Integration of remotely-sensed raster data with a vector-based geographical information system for land-use change detection

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Abstract
Methods of land-use change detection are different for raster and vector data types because of the differences in structures of the two data types. Since large amounts of land-use data (derived from existing maps and aerial photographs) are stored in vector format in a Geographical Information System (GIS), there is a need to develop a change detection algorithm for use with vector-formatted data. Since remotely-sensed images are increasingly being used to derive land-use data, it is necessary to integrate raster data with large volumes of vector data already available in a GIS. This necessitates an efficient and effective data integration technique using which raster data are to be integrated with a vector-based GIS. This paper presents a methodology of data integration of remotely-sensed raster data with vector data. A new approach is developed for land-use change detection for use with vector data in a GIS environment. The approach is based on the mathematical concepts of Sets and Groups, and is successfully implemented for the analysis of historical land-use change from 1931 to 1989 in the River Glen catchment, U.K. Algorithms have been developed for automatic derivation of dynamic statistics of land-use. It is shown that this approach can be efficiently adopted for operational use incorporating products derived from both coarse- and fine-resolution remotely-sensed satellite images once these are integrated with the vector-based GIS.

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