Spatio-Temporal Dynamics and Sensitive Distance Identification of Light Pollution in Protected Areas Based on Muti-Source Data: A Case Study of Guangdong Province, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources
2.2.1. Nighttime Light Data
2.2.2. Human Activity Intensity Data
2.3. Methods
2.3.1. Spatio-Temporal Changes of NTL
2.3.2. Correlation Detection
2.3.3. Sensitive Distance Analysis
3. Results
3.1. Temporal Trend of NTL within PAs
3.2. Spatial Dynamics of NTL within PAs
3.3. Correlation and Sensitive Distance Analysis
4. Discussion
4.1. Set up External NTL Governance Zones
4.2. Adopt Differentiated Protection Measures
4.3. Limitation and Further Study
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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PA Types | Number | Proportion (%) | Area (km2) | Area Proportion (%) |
---|---|---|---|---|
Geo Park | 14 | 1.3 | 835 | 2.8 |
Scenic Resort | 22 | 2.0 | 710 | 2.3 |
Sea Park | 12 | 1.1 | 530 | 1.7 |
Forest Park | 611 | 56.3 | 11,155 | 36.8 |
Wetland Park | 172 | 15.9 | 1408 | 4.6 |
Rocky Desert Park | 3 | 0.3 | 128 | 0.4 |
Nature Reserve | 251 | 23.1 | 15,586 | 51.4 |
Total | 1085 | 100.0 | 30,351 | 100.0 |
Dataset | Spatial Resolution | Temporal Resolution | Data Available | Access Link |
---|---|---|---|---|
NTL data | 30 arc-seconds (around 1000 m) | Annual | 2000–2018 | https://figshare.com/articles/dataset/Harmonization_of_DMSP_and_VIIRS_nighttime_light_data_from_1992-2018_at_the_global_scale/9828827/2 (accessed on 8 August 2022) [36] |
Land-use data | 30 m | Annual | 2013, 2017 | https://zenodo.org/record/5210928#.YtgZf8g17qE (accessed on 8 August 2022) [38] |
POI data | Vector data | Annual | 2012, 2017 | Amap |
Dynamics | Count | Proportion (%) | |||
---|---|---|---|---|---|
NTL decrease | 29 | 2.67 | |||
NTL with no change | 156 | 14.38 | |||
NTL increase | Primary pollution | 319 | 900 | 29.4 | 82.95 |
Secondary pollution | 581 | 53.55 |
(a) Regression Results of 2013 | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
10 km | 20 km | 30 km | 40 km | 50 km | 60 km | 70 km | 80 km | 90 km | 100 km | |
Model (1) | Model (2) | Model (3) | Model (4) | Model (5) | Model (6) | Model (7) | Model (8) | Model (9) | Model (10) | |
Human activity | 1.52 ** (0.073) | 1.38 ** (0.061) | 0.91 ** (0.040) | 0.74 ** (0.033) | 0.70 ** (0.033) | 0.67 ** (0.033) | 0.64 ** (0.033) | 0.60 ** (0.032) | 0.57 ** (0.032) | 0.55 ** (0.032) |
Constant | −0.000 (0.012) | −0.031 * (0.012) | −0.043 ** (0.013) | −0.050 ** (0.013) | −0.052 ** (0.014) | −0.053 ** (0.014) | −0.053 ** (0.015) | −0.054 ** (0.015) | −0.056 ** (0.016) | −0.058 ** (0.017) |
Observations | 1085 | 1085 | 1085 | 1085 | 1085 | 1085 | 1085 | 1085 | 1085 | 1085 |
R2 | 0.28 | 0.32 | 0.32 | 0.32 | 0.30 | 0.28 | 0.26 | 0.24 | 0.23 | 0.21 |
(b) Regression results of 2017 | ||||||||||
10 km | 20 km | 30 km | 40 km | 50 km | 60 km | 70 km | 80 km | 90 km | 100 km | |
Model (1) | Model (2) | Model (3) | Model (4) | Model (5) | Model (6) | Model (7) | Model (8) | Model (9) | Model (10) | |
Human activity | 1.52 ** (0.072) | 1.35 ** (0.060) | 0.89 ** (0.039) | 0.74 ** (0.034) | 0.70 ** (0.033) | 0.67 ** (0.033) | 0.64 ** (0.033) | 0.60 ** (0.033) | 0.57 ** (0.033) | 0.54 ** (0.033) |
Constant | 0.030 * (0.012) | 0.003 (0.012) | −0.008 (0.013) | −0.015 (0.013) | −0.016 (0.014) | −0.016 (0.014) | −0.016 (0.015) | −0.016 (0.016) | −0.018 (0.016) | −0.019 (0.017) |
Observations | 1085 | 1085 | 1085 | 1085 | 1085 | 1085 | 1085 | 1085 | 1085 | 1085 |
R2 | 0.29 | 0.32 | 0.32 | 0.31 | 0.29 | 0.27 | 0.25 | 0.23 | 0.22 | 0.20 |
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Jiang, B.; Li, S.; Li, J.; Zhang, Y.; Zheng, Z. Spatio-Temporal Dynamics and Sensitive Distance Identification of Light Pollution in Protected Areas Based on Muti-Source Data: A Case Study of Guangdong Province, China. Int. J. Environ. Res. Public Health 2022, 19, 12662. https://doi.org/10.3390/ijerph191912662
Jiang B, Li S, Li J, Zhang Y, Zheng Z. Spatio-Temporal Dynamics and Sensitive Distance Identification of Light Pollution in Protected Areas Based on Muti-Source Data: A Case Study of Guangdong Province, China. International Journal of Environmental Research and Public Health. 2022; 19(19):12662. https://doi.org/10.3390/ijerph191912662
Chicago/Turabian StyleJiang, Benyan, Shan Li, Jianjun Li, Yuli Zhang, and Zihao Zheng. 2022. "Spatio-Temporal Dynamics and Sensitive Distance Identification of Light Pollution in Protected Areas Based on Muti-Source Data: A Case Study of Guangdong Province, China" International Journal of Environmental Research and Public Health 19, no. 19: 12662. https://doi.org/10.3390/ijerph191912662