Parameterizing Anthropogenic Heat Flux with an Energy-Consumption Inventory and Multi-Source Remote Sensing Data
Abstract
:1. Introduction
2. Methodology and Procedure
2.1. Estimation of County-Scale Annual Mean AHF with a Top-Down Energy-Consumption Inventory
2.2. RAHF Parameterization of the Beijing–Tianjin–Hebei Region
2.2.1. County-Scale AHF Estimation Model from Multi-Source Remote Sensing Data
2.2.2. RAHF Parameterized Model
2.3. Procedure of the RAHF Parameterization Scheme
3. Study Area and Data
4. Results
4.1. Annual Total AH and Annual Mean AHF Results of Beijing Municipality
4.2. County-Scale Annual Mean AHF Estimation of the Beijing–Tianjin–Hebei Region
4.3. RAHF Results and Validation of the Beijing–Tianjin–Hebei Region
5. Discussion
5.1. Comparison of Three County-Scale AHF Estimation Models
5.2. Accuracy Comparison between RAHF Results and Other AHF Products
5.3. Novelty and Opportunities of Suomi-NPP VIIRS Nighttime Light Data in RAHF Parameterization
6. Conclusions
- For the county-scale AHF estimation model, the mean residual between the AHF estimation result and AHF computed based on the top-down energy-consumption inventory of all municipalities is less than 1%, indicating that the model can be used to estimate the county-scale AHF in the Beijing–Tianjin–Hebei region.
- According to the statistical regression analysis, Suomi-NPP VIIRS NTL data, grid-scale PD, and HSI can all be used to estimate county-scale AHF to some extent. For the estimation of county-scale AHF in the Beijing–Tianjin–Hebei region, the residuals were 0.9%, 0.7% and 0.6%, respectively. HSI can achieve slightly better performance than Suomi-NPP VIIRS NTL data and grid-scale PD.
- The spatial proxy data used to get grid-scale AHF within the districts and counties was established from multi-source remote sensing images. The 500-m resolution grid-scale RAHF was ultimately generated. According to a comparison of RAHF results and other AHF products, the RAHF parameterization scheme can get more refined AHF, and it has obvious advantages in the representation of spatial detail. Furthermore, the RAHF results of different underlying surfaces are also more reasonable.
- After population density, GDP, land use data and DMSP/OLS NTL data, the validity of the Suomi-NPP VIIRS NTL data used in RAHF parameterization has been confirmed. It can produce more accurate and higher spatial resolution AHF results than DMSP/OLS NTL data. The results of this study indicate that Suomi-NPP VIIRS NTL data has a good application prospect for AHF estimation.
Acknowledgments
Author Contributions
Conflicts of Interest
Appendix A
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Municipalities of Beijing–Tianjin–Hebei Region | Mean AHF of Districts (W·m−2) | Mean AHF of Counties (W·m−2) | Mean AHF at Municipal Level (W·m−2) |
---|---|---|---|
Beijing | 24.22 | 1.10 | 18.44 |
Tianjin | 39.21 | 1.02 | 32.85 |
Shijiazhuang | 18.91 | 1.01 | 5.68 |
Cangzhou | 5.16 | 1.05 | 1.56 |
Tangshan | 20.49 | 0.97 | 6.55 |
Xingtai | 7.80 | 1.03 | 1.74 |
Qinghuangdao | 4.32 | 0.99 | 2.42 |
Baoding | 3.47 | 1.02 | 1.31 |
Zhangjiakou | 5.11 | 1.14 | 2.07 |
Handan | 10.16 | 1.04 | 2.96 |
Langfang | 1.18 | 0.97 | 1.05 |
Hengshui | 1.15 | 1.18 | 1.17 |
Chengde | 1.02 | 1.23 | 1.17 |
AHF Categories | Percentage Contribution | AHF Categories | Percentage Contribution |
---|---|---|---|
(W·m−2) | (%) | (W·m−2) | (%) |
<0 | 0 | 50–60 | 5.11 |
0–10 | 39.03 | 60–70 | 2.57 |
10–20 | 6.34 | 70–80 | 2.69 |
20–30 | 6.21 | 80–90 | 2.45 |
30–40 | 4.26 | 90–100 | 5.31 |
40–50 | 4.36 | >100 | 21.66 |
Municipalities of the Beijing–Tianjin–Hebei Region | Residual between AHF Estimation Result of the AHF Estimation Model and AHF Computed Based on Official Statistics |
---|---|
Beijing | 0.6 |
Tianjin | 2.1 |
Shijiazhuang | 0.7 |
Cangzhou | 0.2 |
Tangshan | 0.9 |
Xingtai | 0.2 |
Qinghuangdao | 0.5 |
Baoding | 0.2 |
Zhangjiakou | 1.4 |
Handan | 0.3 |
Langfang | 0.1 |
Hengshui | 0.2 |
Chengde | 1.1 |
Parameters for Statistical Regression | Fitting Equation | R Square |
---|---|---|
AHF-VIIRSnor | AHF = 198.94(VIIRSnor)2 − 59.311(VIIRSnor) + 3.470 | 0.963 |
AHF-PD | AHF = 2 × 10−7(PD)2 + 0.002(PD) + 0.064 | 0.995 |
AHF-HSI | AHF = 48.287(HSI)2 − 17.716(HSI) + 2.541 | 0.989 |
LCCS | Mean AHF (W·m−2) | Max AHF (W·m−2) | |||||
---|---|---|---|---|---|---|---|
Type | Percent | RAHF | LUCY | Flanner | RAHF | LUCY | Flanner |
(%) | |||||||
Agriculture | 55.07 | 0.81 | 0.24 | 1.04 | 0.99 | 34.10 | 20.06 |
Forest | 12.57 | 0.09 | 0.05 | 0.43 | 0.92 | 9.54 | 13.30 |
Grassland | 22.81 | 0.45 | 0.07 | 0.37 | 1.13 | 13.30 | 34.60 |
Settlement | 7.85 | 11.10 | 1.24 | 3.00 | 130.84 | 79.89 | 49.58 |
Water and Wetland | 1.14 | 0.03 | 0.17 | 0.90 | 0.99 | 14.53 | 35.20 |
Others | 0.56 | 0.97 | 0.27 | 1.04 | 4.30 | 21.77 | 35.20 |
Impervious Surface Percent (%) | Mean AHF (W·m−2) | ||
---|---|---|---|
RAHF | LUCY | Flanner | |
0–10 | 1.18 | 0.23 | 1.03 |
10–40 | 3.68 | 0.53 | 1.72 |
40–70 | 9.41 | 0.96 | 2.63 |
70–100 | 33.57 | 2.86 | 5.81 |
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Chen, S.; Hu, D. Parameterizing Anthropogenic Heat Flux with an Energy-Consumption Inventory and Multi-Source Remote Sensing Data. Remote Sens. 2017, 9, 1165. https://doi.org/10.3390/rs9111165
Chen S, Hu D. Parameterizing Anthropogenic Heat Flux with an Energy-Consumption Inventory and Multi-Source Remote Sensing Data. Remote Sensing. 2017; 9(11):1165. https://doi.org/10.3390/rs9111165
Chicago/Turabian StyleChen, Shanshan, and Deyong Hu. 2017. "Parameterizing Anthropogenic Heat Flux with an Energy-Consumption Inventory and Multi-Source Remote Sensing Data" Remote Sensing 9, no. 11: 1165. https://doi.org/10.3390/rs9111165
APA StyleChen, S., & Hu, D. (2017). Parameterizing Anthropogenic Heat Flux with an Energy-Consumption Inventory and Multi-Source Remote Sensing Data. Remote Sensing, 9(11), 1165. https://doi.org/10.3390/rs9111165