Integration of Remote Sensing and GIS to Detect Pockets of Urban Poverty: The Case of Rosario, Argentina

The advent of high spatial resolution, multispectral satellite imagery has allowed analysis of remotely sensed images of urban land cover to become more useful to urban planning and decision making than in the past. The addition of radar imagery at relatively high spatial resolution (6 metres at best), with the advantages that it is not affected by cloud and diurnal light conditions and that it is sensitive to the target's geometric shape, surface roughness and moisture content offers additional capability in this regard. This paper incorporates analysis of Canadian RADARSAT-1 and American Landsat TM satellite imagery and ground-based GIS data to identify known pockets of urban poverty. Poverty is defined, based on a limited number of census variables related to dwelling construction materials and per household overcrowding. The objective is to provide a proof of concept that remote sensing data, especially from synthetic aperture radar, and ground-based GIS data can be successfully integrated for urban planning purposes. The results suggest that the approach used is reasonable and that, with future refinement, it offers planners and decision makers a timely and cost effective means to locate and monitor poverty pockets in urban areas. This is especially important in large, rapidly urbanising areas in the developing world.