Spatio-temporal association of light pollution and urban sprawl using remote sensing imagery and GIS: A simple method based in Otsu's algorithm

Abstract

Automatic thresholding methods are used to detect spatio-temporal changes in the land subject to different natural and anthropogenic processes. Image segmentation plays an important role in this analysis, where urban sprawl detection take place with daylight images. However, recently some investigators have used nocturnal images in remote sensing imagery research. Such georeferenced data represent a good tool for analysis of the light pollution and urban sprawl. There are various physical processes involved in the radiative transfer of the light projected from the cities. Though, with a correct method based on background subtraction, any satellite remotely sensed nocturnal image can be useful in detecting urban sprawl. We base this work on thresholding processes of georeferenced nocturnal satellite images. We used a method combining digital classification techniques, geographic information systems and statistical analyzes. The proposed method is helpful because of a simple implementation and time saving. The pixel intensity of nocturnal images can offer a tool to calculate aspects related to electricity consumption and the efficiency of public lighting. We hope the results motivates other authors to study relationships with other social, natural and economic issues.