A New Approach to Identify on-Ground Lamp Types From Night-Time Iss Images

Abstract

Artificial night-time light (NTL), emitted by various on-ground human activities, becomes further intensive in many regions worldwide. Its adverse effects on humans’ and ecosystems’ health crucially depend on the light spectrum, making the remote discrimination between different lamps a highly important task. However, such studies remain extremely limited, and none of them exploits freely available satellite imagery. In the analysis, the possibility to remotely assess the relative contribution of different lamp types into outdoor lighting is tested. For this sake, the radiometrically calibrated ISS RGB image is used. Spatial resolution of the image is ~20 meters, implying that each pixel may represent a mixture of different lamp types. Unmixing analysis to the detailed spectral signatures of the corresponding in situ measurements is performed, with ‘pure’ lamps’ signatures as the endmembers. Afterward, statistical models to reproduce the results of unmixing based on the broad-band RGB image from the ISS are run. The built models predict well (with R2 reaching ~0.87) the contribution of high-pressure sodium (HPS) and metal-halide (MH) lamps, the most spread in the study area (Haifa, Israel). The restored map for HPS allocation demonstrates high concordance with the network of municipal roads, while that for MH shows notable coincidence with the industrial facilities and the airport.