Explore the Mitigation Mechanism of Urban Thermal Environment by Integrating Geographic Detector and Standard Deviation Ellipse (SDE)
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Datasets
2.3. Methods
2.3.1. Land Surface Temperature Inversion
2.3.2. Research Index Features
2.3.3. Standard Deviation Ellipse (SDE)
2.3.4. Trend Analysis Based on Image Element Scale
2.3.5. Exploration of Spatial Divergence Pattern Factors—Geographic Detector
3. Analysis and Results
3.1. Urban Surface Temperature Classification Method
3.2. Spatial Distribution Characteristics of Urban Thermal Environment
3.3. Temporal Evolution Mechanism of the Urban Thermal Environment
3.4. Comprehensive Detection of Influencing Factors of Urban Thermal Environment
3.4.1. Single Factor Detection
3.4.2. Multi-factor Interaction Detection
4. Discussion
5. Conclusions
- In the future, we should start to strengthen the protection of urban green areas, water bodies, and landscapes to avoid the over-concentration of green areas and the destruction of ecological environments such as water body sewage, and for the improvement of the cooling effect of green areas through homogenization, decentralization, irregularity, and centralization of boundaries.
- Through the analysis, it was found that urban development is related to the urban heat zone. It is important to optimize urban spatial structure and form, control urban building high-density development, in order to appropriately control urban construction intensity, reasonably control the growth scale of construction land, increase the area of non-construction land, and promote intensive, green and low-carbon land use, which can help mitigate the urban heat environment effect. This would actively guide the construction of urban ventilation corridors and sponge cities.
- According to the results of the study on the causal mechanism of urban thermal environment, to improve the urban thermal environment, the protection and maintenance of natural ecosystems such as parks, green areas, and wetlands should be strengthened, and the area of ecological land should be increased in an appropriate amount.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Data Type | Data Description | Data Source | Data Display |
---|---|---|---|
Remote Sensing Data | NPP-VIIRS (500 m resolution NTL data, 2013–2020) [23] | http://ngdc.noaa.gov/eog, accessed on 10 November 2021 | |
Landsat8 TIRS (100 m resolution thermal, 2013–2020, cloud cover < 5%) | http://earthexplorer.usgs.gov, accessed on 10 November 2021 | ||
MODIS 11 L2 (1 km resolution LST product, 2013–2020) | http://lpdaac.usgs.gov, accessed on 10 November 2021 | ||
Digital elevation (ASTER GDEM) | https://earthexplorer.usgs.gov/, accessed on 10 November 2021 | ||
Humanities and Economic Data | Road network | GaoDe Map, accessed on 10 November 2021 | |
Vector boundary | |||
Population data | National Bureau of Statistics of China (http://www.stats.gov.cn/, accessed on 10 November 2021) | ||
Land use data | http://www.globallandcover.com/, accessed on 10 November 2021 | ||
Daily temperature data of the study area from 2013 to 2021 | http://data.cma.cn/, accessed on 10 November 2021 |
Layer | Factors | Formula | |
---|---|---|---|
Natural Factors Indicators | Greenness | NDVI | |
SAVI | |||
Imperviousness | NDBI | ||
IBI | |||
Wetness | MNDWI | ||
TCW | |||
Humanities and Economic Factor Indicators | NLI | ||
PD | |||
RD |
Temperature Level | Lowest Temperature Region | Lower Temperature Region | Low Temperature Region | Medium Temperature Region | High Temperature Region | Higher Temperature Region | Highest Temperature Region |
---|---|---|---|---|---|---|---|
Temperature interval | T < u − 2.5 std | u − 2.5 std ≤ T < u − 1.5 std | u − 1.5 std ≤ T < u − 0.5 std | u − 0.5 std ≤ T < u + 0.5 std | u + 0.5 std ≤ T < u + 1.5 std | u + 1.5 std ≤ T < u + 2.5 std | T ≥ u + 2.5 std |
Factor | 2013 | 2015 | 2018 | 2020 |
---|---|---|---|---|
Imperviousness | 0.608 | 0.673 | 0.488 | 0.537 |
Imperviousness + Greenness | −0.24186 | −0.38674 | −0.27084 | −0.18157 |
Imperviousness + Wetness | −0.45666 | −0.45527 | −0.4473 | −0.42594 |
Imperviousness + PD | 0.261 | 0.27964 | 0.23982 | 0.25941 |
Imperviousness + NIL | 0.4094 | 0.44785 | 0.34508 | 0.38212 |
Imperviousness + RD | 0.40363 | 0.43086 | 0.31862 | 0.38718 |
Greenness | −0.516 | −0.607 | −0.351 | −0.371 |
Greenness + Wetness | −0.55786 | −0.56527 | −0.44834 | −0.52687 |
Greenness + PD | 0.261 | 0.27963 | 0.23982 | 0.25941 |
Greenness + NIL | 0.40852 | 0.448 | 0.34482 | 0.38197 |
Greenness + RD | 0.40357 | 0.43099 | 0.31858 | 0.38715 |
Wetness | −0.439 | −0.500 | −0.36 | −0.404 |
Wetness + PD | 0.2608 | 0.26987 | 0.23952 | 0.25913 |
Wetness + NIL | 0.12008 | 0.40665 | 0.25468 | 0.30644 |
Wetness + RD | 0.38512 | 0.40225 | 0.30683 | 0.37447 |
PD | 0.225 | 0.201 | 0.16 | 0.166 |
PD+NIL | 0.2612 | 0.46001 | 0.24055 | 0.26032 |
PD+RD | 0.26376 | 0.3788 | 0.24426 | 0.26454 |
NIL | 0.355 | 0.043 | 0.248 | 0.234 |
NIL+RD | 0.40855 | 0.40855 | 0.40767 | 0.39634 |
RD | 0.328 | 0.315 | 0.226 | 0.240 |
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Zhao, Y.; Wu, Q.; Wei, P.; Zhao, H.; Zhang, X.; Pang, C. Explore the Mitigation Mechanism of Urban Thermal Environment by Integrating Geographic Detector and Standard Deviation Ellipse (SDE). Remote Sens. 2022, 14, 3411. https://doi.org/10.3390/rs14143411
Zhao Y, Wu Q, Wei P, Zhao H, Zhang X, Pang C. Explore the Mitigation Mechanism of Urban Thermal Environment by Integrating Geographic Detector and Standard Deviation Ellipse (SDE). Remote Sensing. 2022; 14(14):3411. https://doi.org/10.3390/rs14143411
Chicago/Turabian StyleZhao, Yifan, Qirui Wu, Panpan Wei, Hao Zhao, Xiwang Zhang, and Chenkun Pang. 2022. "Explore the Mitigation Mechanism of Urban Thermal Environment by Integrating Geographic Detector and Standard Deviation Ellipse (SDE)" Remote Sensing 14, no. 14: 3411. https://doi.org/10.3390/rs14143411
APA StyleZhao, Y., Wu, Q., Wei, P., Zhao, H., Zhang, X., & Pang, C. (2022). Explore the Mitigation Mechanism of Urban Thermal Environment by Integrating Geographic Detector and Standard Deviation Ellipse (SDE). Remote Sensing, 14(14), 3411. https://doi.org/10.3390/rs14143411