This dataset and code are related to artificial light emissions in the arctic area. They are a supplement to the report "Capabilities and limitations of advanced optical satellite missions for snow, vegetation, and artificial light source applications in Arctic areas".Dataset:The Radiance Light Trends app was used to identify artificial light sources on the Yamal Peninsula in Russia. In order to determine whether a location was lit, a threshold of 5 nW/cm² sr (displayed in yellow in the Radiance Light Trends app) was defined. Visible band daytime imagery from Google Maps and Bing Maps was then used to identify what type of human activity was responsible for the light. The positions of the 78 lit areas and their light source classification are provided in a csv table and kmz file. The classes are defined as: industry, industry / flare, community, ship/ airport, road, water and unknown. This data publication includes the artificial light sources on the Yamal Penninsula (Western Siberia) in .csv and .kmz formats.Code:The data publication includes the python code "Arctic light pollution clustering script", which identifies areas with bright light emissions in the arctic. The script requires the monthly composite images from the Day/Night Band of the Visible Infrared Imaging Radiometer Suite produced by the Earth Observation Group as an input. These data are currently available here: https://eogdata.mines.edu/download_dnb_composites.html
This set of Python-code is used to analyse the variation of VIIRS DNB nighttime imagery. The code is inline documented and the readme provides information on what is needed to run the code, and what order to run it in. These routines were used to produce the data and plots in the paper: Variation of Individual Location Radiance in VIIRS Day/Night Band Monthly Composite Images (Coesfeld et al. 2018).Monthly VIIRS DNB data can be downloaded from NOAA: https://ngdc.noaa.gov/eog/viirs/download_dnb_composites.html
This dataset is related to the question of whether communities inside of certified "International Dark Sky Places" have different levels of lighting change in comparison to communities of similar size that are located further away. It is a supplement to Coesfeld et al. (2019), in which this question was examined for the time period 2012-2018. This dataset contains the boundaries of the analysis areas (i.e. community boundaries) in the directory "Coordinates and Graphs". These boundaries are stored as polygons in plain text format. Additional data related to the publication (e.g. Excel tables containing summary data of measured lighting trends for each community) are also included. Details of the data are available in the data description file.
Radiance Light Trends is a GIS web application that is designed to quickly display information about radiance trends at a specific location (available online at https://lighttrends.lightpollutionmap.info). It uses data from two satellite systems, DMSP-OLS and VIIRS DNB, with data processing by NOAA. New VIIRS layers are added automatically as soon as NOAA makes them available to public.The web application allows the user to examine changes in nighttime light emissions (nearly) worldwide, from 1992 up until last month. From 1992 to 2013, data comes from the Operational Linescan System of the Defense Meteorological Satellite Program (DMSP) satellites. From 2012 to the present, data comes from the Day/Night Band of the Visible Infrared Imaging Radiometer Suite instrument (VIIRS DNB). Due to significant differences in the instruments (as described by Miller et al., 2013), it is not possible to have a single record running from 1992 to today. A description of the VIIRS DNB night lights product used in this application was published by Elvidge et al. (2017), the data used in the app can be accessed from the NOAA Earth Observation Group (EOG) Website: https://ngdc.noaa.gov/eog/download.html