Random forest regression exploring contributing factors to artificial night-time lights observed in VIIRS satellite imagery

Abstract

ABSTRACTArtificial night-time lights (ANTL) pose environmental, economic, and social problems. To effectively manage this issue, it is important to understand the sources that contribute to it. Previous research has presented conflicting views on the relative importance of streetlamps and spill-over light from buildings as contributors to ANTL. In this study, we used satellite images, ground surveys of streetlamps and buildings in the city of Hobart, Tasmania, Australia, to determine the major contributing sources of ANTL. Imagery from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite was used to map ANTL. We developed a predictive random forest regression (RFR) model and found that streetlamps were the major contributor, followed by the building footprint area. We also found that an increase in both the number of streetlamps and buildings leads to an increase in ANTL observable in VIIRS satellite data. The RFR model performed well with an R2 of 0.94 and a median normalised root mean square error of 6.25%.

Keywords

• Artificial night-time lights
• streetlamps
• building footprint area
• Visible Infrared Imaging Radiometer Suite (VIIRS)
• random forest regression (RFR)