Applying Spatial Autocorrelation Techniques to Multi-Temporal Satellite Data for Measuring Urban Sprawl

In last decades the spreading of new buildings, road infrastructures and a scattered proliferation of houses in zones outside urban areas, produced countryside urbanization with no rules, consuming soils and impoverishing the landscape. Such a phenomenon generated a huge environmental impact, diseconomies and a decrease in life quality. Although urban growth is perceived as necessary for a sustainable economy, uncontrolled or sprawling urban growth can cause various problems, such as loss of open space, landscape alteration, environmental pollution, traffic congestion, infrastructure pressure, and other social and economical issues. This study analyzes processes concerning land use change, paying particular attention to urban sprawl phenomenon. The application is based on the integration of Geographic Information Systems and Remote Sensing adopting open source technologies. The objective is to understand size distribution and dynamic expansion of urban areas in order to define a methodology useful to both identify and monitor the phenomenon. The application has been developed in a heavily anthropized area in southern Italy, Apulia region, using free spatial data and free multispectral and multitemporal satellite data (Apulia region was one of the first regions in Italy to adopt open data policies). An integration of free software (Linux Ubuntu, GRASS GIS and Quantum GIS, R) and data (Landsat) has been proposed in order to quantify phenomenon evolution. In order to produce more reliable data, autocorrelation techniques have been implemented in open source software. KeywordsUrban Sprawl; Remote Sensing; Spatial Autocorrelation; Change Detection; Open Source Software; Open Data