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Article

Light-Pollution-Monitoring Method for Selected Environmental and Social Elements

by
Justyna Górniak-Zimroz
*,
Kinga Romańczukiewicz
,
Magdalena Sitarska
and
Aleksandra Szrek
Department of Mining, Faculty of Geoengineering, Mining and Geology, Wrocław University of Science and Technology, 50-370 Wroclaw, Poland
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(5), 774; https://doi.org/10.3390/rs16050774
Submission received: 26 December 2023 / Revised: 22 January 2024 / Accepted: 20 February 2024 / Published: 22 February 2024
(This article belongs to the Special Issue Latest Developments and Applications in Remote Sensing with Nighttime Lights II)
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Abstract

:
Light pollution significantly interferes with animal and human life and should, therefore, be included in the factors that threaten ecosystems. The main aim of this research is to develop a methodology for monitoring environmental and social elements subjected to light pollution in anthropogenic areas. This research is based on yearly and monthly photographs acquired from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (Suomi NPP) satellite; land cover data from the CORINE Land Cover (CLC) program; and environmental data from the European Environment Agency (EEA) and the World Database on Protected Areas (WDPA). The processing of input data for further analyses, the testing of the methodology and the interpretation of the final results were performed in GIS-type software (ArcGIS Pro). Light pollution in the investigated area was analyzed with the use of maps generated for the years 2014 and 2019. The environmental and social elements were spatially identified in five light pollution classes. The research results demonstrate that the proposed methodology allows for the identification of environmental and social elements that emit light, as well as those that are subjected to light pollution. The methodology used in this work allows us to observe changes resulting from light pollution (decreasing or increasing the intensity). Owing to the use of publicly available data, the methodology can be applied to light pollution monitoring as part of spatial planning in anthropogenic areas. The proposed methodology makes it possible to cover the area exposed to light pollution and to observe (almost online) the environmental and social changes resulting from reductions in light emitted by anthropogenic areas.

1. Introduction

The invention of artificial light completely transformed human activity. Over a period of 150 years, the development of lighting technology significantly improved the quality of life. Independence from daylight enabled humans to modify their natural day–night rhythm. Societies started to function in a 24/7 mode. This modification has significantly affected human behavior patterns, which currently extend to both the day and night. Safe communication, work or active leisure can also be performed during the night [1]. Further development in this direction resulted in a popular claim that “cities never sleep.” Artificial lighting is an easily noticed element of technological and scientific progress and has a significant role in the technical development of civilization [2].
Recent decades have seen the rapid development of urban and industrial areas, largely at the expense of diminishing natural and open space areas. Industrialization and urbanization also negatively influence the surface area of the “dark sky” [3,4]. Lighting systems are designed in a manner that does not limit light beam scattering. As a result, the light beam is scattered in all directions. According to research results, light pollution increases by more than 2% per year [3]. The problem was first observed by astronomers, who found that light pollution renders observations of the night sky difficult or even impossible [4]. This phenomenon has been confirmed by observations of the Earth from space, as documented by photographs taken from the International Space Station [3]. They show large areas intensively illuminated during the night and demonstrate the scale of the light pollution problem [5] (Figure 1).
Excessive amounts of artificial light emitted during the night are becoming a regularity in urbanized areas [8]. This phenomenon is also observed in locations outside major populated areas. The glow in the sky reaching far beyond the administrative borders of urbanized areas represents a growing problem. It can be seen at distances of as much as several tens of kilometers from medium-sized urban settlements [1]. The scale of the problem of urban light islands has led many researchers to investigate it from both temporal and spatial perspectives [9]. Remedies can be implemented at the stage of designing lighting systems in public areas. Such an approach is aided by computer software, which allows the lighting system to be visualized and the light intensity to be calculated during the design process [10,11].
Light pollution is a relatively new phenomenon, and therefore, its investigation still requires new and improved monitoring and research methods [12,13]. These include low-altitude night photogrammetry or analyses of satellite images covering the investigated areas [8,14,15,16,17,18,19,20,21]. However, they have some disadvantages, such as low image resolution or the significant impact of weather conditions. Low visibility due to clouds may significantly hinder the analysis of an area. However, in the case of methods based on satellite images, additional attention should be paid to dynamic changes due to municipal lighting, which do not lend themselves easily to observations [22].
Light pollution is a global problem, growing in significance particularly in developed and developing countries. Scientific research performed in a number of disciplines has allowed for the qualification of artificial lighting as an element of anthropopressure, as it disturbs the processes occurring in the human body. It is also a type of pollution, as it negatively influences the natural environment and living organisms [5,23,24]. The adverse effects of artificial lighting—if it is provided in excess and against the natural day–night cycle—became known to researchers when they tried to investigate the causes of the increased and unexplainable incidence rates of some diseases and other health problems. Light of anthropogenic origin causes, inter alia, an increase in the number of civilization disease cases because of the so-called “penetration” of artificial lighting into residential buildings at night. This process disrupts the circadian rhythm and thus leads to disorientation, sleeplessness and difficulties falling asleep. The results include a loss of concentration, lower efficiency at work and reduced resistance to stressful situations. The negative results also include disturbances to the life cycles of animals, leading to modifications in their behavior (e.g., wild animals have been observed to face problems finding food, procreating and migrating) [4,23]. The presence of artificial light at night, especially intensive light, has been demonstrated to disrupt the natural day–night cycle, which is a significant problem for plant growth. This problem also affects the safety of people by contributing to an increase in the number of road accidents due to glare [4].
Therefore, a need exists to limit this type of pollution by reducing the use of artificial lighting while producing a comparable effect. Such reductions are important for maintaining this vital civilizational element, without which, a modern person would be most probably unable to function [2,25]. However, accomplishing this goal depends on cooperation in multiple fields of science and technology regarding both basic and implementation research [26]. The results of such interdisciplinary cooperation should be used to develop appropriate legal regulations, which would enable a reduction in light pollution, the introduction of new technical solutions and a new approach to designing municipal lighting systems in accordance with the principles of sustainable development [15,25]. Municipal lighting is a complex interdisciplinary problem combining environmental protection; biodiversity; natural and cultural landscape protection; sustainable spatial development planning; energy efficiency; and even traffic safety [4,24,27].
Unfortunately, despite the ever-increasing public awareness of the harmful effects of light pollution on humans, animals and plants, there are still insufficient legal regulations to limit it. This also results in a lack of regulation of the methodologies used to study this phenomenon, which are still only applied in scientific research. This results in a lack of tangible uses for the data obtained in environmental policies [4,12,27].
The methodology proposed here for the monitoring of light pollution in selected environmental and social elements has been developed in response to the need to analyze light pollution during the night in anthropogenic areas. This methodology identifies the environmental and social elements of light pollution classes and thus may be useful to urban planning specialists, architects and landscape designers who need to allow for work during nighttime in urbanized areas. Such information may be used, for example, to aid in the identification of anthropogenic areas in which lighting systems require modernization work.
The proposed methodology is presented based on the example of an area covering the town of Bogatynia, with the adjacent Turów mine and power plant. This area is interesting for illustrating the proposed method because of the presence of urban and industrial elements. Light pollution from urban and industrial areas overlaps and jointly affects the surrounding environment and ecosystems. In addition, this is a border area, so the analysis indicates the magnitude of the problem in cross-border terms.

2. Materials and Methods

The environmental and social elements present in areas polluted by anthropogenic light were identified in accordance with the proposed methodology, which involves two main stages (Figure 2), (1) preliminary research and (2) research proper, which includes developing a database containing environmental and social elements and a database of light pollution classes; spatially identifying environmental and social elements prone to light pollution; and representing the results in the form of maps with locations indicating environmental and social elements in light pollution classes.

2.1. Preliminary Research

The increase in the population of the Earth has been accompanied by increasing urbanization; the omnipresence of artificial light during the night; and the resulting glow in the sky extending far beyond the borders of urbanized areas. Research on lighting technologies has led over the years to the development of numerous alternative lighting systems that are significantly different from natural light sources and that cause light pollution. As part of this research, a literature review was performed in order to identify environmental and social elements prone to light pollution.

2.1.1. Selection of Environmental and Social Elements

The introduction of artificial light into the natural environment has disturbed the physiological reactions of organisms because of the modified proportion of darkness and light periods in the daily and annual rhythm and has thus directly affected the functioning of their circadian rhythms [11,26,28].
The problem of the impact of disruption to the circadian rhythm on human functioning and health has been relatively recently addressed in medical sciences. One of the first conclusions was that excessive illumination at night has a negative effect on human somatic and mental health. This observation has motivated further research in this direction. Light pollution is now known to cause, for example, a decrease in melatonin production in humans, which, in turn, disrupts their circadian rhythms [29]. An insufficient amount of the hormone responsible for proper sleep translates into difficulties falling asleep, problems sleeping through the entire night or low quality of sleep. Sleep disorders such as insomnia, delayed sleep phase and somnambulism are especially affected by light in the extended blue band. As a result, the body does not function properly during the day. Studies have also demonstrated that the effects of light pollution include an increased risk of reproductive dysfunctions, with higher incidences of breast cancer in women and prostate cancer in men, as well as metabolic and cardiovascular diseases [29,30,31,32].
Light pollution also induces many changes in ecological systems. Many species of animals follow daily and annual light cycles as determiners of their biological activity. The disturbance of these cycles affects important biological processes [26,32,33]. Such abnormalities cause disorders in the processes related to finding food; reproduction; and sleep, or its variant, hibernation, as well as migration and many others, including the regeneration of organisms on the cellular, tissue and organ levels. Studies have shown that light pollution can have various effects on the physiology of diurnal species by modifying, for example, their circadian rhythms and the processes they regulate or their behavior (e.g., spatial orientation, food search, reproductive activity, ability to escape from predators). Research results clearly indicate that blue light disrupts the behavior, reproductive cycles and natural intra- and inter-species interactions of birds, mammals and fish from local habitats. Light within a longwave spectrum (white, red) disturbs the sense of orientation in birds migrating at night. In addition, it is attractive to birds, which may, as a result, collide with elements of the lighting infrastructure. In unfavorable weather conditions, when natural “signposts” are obscured by clouds or fog, migrating birds lower their flight altitude. Densely arranged buildings in city centers limit their ability to orientate themselves in space and cause them to confuse artificial lighting with celestial bodies, which often results in fatal collisions [32,34].
Therefore, reducing artificial light pollution is important from the perspective of nature protection, especially in places that are attractive habitats for species, and in particular for sensitive species. Elements of the network in ecological corridors with regional and supra-regional importance should also be included in actions aimed at reducing light pollution.
This literature review allowed the authors to select environmental and social elements affected by the emission of anthropogenic light [2,4,12,13,24,25,26,27,28,29,30,31,33,34,35,36,37,38,39,40,41,42,43,44]. Environmental elements are understood here as all forms of natural protection: national parks, nature reserves, natural monuments, landscape parks, Natura 2000 areas (Habitats Directive Sites, Birds Directive Sites), landscape and nature complexes, ecological areas and documentation sites. Social elements are objects related to the activity of the inhabitants of a particular area, i.e., buildings; industrial areas; commercial and service areas; communication areas; green areas; sports; recreation and leisure areas; agricultural areas; forests; and water areas.

2.1.2. Selection of Sensors

The development of measurement technologies and methods has allowed for the monitoring of light pollution in particular areas and for periods spanning years. Light pollution is monitored with the use of, inter alia, remote sensing methods on the basis of data obtained from satellite images [11,13,14,15,16,17,19,20,34,45,46]; from images taken by astronauts from the International Space Station [18,19,20,35,45,47,48]; from stratospheric balloon observation platforms [49]; from flights by unmanned aerial vehicles [8,13] or by airplanes [50]; and from photometers [13,19,20,51,52,53].
This research focuses on analyzing satellite images. Satellite night data have been available since 1992 as a result of launching, in the 1960s, a series of Earth-observing satellites for the Defense Meteorological Satellite Program (DMSP). These satellites are located at an altitude of about 830 km in the low-altitude, sun-synchronous polar orbit. Satellites in such an orbit move from pole to pole, and their position is synchronized relative to the Sun. As a result, they are always over a certain point at the same local time [54].
The orbital period of the DMSP satellites is 101 minutes, and the width of the recorded scene is 3000 km; thus, data for the entire Earth are acquired every 24 h. The satellites are provided with an Operational Linescan System (OLS) sensor, which operates in the visible, near-infrared (VNIR) (0.4–1.1 µm) and thermal (TIR) (10.5–12.6 µm) ranges. The images are captured with a spatial resolution of 0.56 km, but they are “smoothed” to a block of 5 × 5 pixels with a resolution of approx. 2.8 km. Night images are acquired with the use of a photomultiplier tube, which amplifies the signal from the VNIR band, thus allowing weak radiance values to be recorded [55].
Night data from the DMSP OLS have been widely used, e.g., in research on estimating light pollution [56], spatial analyses of city boundaries based on night lights [57], mapping urban areas [58] and modeling population density based on night images [59].
The popularity of the DMSP OLS data is due to their advantages, such as daily acquisition frequency, an archive spanning many years (1992–2013) and free and easy access to the dataset. However, they have some disadvantages. A noticeably low spatial resolution is an obstacle in the study of small areas and affects the blooming effect—the illuminated areas have excessive brightness due to light sources from neighboring areas, especially sources that intensively reflect light (e.g., water). The sensor has a high threshold for the minimum value of the recorded signal (~0.5 nW/cm2/sr), and signal quantization is limited to 6 bits. No information is provided about in-flight signal gain changes, and a standard high gain results in light saturation being recorded in urban areas [60].
The Suomi National Polar-Orbiting Partnership (Suomi NPP) satellites and the National Oceanic and Atmospheric Administration (NOAA-20) satellites were placed in space in 2011 and 2017, respectively. Their equipment includes the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor, which is capable of night imaging and which provides data of higher quality than the OLS sensor. Both satellites are part of the Joint Polar Satellite System (JPSS) of environmental satellites—a program led by NASA and NOAA. They are located at an altitude of approximately 830 km in the low-altitude, sun-synchronous polar orbit, and they orbit the Earth 14 times a day (orbital period of 101 min) providing global data coverage at 24 h intervals. In addition, scenes of approx. 3000 km in width are recorded by both satellites at fifty-minute intervals, and thus, the data are available twice a day [61]. The main purpose of these satellites is to monitor the environment and to provide meteorological and atmospheric data. These satellites are equipped with the Cross-Track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS), which record data on temperature and humidity; the Clouds and the Earth’s Radiant Energy System (CERES), which provides data on solar radiation and on the thermal parameters of the Earth; and the VIIRS sensor, which is of importance in research on night lights [62].
The VIIRS sensor provides information on land, oceans and the atmosphere and has 21 channels covering a spectral resolution of 0.4–13.0 µm. An additional panchromatic day–night band (DNB) with a spectral resolution of 0.4–0.9 µm enables the observation of night lights [63]. The night scene is recorded around 1:30 a.m. (Suomi NPP) and 2:30 a.m. (NOAA-20) local time. In products provided by NASA, its spatial resolution of 750 m is sampled to 500 m, 1 km and 0.05 degrees (15 arc seconds). The VIIRS DNB has high sensitivity in low-light conditions; it detects light from 0.02 nW/cm2/sr, and the data are calibrated on the basis of a solar diffuser—as a result, no saturation occurs in urban areas (as is the case with the OLS data) [64].
Data from the VIIRS DNB are widely used not only in research on light pollution [65,66,67,68] but also for detecting [69] and monitoring the intensity of ship traffic [70], automatically detecting lightning [71] or fires at night [72]; developing distribution maps of impervious surfaces [73]; detecting blackouts after severe storms [74]; and characterizing aerosols in the atmosphere [75].
Night data are also recorded by satellite platforms and systems other than the above-described. Although their main advantage is a high spatial resolution of up to 0.7 m (EROS-B), these systems also have significant disadvantages, such as irregular (Landsat 8) or sporadic recording of night data; no post-correction data available for download (Aerocube 4, Aerocube 5, SAC-C HSTC, SAC-D HSC, CUMULOS); a long revisit time (LuoJia1-01); and download options limited to accessing data only “on demand” (commercial satellites such as EROS-B, JL1-07 and Jilin-1) [76].
This research on light pollution is based on satellite data from the Suomi NPP VIIRS. A lower spatial resolution and the particular span of the analyzed years disqualified data from the DMSP OLS, while irregular acquisition and limited availability disqualified data from the other mentioned satellite systems.
The VIIRS DNB sensor does not measure light pollution directly—it only provides information about the value of the recorded radiance. This value is converted into the mag/arcsec2 unit (magnitudes per square arc second) and used to define the level of light pollution [77].
Converting radiance to mag/arcsec2 requires allowing for both the wavelength and the influence of the atmosphere, specifically for the influence of Rayleigh and Mie scattering. In cases when larger areas are surveyed, allowance should be also made for the curvature of the Earth and the variable elevation of the terrain. Such conversions were used in developing the world atlas of artificial night sky brightness [78]. However, in scientific research, raw values provided by the sensor are often, with some simplification, used as information about light pollution [22,57,66,79].

2.1.3. Selection of Research Area

This study was performed on an area of 310.33 km2 located on the border of Poland, Germany and the Czech Republic (Figure 3) and included sources of anthropogenic light emissions. The range of the study area was selected by visually analyzing the distribution of radiance values presented on a map portal in 2014 and 2019. The largest emitters with high radiance values in this area include Bogatynia (PL), Turoszów (PL), Bertsdorf-Hörnitz (DE), Hirschfelde (DE), Mittelherwigsdorf (DE), Olbersdirf (DE) Zittau (DE) and Hradek nad Nisou (CZ), as well as the industrial areas of the PGE Górnictwo i Energetyka Konwencjonalna S.A. Capital Group: the Turów Lignite Mine (PL) and the Turów Power Plant (PL).

2.1.4. Selection of Data Sources

The environmental and social data were obtained from the CORINE Land Cover (CLC) program, the European Environment Agency (EEA) and the World Database on Protected Areas (WDPA). The light pollution data were obtained from a map portal: www.lightpollutionmap.info [80].
The CLC program is a European database providing information on land cover and land use. These data are periodically updated, allowing for the observation of changes taking place over the years. The first CLC inventory took place in 1990. The data were first updated in 2000 and then in 2006, 2012 and 2018. The data, their detail levels and their objects have been adapted to the needs defined by the European Union and the EEA, which coordinates CLC projects in Europe. The CLC program is part of the pan-European component of the Copernicus Land Monitoring Service [81], which is part of the European Copernicus program coordinated by the EEA, which, in turn, provides information on the environment based on a combination of data acquired from airborne and space observation systems, as well as from in situ monitoring. The data collected in the CLC program are assigned to land cover classes grouped into three levels. The first level comprises the main types of the Earth’s cover, i.e., anthropogenic areas; agricultural areas; forests and semi-natural ecosystems; wetlands; and waters. The second level distinguishes 15 forms of land cover, which are presented on maps on scales from 1:500,000 to 1:1,000,000. The third level identifies 44 classes based on land cover databases in European countries [81].
The CLC data for the entirety of Europe are available free of charge on the website of the Copernicus program [81]. These data are available in both raster versions (100 m resolution) and vector versions (ESRI and SQLite geodatabases). The minimum mapping unit (MMU) for CLC is 25 ha for surface phenomena and 100 m for linear phenomena. The time series (1990, 2000, 2006, 2012 and 2018) are supplemented with layers of changes that highlight land cover transformations over a surface area of 5 ha. The database was developed in the ETRS 1989 LAEA system.
The main objective of the EEA is to communicate data and support decision-making processes aimed at improving the natural environment, as well as to increase the importance of environmental issues in the policies of EEA member countries. As part of its activities, the EEA is involved in the European Environment Information and Observation Network (Eionet), which acts as a partner network between the EEA and the countries associated with Eionet. Units at the European level closely cooperate with national contact points for the development and protection of the natural environment. Such points include national agencies, environmental organizations and ministries. EEA is responsible for extending the information network and for cooperating with other entities, while the national partners are responsible for coordinating the network in the member states and enhancing cooperation between local institutions [82].
One result of this cooperation is publicly available databases on the condition and protection of the environment. They include maps, indicators, publications and spatial data. The elements made available under Eionet involve integrated Natura 2000 data, which are provided in the shapefile format, have a polygon geometry and are one of the key elements for environmental protection in the EU. The coordinate system of the dataset is ETRS 1989 LAEA. The areas are updated once a year and include data provided by EEA member states.
A much larger global database related to environmental protection is the World Database of Protected Areas (WDPA). This database was developed on the initiative of the United Nations Environment Program (UNEP) and the International Union for Conservation of Nature (IUCN). It is managed by the UN Environment Program World Conservation Monitoring Center (UNEP-WCMC), together with national authorities and NGOs. The spatial data in the WDPA are classified as protected areas based on an IUCN entry. They cover all IUCN management categories: Strict Nature Reserve, Wilderness Area, National Park, Natural Monument, Habitat/Species Management, Protected Landscape/Seascape and Managed Resource Protected Area. The environmental data in the WDPA are updated monthly and made available via point- and polygon-type shapefiles in the WGS-1984 coordinate system. The data are available via the Protected Planet website [83,84].
Map portals [80,85] enable easy access to night image data in the form of annual or monthly compositions obtained from the DMSP (1992–2013) and VIIRS (2012–2021) sensors and to data provided by Sky Quality Meters and Sky Quality Cameras. The above data are supplemented by a World Atlas 2015 layer showing light pollution, sky observation points and an orthophotomap [86]. Night data are expressed in radiance values [nW/cm2/sr] (for the World Atlas 2015, the radiance values were converted to mag/arcsec2 [78]). In addition to displaying data with an option to adjust the transparency of the radiance layer, the service also provides such functions as measuring distance, reading values from a selected point or area or downloading a selected map section in the GeoTIFF file extension [80]). The application was developed as part of the GEOEssential project, partly funded by the European Union under the Horizon 2020 ERA-PLANET research and innovation program and by the GFZ German Research Center for Geosciences. The aim of this project is to provide reliable data to monitor the state of the environment from such sources as the Global Earth Observation System of Systems and the Copernicus program [87].
The radiance data available on the map (2012–2021) were obtained from a Black Marble suite product—NASA’s VIIRS/NPP Lunar BRDF-Adjusted Nighttime Lights (NTL) yearly composites [88], which are generated from daily NTL data after atmospheric correction (removal of the influence of aerosols, water vapor and ozone) and after lunar-BRDF (Bidirectional Reflectance Distribution Function) processing, which estimates and removes the influence of moonlight based on albedo. The range of valid radiance values is determined with the use of the Boxplot metric [89], and values outside the range are removed from the composite. Background noise is also removed—values with a radiance lower than 0.5 nW/cm2/sr are set to zero. In addition, because of the presence of snow, which increases the scattering of reflected light, and the influence of the view angle on artificial light sources, these products are generated for many categories of view angles (i.e., near-nadir, off-nadir and all angles) and for the presence or absence of snow. Atmospheric correction is not applied to recorded artificial lights [90,91].
Changes in light emissions observed for any given area may, to some extent, result from actual changes in the installed lighting. However, this fact applies to longer periods, and in the case of shorter periods, this volatility is typically due to, among others, the view angles that comprise the monthly composite. In many areas, particularly in those at high latitudes, seasonal cycles are also observed. In areas with snowfall, the analyses are recommended to be limited to the months with a low probability of snow covering the ground. In addition, no data are available at high latitudes during the summer, as stray light shines on the satellite sensor [85].

2.2. Proper Research

The monitoring of light pollution for selected environmental and social elements was performed on the basis of a new data-processing methodology. The methodology is schematically shown in Figure 4. The data were transformed into a coherent ETRS 1989 Poland CS92 coordinate system and cropped to the boundaries of the research area. Subsequently, light pollution classes were defined, and raster data were converted into vector data. The environmental and social data were cropped to match the classes, and then, the environmental and social elements were identified and analyzed within these classes. In the next step, the resultant maps were developed, and the surface area of the identified elements was calculated for individual classes.
The database was developed on the basis of night satellite images from 2014 and 2019, land cover from 2012 and 2018 and protected areas from 2017–2022. After the collected data were verified to be useful, they were further processed.
The night satellite data used in this study were downloaded from a map portal [80] as annual data in the GeoTIFF format. The collected raster data were transformed from the WGS84 system into the ETRS 1989 Poland CS92 system and subsequently cropped to the defined research area. The rasters were classified, and the number and ranges of classes were selected on the basis of the distribution of radiance values and the visibility of the phenomena. The class selection method was verified on the basis of the literature [12,56,65,79]. In the next step, the rasters were reclassified in such a manner that the classes identified by radiance values were assigned pixel values from 1 to 5. Table 1 contains information about the defined names of light pollution classes, their corresponding ranges of radiance values and the pixel values assigned to each of the classes. Figure 5 shows the radiance observed in the study area in 2014 and 2019, divided into individual classes.
The classified raster data were converted into vector data with the Raster to Polygon tool in ArcGIS Pro (ESRI) in order to enable a comparison of the collected environmental and social data with the light pollution emission rasters. The polygons obtained for each light pollution class were added to the light pollution database for 2014 and 2019 (Figure 6).
The evaluation, selection and processing of input data for analyses, as well as the testing of the methodology and the interpretation of the final results, were performed in GIS-type software in ArcGIS Pro (ESRI).
After analyzing the data from the CLC program for 2012 and 2018, we decided to perform further analysis of vector data in the form of the ESRI geodatabase for 2018. These data were selected after analyzing the layers showing changes in the land cover. The selected research area was found to show insignificant surface changes in the selected land cover classes and the data from 2012 and 2018. Subsequently, the data were cropped to fit the research area and divided into land cover classes according to the key used for the first level of detail in the CLC program and with an additional division of anthropogenic areas. The classification is presented in Table 2. For each class, a vector layer was developed, which is a component of the database of environmental and social elements in the ETRS 1989 Poland CS92 coordinate system.
The input data for the protected areas were obtained from two databases. The data on the Natura 2000 sites were retrieved from the years 2017–2022, and the remaining WDPA data were current as of January 2023. Both the EEA Natura 2000 data and the WDPA data were transformed into the ETRS 1989 Poland CS92 uniform coordinate system. In the next step, vector layers were developed for national parks, nature reserves, nature monuments, landscape parks and the Natura 2000 sites. These layers were subsequently stored in the database of environmental and social elements. In the next step, the data were cropped to the borders of the research area and indicated with a dark green color, and the layer was named “protected areas” (PA).
The environmental and social data were processed into the input database for the model. The structure of the database is shown in Figure 7.

3. Results and Discussion

The aim of this research was to develop and test a model for identifying environmental and social elements in light pollution classes for the selected area in the years 2014 and 2019. The information from Table 1 served to distinguish five classes of light pollution. Figure 8 shows their percentage share in the research area, with a total surface of 310.33 km2. In 2019, as compared with 2014, the area of class 1 (no pollution) increased by 12.5%. On the other hand, the area of class 2 (low pollution) decreased by 11.0%, and the areas of class 3 (medium pollution) and class 4 (high pollution) decreased by approx. 1.5% compared with 2014. A slight change is also visible for the area of class 5 (very high pollution)—it increased by approx. 1% in comparison with the area in 2014.
The environmental and social elements classified by light pollution are presented in the form of maps (Table 3) and graphs showing the percentage share (Figure 9), as well as in Appendix A, which contains the calculation results for the areas of environmental and social elements in individual light pollution classes.
The areas identified in class 1 include a large share of agricultural areas (AAs) (2014: 54.3%, 2019: 61.9%), as well as forest areas (FAs) and protected areas (PAs) overlapping with forest areas (2014: 44.0%, 2019: 36.7%). On the other hand, anthropogenic areas (AnAs) were identified at below 1%, and industrial areas (IAs) were identified at 0.1% in 2014 and 0.6% in 2019. Class 2 was found to include more than 60% of agricultural areas and approx. 30% of forest areas (including protected areas) and showed an increase in anthropogenic areas (2014: 4.9%, 2019: approx. 5.8%) and industrial areas (2014: 2.7%, 2019: 5.2%). Class 3 included more than 50% of agricultural areas and approx. 16% of forest areas (for both 2014 and 2019). Anthropogenic areas increased from 16.1% in 2014 to 19.7% in 2019, and industrial areas decreased from 13.5% in 2014 to 9.1% in 2019. Class 4 comprised approx. 27% of agricultural areas (for both 2014 and 2019) and showed an increase in forest areas (2014: 9.1%, 2019: 13.3%) and a slight decrease in both anthropogenic areas (2014: 26.8%, 2019: 24.7%) and industrial areas (2014: 36.5%, 2019: 34.3%). In class 5, approx. 11% were agricultural areas (for both 2014 and 2019). The shares for the remaining areas were as follows: forest areas (2014: 29.0%, 2019: 19.7%), anthropogenic areas (2014: 37.3%, 2019: 43.3%) and industrial areas (2014: 22.5%, 2019: 26.4%). Water areas were omitted because of their very small share (below 0.8%).
As illustrated in Table 3 and Figure 8, the surface areas in individual light pollution classes were modified within the research area, and therefore, the environmental and social elements in the classes changed. The results show changes resulting from light pollution reducing, increasing or decreasing its intensity.
The literature proves that the highest light pollution emissions are from anthropogenic areas, including industrial areas. The greatest change in the spatial distribution of light pollution occurred between class 1 and class 2. This was due to changes that occurred in 2016 in the industrial area of the Turów Lignite Mine, which is a branch of the PGE Górnictwo i Energetyka Konwencjonalna S.A. Capital Group in Poland. On the night of 26/27 September 2016, a landslide process began on the external heap involving a sudden and uncontrolled movement of earth masses. The deformations destroyed belt conveyors on individual dumping levels and stopped further dumping works. A Z-47 stacker was also trapped in the area, covered by the movement of dumping masses, and as a result, it was displaced and damaged. The damage also caused the external lighting of the machines to fail [92]. Employees in the mine work in a continuous four-shift system. Therefore, the illumination at the Turów Lignite Mine includes roads; bus yards; infrastructure yards; equipment and resource yards; warehouses; basic machinery; conveyor drive stations; coal bunkers; a sorting area; and electrical substations [93]. Table 4 compares data on light pollution for KWB Turów downloaded from a portal [85] with image data showing the condition of the external heap before and after the event. The data on light pollution were downloaded for the year 2016 for January and October in the form of monthly mosaics [85]. The downloaded photos show the difference in light emissions for the analyzed area. The orthophotomaps were downloaded from the portal of the Head Office of Geodesy and Cartography (GUGiK), for the years 2016 and 2019 [94]. The orthophotomaps show changes that occurred on the surface of the external heap as a result of the landslide.
As presented in the results of this research, the event at the Turów Mine modified the spatial distribution of light pollution classes. This modification is visible in satellite night data available on the aforementioned map portal [80]. This research demonstrates that such data can be used in light pollution analyses following the methodology proposed here. In the case of a deliberate modification or malfunction of the lighting system, the resulting change will be detected by the VIIRS sensor. After classifying the environmental and social elements by light pollution, it is possible to indicate both the areas most prone to this type of pollution and the anthropogenic areas that require changes in their lighting policies. For the ecosystems most degraded by light pollution, efforts should be made to develop recovery programs that would involve modifying lighting systems in adjacent anthropogenic areas. On the other hand, the introduced lighting modifications should be verified by monitoring the spatial distribution of light pollution classes based on the methodology proposed in this work.
Protecting the “dark sky” is currently a major global problem, the effects of which we can observe mainly in developed and developing countries. The lighting of urban agglomerations and industrial areas significantly exceeds the needs of their populations. It is no longer just lighting necessary for people to function safely at night. It is often decorative lighting for buildings, monuments, etc., and it is not necessary. Reducing it would improve the situation. Unfortunately, we can only rely on the common sense of those responsible because of a lack of regulation in this area [4,24,27].
Global pollution has led to the introduction of sustainable development policies on a world scale. Of the Sustainable Development Goals, of which there are seventeen, light pollution affects six [96]. The second goal talks about combating hunger through, for example, sustainable agriculture. Currently, there are increasing difficulties in producing enough food because of declining crop yields, and this problem is expected to escalate [97]. This is influenced by several factors, one of which is the declining number of insects worldwide, including those that pollinate crops. The decline in their numbers is, among other things, the result of developmental abnormalities at the larval stage due to disturbances from excess light in the environment [36,37].
Increasing light pollution in inhabited areas is increasing the incidence of diseases of civilization in developed countries, especially mental illnesses and increased rates of addiction. Combating these problems of humanity is first and foremost goal 3, but also goal 11, which regards the sustainable planning of urban settlements and reducing their environmental impact by creating, at least, green areas for the public. Such measures aim not only to improve the air quality of cities but also to increase the well-being of the people living in them [4,31,38,39,40,96].
Objective 7 includes provisions for the efficient use of energy. It is not sustainable to increase energy consumption for late-night public lighting. Notably, only a slight percentage of the population can benefit from this. Reducing energy consumption should take in as many areas of society as possible. That is a guarantee for the success of the set goals [41,42,96].
We then examined objectives 14 and 15, the intention of which is to protect the seas and oceans and to protect terrestrial ecosystems and their biodiversity. The main thrust of these objectives is to protect ecosystems by reducing the degradation of natural habitats. Many marine and terrestrial animal species are very sensitive to light, which regulates their diurnal and annual cycles. Any disturbance in this area has far-reaching consequences, culminating in a reduction in biodiversity in areas affected by high levels of light pollution. There are numerous reports in the scientific literature on the negative effects of excess light on, for example, the procreation of marine and terrestrial animals. Unfortunately, there is not even a mention of the need to draw attention to the harmful effects of light pollution and the possibility of reducing them in protected areas in the objectives themselves [13,14,15,24,26,43,96].
Recent years have seen an increase in people’s awareness of light pollution, as evidenced by the creation of so-called “darkness reserves”. Currently, there are more than 30 of them in the world. The first one in Europe was the Izera Dark-Sky Park, established in 2009. It is located not far from Bogatynia, on which the proposed methodology for studying light pollution in anthropogenic (urban, industrial) areas was demonstrated. These are isolated areas offering significant areas of dark skies for astronomical observations. However, they are often also part of national parks, landscape parks or reserves due to their beneficial impact on local ecosystems [44].
Unfortunately, an analysis of legislation has shown that the protection of dark skies is not regulated by law in the same way as reducing chemical pollution or even noise. Numerous European Commission documents focus on environmental protection and the improvement of biodiversity, but they do not contain provisions for reducing light pollution even in protected areas (EU Biodiversity Strategy for 2023 [98], Directive 2009/147/EC [99]). It is worth noting that Directive 2002/49/EC [100] on the assessment and management of environmental noise was established in 2002. This document obliged EU countries to draw up strategic noise maps as a basis for action plans aimed at preventing and reducing noise (Directive 2002/49/EC [100]). In light of the current scientific results on the negative impact of light pollution on living organisms and its link to the strategic Sustainable Development Goals of the UN member states, it is reasonable to consider similar solutions and provisions for ALAN.
A legal analysis of the documents for the presented area showed that, at present, there are no relevant legal regulations concerning the intensity of lighting in the city and commune of Bogatynia [101,102]. Relevant provisions were also not found in the documentation of the Environmental Protection Program for Bogatynia [103]. As in the case of the previously cited documents, including the EU Directives, there are provisions on the risks of noise, but there are no annotations on reducing light pollution.

4. Conclusions

The literature review indicated that the analysis of light pollution is a relatively new problem and requires new research and monitoring methods [12,13]. The research described here addresses this demand. It proposes a methodology for monitoring the spatial distribution of light pollution along with the identification of environmental and social elements in light pollution classes. The method also enabled the definition of five light pollution classes: class 1—no pollution, class 2—low pollution, class 3—medium pollution, class 4—high pollution and class 5—very high pollution. This research was based on publicly available data covering environmental and social elements and spatial light distribution. As a result, the methodology can be employed by any user interested in the subject. The user can obtain annual or monthly data as needed for particular research and describe light pollution in any area and time (since 1992). The results demonstrate variations attributed to light pollution, encompassing alterations in its intensity, whether through reduction or augmentation.
Owing to this new methodology, experts involved in spatial planning (e.g., urban planners, architects, landscape designers) can identify the places most prone to light pollution (built-up areas, protected areas), as well as places with the highest light emissions and indicate areas where the lighting system should be modified.
Once identified in these areas, artificial lighting can be reduced while maintaining a comparable effect. This is extremely important to preserving an essential element of civilization, as modern humans would not be able to function without it [2,25]. However, achieving this depends on the ability of many scientific and technical disciplines to work together in both basic research and implementation research. The results of this interdisciplinary cooperation should be used to develop appropriate legislation or technical solutions [26]. The acquisition of knowledge about the areas most exposed to light pollution makes it possible to introduce changes in the management of anthropogenic space, resulting in a reduction in environmental, social and economic impacts [25]. The inefficient use of artificial light causes, for example, the disturbance of the light–dark cycle, adversely affecting humans, flora and fauna (ecological effect); losses due to a waste of produced electricity (economic impacts); and the deterioration of the ability of nighttime traffic participants to see, reducing safety levels (social effect). With changes in the type of lighting, we can achieve environmental, social and economic benefits [4,23].
The methodology proposed here makes it possible to control and evaluate changes made to the lighting policies of anthropogenic areas. Being based on publicly available data and, thus, relatively easy to implement, the proposed method can be successfully used as a monitoring method at any stage of project implementation.

Author Contributions

J.G.-Z.: term, conceptualization, supervision. K.R.: software, data curation, formal analysis, visualization, investigation. M.S.: investigation, administration. A.S.: software, data curation, formal analysis, visualization, investigation. All authors: methodology, writing, reviewing, editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the budget of the Faculty of Geoengineering, Mining and Geology Wrocław University of Science and Technology within Excellence Initiative – Research University (No 8211204601).

Data Availability Statement

The environmental and social data used in the study came from the CORINE Land Cover (CLC) programme, the European Environment Agency (EEA) and the World Database of Protected Areas (WDPA). Light pollution data were obtained from the map portal.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Calculation results for the areas of environmental and social elements in individual light pollution classes in 2014 and 2019.
2014Area of Terrain Coverage in Light Pollution Classes
Number
of Classes		Class 1 [km2]Class 2 [km2]Class 3 [km2]Class 4 [km2]Class 5 [km2]
AnA0.029.954.5814.963.95
IA0.205.583.8520.322.39
AA6.86126.4715.0715.271.19
FA5.5660.374.715.083.08
WA0.000.530.210.120.00
Part of Terrain Coverage Area in Relation to Light Pollution Class Areas.
Number
of Classes		Class 1 [%]Class 2 [%]Class 3 [%]Class 4 [%]Class 5 [%]
AnA0.14.916.126.837.3
IA1.62.713.536.522.5
AA54.362.353.027.411.2
FA44.029.816.69.129.0
WA0.00.30.80.20.0
2019Area of Terrain Coverage in Light Pollution Classes.
Number
of Classes		Class 1 [km2]Class 2 [km2]Class 3 [km2]Class 4 [km2]Class 5 [km2]
AnA0.319.894.9212.465.87
IA0.298.892.2717.313.58
AA31.95103.8813.7613.821.45
FA18.9546.513.956.712.68
WA0.080.550.050.190.00
Part of Terrain Coverage Area in Relation to Light Pollution Class Areas.
Number
of Classes		Class 1 [%]Class 2 [%]Class 3 [%]Class 4 [%]Class 5 [%]
AnA0.65.819.724.743.3
IA0.65.29.134.326.4
AA61.961.255.127.410.7
FA36.727.415.813.319.7
WA0.20.30.20.40.0

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Figure 1. Night view of Western Europe (Photograph ISS028-E-24360 taken on 10 August 2011 at 00:25 Greenwich Time with a digital Nikon D3S camera provided with a 28 mm lens; author: William L. Stefanov, Jacobs/ESCG NASA-JSC [6]) (a) and Warsaw (Photograph ISS066-E-172754 taken on 17 March 2022 at 21.27:24 GMT time with a digital Nikon D5 camera provided with a 400 mm lens [7]) (b) from the International Space Station—NASA (National Aeronautics and Space Administration) [6,7].
Figure 1. Night view of Western Europe (Photograph ISS028-E-24360 taken on 10 August 2011 at 00:25 Greenwich Time with a digital Nikon D3S camera provided with a 28 mm lens; author: William L. Stefanov, Jacobs/ESCG NASA-JSC [6]) (a) and Warsaw (Photograph ISS066-E-172754 taken on 17 March 2022 at 21.27:24 GMT time with a digital Nikon D5 camera provided with a 400 mm lens [7]) (b) from the International Space Station—NASA (National Aeronautics and Space Administration) [6,7].
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Figure 2. Light-pollution-monitoring methodology for selected environmental and social elements.
Figure 2. Light-pollution-monitoring methodology for selected environmental and social elements.
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Figure 3. Location of the research area near Bogatynia City.
Figure 3. Location of the research area near Bogatynia City.
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Figure 4. Schematic diagram of the methodology for identifying environmental and social elements in light pollution classes.
Figure 4. Schematic diagram of the methodology for identifying environmental and social elements in light pollution classes.
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Figure 5. Recorded radiance in the study area in 2014 (a) and 2019 (b). Compiled by the authors on the basis of the data in [80].
Figure 5. Recorded radiance in the study area in 2014 (a) and 2019 (b). Compiled by the authors on the basis of the data in [80].
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Figure 6. Visualization of light pollution classes.
Figure 6. Visualization of light pollution classes.
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Figure 7. Structure of the environmental and social database.
Figure 7. Structure of the environmental and social database.
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Figure 8. Percentage share of the surface areas for light pollution classes in the research area in 2014 and 2019.
Figure 8. Percentage share of the surface areas for light pollution classes in the research area in 2014 and 2019.
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Figure 9. Percentage share of the areas with environmental and social elements in individual light pollution classes in 2014 and 2019.
Figure 9. Percentage share of the areas with environmental and social elements in individual light pollution classes in 2014 and 2019.
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Table 1. Light pollution classes.
Table 1. Light pollution classes.
Class
Number		Class
Name		Radiance Value
[nW/cm2/sr]		Pixel
Value		Symbolization
1No pollution0.0001Remotesensing 16 00774 i001
2Low pollution0.001–1.5002Remotesensing 16 00774 i002
3Medium pollution1.501–3.0003Remotesensing 16 00774 i003
4High pollution3.001–15.0004Remotesensing 16 00774 i004
5Very high pollution15.001–60.0005Remotesensing 16 00774 i005
Table 2. Environmental and social elements. Own study based on the CORINE Land Cover [81].
Table 2. Environmental and social elements. Own study based on the CORINE Land Cover [81].
NameSymbolDescriptionCode
CLC		Symbolization
Anthropogenic areasAnAContinuous urban fabric 111 Remotesensing 16 00774 i006
Discontinuous urban fabric112
Communication areas122
Green urban areas141
Sport and leisure facilities142
Industrial
area		IAIndustrial or commercial units
Mineral extraction sites
Dump sites		121
131
132		Remotesensing 16 00774 i007
Agricultural
areas		AANon-irrigated arable land
Pastures
Complex cultivation patterns
Land principally occupied by agriculture, with significant areas of natural vegetation		211
231
242
243		Remotesensing 16 00774 i008
Forest areasFABroad-leaved forest
Coniferous forest
Mixed forest
Natural grasslands
Transitional woodland–shrub		311
312
313
321
324		Remotesensing 16 00774 i009
Water areasWAWater bodies512Remotesensing 16 00774 i010
Table 3. Identified environmental and social elements in light pollution classes.
Table 3. Identified environmental and social elements in light pollution classes.
Class20142019
Class 1
No pollution		Remotesensing 16 00774 i011Remotesensing 16 00774 i012
Class 2
Low pollution		Remotesensing 16 00774 i013Remotesensing 16 00774 i014
Class 3
Medium pollution		Remotesensing 16 00774 i015Remotesensing 16 00774 i016
Class 4
High pollution		Remotesensing 16 00774 i017Remotesensing 16 00774 i018
Class 5
Very high pollution		Remotesensing 16 00774 i019Remotesensing 16 00774 i020
Table 4. Comparison of the area of the external heap at KWB Turów before and after the event.
Table 4. Comparison of the area of the external heap at KWB Turów before and after the event.
State Before EventState After Event
23 May 2016
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Orthophotomap [94]
January 2016
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Image and data processing by NOAA’s National Geophysical Data Center, Microsoft® Bing™ Maps Platform APIs. [95]		October 2016
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Górniak-Zimroz, J.; Romańczukiewicz, K.; Sitarska, M.; Szrek, A. Light-Pollution-Monitoring Method for Selected Environmental and Social Elements. Remote Sens. 2024, 16, 774. https://doi.org/10.3390/rs16050774

AMA Style

Górniak-Zimroz J, Romańczukiewicz K, Sitarska M, Szrek A. Light-Pollution-Monitoring Method for Selected Environmental and Social Elements. Remote Sensing. 2024; 16(5):774. https://doi.org/10.3390/rs16050774

Chicago/Turabian Style

Górniak-Zimroz, Justyna, Kinga Romańczukiewicz, Magdalena Sitarska, and Aleksandra Szrek. 2024. "Light-Pollution-Monitoring Method for Selected Environmental and Social Elements" Remote Sensing 16, no. 5: 774. https://doi.org/10.3390/rs16050774

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