Spatio-temporal variations of night-time lights at early stages of the COVID-19 epidemic in the United States

The COVID-19 epidemic swept the world and continued to spread. Without effective medical treatments and vaccine during the early stage of the pandemic, local governments in various countries had to lock down cities and adopt non-pharmaceutical interventions (NPIs), such as the stay-at-home order, social distancing, and so on. NPIs against the COVID-19 epidemic have significantly changed socioeconomic activities in cities. However, characteristics and patterns of urban socio-economic activities under this influence are still unclear. Benefiting from the development of earth observation technologies, such large-scale changes in socioeconomic activities are enough to be captured by satellites through remotely sensed night-time lights (NTL). In this study, we selected 20 major cities in the United States including New York, Chicago and Los Angeles to analyze spatio-temporal variations of NTL caused by the lockdown of cities. The first round of COVID-19 epidemic occurred in the United States in mid-March 2020. Since March 2020, American cities have successively issued stay-at-home orders, but there are differences in the time and strictness of policy implementation. Large cities have a higher population density and a higher intensity of social activities, so they are more susceptible to infectious diseases. The diversity of lockdown dates and strictness of lockdowns in cities in the United States are conducive to investigating the spatio-temporal variations of NTL. We acquired monthly averaged NPP VIIRS products of February, March and April, 2020, which are from Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (NPP). We further analyzed the spatial pattern, distance decay and disparities in land use types of changes in NTL. Results show that NTL generally dimmed by 5-8% in U.S. cities caused by the lockdown of cities. There are 6 cities where the luminous brightness has dropped by more than 10%: Chicago, Dallas, Denver, Detroit, Minneapolis, and St. Louis. Among them, Minneapolis has the largest decrease in luminous brightness, with a decrease of about 40% in March. The spatial change of NTL shows obvious "core-periphery" pattern that the reduction of NTL declines with the distance from the city center. This is mainly because the central area of the city is a concentrated commercial area. After the closure of the city, commercial activities have dropped significantly, resulting in an obvious reduction in NTL around city centers. The reduction of NTL varies among diverse urban land use types. In New York, NTL decreased the most on land for residence and aviation facilities by 12% and 11%, respectively. In Chicago, NTL generally decreased by 20% in all types of urban land, and NTL recovered after one month of the lockdown of cities in other urban land except sports facilities land. This study only analyzes the spatio-temporal changes of NTL. In the future, it can be combined with multi-source data to explain the driving force of NTL changes. Nighttime light remote sensing effectively reflects urban socio-economic dynamics with an important application in monitoring and assessing socio-economic impacts of emergencies. © 2022 National Remote Sensing Bulletin. All rights reserved.

GDP Forecasting Model for China’s Provinces Using Nighttime Light Remote Sensing Data

In order to promote the economic development of China’s provinces and provide references for the provinces to make effective economic decisions, it is urgent to investigate the trend of province-level economic development. In this study, DMSP/OLS data and NPP/VIIRS data were used to predict economic development. Based on the GDP data of China’s provinces from 1992 to 2016 and the nighttime light remote sensing (NTL) data of corresponding years, we forecast GDP via the linear model (LR model), ARIMA model, ARIMAX model, and SARIMA model. Models were verified against the GDP records from 2017 to 2019. The experimental results showed that the involvement of NTL as exogenous variables led to improved GDP prediction.

Advances in the use of nighttime light data to monitor and assess coastal fisheries under the impacts of human activities and climate and environmental changes: A case study in the Beibu Gulf

Nighttime light remote sensing has attracted increasing attention in fishery management due to its unique advantages in observing light fisheries. NASA's Black Marble product suite (VNP46A1) and Luojia 1–01 images are the latest nighttime light data with improved spatial and temporal resolutions. This paper developed an effective night-light fishing boat (NLFB) dataset to monitor and assess the coastal light fisheries in the Beibu Gulf. Daily NLFB data from 2018 to 2020 were used to map the spatiotemporal variations in fishing intensity, number of fishing boats and locations of core fishing areas. The influencing mechanisms of climate and environmental changes, fishery closures, COVID-19 and typhoons on the temporal and spatial variations in light fisheries were evaluated and discussed. Combined with the fishery boundaries defined by the China-Vietnam bilateral agreements, nighttime light remote sensing revealed the effective management of and restrictions on (such as fishery closures and lockdowns) fishing activities in the Chinese fishery area. The Vietnamese fishery area was under higher fishing pressure, especially in densely populated estuaries where the fish spawn and feed. Therefore, a cross-regional marine protected area (MPA) is recommended as a realistic strategy for the sustainable management of light fisheries in the Beibu Gulf. Nighttime light remote sensing provides a useful opportunity to combat illegal, unreported and unregulated (IUU) fishing in the future.