In an article published in Remote Sensing, researchers proposed crowdsourcing images of streetlights as a novel data source. The authors created NightUp Castelldefels, a prototype for a citizen science experiment that gathered information regarding the hue of streetlights.
The color from the collected pictures was specifically extracted and compared to an official database to demonstrate that streetlights could be categorized according to their color from images captured by smartphone users. The findings were contrasted with those from 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 vicinity's artificial night-time lighting. With the possibility of collecting accurate, significant, and inexpensive data, this work opened a new path in analyzing the color of night-time artificial lights.
Artificial Lights as an Indicator of Human Activity
Night-time artificial lights are a significant environmental and human activity indicator. Their knowledge allows the estimation of a wide range of intricate global parameters, with applications in the economy, gross domestic product forecasts, energy consumption, population density, and environmental sciences. The findings of artificial night-time lights experiment also assist in predicting sky brightness, carbon dioxide emissions, and landscape connectivity.
Essential data can also be gleaned from the spectra of light sources. Numerous ecological and health indicator studies have found that the blue region of the spectrum primarily impacts living things.
Recent studies have shown that various visible spectrum bands have diverse effects on various biological processes, dramatically influencing the flora, wildlife, and human populations.
There are currently not enough field studies linking the effects of colored ambient light on health. The color temperature of the lighting systems utilized in various parts of the world has been tested using a variety of approaches. Photometric techniques are difficult and expensive due to their spectrum and spatial resolution limits. Therefore, the primary data source for this type of study is images.
Only two sources of colored satellite imagery are now accessible, those produced by commercial satellites and those shot by the International Space Station (ISS) astronauts using a commercial single-lens digital reflex camera. However, both sources contain significant flaws.
Streetlight databases, which often include details about the multispectral properties of lighting technology and the location, can also infer blue light emissions. However, obtaining such information is challenging since local governments often run public lighting databases, and their usability may differ depending on the local authority. Private illumination contributes significantly to light pollution, for which no database exists. Researchers have employed satellite photos to evaluate and characterize visible light in particular places to get around these issues.
In this work, using inexpensive light sensors such as cameras on smartphones identifies the color of night-time artificial lights.
This method addressed problems with other data sources while providing a low-cost way to supplement and enhance satellite data. With the help of smartphone users taking pictures of streetlights, it was possible to reach urban regions that were not covered by data sets and to gather data with an unprecedented spatial resolution regarding the color of night-time artificial lights.
Because the photographs were taken directly at the light source, they were not impacted by the color distortions caused by the reflection in satellite images. The citizen science experiment NightUp was created to achieve this in which volunteers used their smartphones to take pictures of artificial lights. In this paper, the authors analyzed the data gathered during the pilot NightUp program in Castelldefels (Barcelona, Spain), demonstrating the viability of mobile phones and citizen science participation as critical data sources for artificial light research at night.
A simple but rigorous data collection method was required to ensure that citizen science practitioners delivered high-quality data with the least amount of training. Effective engagement strategies to recruit and retain volunteers were the two main components of the achievement of a citizen science experiment such as NightUp.
For the pilot phase, the study primarily concentrated on the latter component and created a mobile phone application with a simple user experience and visual instructions, enabling participants to gather data without needing specialized tools or training.
The devices were calibrated to compare findings but assuming each person could calibrate their device was impractical. When the device permitted it, the app adjusted the white balance of the phone camera to a color temperature of 3000 K.
The NightUp citizen science pilot program (NightUp Castelldefels), created to test the idea that additional calibration was not required to discern between warmer and colder light sources, provided the data used in this research.
The information gathered from the proposed citizen science study might not be simple to get for many cities or regions. As a result, an experiment such as NightUp citizen science, once confirmed, might be a significant source of information for the scientific community studying light pollution.
The NightUp citizen science app logged the smartphone's location when the photo was taken to display the gathered information on a map. Therefore, the scientific community studying light pollution might benefit from maps such as these to quantify the effects of blue light on living things and its contribution to global light pollution output.
Citizen Science Enhances Light Pollution Detection
The implications of light pollution on the health and behavior of people, animals, and plants vary greatly depending on the visible spectrum's range. The NightUp citizen science experiment aimed to gather data about the spatial distribution of color of night-time artificial lights, an effective tool for understanding light pollution.
Users were instructed to capture pictures of streetlights using a cross-platform mobile application. An algorithm was created to identify and extract the color of the lamps in the photographs. A map was designed to show the color of lamps in various places using the NightUp citizen science data geolocation.
With this data, the NightUp citizen science experiment was tested under several circumstances, checking the precision of the data acquisition and the method for extracting colors.
In particular, the findings demonstrated that without any unique calibration of the device, the proposed method provided an estimation of the color of night-time artificial light that was sufficiently accurate to distinguish between warmer and colder light sources.
In conclusion, the NightUp citizen science experiment made it possible to efficiently and precisely estimate the colors of streetlights in heavily populated areas. Local governments might utilize the data from this analysis to optimize outdoor multispectral lighting properties and solve light pollution issues in their communities. Moreover, the data gathered by the novel citizen science approach could also enable scientists working on light pollution to use it to further their research on the impact of light color.
Muñoz-Gil, G., Dauphin, A., Beduini, F.A., 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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