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Quantifying Urban Sprawl with Spatial Autocorrelation Techniques using Multi-Temporal Satellite Data

Gabriele Nolè, Rosa Lasaponara, Antonio Lanorte, Beniamino Murgante

Source Title: International Journal of Agricultural and Environmental Information Systems (IJAEIS)5(2)

ISSN: 1947-3192|EISSN: 1947-3206|EISBN13: 9781466652446|DOI: 10.4018/IJAEIS.2014040102

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MLA

Nolè, Gabriele, et al. "Quantifying Urban Sprawl with Spatial Autocorrelation Techniques using Multi-Temporal Satellite Data." IJAEIS vol.5, no.2 2014: pp.19-37. http://doi.org/10.4018/IJAEIS.2014040102

APA

Nolè, G., Lasaponara, R., Lanorte, A., & Murgante, B. (2014). Quantifying Urban Sprawl with Spatial Autocorrelation Techniques using Multi-Temporal Satellite Data. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 5(2), 19-37. http://doi.org/10.4018/IJAEIS.2014040102

Chicago

Nolè, Gabriele, et al. "Quantifying Urban Sprawl with Spatial Autocorrelation Techniques using Multi-Temporal Satellite Data," International Journal of Agricultural and Environmental Information Systems (IJAEIS) 5, no.2: 19-37. http://doi.org/10.4018/IJAEIS.2014040102

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Abstract

This study deals with the use of satellite TM multi-temporal data coupled with statistical analyses to quantitatively estimate urban expansion and soil consumption for small towns in southern Italy. The investigated area is close to Bari and was selected because highly representative for Italian urban areas. To cope with the fact that small changes have to be captured and extracted from TM multi-temporal data sets, we adopted the use of spectral indices to emphasize occurring changes, and geospatial data analysis to reveal spatial patterns. Analyses have been carried out using global and local spatial autocorrelation, applied to multi-date NASA Landsat images acquired in 1999 and 2009 and available free of charge. Moreover, in this paper each step of data processing has been carried out using free or open source software tools, such as, operating system (Linux Ubuntu), GIS software (GRASS GIS and Quantum GIS) and software for statistical analysis of data (R). This aspect is very important, since it puts no limits and allows everybody to carry out spatial analyses on remote sensing data. This approach can be very useful to assess and map land cover change and soil degradation, even for small urbanized areas, as in the case of Italy, where recently an increasing number of devastating flash floods have been recorded. These events have been mainly linked to urban expansion and soil consumption and have caused loss of human lives along with enormous damages to urban settlements, bridges, roads, agricultural activities, etc. In these cases, remote sensing can provide reliable operational low cost tools to assess, quantify and map risk areas.

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Quantifying Urban Sprawl with Spatial Autocorrelation Techniques using Multi-Temporal Satellite Data

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