How can GIS be used to measure light pollution?

1 What is GIS?

GIS is a system that captures, stores, analyzes, and displays spatial data. Spatial data is any information that has a geographic location, such as coordinates, addresses, or boundaries. GIS can integrate different types of data, such as satellite images, census records, or environmental indicators, and visualize them on maps or charts. GIS can also perform complex calculations and analyses on spatial data, such as measuring distances, areas, or patterns.

2 Why use GIS for light pollution?

GIS can be a valuable tool for environmental services professionals to measure and map light pollution. For instance, GIS can utilize remote sensing data from satellites or aerial sensors to capture the intensity and distribution of artificial light sources at night. It can also combine remote sensing data with other spatial data, such as land use, population, or elevation, to identify the sources and impacts of light pollution. Additionally, GIS can create indices or indicators of light pollution to quantify and compare the levels of light pollution across different regions or time periods. Furthermore, GIS can generate maps or charts that visualize the spatial patterns and trends of light pollution, such as hotspots, clusters, or gradients. Lastly, GIS can apply spatial analysis techniques to model and predict the spatial variation and correlation of light pollution with other variables, such as socioeconomic factors, biodiversity, or health outcomes.

3 What are some GIS tools for light pollution?

GIS tools are useful for light pollution measurement and mapping. ArcGIS is a popular and comprehensive GIS software that provides various tools and extensions for remote sensing, spatial analysis, and mapping. It also has a web-based platform, ArcGIS Online, which allows users to access and share data and maps online. QGIS is a free and open-source GIS software that supports many data formats and standards, and can be customized and extended by users. Google Earth is a web-based platform that allows users to explore satellite imagery, maps, and terrain of the Earth. It also has a feature called Earth at Night which displays the global distribution of artificial lights at night. VIIRS is a satellite sensor that collects high-resolution data on visible and infrared light, with a special product called the Day/Night Band that can detect low-light signals at night. DMSP is another satellite sensor that collects low-resolution data on visible and infrared light, with a product called the Operational Linescan System that captures the global distribution of artificial lights at night. The World Atlas of Artificial Night Sky Brightness is a web-based map that shows the artificial sky brightness at the zenith for any location on Earth, based on data from VIIRS and DMSP as well as models of light scattering by the atmosphere.

4 How to use GIS for light pollution?

In order to use GIS for light pollution measurement and mapping, one must first define the research question and objectives. Then, data must be acquired and prepared, which may involve preprocessing, georeferencing, or projecting the data. After this, analysis should be performed, such as calculating indices or indicators of light pollution or applying spatial analysis techniques. Finally, the results should be interpreted and communicated, possibly with visual aids like legends, labels, or colors to enhance the maps or charts.

5 What are some challenges and limitations of using GIS for light pollution?

Using GIS for light pollution measurement and mapping can also present certain challenges and limitations. For instance, you may not have access to the necessary data sources, such as remote sensing data, land use data, or population data. Additionally, complex or time-consuming data processing and analysis tasks may be required, such as preprocessing, georeferencing, projecting, calculating, or modeling. Moreover, interpreting and communicating your data and findings accurately can be difficult for your audience, such as explaining concepts or assumptions, or highlighting implications or recommendations. Furthermore, ethical or social aspects of using and sharing spatial data need to be taken into account, such as privacy, security, or ownership.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?