Elsevier

Remote Sensing of Environment

Volume 126, November 2012, Pages 39-50
Remote Sensing of Environment

Aerial survey and spatial analysis of sources of light pollution in Berlin, Germany

https://doi.org/10.1016/j.rse.2012.08.008Get rights and content

Abstract

Aerial observations of light pollution can fill an important gap between ground based surveys and nighttime satellite data. Terrestrially bound surveys are labor intensive and are generally limited to a small spatial extent, and while existing satellite data cover the whole world, they are limited to coarse resolution. This paper describes the production of a high resolution (1 m) mosaic image of the city of Berlin, Germany at night. The dataset is spatially analyzed to identify the major sources of light pollution in the city based on urban land use data. An area-independent ‘brightness factor’ is introduced that allows direct comparison of the light emission from differently sized land use classes, and the percentage area with values above average brightness is calculated for each class. Using this methodology, lighting associated with streets has been found to be the dominant source of zenith directed light pollution (31.6%), although other land use classes have much higher average brightness. These results are compared with other urban light pollution quantification studies. The minimum resolution required for an analysis of this type is found to be near 10 m. Future applications of high resolution datasets such as this one could include: studies of the efficacy of light pollution mitigation measures, improved light pollution simulations, economic and energy use, the relationship between artificial light and ecological parameters (e.g. circadian rhythm, fitness, mate selection, species distributions, migration barriers and seasonal behavior), or the management of nightscapes. To encourage further scientific inquiry, the mosaic data is freely available at Pangaea: http://dx.doi.org/10.1594/PANGAEA.785492.

Highlights

► A 391 square kilometer urban light pollution map is produced with 1 m resolution. ► Geospatial analysis of the map compares lighting to land use type. ► Lighting associated with streets accounts for 1/3 of the total zenith uplight. ► Land use types of differing areas are compared equivalently using mean brightness. ► The utility of night aerial photography for light pollution studies is demonstrated.

Introduction

The presence of light at night is one of the most obvious hallmarks of human habitance in an ecosystem. Humans light their nighttime environment for many reasons, including shift work, advertising, the desire to make navigation easier, and to reduce perceptions of fear associated with darkness. As with many other anthropogenic environmental changes, the immediate benefits of artificial light at night are far more obvious than the potential consequences, but negative externalities do, however, exist. Due to a recognition of these various negative and generally unintended consequences, “light pollution” has become a widely studied and discussed topic.

Light pollution was first discussed by astronomers, because the diffuse light of urban skyglow impedes visual observation of dim celestial objects near cities (Riegel, 1973). More recently, “ecological light pollution” has been recognized as a major stressor for nocturnal organisms and as a threat to biodiversity (Hölker, Wolter, Perkin and Tockner, 2010, Longcore and Rich, 2004). While the biological effects of direct light are more obvious (e.g. McFarlane, 1963), skyglow can also influence animal behavior (Moore et al., 2000) and possibly arthropod navigation (Kyba et al., 2011a). Light at night also has manifold health consequences for humans (Boivin et al., 1996, Fonken and Nelson, 2011, September, Kloog et al., 2011, Navara and Nelson, 2007), although in this case the most important sources of light are probably indoors. Large direct costs are associated with providing artificial light at night (Gallaway et al., 2010), and the carbon released into the atmosphere as a result of electrical generation represents another negative externality of artificial lighting. The development of light sources with higher luminous efficiencies has often not resulted in a commensurate drop in cost, but instead, due to the so called “rebound effect”, decreases in the cost of lighting have resulted in increasing levels of artificial light at night (Hölker, Moss, et al., 2010). As a result, the flux of light from some cities is large enough to influence atmospheric chemistry (Stark et al., 2011).

The first measurements of light pollution, in this case meaning urban skyglow, were also performed by astronomers (Berry, 1976, Bertiau et al., 1973, Treanor, 1973, Walker, 1970). The development of inexpensive light meters has led to measurement of the night sky luminance under different meteorological conditions (Kyba et al., 2011b, Lolkema et al., 2011), compilation of ground based maps of sky radiance for individual cities (Biggs et al., 2012, Pun and So, 2011), and the establishment of night sky monitoring networks (e.g. http://www.sqm-network.com). A digital record of artificial light monitored from space by the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) exists for all years since 1992. Measurement of night lighting is an accidental by-product of this military program, but despite the shortcomings of the data (poor spatial and intensity resolution), the image of “Earth at Night” is iconic, and these data have been used in close to one hundred peer-reviewed publications. The reason these data are so useful is that light at night is a unique marker of human activity.

The aforementioned measurements allow comparisons of surface and sky brightness in different locations, but in general they do not provide information as to which sources are responsible for light pollution, and to what degree. Even the images taken by astronauts aboard the ISS (Elvidge et al., 2007, European Space Agency (ESA), 2012) with less than 50 m resolution are far too coarse to distinguish between the different sources of light (e.g. street vs. garden lighting). In some instances it is possible to perform “natural experiments”, by investigating the change in the luminance of the sky when a particular class of lighting is turned off. For example, in a study of the sky over Reykjavik, Iceland, Hiscocks and Guðmundsson (2010) found that the sky luminance reduced by approximately half when street lighting was turned off during the opening ceremony of the Reykjavik International Film Festival in 2006. Such experiments may become possible in other locations, if dimmable streetlights become common. The most certain method for ascertaining the sources of light pollution is a ground based survey, in which outdoor lamps are visually inspected and counted (e.g. Luginbuhl et al., 2009). This approach allows a direct estimation of the amount of light produced by the different sources (e.g. advertising vs. street lighting). In Flagstaff, Arizona, the largest sources of upward directed lighting were found to be commercial lighting (36%), sports fields (32%), roadways (12%), and residential lighting (9%) (Luginbuhl et al., 2009).

For purposes of categorizing the sources of light pollution, aerial surveys have the potential to bridge the gap between the problematically low resolution of current satellite imagery (Levin & Duke, 2012) and the tremendous effort required to perform a ground based survey at large spatial scales. Several analyses that are not currently feasible with satellite images or ground based surveys would be possible using a high resolution mosaic image of a city taken from the air at night (e.g. identification of migration barriers for animals on land, in rivers, or in the air). Nighttime aerial surveys have been taken in several cities around the world, but the data from these campaigns have not been made public. In addition to making such a dataset public, this paper describes in detail the process of acquiring and processing the aerial data and identifies some of the potential pitfalls in such an acquisition. The first ever high resolution spatial analysis of light sources in an urban area is presented, along with suggested possibilities for future ecological, economic, and social applications of aerial nighttime data.

Section snippets

Methods

A schematic flowchart of the steps necessary to acquire the images and process them is shown in Fig. 1. The acquisition, image processing, and analyses are described in detail below.

Mosaic image

The mosaic image shown in Fig. 5 was generated from 2647 individual images taken through the broadband (luminance) filter as described in Section 2.1. When viewed at full (1 m) resolution it was easy to identify individual light sources and, in some unlit areas, even to distinguish between the physical headlights of cars and the light they projected on the road. The improvement in resolution compared to space-based measurements was tremendous. This can be seen in Fig. 5, Fig. 6, which compare

Conclusions

By developing a high resolution map of zenith directed artificial light in the city of Berlin, we were able to analyze the degree to which different land use classes are responsible for local light pollution. Light emission is an anthropogenic factor with far-reaching consequences, and should be acknowledged as a source of pollution similar to noise, soil, water, and air pollution. The establishment of local lighting concepts (e.g. Berlin Senate Department for Urban Development & the

Acknowledgments

This work was supported by the Verlust der Nacht project (funded by the Federal Ministry of Education and Research, Germany, BMBF-033L038A), the project (IFV) Lichtimmissionen im öffentlichen Raum (funded by the Berlin Senate Department for Economics, Technology and Research, Germany), and by focal area MILIEU (FU Berlin). We are grateful to the Berlin Senate Department for Urban Development and the Environment for providing the land use data of Berlin, ESA/NASA for providing the nighttime

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