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The aim of the research contained in this thesis was to develop a model of long-term upland catchment runoff that can be used for ungauged catchments. This is a problem due to the complex spatial and temporal nature of runoff and the main contributing processes, precipitation (P) and evapotranspiration (Et). It is also a problem due to the lack of suitable data on which to base and test models of these processes, particularly in remote upland areas such as the north-west of England, the study area of this research. Long-term runoff is important since it represents the maximum rate at which water is available for human use and management, for assessment of water resource yield and for prediction of extreme events that are particularly important in respect to climate change. Methods currently in use by water companies in the UK, such as North West Water Limited (NWW), are inadequate since they fail to account for the spatial and temporal nature of runoff. New more reliable methods are therefore required which will equip water managers with flexible and responsive runoff modelling tools based upon routinely available data and that are sensitive to the complex physical nature of the processes involved. A physically based distributed runoff model was developed using GIS technology and spatial data to interpolate and extrapolate available point-based hydrometeorological data. The strategy required the development of models to derive areal representations of P and Et. For the P modelling several interpolation techniques and artificial neural network models were investigated. The results were evaluated against an independent data set. The results showed that a geostatistical interpolation technique, detrended Kriging, which uses pointbased precipitation and spatial elevation data provided the most accurate estimates when compared to other methods. The models of Et involved extrapolation of point-based Et values derived from the Penman-Monteith formula (Monteith, 1965), using spatial land cover data. A point-based temperature function model (Wright and Harding, 1993) that reduces the Penman estimates of Et for upland sites was spatially implemented using spatial temperature and elevation data. No independent data were available for model evaluation but first estimates of errors were gained through comparison of errors of runoff and precipitation estimates. Overall it was found that the most accurate E, model results were derived when the temperature function model was not implemented. Evidence of whether or not a lumped or heterogeneous land cover representation provided the more accurate results was unclear. Error evaluation and sensitivity analysis of the modelled runoff was carried out using measured runoff records and the results were compared to those produced using the North West Water model. It was found that the GIS-based model provided improved estimates of long-term average annual runoff for upland catchments. The largest component of the errors of the GIS-based method were associated with the Et estimates. This was principally a result of poor quality and limited availability of data for the study area. The research highlights many wider issues related to the use of GIS and spatial data for hydrological modelling.

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Underlying the majority of remotely-sensed data analysis is the assumption that geographical phenomena, such as rivers, heather-moors and the dynamics associated with such objects, can be adequately detected and identified through the use of spectral and other visual information alone. There is a common misconception that any major deficiencies of quantitative analyses are "hardware problems": that by increasing the spectral, spatial, radiometric and temporal resolutions of sensors, geographical phenomena will be identified with similarly increasing accuracy and reliability. This, however, is an unrealistic viewpoint. This thesis has developed a prototype of an automated system based on the principle that by considering the "real-world" properties of the land, a more effective and robust analysis of its dynamic nature can ensue. SYMOLAC is an automated SYstem for MOnitoring LAnd Cover based upon theories of artificial intelligence. It has been developed within a specifically designed hybrid software environment called ETORA, an Environment for Task-Orientated Analysis. This prototype environment allows SYMOLAC to utilise disparate sources of spatial data, to reason with both quantitative and qualitative knowledge, to model disparate domain uncertainties, and to exploit the functionality of third-party software components. Unlike standard approaches, it allows an automated analysis to focus on each particular domain task and how it may best be performed with the available data, knowledge and software resources. The detection of forest felling and the subsequent update of the Land Cover of Scotland (1988) dataset forms the initial application of SYMOLAC. It is concluded that the system's approach is flexible, extensible and adaptable, and demonstrates one way in which satellite imagery can offer <I>potential </I>to the future monitoring of complex land cover change without the need for human intervention.

Region called "Sokolovsko" is an area in the Czech Republic in which a coal mining has caused a great interference with the appearance of the landscape. With a subsequent reclamation, the affected areas are recreated into new landscapes, however on the research base, the non-interference approach is applied in order to follow the principles of a natural succession. This diploma thesis examines the influence of the origin, respectively the relief of the dump area on the intensity of the spontaneous vegetation growth, within the example of the Velká podkrušnohorská spoil heap, based on the data collected by remote sensing techniques. The vegetation indices NDVI and SAVI were used to reveal the intensity of the vegetation cover on the area of the interest. It is clear from the results that the vegetation growth is considerably faster in the areas with the original, wavy relief. It was also found that the vegetation growth of the non reclaimed area of Velká podkrušnohorská spoil heap in the parts of which the relief was settled at the time of the origin differs from the non reclaimed area of which the relief was left in the original wavy surface and later over layered with a new material. Finally it was made a comparison between the non reclaimed part of the Velká podkrušnohorská spoil heap whose...

This thesis starts from the proposition that the ingrained perception of urban green space as being synonymous only with fairly well maintained amenity parkland is too narrow and generally overlooks the many environmental and social benefits that other types of green space and their natural habitats bestow on urban residents and wildlife. A critical review of the literature on the benefits which different kinds of green space confer on urban residents in environmental, social, health and well being and economic terms confirms the need for a more holistic approach to the study of green spaces in cities and also highlights the need to develop and realise a more comprehensive "ontology" of urban green space in tropical countries, a fundamental task which is a main concern of the present thesis. From reviewing the classification schemes or typologies used in different countries to formally recognise and to distinguish different types of green space, the author develops a new, expanded typology for urban green space adapted to Malaysian conditions, aiming to use this as far as possible as a framework to categorise the green spaces of Kuala Lumpur (KL). KL provides a particularly interesting case study as a rapidly growing city in a developing country with a tropical climate, a context where there has been relatively little research on urban green space, despite shade being particularly appreciated in very hot climates. Also KL has experienced much loss of green space in recent decades: on its periphery from urban expansion; and around the city centre from the drive, fuelled by economic growth, to use central land more intensively. The main empirical analysis in the thesis uses data obtained from remotely sensed satellite images of high resolution (from the IKONOS satellite) to try to identify all vegetated forms of land cover in KL and to discern their nature, primarily whether trees, shrubs or grass, regardless of their location, using object oriented software to process the IKONOS data. The degree to which the different types and functions of green space can be identified from IKONOS imagery using both semi-automated and manual methods of visual interpretation is then compared. The results show that, using high resolution IKONOS imagery, it is not possible to identify unambiguously all the types of green land use or green land cover that are found in the proposed, new typology of green space, either by using semi-automatic classification or by visual interpretation, although the latter enables more types of green space to be distinguished with confidence. A key result of the preceding analysis, nevertheless, is to produce maps of green space showing the foregoing 3 classes of vegetation (plus water, bare ground and built up areas) for the entire city in very fine detail using first a semi-automated classification followed by selective manual revision. This produces a more complete picture of the geography of these 3 basic types of green space across the whole city than the typical picture purely or mainly of public parks generated from the typologies used by city governments in developing countries, including KL, simply reflecting their traditional concerns being largely restricted to the latter kinds of green space. These finely detailed maps showing the complex mosaic of green space are, in some respects, the most important result of the thesis. These maps of green space produced from satellite data are linked in a geographic information system (GIS) with data on land use for small land parcels and, using dasymetric methods, with data on population from the census to produce a range of alternative, illuminating perspectives on the nature and extent of green space across the whole city, often at a very fine geographical scale, and including an analysis of the relative provision (or lack thereof) of green space over the whole city; this also yields insight into the role of particular green spaces in the wider urban system. Subsequently, the use of GIS operations enables officially recognised green spaces and the even more extensive and diverse areas of green space not officially recognised to be mapped and examined separately, possibly for the first time in KL. A social survey designed mainly for urban planners and landscape architects in KL was carried out mainly to learn and study their views on the nature, roles and benefits of urban green space, on the new expanded typology, on the problems of protecting urban green space in KL and on what attributes of green spaces they considered should be seen as most important when considering how much priority a particular green space should be given for preservation. From some 38 environmental and social criteria the 41 respondents considered very important, 31 criteria (13 environmental and 18 social) were chosen as attributes to use in evaluating 17 different green spaces of various types in different parts of the city through assessment on site by a small team of trained assessors. A smaller subset of 4 environmental and 3 "social" (actually all accessibility) criteria, selected from the foregoing 31 criteria, was identified which could be estimated "remotely" by "desk based" methods i.e. by using the satellite data and the population data held in our GIS, as well as by direct field survey. It was then possible to compare the 3 sets of evaluations for the 17 green areas in the form of overall rankings in turn on the environmental and then accessibility criteria: firstly the ranks of the sites on all 13 environmental criteria, then on the subset of 4 environmental criteria (both of the latter from field assessment) and finally on the same subset of 4 criteria estimated "remotely". The equivalent overall rankings for the 18 social amenity criteria, then the subset of 3 accessibility indicators from field observation and lastly the same subset of 3 but estimated remotely were then compared. The results showed clear similarities and strong correlations between the three sets of evaluations for the 4 environmental criteria measuring aspects of vegetative cover and "green connectivity" but less consistent similarity for the social and accessibility measures, with only weak correlations between rankings on the field and remote estimates for the 3 accessibility indices. The main conclusion is therefore that "remote" evaluation could potentially have a useful role, complementary to ground surveys, in monitoring and assessing green spaces as regards some key environmental criteria and, more debatably, may also be able to provide useful measures of accessibility, which are difficult to estimate from field visits. However, observation on site is necessary for assessment of nearly all the social criteria relevant to evaluating urban green spaces.

Heterogeneous geospatial big data, from multi-modal Earth Observation (EO) data to geo-social media data, has become more and more accessible in recent years. This provides a potential data source for automatically extracting and mapping key geographical characteristics, hence mitigating the global mapping problem using current data mining methods. These automated geographic feature mapping techniques, especially for man-made infrastructure, are crucial to a lot of our socio-economic existence. Machine learning techniques, among many other data mining methodologies, have demonstrated better performance across a wide range of academic domains, most notably natural language processing and computer vision. In recent times, there has been a growing interest in research on ML-based Geospatial Artificial Intelligence (GeoAI), particularly in its ability to support autonomous mapping with heterogeneous geographical data. Though the potential is high and obvious, it remains a major challenge to handle inherent heterogeneity and empower data synergy when building robust and scalable GeoAI models for large-scale automated mapping purposes. For geospatial analysis, citizen science initiative is seen to be the most effective. This is a result of the fast development of Web 2.0 and crowdsourcing/Volunteered Geographic Information (VGI) technologies. These technologies enable even regular users or volunteer mappers to develop, gather, and distribute geospatial data using a variety of digital devices (such as desktop computers, mobile tablets, and smartphones). The technological obstacles to digital mapping have been significantly reduced by ongoing crowdsourcing and VGI efforts. In the real world, though, problems with global mapping have persisted for a considerable amount of time even in higher-income nations. Intelligent automated mapping techniques for geospatial analysis are desperately needed in this situation since they may effectively and efficiently close significant data gaps across nations. The research effort reported in this dissertation explores the possibilities of using citizen science or VGI to conduct geospatial analysis of different man-made infrastructures using ML from diverse geospatial data sources (e.g., multi-modal EO data, OSM, and GIS data). Three main research questions (RQs), derived from data-driven, method-driven, and application-driven research perspectives, are established to better address the issue of geospatial analysis with remote sensing and citizen science. The thesis especially goes in this direction by i) investigating the data-driven issue that combines ML for segmentation tasks; ii) creating strategies to deal with VGI data noises; and iii) using the created strategies in various mapping tasks. This creates even more intriguing possibilities for related works in the future.

Water quality and nonpoint source (NPS) pollution are important issues in many areas of the world, including the Greater Toronto Area where urban development is changing formerly rural watersheds into impervious surfaces. Impervious surfaces (i.e. roads, sidewalks, parking lots, strip malls, building rooftops, etc.) made out of impenetrable materials directly impact hydrological attributes of a watershed. Therefore, understanding the degree and spatial distribution of impervious surfaces in a watershed is an important component of overall watershed management. According to Environment Canada’s estimates, road salts, also considered nonpoint source pollutants, represent the largest chemical loading to Canadian surface waters. The main objective of this study is to verify the often assumed correlation between impervious surfaces and chlorides that result from the application of road salts, focusing on a case study in the selected six major watersheds within the Greater Toronto Area. In this study, Landsat-5 TM images from 1990, 1995, 2000, and 2005 were used in mapping urban impervious surface changes within the study area. Pixel-based unsupervised classification technique was utilized in estimation of percentage impervious surface coverage for each watershed. Chloride concentrations collected at Water Quality Monitoring Stations within the watersheds were then mapped against impervious surface estimates and their spatiotemporal distribution was assessed. In a GIS environment, remotely sensed impervious surface maps and chloride maps were overlaid for the investigation of their potential correlation. The main findings of this research demonstrate an average of 12.9% increase in impervious surface areas as well as a three-fold increase in chloride concentrations between 1990 and 2005. Water quality monitoring stations exhibiting the highest amounts of chloride concentrations correspond with the most impervious parts of the watersheds. The results also show a correlation (coefficient of determination of 0.82) between impervious surfaces and chloride concentrations. The findings demonstrate that the increase in imperviousness do generate higher chloride concentrations. Correspondingly, the higher levels of chloride can potentially degrade quality of surface waters in the region. Through an innovative integrated remote sensing approach, the study was successful in identifying areas most vulnerable to surface water quality degradation by road salts.

Water quality and nonpoint source (NPS) pollution are important issues in many areas of the world, including the Greater Toronto Area where urban development is changing formerly rural watersheds into impervious surfaces. Impervious surfaces (i.e. roads, sidewalks, parking lots, strip malls, building rooftops, etc.) made out of impenetrable materials directly impact hydrological attributes of a watershed. Therefore, understanding the degree and spatial distribution of impervious surfaces in a watershed is an important component of overall watershed management. According to Environment Canada’s estimates, road salts, also considered nonpoint source pollutants, represent the largest chemical loading to Canadian surface waters. The main objective of this study is to verify the often assumed correlation between impervious surfaces and chlorides that result from the application of road salts, focusing on a case study in the selected six major watersheds within the Greater Toronto Area. In this study, Landsat-5 TM images from 1990, 1995, 2000, and 2005 were used in mapping urban impervious surface changes within the study area. Pixel-based unsupervised classification technique was utilized in estimation of percentage impervious surface coverage for each watershed. Chloride concentrations collected at Water Quality Monitoring Stations within the watersheds were then mapped against impervious surface estimates and their spatiotemporal distribution was assessed. In a GIS environment, remotely sensed impervious surface maps and chloride maps were overlaid for the investigation of their potential correlation. The main findings of this research demonstrate an average of 12.9% increase in impervious surface areas as well as a three-fold increase in chloride concentrations between 1990 and 2005. Water quality monitoring stations exhibiting the highest amounts of chloride concentrations correspond with the most impervious parts of the watersheds. The results also show a correlation (coefficient of determination of 0.82) between impervious surfaces and chloride concentrations. The findings demonstrate that the increase in imperviousness do generate higher chloride concentrations. Correspondingly, the higher levels of chloride can potentially degrade quality of surface waters in the region. Through an innovative integrated remote sensing approach, the study was successful in identifying areas most vulnerable to surface water quality degradation by road salts.