Remote sensing for efficient describe residential land use density structures "case study of Barcelona Metropolitan Area"

Resum

Most major metropolitan areas face the growing problems of urban sprawl, loss of natural vegetation and open space. Almost everyone has seen these changes to their local environment but without a clear understanding of their impact. Remote sensing technology offers the potential for acquisition of detailed and accurate land-use information for management and planning of urban regions. However, Satellite data is particularly useful for detecting major changes in urban land-use because of frequent coverage, low cost and the possibility of overlaying images from different dates exactly on top of each other. The determination of land-use data with high geometric and thematic accuracy is generally limited by the availability of adequate remote sensing data, in terms of special and temporal resolution and digital analysis image techniques. This study introduces a methodology using information on spatial images to describe urban land-use density and changes. The analysis is based on spatial analysis of land-cover structure mapped from digitally classified satellite images of the metropolitan region of Barcelona. The results show a useful separation and characterization of various types of land-uses of this area and several important structural land-cover features were identified for this study. The analysis shows the importance of the special measurements as second order image information that can contribute to more detailed mapping of urban areas and towards a more accurate characterization of spatial urban growth pattern. However, Improve classification categories one of the image processing targets based on different kind of analyses to obtain the missing data or to divide the existing one for more class’s levels. The first level of Residential urban fabric category obtained from satellite images data sources as a homogeneous data (undivided data). When we are talking about residential density that’s mean the occupation of construction building areas of lands because the volume is not exist in our case of study so the neighbour categories such as Green, Street and industrial areas will affect on dividing the Residential density levels. Our data source is formed by classified Spot 5 (year 2004) satellite image (False Colour image with 10m resolution) which cover the metropolitan area of Barcelona. This paper focused on the development of a methodology based on segmentation and buffer zone analysis for urban residential areas that may improve the urban investigation.