The world of remote sensing and GIS is changing at a pace. This volume brings together developments in these complimentary fields, with a particular emphasis on mathematical techniques and their application. It covers techniques such as fuzzy classification, artificial neural networks, geostatistical techniques, texture classification, fractals, per-parcel classification, raster and vector data integration, and process modelling. These techniques are considered with reference to a range of applications including land cover and land use mapping, cloud tracking, snow cover mapping, air temperature monitoring, topographic mapping, geological classification, and soil erosion modelling. The book covers the development of remote sensing methods and the integrated use of remote sensing and GIS analysis, and contains international contributors from academia, industry and research institutes.
Techniques for the analysis of spatial data; land cover classification revisited; image classification with a neural network - from completely-crisp to fully-fuzzy situations; cloud motion analysis; methods for estimating image signal-to-noise ratio (SNR); modelling and efficient mapping of snow cover in the UK for remote sensing validation; using variograms to evaluate a model for the spatial prediction of minimum air temperature; modelling the distribution of cover fraction of a geophysical field; classification of digital image texture using variograms; geostatistical approaches for image classification and assessment of uncertainty in geologic processing; a syntactic pattern-recognition paradigm for the derivation of second-order thematic information from remotely sensed images; the role of classified imagery in urban spatial analysis; image classification and analysis using integrated GIS; per-field classification of land use using the forthcoming very fine spatial resolution satellite sensors - problems and potential solutions; modelling soil erosion at global and regional scales using remote sensing and GIS techniques; extracting information from remotely sensed and GIS data.