at a local level of scale.

A new Spatial Decision Support System (SDSS) has been designed and developed to address a wide spectrum of semi-structured spatial decision problems. These problems are related to site selection, site ranking and impact assessment. The proposed SDSS is conceptualised as a holistic, informed and impact-based multicriteria decision framework. The system has been developed using the .NET C# programming language and open source geoinformatics technologies such as DotSpatial and SpatiaLite. A combination of existing Multi Criteria Decision Analysis (MCDA) and Artificial Intelligence (AI) techniques, with a few novel variations have been developed and incorporated into the SDSS. The site selection module utilises a theme-based Analytical Hierarchy Process (AHP) and Weighted Linear Combination (WLC). Two site ranking techniques have been introduced in this research. The first technique is based on the systematic neighbourhood comparison of sites with respect to key indicators. The second technique utilises multivariate ordering capability of the one-dimensional Self-Organizing Maps (SOM) to rank the sites. The site impact assessment module utilises a theme-based Rapid Impact Assessment Matrix (RIAM). A spatial variant of the General Regression Neural Networks (GRNN) with a genetic algorithm for optimisation has been developed for the prediction and regression analysis. A number of other spatial knowledge discovery and geovisual-analytics tools have been provided in the system to facilitate spatial decision making process. An application of the SDSS has been presented to investigate the potential of Coalbed Methane (CBM) development in Wales, UK. Most potential sites have been identified by utilising the site selection and site ranking tools of the developed SDSS. An impact assessment has been carried out on the best sites by using Rapid Impact Assessment Matrix. Further analysis has uncovered the spatial variability expected in the potential impacts of the sites, considering key indicators. The application has demonstrated that the developed system can help the decision makers in providing a balanced regime of social, environmental, public health and economic aspects into the decision making process for engineering interventions. The generic nature of the developed system has extended the concept of Spatial Decision Support System to address a range of spatial decision problems, thereby enhancing the effectiveness of the decision making process. The developed system can be considered as a useful modern governance tool, incorporating the key factors into decision making and providing optimal solutions for the critical questions related to energy security and economic future of the region.

Oil pipelines play a significant role in crude oil transportation and bring danger close to communities along their paths. Pipeline accidents happen every now and then due to factors ranging from operational cause to third party damage. In the Niger Delta pipeline system, interdiction is common; therefore, every length and breadth of land covered by a pipeline is vulnerable to oil pollution, which can pose a threat to land use. Weak enforcement of rights of way led to encroachment by farmers and human dwellings, thereby bringing people in close proximity to pipelines. Considering the impact exposure can have on human health, a method was developed for identifying vulnerable communities within a designated potential pipeline impact radius, and generic assessment criteria developed for assessing land use exposure. The GIS based model combines four weighted criteria layers, i.e. land cover, population, river and pipeline buffers in a multi-criteria decision making with analytical hierarchy process to develop an automated mapping tool designed to perform three distinct operations: firstly, to delineate pipeline hazard areas; secondly, establish potential pipeline impact radius; and thirdly, identify vulnerable communities in high consequence areas. The model was tested for sensitivity and found to be sensitive to river criterion; transferability on the other hand is limited to similar criteria variables. To understand spatial distribution of oil spills, 443 oil spill incidents were examined and found to tend towards cluster distribution. Meanwhile, the main causes of spills include production error (34.8%) and interdiction (31.6%); interdiction alone discharged about 61.4% of crude oil. This brings to light the significance of oil pipeline spills and the tendency to increase the risk of exposure. The generic assessment criteria were developed for three land uses using CLEA v 1.06 for aromatic (EC5-EC44) and aliphatic (EC5-EC44) fractions. The use of the model and screening criteria are embedded in a framework designed to stimulate public participation in pipeline management and pipeline hazard mitigation, which policy makers and regulators in the oil industry can find useful in pipeline hazard management and exposure mitigation.

In Britain, projected population rise and climate change threaten future water availability. UK water companies run education programmes to encourage more efficient usage, but these tend to focus on primary schools and adults, missing the opportunity to engage secondary school pupils as the next generation of homeowners and bill payers. Educational interventions also traditionally follow the theory of rational choice, envisaging learners as able to change their attitudes and behaviours in accordance with newly acquired information. Sociological research on social practices and ordinary consumption, however, sees water as playing an inconspicuous role in daily domestic activities. Technological infrastructure and prevalent social norms mould behaviour and limit the ability of water users to alter their consumption. This interdisciplinary thesis attempts to break the impasse between works from educational and sociological perspectives, using the theoretical lens of water citizenship. A review of current water education provision in the East Midlands region was undertaken, and a school-based study involving questionnaires, focus groups and exploratory lessons around water. The young people involved in the study tended to show ambivalence towards water conservation, despite general pro-environmental motivations. While some teenagers perceived they were ‘doing their bit’ for the environment, this tended to be limited to accepting and invoking ‘water saving tips’, and many teenagers eschewed water conservation altogether. These findings indicate that innovative educational programmes are needed to raise the standard of water literacy in the UK. This thesis argues firstly for making water use more ‘visible’ in daily activities, by deconstructing the routines and habits that use water, and by recognising the influences that social norms exert on water use. Secondly, it argues that educational initiatives for water literacy could develop young people’s sense of citizenship and responsibility towards water resources by connecting personal actions to impacts at local, national and global scales.