With the imminent launch of a new generation of very high spatial resolution satellite sensors, 1-5m image data are soon to be available. This thesis explores the potential application of these data to infer information on urban land use. Distinctions are sought between broad categories such as industrial, commercial and residential, as well as finer residential categories indicative of housing age and type. Unlike conventional classification procedures, where land cover is inferred from spectral reflectance, it has been suggested that land use requires information on the spatial and morphological properties of the principal scene objects (<I>e.g.</I> buildings, roads) within the image. However, this has received little formal investigation. This hypothesis is examined quantitatively using Ordnance Survey 1:1250 digital map data (an 'optimum' segmentation of the scene, without the problems of mixed pixels, misclassification, shadowing and occlusion associated with remotely sensed imagery). Differences are observed between areas of contrasting land use, in particular the structure of the road network and the specific composition of buildings (their size and shape) within an area. The ability to extract those scene objects from remotely sensed data is subsequently assessed using airborne imagery, resampled to a number of spatial resolutions between 1 and 10m, and a variety of segmentation procedures (multi-spectral classification, edge-detection and region-growing). This indicates that, while thematic accuracy increases at finer resolutions, it is not possible to extract building and road features as discrete entitles unambiguously and consistently. Further experiments using even higher resolution data (up to 25cm) exhibit similar problems. Despite this, a final set of experiments examine the structural properties of a segmented image. Using a graph-based tool, relations such as the adjacency between regions are shown to vary between areas of contrasting land use. Image structure is also successfully used as a novel means of removing 'noise' from classified imagery.

The repair cost for deteriorating sewers had led to the growing use of closed circuit television surveys to assess their condition. A more objective assessment of these sewers was required since information from these surveys was being used to allocate scarce resources. The useful information produced by TV surveys was increased by the adoption of the principle of an optical section. A special light-head was placed in front of a TV camera such that a ring of light was projected onto the pipe, perpendicular to the pipe's axis. The camera viewed this ring of light and any deformation in the pipe was reproduced by the light-ring. Using a microcomputer as the controller, a framestore digitised the video signal from the survey camera, and the picture information was downloaded into the computer memory. The initial research period was spent developing a reliable algorithm which could accurately identify the light-ring with the minimum number of errors. This was achieved by examining the digital map of the picture for specified gradients of light which generally only occurred at the light-ring. A mathematical representation of the profile coordinates was created using Fourier Desciptors. This model allowed diameter measurements to be extracted to an accuracy of 0.2% when using a solid state camera, and each profile could be stored in 100 bytes. This system was proved in field trials. It was shown by Cluster Analysis that the deformed shape of a pipe could be classified by the use of its Fourier Desciptors. It is expected that when a large enough database has been gathered the structural stability of a sewer may be automatically assessed from this mathematical representation of its profile.

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This applied study investigates the capability of field and airborne hyperspectral remote sensing to detect the spectral and spatial characteristics of sub-surface soil disturbance from associated overlying subtle canopy scale vegetation stress features. A 9 km stretch of buried gas pipeline, in Aberdeenshire, was used as a real world case study. Hyperspectral techniques, in particular derivative analysis, have a number of advantages over existing broadband approaches under operational conditions, reducing changes in illumination or background reflectance and aiding in suppressing the continuum caused by other leaf biochemicals. Various peaks in red-edge derivative spectra are also well correlated with plant pigment concentrations thus being able to detect more subtle stress features than conventional broadband reflectance approaches. The capability of these hyperspectral techniques to detect a generic stress response to gas induced soil oxygen depletion, which could also result from soil compaction and water logging due to sub-surface soil disturbance, has shown great potential under controlled conditions. However, their transferability to heterogeneous, canopy scale, field conditions for operational applications has not been published yet. This thesis aims to develop a rigorous method for spatially intensive field spectroradiometry acquisition to identify the full spatial and spectral (VIS-NIR) characteristics of subtle, canopy scale, surface vegetation stress features that maybe indicative of sub-surface soil disturbance to inform operational sensor specifications. In order to achieve this aim field spectroscopy data of barley, wheat, oilseed rape and grassland were acquired at selected transects perpendicular to a buried gas pipeline. The application of derivative analysis, vegetation band ratios and in particular the Smith et al (2004) 725:702 nm ratio, Lagrangian red-edge and continuum removal are evaluated to identify the optimal hyperspectral analytical technique for detecting vegetation stress associated sub-surface soil disturbance under operational conditions. Moreover, the ability of operational airborne hyperspectral sensors to detect the same stress features in the field data is investigated through field spectroradiometry data simulating sensor spectral Gaussian point spread functions and acquired CASI-2 imagery of the study area. First derivative analysis coupled with the 723:700 and 725:702 nm ratios was the most effective hyperspectral approach for detecting vegetation stress associated with pipeline soil disturbance. The 725:702 nm ratio of Smith et at. (2004) and a 723:700 nm ratio performed consistently well detecting stress for all sites over two consecutive field seasons under different cropping regimes, barley being particularly stress sensitive. The ratios exhibited a parabolic trend of decreasing ratio values with proximity to the pipeline, whilst being insensitive to soil background effects, intimating their transferability to heterogeneous field conditions. Field spectroradiometry data simulating the default channel settings for CASI-2, AVIRIS and programmable 725:702 and 723:700 nm wavelengths for CASI-2, AVIRIS and Eagle hyperspectral airborne sensors revealed that channel centre wavelength positions influenced the sensors ability to detect stress. The CASI-2 713:703 and 743:703 nm default channels were able to distinguish vegetation stress associated with pipeline earthworks to the same degree as the 725:702 and 723:700 nm wavelengths. Under operational conditions the CASI-2 743:703 nm ratio was also capable of detecting barley field stress identified by the original field spectra 723:700 and 725:702 nm ratios.

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A comprehensive series of calibrations of a Galileo-Santoni Special "A" stereometric camera pair was carried out in preparation for the structural deformation measurement of a model box girder bridge. The calibrations are described here. A qoniometer was built for the infinite focus calibration which in turn was required as a basis for comparison of the close focus calibrations. A new method of calibrating the cameras at less than 4m object distance was then devised, which proved capable of very high precision. It forms the only truly original part of this work. Results of all the calibrations were then examined and an unexpected change in principal distance was found at the close focus. The change is shown to be dependent on the positions of the entrance and exit pupils. The opportunity is taken to explain an effect known as the variation of principal distance with object distance, and to show that this variation is, in fact, non-existent. A simulation of the structural project was created in the laboratory and photographed. The photogranmetric results were obtained first using the maker's value of the principal distance, then using the principal distance derived from the infinite focus calibration and finally from the values obtained in the actual close focus calibration. The three results demonstrate the increase in accuracy obtained by calibration, and also the importance of the lens effect described above. Details are given of the photogrammetric process used on the box girder bridge. Finally, a critical appraisal is made of the use of photogrammetry in a project such as this.