Artificial Light at Night Advances Spring Phenology in the United States
Abstract
:1. Introduction
- (1)
- How is spring phenology affected by ALAN and in relation to other phenology cues?
- (2)
- Does the impact on spring phenology vary spatially and, if so, what are the underlying mechanisms explaining the spatial pattern?
- (3)
- How does the ALAN’s impact in urban areas differ from that in natural ecosystems?
2. Materials and Methods
2.1. Datesets and Preprocessing
2.1.1. Satellite-Based Spring Phenology Data
2.1.2. Nighttime Light Images
- (1)
- Visible Infrared Imaging Radiometer Suite (VIIRS, 2012–2018) nighttime light images from Suomi-NPP satellite. VIIRS, a new generation of NTL sensor (2012) with an on-board calibration system, provides 500-m monthly NTL images in consistent and non-saturated radiance values [38,39]. We resampled the VIIRS images into 1-km resolution and aggregated them into annually averaged NTL images from 2012 to 2018.
- (2)
- Long-term NTL time series from harmonized DMSP-OLS and VIIRS data (DMSP-VIIRS, 2001–2018) [40]. The DMSP-VIIRS data provides annually averaged NTL with 1-km spatial resolution from 2001–2018. This harmonized NTL data reduces the cross-sensor difference between DMSP-OLS and VIIRS, expanding the temporal range of NTL time series. Note that the NTL intensity of the harmonized NTL data is still the relative NTL intensity.
2.1.3. Climate Data
2.1.4. Land Cover Maps
2.2. Analysis
3. Results
3.1. Phenological Impact of ALAN
- (1)
- Data availability. Subjected to the availability of NTL data and the corresponding phenology and climate data, VIIRS data were only available for 6.12 ± 1.17 yr. for each pixel, while DMSP-OLS data is available for 12.2 ± 3.28 yr. The partial correlation analysis was vulnerable to the impact of short-term observations [53].
- (2)
- Overpass time. DMSP-OLS (7:30 p.m.) has a more ideal overpass time than VIIRS (1:30 a.m.) because the peak lighting hour is approximately from sunset to 22:00 p.m. [54]. Using DMSP-OLS data enables us to evaluate the phenological response to ALAN when most artificial lights are on.
- (3)
- Despite a longer temporal coverage of DMSP-VIIRS data, the uncertainties of cross-sensor calibration algorithm to generate DMSP-VIIRS data may propagate to and confound our analysis [55].
3.2. Divergent Spatial Pattern of the Phenological Impact of ALAN
3.3. Urban. vs. Non-Urban. Area
4. Discussions
4.1. Mechanisms of the Divergent Spatial Pattern
4.1.1. The Interplay among Chilling, Forcing and Photoperiod
4.1.2. Chilling Insufficient
4.1.3. Differences in Species’ Life Strategies
4.2. Implications
- (1)
- the interplay among forcing, chilling and photoperiod;
- (2)
- chilling insufficiency, as we found the phenological impact of ALAN was sensitive to the accumulated chilling days;
- (3)
- the differences in life strategies of species. The results have an implication to understand the interaction mechanism between phenology shifts and environmental stimuli. It helps to better forecast how phenology will be influenced by and feedback to the warming climate and the environment being modified by human activities. Revealing the spatial variation of the impact of ALAN and its linkage to other environmental cues of phenology is a complement to in situ observation and experiment-based studies. Our results imply that previous studies about daylength and photoperiod may underestimate its actual potential in shifting phenology, especially for places where ALAN is prevailing. Hence, the long-lasting controversy on how ALAN (or photoperiod) affects phenology can be a result of not taking ALAN into consideration (for studies related to photoperiod) and/or the divergent spatial pattern of the ALAN-SOS relationship (for studies related to photoperiod and ALAN). We foresee a promising potential in improving the existing temperature-photoperiod based phenology models by incorporating the impact of ALAN [66].
4.3. Limitations
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Zheng, Q.; Teo, H.C.; Koh, L.P. Artificial Light at Night Advances Spring Phenology in the United States. Remote Sens. 2021, 13, 399. https://doi.org/10.3390/rs13030399
Zheng Q, Teo HC, Koh LP. Artificial Light at Night Advances Spring Phenology in the United States. Remote Sensing. 2021; 13(3):399. https://doi.org/10.3390/rs13030399
Chicago/Turabian StyleZheng, Qiming, Hoong Chen Teo, and Lian Pin Koh. 2021. "Artificial Light at Night Advances Spring Phenology in the United States" Remote Sensing 13, no. 3: 399. https://doi.org/10.3390/rs13030399