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Urban areas are complicated due to the mix of man-made features and natural features. A higher level of structural information playsan important role in land cover/use classification of urban areas. Additional spatial indicators have to be extracted based on structuralanalysis in order to understand and identify spatial patterns or the spatial organization of features, especially for man-made feature.It’s very difficult to extract such spatial patterns by using only classification approaches. Clusters of urban patterns which areintegral parts of other uses may be difficult to identify. A lot of public resources have been directed towards seeking to develop astandardized classification system and to provide as much compatibility as possible to ensure the widespread use of such categorizeddata obtained from remote sensor sources.  In this paper different methods applied to understand the change in the land use areas by understanding and monitoring the change inurban areas and as its hard to apply those methods to classification results for high elements quantities, dusts and scratches (Rocaand Alhaddad, 2005). This paper focuses on a methodology developed based relation between urban elements and how to join thiselements in zones or clusters have commune behaviours such as form, pattern, size. The main objective is to convert urban classcategory in to various structure densities depend on conjunction of pixel and shortest distance between them, Delaunay triangulationhas been widely used in spatial analysis and spatial modelling. To identify these different zones, a spatial density-based clusteringtechnique was adopted. In highly urban zones, the spatial density of the pixels is high, while in sparsely areas the density of points ismuch lower. Once the groups of pixels are identified, the calculation of the boundaries of the areas containing each group of pixelsdefines the new regions indicate the different contains inside such as high or low urban areas. Multi-temporal datasets from 1986,1995 and 2004 used to urban region centroid to be our reference in this study which allow us to follow the urban movement, increaseand decrease by the time. Kernel Density function used to Calculates urban magnitude, Voronoi algorithm is proposed for derivingexplicit boundaries between objects units. To test the approach, we selected a site in a suburban area in Barcelona Municipality, theSpain

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

METHODS FOR MONITORING THE DETECTION OF MULTI-TEMPORAL LAND USE CHANGE THROUGH THE CLASSIFICATION OF URBAN AREAS