Optimization of sensor locations for a light pollution monitoring network. (August 2023)

• Record Type: Journal Article
• Authors: Lopez-Farias, Rodrigo; Valdez, S. Ivvan; Paredes-Tavares, Jorge; Lamphar, Hector
• Is Part Of: Journal of quantitative spectroscopy & radiative transfer. Volume 304(2023)
• Publication Date: 2023-08
• Subjects: Light pollution -- Sensor network -- Discrete global optimization -- Environmental vulnerability; Spectrum analysis -- Periodicals; Radiation -- Periodicals; Analyse spectrale -- Périodiques; Rayonnement -- Périodiques; Radiation; Spectrum analysis; Periodicals; 543.0858
• DOI: 10.1016/j.jqsrt.2023.108584
• Languages: English
• ISSNs: 0022-4073

Abstract

Highlights: An objective function is needed to evaluate the suitability of the near-optimum location of light pollution sensors. The representativeness of a set of locations are related not only to the light intensity over a satellite image, but also to the environmental vulnerability of each location. Measurements in the most vulnerable areas of a city may optimally represent a wide region of the studied territory. Abstract: Light Pollution is an environmental problem that needs to be retrieved by experimental means. However, to the best of our knowledge, there is no methodology nor a quantitative procedure to determine an optimal light pollution monitoring network. In this work, we propose a methodology for locating sensors in a light pollution monitoring network by formulating an optimization problem. We introduce an objective function that measures the representativeness of a set of locations using the spatial semi-variance over an image, and different levels of monitoring needs according to the environmental vulnerability of each location. To apply the methodology to a region of interest, we consider three inputs: a Nighttime-Light Image NTLI, an Environmental Vulnerability Map, and a constrained number of sensors. The output is a set of coordinates to locate sensors that consider the intensity of luminosity in nighttime images and its environmental impact. A case study shows that the methodology locates sensors in the most vulnerable areas in which measurements may optimally represent a wide region of the studied territory.