Large-area urban growth observations—a hierarchical kernel approach based on image texture

Abstract

Geografisk Tidsskrift, Danish Journal of Geography 105(2):39–47, 2005 A multi-scale classification algorithm based entirely on computation of image texture in moving windows is presented and discussed. The rationale for using kernel-based methods as opposed to segment-based methods for texture-based land cover classification is discussed, as well as, the need for identifying strategies that may limit the effects of the inherent weaknesses of the kernel-based methods. Part of the proposed solution to this problem is to omit the search for an optimum kernel size and rather use different sizes concurrently followed by a weighted summary overlay process based on associated scores. The spatial growth pattern of the city of Accra, Ghana, is assessed over a 17 years period using the algorithm. Land-cover maps showing the extension of the new residential areas on the fringe of Accra are produced from Landsat ETM satellite imagery. These areas constitute the rapidly expanding frontier of urban Accra into its rural surroundings. The classification discriminates between new residential areas that have a distinct urban appearance and areas that are in a transition phase between rural and urban use. Finally, comparisons are made to a previous satellite-based study of land cover changes for the period 1985–1991.