First Estimation of Global Trends in Nocturnal Power Emissions Reveals Acceleration of Light Pollution
Abstract
:1. Introduction
2. Materials and Methods
2.1. Background
2.2. Calibration of Uncalibrated DMSP-OLS Data
2.3. Smoothing and Recalibration of All Data Series Using Energy Consumption Data
- (i)
- Using the 2012 VIIRS image, we fitted a linear least-squares regression between the total light emitted (per province, as the sum of values from all pixels within that province) and the energy consumption of municipal street lighting in 2012. The slope of this regression yields an estimated conversion factor between provincial energy consumption and the expected radiance produced by the lights. We then converted the radiance units of nW cm−2 sr−1 into total radiant energy within the range of wavelengths detected by the satellite (in W), assuming an isotropic distribution of radiance (equal radiance in all directions). This conversion was undertaken for all satellite images.
- (ii)
- We then plotted the relationship between annual total light emitted (converted to detectable radiant energy in W) and estimated detectable radiant energy from the energy consumption data for each province (as a proxy of the real change slope). We calculated the mean residual from this relationship for each province. In this case a positive residual value for a province indicates a higher-than-expected energy consumption value for a given observed radiance; this may be due to, for example, effective shielding of street lighting or energy inefficient lighting within the province. Conversely a negative residual value may indicate emissions from private or commercial sources, low shielding of lighting or more efficient street lighting.
- (iii)
- We then fitted separate linear regressions for each image, between the total light emitted (converted to radiant energy in W) and estimated detectable radiant energy from the energy consumption data, adjusted by subtracting the province-specific residual values. The slope for each line was recalculated at this stage.
- (iv)
- We then subtracted the mean residual value for each province across images (assumed to represent consistent, localised differences in lighting efficiency and type) from the provincial data and recalculated the regression slopes for each image. The coefficients of this regression were then used to recalibrate the values to convert each image to radiant energy in W. Steps ii-iv were repeated iteratively until radiance values converged on stable values, and the converged values were used to calibrate global images.
2.4. Adjusting National Data for Differing Times of Acquisition
2.5. Adjusting Data for Increased Emission of Blue Light from LEDs
2.6. Details of the Method, Limitations, and Validation
3. Results and Discussion
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Sánchez de Miguel, A.; Bennie, J.; Rosenfeld, E.; Dzurjak, S.; Gaston, K.J. First Estimation of Global Trends in Nocturnal Power Emissions Reveals Acceleration of Light Pollution. Remote Sens. 2021, 13, 3311. https://doi.org/10.3390/rs13163311
Sánchez de Miguel A, Bennie J, Rosenfeld E, Dzurjak S, Gaston KJ. First Estimation of Global Trends in Nocturnal Power Emissions Reveals Acceleration of Light Pollution. Remote Sensing. 2021; 13(16):3311. https://doi.org/10.3390/rs13163311
Chicago/Turabian StyleSánchez de Miguel, Alejandro, Jonathan Bennie, Emma Rosenfeld, Simon Dzurjak, and Kevin J. Gaston. 2021. "First Estimation of Global Trends in Nocturnal Power Emissions Reveals Acceleration of Light Pollution" Remote Sensing 13, no. 16: 3311. https://doi.org/10.3390/rs13163311
APA StyleSánchez de Miguel, A., Bennie, J., Rosenfeld, E., Dzurjak, S., & Gaston, K. J. (2021). First Estimation of Global Trends in Nocturnal Power Emissions Reveals Acceleration of Light Pollution. Remote Sensing, 13(16), 3311. https://doi.org/10.3390/rs13163311