Modelling of light pollution in suburban areas using remotely sensed imagery and GIS
Introduction
Night light emissions that originate mainly from large urban areas are among the main elements of environmental pollution. The rapid growth of night sky brightness due to light pollution is not only damaging the perception of the starry sky, but is also silently altering even the perception of moonlit nights by mankind (Cinzano et al., 2001a).
Astronomers are among the worst affected by urban sky glow (IDSA, 1996, Falchi and Cinzano, 2000), but environmentalists are also worried about the direct effects on wildlife, as well as the reduction in the overall ‘quality of life’ for the people of Europe. Among the negative effects of light pollution are: (i) disturbance of biological rhythms, (ii) psychological effects, and (iii) environmental degradation (Shaflik, 1997, Borg, 1996).
Over the last decades, many scientists have modelled light pollution in various ways, e.g. creating maps showing the sky glow variation at different altitudes and azimuths from different observation sites (Garstang, 1986), mapping artificial sky brightness in large territories (Cinzano et al., 2000, Cinzano et al., 2001b), spatial population definition using DMSP–OLS data (Elvidge et al., 1997), or urban area mapping (Imhoff et al., 1997). The interest in light pollution has been growing in many fields of science, extending from the traditional field of astronomy to atmospheric physics, environmental sciences, natural sciences and even human sciences (Cinzano et al., 2001a, Cinzano et al., 2001b; Doll et al., 2000), although the Greek literature on the issue is rather limited (Chalkias et al., 2002).
Artificial lighting disturbs the ‘tranquility’ (grade of naturality) of an area. This kind of pollution is directly correlated to the presence of human activities and for this reason is considered of high interest. Tranquility maps are a valuable tool for the classification of parts of the countryside, as well as for the classification of areas that are relatively undisturbed by noise and visual intrusion, areas representative of ‘unspoilt’ countryside.
Tranquility can be defined as ‘the sense of peace, quiet and natural pureness of the countryside’ (Bell, 1999). The main factors that disturb tranquility are:
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light pollution (buildings, human constructions, artificial lighting, etc.)
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noise pollution (road traffic, industry, railroads, etc.)
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absence of woodland.
While tranquility disturbance is profound in modern urban areas, suburban and rural areas also face the same problems due to urban growth, intense cultivation activities, transportation network expansion, etc.
This study was conducted within the framework of the European project MANTLE (mapping night-time light emissions). The main target of the MANTLE project was to assess the potential of using satellite resources to produce maps of light emissions and urban night-time light intensity levels in the EU.
The scope of this study is to develop and present a prototype methodology for modelling light pollution, as well as to estimate the grade of light pollution in Athens suburban areas by creating various corresponding maps.
Section snippets
Data-methodology
For the night light emission study, satellite data were used from the Defense Meteorological Satellite Program (DMSP)/OLS: Operational Linescan System of the USA. The DMSP satellites, with the onboard OLS, have the capability to detect faint sources of visible near-infrared (VNIR) emissions on the Earth's surface, making it possible to detect cities and towns (Elvidge et al., 1997, Croft, 1978). This capability allows the mapping of urban night-time light emissions (upward light emissions) from
Analysis
Our main intention was to identify the amount of light pollution due to artificial nightlights, in the greater Athens area and estimate the grade of light pollution in suburban areas. Two basic parameters of light pollution were studied. The first concerns direct visual contact with nightlights, while second involves indirect light pollution (visual contact with the sky glow dome over the Athens metropolitan area). Fig. 3 presents the basic stages, as well as the data flow of the proposed
Conclusions
The proposed methodology is an application of RS and GIS technology in the assessment of light pollution. DMSP data relevant to nightlight pollution and DEM are the main data sets of the study. The analysis of these datasets using and enhancing GIS functionality produces maps of direct and indirect light pollution. These maps provide decision makers and researchers with useful information about the spatial dispersion of disturbed and relatively tranquil areas in the countryside.
The analysis of
Acknowledgements
This study was performed in the framework of EU project MANTLE (Mapping Night-Time Light Emissions in the EU Using Satellite Observed Visible-Near Infrared Emissions As a Policy Tool) under contract IST-1999-14208, the support of which is greatly acknowledged.
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