Automated Extraction of Street Lights From JL1-3B Nighttime Light Data and Assessment of Their Solar Energy Potential

Abstract

To realize energy conservation and environmental protection, solar street lights have been widely used in urban areas in China. To reasonably and effectively utilize solar street lights, the original street lights must be located, and the solar street light potential must be assessed. The Jilin1-03B (JL1-3B) satellite provides next-generation nighttime light data with a high spatial resolution and in three spectral bands. Consequently, the street lights can be extracted from the nighttime light data. We used the road network dataset from the open street map with a specific buffer to extract the road area as a constraint region. Next, the grayscale brightness of JL1-3B images was obtained by integrating all the three bands to locate the street light by using a local maximum algorithm. Then, the values of the original three bands were utilized to classify the types of street lights as high-pressure sodium (HPS) lamps or light-emitting diode lamps. Finally, we simulated the replacement of all the HPS lamps with solar street lights and assessed the corresponding solar energy potential by using the digital surface model data and hourly cloud cover data through the SHORTWAVE-C model. The accuracy of location of the street lights was approximately 90%. Replacing an HPS lamp by one solar street light for 20 years can save 1.85 × 104 kWh of electrical energy, 7.41 t of standard coal, 5.03 t of C emissions, 18.47 t of CO2 emissions, 0.55 t of SO2 emissions, and 0.28 t of NOX emissions.