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Routine monitoring of Arctic glaciers and ice caps is only possible using synthetic aperture radar (SAR). To make effective use of SAR, an algorithm to geocode SAR imagery is developed; the algorithm utilises a DEM to correct for terrain effects. Methods for collecting <I>in situ</I> data are also described; the data are then used to validate backscattering models. It is shown that during the summer melt season the physical optics model provides the best prediction of ERS-1 SAR backscatter from Arctic glaciers. High resolution satellite imagery is required to classify the surface facies on Arctic glaciers and ice caps. The potential of Landsat MSS and ERS-1 SAR for this application is explored. A Landsat TM image discriminates five distinct facies in cloud-free conditions, while a SAR image is unable to discriminate many surface facies. To improve the capability of SAR, backscatter values are normalised and dual-look images are used. Normalisation removes the dependency on incidence angle; this is achieved using the physical optics model. Images from an ascending and descending satellite pass are combined and it is demonstrated that up to five surface facies can be discriminated. It is estimated that the classification accuracy of a normalised, dual-look SAR image is about 65%, compared with 80% from a TM image; the SAR-derived ELA is within ± 5 m of the TM-derived ELA. A hybrid-classification scheme is applied to normalised, dual-look ERS-1 SAR imagery of Finsterwalderbreen and Austfonna, acquired at the end of a balance year. ELA measurements are used to calculate net mass balance and the potential of carrying out routine monitoring is assessed. The findings suggest that a combination of satellite reconnaissance and the direct glaciological method can provide a cost effective solution for routinely and systemically measuring the net mass balance of Arctic glaciers that are terminated by land and not highly deformed. However, the solution is presently not recommended for use on Arctic ice caps because of image requirements.

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Accurate and objective fine-scale information on urban land use is important for the development of cohesive urban planning policies. Earth observation images represent one source of such information although historically the results of automated urban land use mapping have been poor. It is argued that this due to the widespread use of spectrally-based pattern recognition approaches that assume a direct relationship between detected spectral response and urban land use. Evidence is presented that shows that detected spectral response of urban scenes is only indirectly related to land use. In response a <i>function-from-form</i> approach is developed to infer urban land use in two-stages: (i) the derivation of land cover from a multispectral image data set, and (ii) the inference of land use on the basis of the structure of the land cover. A novel structural pattern recognition system called SAMS (Structural Analytical Modelling System), consisting of XRAG (eXtended Relational Attribute Graph) - a data model that represents the structure of image regions - is developed to achieve this two-stage inference. SAMS is used in several experiments that reveal building proximity and morphology, as opposed to the topological spatial organisation of urban land cover, facilitates the statistical discrimination of different urban land use types. Further analysis reveals, however, that the spectral inference of urban land cover from multispectral images results in very complex apparent scene structure. In response to this, an automated approach is developed to reduce the complexity of scene region-structure. This procedure is, however, unable to achieve a building region-structure that allows unambiguous urban land use discrimination. Nevertheless, the results presented in this thesis demonstrate that it is possible to distinguish urban land use types using structural pattern recognition approaches to analyse urban land cover regions, although the accurate inference of urban land cover remains a significant barrier to its application.