RADARSAT Fine-Beam SAR Data for Land-Cover Mapping and Change Detection in the Rural-Urban Fringe of the Greater Toronto Area

This research investigates the capability of the multitemporal RADARSAT Fine-Beam C-HH SAR imagery for land use/land-cover mapping and change detection in the rural-urban fringe of the Greater Toronto Area (GTA). Five-date RADARSAT fine-beam SAR images were acquired during May to August in 2002. One scene of Landsat TM imagery was acquired in 1988 for change detection. The major land use/land-cover classes were high-density built-up areas, low-density built-up areas, roads, forests, parks, golf courses, water and three types of agricultural lands. These ten classes were chosen to characterize the complex land use/land-cover types in the rural-urban fringe of the GTA. The results demonstrated that, for identifying land use/land-cover classes, five-date raw SAR imagery yielded very poor result due to speckles. Much better results were achieved with combined Mean, Standard Deviation and Correlation texture images using artificial neural networks (ANN) and with raw images using object-based classification. The change detection procedure was able to identify the areas of significant changes, for example, major new roads, new low-density and high-density built up areas and golf courses, even though the overall accuracy of the change detection was rather low.