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Forecasting High Correlation Transition of Agricultural Landscapes into Urban Areas: Diachronic Case Study in North Eastern Italy

Forecasting High Correlation Transition of Agricultural Landscapes into Urban Areas: Diachronic Case Study in North Eastern Italy

Federico Martellozzo
Copyright: © 2012 |Volume: 3 |Issue: 2 |Pages: 13
ISSN: 1947-3192|EISSN: 1947-3206|EISBN13: 9781466610668|DOI: 10.4018/jaeis.2012070102
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MLA

Martellozzo, Federico. "Forecasting High Correlation Transition of Agricultural Landscapes into Urban Areas: Diachronic Case Study in North Eastern Italy." IJAEIS vol.3, no.2 2012: pp.22-34. http://doi.org/10.4018/jaeis.2012070102

APA

Martellozzo, F. (2012). Forecasting High Correlation Transition of Agricultural Landscapes into Urban Areas: Diachronic Case Study in North Eastern Italy. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 3(2), 22-34. http://doi.org/10.4018/jaeis.2012070102

Chicago

Martellozzo, Federico. "Forecasting High Correlation Transition of Agricultural Landscapes into Urban Areas: Diachronic Case Study in North Eastern Italy," International Journal of Agricultural and Environmental Information Systems (IJAEIS) 3, no.2: 22-34. http://doi.org/10.4018/jaeis.2012070102

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Abstract

One of the most critical challenges modern society is facing deals with the uncontrolled spread of urban environment among surrounding natural environments, with agricultural and wild landscapes being the ones that suffer the effects of urban and suburban pressure (Ewing, 1994). This phenomenon known as urban sprawl is controversial because even if its conceptually well known there is not a universally shared definition of causes and consequences (Brueckner, 2002). The main aim of this research is to develop a methodology to successfully investigate the evolution trend of sprawl on a specific case study and to get empirical evidences of the tight correlation between urban growth and loss of agricultural and natural lands over time. The research is developed in two phases: the diachronic analysis of landcover changes occurred in the recent past through the use of satellite imagery; the forecast of possible landcover scenarios through the use of cellular automata model. Combining the results obtained in the two phases will build up a longer time span on which investigate the phenomena previously described.

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