Characterizing Light Pollution Trends across Protected Areas in China Using Nighttime Light Remote Sensing Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources
2.2.1. Nighttime Light Imagery
2.2.2. Protected Areas Dataset
2.3. Methods
2.3.1. Nighttime Light Index
2.3.2. Spatial Pattern Analysis
2.3.3. Spatial Trend Analysis
2.3.4. Division of Light Pollution Change Levels in PAs
3. Results
3.1. The Trends and Patterns of Light Pollution in PAs from 1992 to 2012
3.2. Classification of Light Pollution Change Levels in PAs
3.3. Cause Analysis of Light Pollution Change in PAs
4. Discussion
5. Conclusions
- (1)
- The TNL and NLM indexes were employed to analyze the trend in light pollution in the PAs from 1992 to 2012 across China. Compared with 1992, the percentage of dark PAs decreased by more than 20% by 2012. From 1992 to 2012, most PAs (57.30%) experienced an increase in light pollution, and these PAs were mainly located in eastern and central China and a small part of western China. According to the results of the hot spot analysis, the distribution of changes in TNL and NLM both had obvious spatial agglomeration characteristics.
- (2)
- According to the TNL index and the NLM index, the PAs impacted by light pollution were divided into eight categories. Most PAs showed a stable trend (41%), but approximately 10% showed a high increasing trend; however, of those with a high increasing trend, those that had a high increasing trend with low density accounted for a small percentage, i.e., only 1%, and were mainly located in large-area PAs in western China.
- (3)
- High-resolution satellite images and statistical data were selected to analyze the causes of light pollution changes in PAs. The results showed that decreasing distance to an urban area, mineral exploitation and tourism development may increase light pollution in PAs. In contrast, the migration of residents away from an area may be one explanation for the decrease in light pollution.
Author Contributions
Funding
Conflicts of Interest
References
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Class | Criteria | Description | |
---|---|---|---|
Class 1 | TNL < 0 | NLM < 0 | Decreasing trend (DT) |
Class 2 | TNL = 0 | NLM = 0 | Stable trend (ST) |
Class 3 | 0 < TNL < 300 | 0 < NLM < 1 | Low increasing trend with low density (LL) |
Class 4 | 1 ≤ NLM < 40 | Low increasing trend with high density (LH) | |
Class 5 | 300 ≤ TNL < 1000 | 0 < NLM < 1 | Medium increasing trend with low density (ML) |
Class 6 | 1 ≤ NLM < 40 | Medium increasing trend with high density (MH) | |
Class 7 | 1000 ≤ TNL <61,000 | 0 < NLM < 1 | High increasing trend with low density (HL) |
Class 8 | 1 ≤ NLM < 40 | High increasing trend with high density (HH) |
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Jiang, W.; He, G.; Leng, W.; Long, T.; Wang, G.; Liu, H.; Peng, Y.; Yin, R.; Guo, H. Characterizing Light Pollution Trends across Protected Areas in China Using Nighttime Light Remote Sensing Data. ISPRS Int. J. Geo-Inf. 2018, 7, 243. https://doi.org/10.3390/ijgi7070243
Jiang W, He G, Leng W, Long T, Wang G, Liu H, Peng Y, Yin R, Guo H. Characterizing Light Pollution Trends across Protected Areas in China Using Nighttime Light Remote Sensing Data. ISPRS International Journal of Geo-Information. 2018; 7(7):243. https://doi.org/10.3390/ijgi7070243
Chicago/Turabian StyleJiang, Wei, Guojin He, Wanchun Leng, Tengfei Long, Guizhou Wang, Huichan Liu, Yan Peng, Ranyu Yin, and Hongxiang Guo. 2018. "Characterizing Light Pollution Trends across Protected Areas in China Using Nighttime Light Remote Sensing Data" ISPRS International Journal of Geo-Information 7, no. 7: 243. https://doi.org/10.3390/ijgi7070243