This work deals with the identification of potentially contaminated areas using remote sensing, geographic information systems (GIS) and multi-criteria spatial analysis. The identification of unknown illegal landfills is a crucial environmental problem in all developed and developing countries, where a large number of illegal waste deposits exist as a result of fast, and relatively unregulated, industrial growth over the past century. The criteria used to perform the spatial analysis are here selected by considering the characteristics which are 'desirable' for an illegal waste disposal site, chiefly related to the existence of roads for easy access and to a low population density which facilitates unnoticed dumping of illegal waste materials. A large dataset describing known legal and illegal landfills and the context of their location (population, road network, etc.) was used to perform a spatial statistical analysis to select factors and criteria allowing for the identification of the known waste deposits. The final result is a map describing the likelihood of an illegal waste deposit to be located at any arbitrary location. Such a probability map is then used together with remote sensing techniques to narrow down the set of possibly contaminated sites (Silvestri and Omri, 2008), which are candidates for further analyses and field investigations. The importance of the integration of GIS and remote sensing is highlighted and represents a key instrument for environmental management and for the spatially- distributed characterization of possible uncontrolled landfill sites. © 2009 Taylor & Francis.

GIS, multi-criteria and multi-factor spatial analysis for the probability assessment of the existence of illegal landfills / Biotto G.; Silvestri S.; Gobbo L.; Furlan E.; Valenti S.; Rosselli R.. - In: INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE. - ISSN 1365-8816. - ELETTRONICO. - 23:10(2009), pp. 1233-1244. [10.1080/13658810802112128]

GIS, multi-criteria and multi-factor spatial analysis for the probability assessment of the existence of illegal landfills

Silvestri S.;
2009

Abstract

This work deals with the identification of potentially contaminated areas using remote sensing, geographic information systems (GIS) and multi-criteria spatial analysis. The identification of unknown illegal landfills is a crucial environmental problem in all developed and developing countries, where a large number of illegal waste deposits exist as a result of fast, and relatively unregulated, industrial growth over the past century. The criteria used to perform the spatial analysis are here selected by considering the characteristics which are 'desirable' for an illegal waste disposal site, chiefly related to the existence of roads for easy access and to a low population density which facilitates unnoticed dumping of illegal waste materials. A large dataset describing known legal and illegal landfills and the context of their location (population, road network, etc.) was used to perform a spatial statistical analysis to select factors and criteria allowing for the identification of the known waste deposits. The final result is a map describing the likelihood of an illegal waste deposit to be located at any arbitrary location. Such a probability map is then used together with remote sensing techniques to narrow down the set of possibly contaminated sites (Silvestri and Omri, 2008), which are candidates for further analyses and field investigations. The importance of the integration of GIS and remote sensing is highlighted and represents a key instrument for environmental management and for the spatially- distributed characterization of possible uncontrolled landfill sites. © 2009 Taylor & Francis.
2009
GIS, multi-criteria and multi-factor spatial analysis for the probability assessment of the existence of illegal landfills / Biotto G.; Silvestri S.; Gobbo L.; Furlan E.; Valenti S.; Rosselli R.. - In: INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE. - ISSN 1365-8816. - ELETTRONICO. - 23:10(2009), pp. 1233-1244. [10.1080/13658810802112128]
Biotto G.; Silvestri S.; Gobbo L.; Furlan E.; Valenti S.; Rosselli R.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/718465
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