Change-detection accuracy assessment using SPOT multispectral imagery of the rural-urban fringe

Abstract

Studies are being undertaken to determine the capabilities of SPOT multispectral (XS) imagery for providing information on rural-to-urban land conversion. Four procedures are tested. These are visual analysis of two images, visual analysis of a multidate image consisting of two XS2 bands, supervised classification of the two images and supervised classification of the multidate image. Results show that visual analysis of two images and supervised classification of the multidate image provide the best overall classification accuracies at approximately 80%. The best change-detection accuracy of 60% is achieved with supervised classification of the multidate image. Change/no change accuracies are greater than 90%. Although classification accuracies are slightly lower than those achieved with Landsat multispectral scanner imagery, on the SPOT imagery changes in much smaller parcels of land can be observed with greater spatial precision.