Identifying Surface Urban Heat Island Drivers and Their Spatial Heterogeneity in China’s 281 Cities: An Empirical Study Based on Multiscale Geographically Weighted Regression
Abstract
:1. Introduction
2. Study Area and Data
2.1. Study Area
2.2. Data
2.2.1. CSUHI Dataset
- The LST data from 2001 to 2018 were provided by the Terra/Aqua MODIS 8-day 1 km LST/LSE products (MOD11A2 for Terra MODIS and MYD11A2 v006 for Aqua MODIS and the data of MYD11A2 are from 2003) [51].
- The European Space Agency’s Climate Change Initiative (ESA CCI) land cover data at 300 m resolution from 2001 to 2018 [52] were employed to delineate urban and rural areas.
- Shuttle Radar Topography Mission (SRTM) data at 90 m resolution from 2000 was employed to determine the elevation of urban and rural areas.
- The above 281 cities’ administrative areas were defined based on data from the National Geomatics Center of China (NGCC).
2.2.2. Variable Selection and Data Source
- ΔNDVI was derived from the 16-day 1 km MODIS NDVI products (MYD13A2 v006) in 2005, 2010, 2015, and 2018 [53].
- ΔPre was derived from the monthly 1 km precipitation raster product (National Tibetan Plateau Data Center. Available online: http://data.tpdc.ac.cn (accessed on 21 October 2021)) in 2005, 2010, 2015, and 2018, which was spatially downscaled from the Climatic Research Unit (CRU) time-series dataset combined with the climatology dataset of WorldClim [54].
- ΔPM2.5 was derived from the yearly 1 km ChinaHighAirPollutants (CHAP) dataset, which was constructed from the MODIS/Terra+Aqua multiangle implementation of atmospheric correction (MAIAC) aerosol optical depth products together with abundant natural and human factors using the Space–Time Extra-Trees (STET) model [55,56].
- An integrated and consistent annual NTL product was employed from a harmonized global nighttime light dataset [57] for 2005, 2010, 2015, and 2018. This dataset employed Defense Meteorological Satellite Program (DMSP) data and simulated DMSP-like NTL observations from Visible Infrared Imaging Radiometer Suite (VIIRS) data to harmonize the intercalibrated NTL observations and showed consistent temporal trends. This study only used pixels with more than seven digital number (DN) values to improve the data’s reliability.
- Population data was extracted from the China Urban Statistical Yearbook for 2005, 2010, 2015, and 2018.
3. Methods
3.1. SUHI Intensity Calculation
3.2. Ordinary Least Square Model
3.3. Multiscale Geographically Weighted Regression Model
4. Results
4.1. Results of the OLS Analysis
4.2. MGWR Results
5. Discussion
5.1. Multiscale Extensions of Geographically Weighted Regression
5.2. Seasonal Variation of SUHIs and Vegetation
5.3. Spatial Context and Population
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Variable | Number of Observations | Mean | STD | 1st Quartile | 2nd Quartile | 3rd Quartile | Reference | |
---|---|---|---|---|---|---|---|---|
SUHI (°C) | Daytime | 281 | 0.856 | 1.125 | 0.470 | 1.033 | 1.465 | - |
Nighttime | 281 | 0.703 | 0.571 | 0.439 | 0.702 | 0.966 | ||
ΔNDVI (1/103) | 281 | −2.218 | 1.171 | −3.027 | −2.377 | −1.502 | [58,59] | |
ΔPM2.5 (μg/m3) | 281 | 0.303 | 2.108 | −0.757 | 0.241 | 1.319 | [60,61] | |
ΔPre (mm) | 281 | 4.939 | 24.033 | −5.315 | 5.218 | 15.210 | [30,62] | |
NTL (DN value) | 281 | 20.70 | 5.607 | 17.502 | 19.255 | 21.968 | [28,63] | |
Pop (104 Person) | 281 | 4.328 | 3.073 | 2.382 | 3.664 | 5.675 | [4,40] |
2005 | 2010 | 2015 | 2018 | |||||
---|---|---|---|---|---|---|---|---|
Daytime | Nighttime | Daytime | Nighttime | Daytime | Nighttime | Daytime | Daytime | |
ΔNDVI | −0.735 *** | 0.208 *** | −0726 *** | 0.337 *** | −0.790 *** | 0.399 *** | −0.776 *** | 0.386 *** |
ΔPRE | −0.013 | −0.014 | 0.068 * | 0.063 | 0.037 | 0.100 | −0.008 | 0.132 ** |
NTL | 0.082 ** | 0.205 *** | 0.083 ** | 0.056 ** | 0.121 *** | 0.139 *** | 0.131 *** | 0.059 |
ΔPM2.5 | 0.000 | −0.012 | −0.036 | −0.043 | 0.068 | −0.02 | 0.092 *** | 0.055 |
POP | 0.003 | 0.001 | 0.019 | 0.012 | 0.034 | 0.06 | 0.04 | 0.074 |
R2 | 0.545 | 0.087 | 0.540 | 0.135 | 0.666 | 0.181 | 0.658 | 0.176 |
Adj. R2 | 0.537 | 0.071 | 0.532 | 0.120 | 0.660 | 0.166 | 0.652 | 0.161 |
AICC | 592.758 | 788.989 | 595.728 | 773.649 | 505.325 | 758.541 | 510.183 | 757.353 |
RSS | 128.370 | 257.435 | 129.729 | 243.806 | 94.148 | 231 | 96.043 | 231.459 |
2005 | 2010 | 2015 | 2018 | |||||
---|---|---|---|---|---|---|---|---|
Day | Night | Day | Night | Day | Night | Day | Night | |
Moran’s Index | 0.408 | 0.261 | 0.475 | 0.334 | 0.544 | 0.246 | 0.546 | 0.260 |
Z-score | 17.31 | 11.06 | 20.19 | 16.06 | 22.93 | 10.402 | 22.08 | 10.538 |
p-value | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 |
2005 | 2010 | 2015 | 2018 | |||||
---|---|---|---|---|---|---|---|---|
Daytime | Nighttime | Daytime | Daytime | Nighttime | Nighttime | Daytime | Nighttime | |
Bandwidth | 73 | 88 | 63 | 86 | 74 | 98 | 83 | 78 |
R2 | 0.804 | 0.588 | 0.851 | 0.868 | 0.632 | 0.609 | 0.861 | 0.619 |
Adj. R2 | 0.756 | 0.508 | 0.810 | 0.842 | 0.545 | 0.543 | 0.832 | 0.535 |
AICC | 479.856 | 662.772 | 421.886 | 342.88 | 652.109 | 634.155 | 362.921 | 655.341 |
RSS | 55.396 | 116.224 | 41.919 | 36.968 | 103.365 | 110.241 | 39.175 | 107.310 |
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Niu, L.; Zhang, Z.; Peng, Z.; Liang, Y.; Liu, M.; Jiang, Y.; Wei, J.; Tang, R. Identifying Surface Urban Heat Island Drivers and Their Spatial Heterogeneity in China’s 281 Cities: An Empirical Study Based on Multiscale Geographically Weighted Regression. Remote Sens. 2021, 13, 4428. https://doi.org/10.3390/rs13214428
Niu L, Zhang Z, Peng Z, Liang Y, Liu M, Jiang Y, Wei J, Tang R. Identifying Surface Urban Heat Island Drivers and Their Spatial Heterogeneity in China’s 281 Cities: An Empirical Study Based on Multiscale Geographically Weighted Regression. Remote Sensing. 2021; 13(21):4428. https://doi.org/10.3390/rs13214428
Chicago/Turabian StyleNiu, Lu, Zhengfeng Zhang, Zhong Peng, Yingzi Liang, Meng Liu, Yazhen Jiang, Jing Wei, and Ronglin Tang. 2021. "Identifying Surface Urban Heat Island Drivers and Their Spatial Heterogeneity in China’s 281 Cities: An Empirical Study Based on Multiscale Geographically Weighted Regression" Remote Sensing 13, no. 21: 4428. https://doi.org/10.3390/rs13214428