Assessing Light Pollution Using POI and Luojia1-01 Night-Time Imagery From a Quantitative Perspective at City Scale

Abstract

In previous studies using night-time light (NTL) image in analyzing light pollution, most of the researchers focused on national or regional scale analysis. While in this article we focus on the perception of light pollution's influence to the environment of human settlement. We propose an analysis method mainly utilizing NTL images and a city's point of interest (POI) data to assess the light pollution from the aspect of its impact on the environment of city residents. The method quickly provides light pollution analysis at a fine spatial scale. We also address the POI data in a novel aggregating algorithm to better construct the area of interest, which can conquer the limitation of spatial resolution of NTL data in some extent. By doing the assessment in two Chinese medium-size cities, light pollution sources, the pollution level for each residence are found and analyzed. Furthermore, several light pollution patterns are discovered and interpreted. The result of the experiment demonstrates our assessment method provides a fast way to analyze light pollution patterns and can show the detailed light pollution situation in a city. Index Terms-Night-time light (NTL), point of interest (POI), remote monitoring, urban light pollution.

Evaluation Model of Location Light Pollution Level based on Analytic Hierarchy Process and Entropy Weight Method

“…HAO drew the light pollution map using the night light image and data obtained from Luojia 1 satellite [8]. Zhao based on the night-time light image obtained from satellite observation and the data of urban points of interest to evaluate the light pollution [9]. However, this observation satellite system also has some defects.…”

Predicting County-Level Population From VIIRS Nighttime Light Imagery With Deep Learning

“…NTL imagery data was explored by the research community to determine changes in the vulnerability of various ecosystems due to light pollution [8], [9], identify gas flaring sites [10], approximate CO2 emissions [11], detect changes in regional economies [12], [13], [14], analyze poverty at county-level resolution [15], [16], predict variations in electricity utilization in lower-middle-income countries [17], [18], [19], [20] predict the occurrence of power outages caused by storms [21], determine levels of urbanization [22], [23] estimate the population density in point of interest regions (Beijing city) [24], and predict gridded population in Chongqing municipality [25]. Our study focuses on predicting county-level population in the United States using the VIIRS DNB monthly composites.…”

Illuminated border: Spatiotemporal analysis of COVID-19 pressure in the Sino-Burma border from the perspective of nighttime light

“…To assess the epidemic pressure on the Sino-Burma border, we studied the nighttime light variability using daily data from the visible infrared imaging radiometer suite (VIIRS). Satellite remote sensing images are an objective data source widely used in economic development, ecological environment and urban area monitoring, population mapping, quantification of electricity consumption, and light pollution assessment ( Cai et al, 2017 , Havrlant et al, 2021 , Lin and Shi, 2020 , Shan et al, 2019 , Wang et al, 2020 , Yu et al, 2015 , Zhao et al, 2021a ). Many scholars have studied the application of nighttime light remote sensing in epidemic situations.…”