In a paper published in Remote Sensing, the researchers proposed crowdsourcing images of streetlights as a new source of data. The authors created NightUp Castelldefels, a prototype of a citizen science experiment that collected information about the hue of streetlights.
Study: Citizen science to assess light pollution with mobile phones. Image credit: Peyker/Shutterstock.com
The color of the collected images was specifically extracted and compared with an official database to show that streetlights could be classified according to their color from images captured by smartphone users. The findings were contrasted with those of one of the most recent sources for this type of research.
The comparison demonstrated how the two methods provided different but complementary insights into the surrounding nocturnal artificial illumination. With the possibility of collecting accurate, meaningful and economical data, this work opened a new path in the analysis of the color of artificial lights at night.
Artificial lights as an indicator of human activity
Artificial lights at night are an important environmental and human activity indicator. Its knowledge enables the estimation of a wide range of complex global parameters, with applications in economics, gross domestic product forecasts, energy consumption, population density and environmental sciences. Findings from the nocturnal artificial lights experiment also help predict sky brightness, carbon dioxide emissions, and landscape connectivity.
Essential data can also be collected from the spectra of the light sources. Numerous studies of ecological and health indicators have found that the blue region of the spectrum mainly affects living things.
Recent studies have shown that various bands of the visible spectrum have diverse effects on various biological processes, dramatically influencing flora, wildlife, and human populations.
Currently there are not enough field studies linking the effects of colored ambient light on health. The color temperature of lighting systems used in various parts of the world has been tested using a variety of approaches. Photometric techniques are difficult and expensive due to their spectral and spatial resolution limits. Therefore, the primary source of data for this type of study is images.
Only two sources of color satellite imagery are now available, those produced by commercial satellites and those shot by astronauts on the International Space Station (ISS) with a commercial single-lens reflex camera. However, both sources contain significant flaws.
Streetlight databases, which often include details about the multispectral properties of the lighting technology and location, can also infer blue light emissions. However, obtaining this information is a challenge as local governments often manage public lighting databases and their usability can vary by local authority. Private lighting contributes significantly to light pollution, for which no database exists. Researchers have used satellite photographs to assess and characterize visible light in specific locations to avoid these problems.
In this work, the use of inexpensive light sensors such as smartphone cameras identifies the color of artificial lights at night.
This method addressed problems with other data sources while providing a low-cost way to supplement and improve satellite data. With the help of smartphone users taking photographs of streetlights, it was possible to reach urban regions that were not covered by datasets and collect data with an unprecedented spatial resolution regarding the color of artificial lights nocturnal
Because the photographs were taken directly at the light source, they were not affected by the color distortions caused by reflection in satellite images. To achieve this, the citizen science experiment NightUp was created in which volunteers used their smartphones to take photos of artificial lights. In this paper, the authors analyzed data collected during the NightUp pilot program in Castelldefels (Barcelona, Spain), demonstrating the feasibility of mobile phones and citizen science participation as critical data sources for nighttime research. of artificial light.
Proof of concept experiments
A simple yet rigorous data collection method was needed to ensure that citizen science practitioners provided high-quality data with the least amount of training. Effective engagement strategies for recruiting and retaining volunteers were the two main components of achieving a citizen science experiment like NightUp.
For the pilot phase, the study focused primarily on the latter component and created a mobile phone app with a simple user experience and visual instructions, allowing participants to collect data without the need for specialized tools or formation.
Devices were calibrated to compare findings, but assuming that each person could calibrate their device was impractical. When the device allowed it, the app adjusted the phone’s camera white balance to a color temperature of 3000K.
The citizen science pilot program NightUp (NightUp Castelldefels), created to test the idea that no additional calibration was needed to discern between warmer and cooler light sources, provided the data used in this research.
The information gathered from the proposed citizen science study may not be readily available for many cities or regions. As a result, an experiment like NightUp citizen science, once confirmed, could be an important source of information for the scientific community studying light pollution.
The citizen science app NightUp recorded the location of the smartphone when the photo was taken to display the collected information on a map. Therefore, the scientific community studying light pollution could benefit from maps like these to quantify the effects of blue light on living things and its contribution to the global output of light pollution.
Citizen science improves the detection of light pollution
The implications of light pollution for the health and behavior of people, animals and plants vary widely across the range of the visible spectrum. The citizen science experiment NightUp aimed to collect data on the spatial distribution of the color of artificial lights at night, an effective tool for understanding light pollution.
Users were asked to capture images of the streetlights using a cross-platform mobile application. An algorithm was created to identify and extract the color of lamps from photographs. A map was designed to show the color of lights in various locations using NightUp citizen science data geolocation.
With this data, the NightUp citizen science experiment was tested under various circumstances, checking the accuracy of the data acquisition and color extraction method.
In particular, the findings showed that without any unique device calibration, the proposed method provided an estimate of the color of artificial nighttime light that was accurate enough to distinguish between warmer and cooler light sources.
In conclusion, the NightUp citizen science experiment made it possible to efficiently and accurately estimate streetlight colors in highly populated areas. Local governments could use the data from this analysis to optimize outdoor multispectral lighting properties and solve light pollution problems in their communities. In addition, the data collected by the new citizen science approach could also allow scientists working on light pollution to use it to further their research on the impact of light color.
reference
Muñoz-Gil, G., Dauphin, A., Beduini, FA, Sánchez de Miguel, A. (2022) Citizen Science to Assess Light Pollution with Mobile Phones. Remote Sensing, 14(19), 4976. https://www.mdpi.com/2072-4292/14/19/4976
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