Zamorano, J., Sánchez de Miguel, A., Ocaña, F., Pila-Diez, B., Gómez Castaño, J., Pascual, S., et al. (2016). Testing sky brightness models against radial dependency: a dense two dimensional survey around the city of Madrid, Spain. JQSRT, 181, 52–66.
Abstract: We present a study of the night sky brightness around the extended metropolitan area of Madrid using Sky Quality Meter (SQM) photometers. The map is the first to cover the spatial distribution of the sky brightness in the center of the Iberian peninsula. These surveys are neccessary to test the light pollution models that predict night sky brightness as a function of the location and brightness of the sources of light pollution and the scattering of light in the atmosphere. We describe the data-retrieval methodology, which includes an automated procedure to measure from a moving vehicle in order to speed up the data collection, providing a denser and wider survey than previous works with similar time frames. We compare the night sky brightness map to the nocturnal radiance measured from space by the DMSP satellite. We find that i) a single source model is not enough to explain the radial evolution of the night sky brightness, despite the predominance of Madrid in size and population, and ii) that the orography of the region should be taken into account when deriving geo-specific models from general first-principles models. We show the tight relationship between these two luminance measures. This finding sets up an alternative roadmap to extended studies over the globe that will not require the local deployment of photometers or trained personnel.
|
Zeng, C., Zhou, Y., Wang, S., Yan, F., & Zhao, Q. (2011). Population spatialization in China based on night-time imagery and land use data. International Journal of Remote Sensing, 32(24), 9599–9620.
Abstract: Population is a key indicator of socioeconomic development, urban planning and environmental protection, particularly for developing countries like China. But, census data for any given area are neither always available nor adequately reflect the internal differences of population. The authors tried to overcome this problem by spatializing the population across China through utilizing integer night-time imagery (Defense Meteorological Satellite Program/Operational Linescan System, DMSP/OLS) and land-use data. In creating the population linear regression model, night-time light intensity and lit areas, under different types of land use, were employed as predictor variables, and census data as dependent variables. To improve model performance, eight zones were created using night-time imagery clustering and shortest path algorithm. The population model is observed to have a coefficient of determination (R 2) ranging from 0.80 to 0.95 in the research area, which remained the same in different years. A comparison of the results of this study with those of other researchers shows that the spatialized population density map, prepared on the basis of night-time imagery, reflects the population distribution character more explicitly and in greater detail.
|
