Factors Influencing Spatiotemporal Changes in the Urban Blue-Green Space Cooling Effect in Beijing–Tianjin–Hebei Based on Multi-Source Remote Sensing Data
Abstract
:1. Introduction
2. Data and Methodology
2.1. Study Area
2.2. Data Sources
2.3. Methods
2.3.1. HMI Model
- -
- Cooling Capacity Index CC (Cooling Capacity)
2.3.2. Wavelet Analysis
3. Results
3.1. Temporal and Spatial Changes in HMI
3.2. Environmental Factors Affecting Cooling Capacity in Blue-Green Spaces
3.2.1. Relationships between Cooling Capacity and Individual Factors
3.2.2. Relationships between Cooling Capacity and Multiple Environmental Factors
4. Discussion
4.1. Limitations of the Model
4.2. Urban Blue-Green Spatial Planning
5. Conclusions
- (1)
- Low HMI values in the Beijing–Tianjin–Hebei region from 1990 to 2020 were mainly distributed in the various central cities in the region. In addition, the stage in which the time-series change occurred was remarkably important. In particular, the average HMI of the urban area of Beijing decreased by 0.2 during the study period (decreased rapidly in the early stage, slowed in the late stage, and exhibited a small upward trend during 2010–2020). The cities and districts in the study area were in different stages of development during the same period of time. The primary driver behind the observed changes was the persistent impact of urban expansion and corresponding blue-green space reduction.
- (2)
- The HMI exhibited coherence with environmental factors that was mainly reflected at the hourly time–frequency scale. The main factors controlling the HMI varied on different time–frequency scales. Overall, the inclusion of four variables demonstrated the best coherence (AWC of 0.82). These variables were the most effective explanatory variables for changes in the HMI. The individual variable that best explained the changes in the HMI was air temperature, while the best bivariate combination was air temperature and wind speed.
- (3)
- When planning blue-green spaces, it is particularly important to consider air temperature, precipitation, and wind speed to improve the sustainability of a city and the comfort of its residents. It is possible to establish cool microclimate zones, reduce surface temperatures, and mitigate the UHI effect by concentrating multi-layered greenery in hot spots, installing rain gardens and permeable paving, considering wind channel design, and using highly reflective materials and green roofs.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Gong, P.; Liu, H.; Zhang, M.; Li, C.; Wang, J.; Huang, H.; Clinton, N.; Ji, L.; Li, W.; Bai, Y.; et al. Stable Classification with Limited Sample: Transferring a 30-m Resolution Sample Set Collected in 2015 to Mapping 10-m Resolution Global Land Cover in 2017. Sci. Bull. 2019, 64, 370–373. [Google Scholar] [CrossRef] [PubMed]
- Mirzaei, P.A. Recent Challenges in Modeling of Urban Heat Island. Sustain. Cities Soc. 2015, 19, 200–206. [Google Scholar] [CrossRef]
- Huang, C.; Xu, N. Quantifying Urban Expansion from 1985 to 2018 in Large Cities Worldwide. Geocarto. Int. 2022, 37, 18356–18371. [Google Scholar] [CrossRef]
- Estoque, R.C.; Murayama, Y.; Myint, S.W. Effects of Landscape Composition and Pattern on Land Surface Temperature: An Urban Heat Island Study in the Megacities of Southeast Asia. Sci. Total Environ. 2017, 577, 349–359. [Google Scholar] [CrossRef] [PubMed]
- Yang, L.; Qian, F.; Song, D.-X.; Zheng, K.-J. Research on Urban Heat-Island Effect. Procedia. Eng. 2016, 169, 11–18. [Google Scholar] [CrossRef]
- Kamath, H.G.; Martilli, A.; Singh, M.; Brooks, T.; Lanza, K.; Bixler, R.P.; Coudert, M.; Yang, Z.-L.; Niyogi, D. Human Heat Health Index (H3I) for Holistic Assessment of Heat Hazard and Mitigation Strategies beyond Urban Heat Islands. Urban Clim. 2023, 52, 101675. [Google Scholar] [CrossRef]
- Santamouris, M.; Ding, L.; Fiorito, F.; Oldfield, P.; Osmond, P.; Paolini, R.; Prasad, D.; Synnefa, A. Passive and Active Cooling for the Outdoor Built Environment—Analysis and Assessment of the Cooling Potential of Mitigation Technologies Using Performance Data from 220 Large Scale Projects. Sol. Energy 2017, 154, 14–33. [Google Scholar] [CrossRef]
- Chen, S.; Bao, Z.; Ou, Y.; Chen, K. The Synergistic Effects of Air Pollution and Urban Heat Island on Public Health: A Gender-Oriented Nationwide Study of China. Urban Clim. 2023, 51, 101671. [Google Scholar] [CrossRef]
- Ou, Y.; Bao, Z.; Ng, S.T.; Song, W.; Chen, K. Land-Use Carbon Emissions and Built Environment Characteristics: A City-Level Quantitative Analysis in Emerging Economies. Land Use Policy 2024, 137, 107019. [Google Scholar] [CrossRef]
- Pritipadmaja, P.; Garg, R.; Sharma, A. Assessing the Cooling Effect of Blue-Green Spaces: Implications for Urban Heat Island Mitigation. Water 2023, 15, 2983. [Google Scholar] [CrossRef]
- Zhou, D.; Bonafoni, S.; Zhang, L.; Wang, R. Remote Sensing of the Urban Heat Island Effect in a Highly Populated Urban Agglomeration Area in East China. Sci. Total Environ. 2018, 629, 415–429. [Google Scholar] [CrossRef] [PubMed]
- Carvalho, D.; Martins, H.; Marta-Almeida, M.; Rocha, A.; Borrego, C. Urban Resilience to Future Urban Heat Waves under a Climate Change Scenario: A Case Study for Porto Urban Area (Portugal). Urban Clim. 2017, 19, 1–27. [Google Scholar] [CrossRef]
- Huang, C.; Xu, N. Climatic Factors Dominate the Spatial Patterns of Urban Green Space Coverage in the Contiguous United States. Int. J. Appl. Earth Obs. Geoinf. 2022, 107, 102691. [Google Scholar] [CrossRef]
- Beauchamp, P.; Adamowski, J. An Integrated Framework for the Development of Green Infrastructure: A Literature Review. Eur. J. Sustain. Dev. 2013, 2, 1. [Google Scholar] [CrossRef]
- Sinnett, D.; Jerome, G.; Burgess, S.; Smith, N.; Mortlock, R. Building with Nature—A New Benchmark for Green Infrastructure. Town Ctry. Plan. 2017, 86, 427–431. [Google Scholar]
- Houkai, W.E.I.; Meng, N.; Le, L.I. China’s Strategies and Policies for Regional Development During the Period of the 14th Five-Year Plan. Chin. J. Urban Environ. Stud. 2020, 8, 2050008. [Google Scholar] [CrossRef]
- Fan, J.; Xiao, Z. Analysis of Spatial Correlation Network of China’s Green Innovation. J. Clean. Prod. 2021, 299, 126815. [Google Scholar] [CrossRef]
- Xu, N.; Lu, H.; Li, W.; Gong, P. Natural Lakes Dominate Global Water Storage Variability. Sci. Bull. 2024, 69, 1016–1019. [Google Scholar] [CrossRef]
- Huang, M.; Cui, P.; He, X. Study of the Cooling Effects of Urban Green Space in Harbin in Terms of Reducing the Heat Island Effect. Sustainability 2018, 10, 1101. [Google Scholar] [CrossRef]
- Yu, Z.; Yang, G.; Zuo, S.; Jørgensen, G.; Koga, M.; Vejre, H. Critical Review on the Cooling Effect of Urban Blue-Green Space: A Threshold-Size Perspective. Urban For. Urban Green. 2020, 49, 126630. [Google Scholar] [CrossRef]
- Jiang, Y.; Huang, J.; Shi, T.; Li, X. Cooling Island Effect of Blue-Green Corridors: Quantitative Comparison of Morphological Impacts. Int. J. Environ. Res. Public Health 2021, 18, 11917. [Google Scholar] [CrossRef] [PubMed]
- Şimşek, Ç.K.; Serter, G.; Ödül, H. A Study on the Cooling Capacities of Urban Parks and Their Interactions with the Surrounding Urban Patterns. Appl. Spat. Anal. 2022, 15, 1287–1317. [Google Scholar] [CrossRef]
- Sheng, S.; Wang, Y. Configuration Characteristics of Green-Blue Spaces for Efficient Cooling in Urban Environments. Sustain. Cities Soc. 2024, 100, 105040. [Google Scholar] [CrossRef]
- Quan, S.; Li, M.; Li, T.; Liu, H.; Cui, Y.; Liu, M. Nonlinear Effects of Blue-Green Space Variables on Urban Cold Islands in Zhengzhou Analyzed with Random Forest Regression. Front. Ecol. Evol. 2023, 11, 1185249. [Google Scholar] [CrossRef]
- Xue, X.; He, T.; Xu, L.; Tong, C.; Ye, Y.; Liu, H.; Xu, D.; Zheng, X. Quantifying the Spatial Pattern of Urban Heat Islands and the Associated Cooling Effect of Blue–Green Landscapes Using Multisource Remote Sensing Data. Sci. Total Environ. 2022, 843, 156829. [Google Scholar] [CrossRef]
- Amani-Beni, M.; Zhang, B.; Xie, G.-D.; Shi, Y. Impacts of Urban Green Landscape Patterns on Land Surface Temperature: Evidence from the Adjacent Area of Olympic Forest Park of Beijing, China. Sustainability 2019, 11, 513. [Google Scholar] [CrossRef]
- Zhou, W.; Cao, W.; Wu, T.; Zhang, T. The Win-Win Interaction between Integrated Blue and Green Space on Urban Cooling. Sci. Total Environ. 2023, 863, 160712. [Google Scholar] [CrossRef] [PubMed]
- Hamel, P.; Guerry, A.D.; Polasky, S.; Han, B.; Douglass, J.A.; Hamann, M.; Janke, B.; Kuiper, J.J.; Levrel, H.; Liu, H.; et al. Mapping the Benefits of Nature in Cities with the InVEST Software. npj Urban Sustain. 2021, 1, 25. [Google Scholar] [CrossRef]
- Kunapo, J.; Fletcher, T.D.; Ladson, A.R.; Cunningham, L.; Burns, M.J. A Spatially Explicit Framework for Climate Adaptation. Urban Water J. 2018, 15, 159–166. [Google Scholar] [CrossRef]
- Zawadzka, J.E.; Harris, J.A.; Corstanje, R. Assessment of Heat Mitigation Capacity of Urban Greenspaces with the Use of InVEST Urban Cooling Model, Verified with Day-Time Land Surface Temperature Data. Landsc. Urban Plan. 2021, 214, 104163. [Google Scholar] [CrossRef]
- Battemarco, B.P.; Tardin-Coelho, R.; Veról, A.P.; de Sousa, M.M.; da Fontoura, C.V.T.; Figueiredo-Cunha, J.; Barbedo, J.M.R.; Miguez, M.G. Water dynamics and blue-green infrastructure (BGI): Towards risk management and strategic spatial planning guidelines. J. Clean. Prod. 2022, 333, 129993. [Google Scholar] [CrossRef]
- Ji, L.; Shu, C.; Gaur, A.; Wang, L.; Lacasse, M. A State-of-the-Art Review of Studies on Urban Green Infrastructure for Thermal Resilient Communities. Build. Environ. 2024, 111524. [Google Scholar] [CrossRef]
- Jiang, Y.; Huang, J.; Shi, T.; Wang, H. Interaction of Urban Rivers and Green Space Morphology to Mitigate the Urban Heat Island Effect: Case-Based Comparative Analysis. Int. J. Environ. Res. Public Health 2021, 18, 11404. [Google Scholar] [CrossRef] [PubMed]
- Wu, A.-B.; Chen, F.-G.; Zhao, Y.-X.; Qin, Y.-J.; Liu, X.; Guo, X.-P. Multi-scenario Simulation of Construction Land Expansion and Its Impact on Ecosystem Carbon Storage in Beijing-Tianjin-Hebei Urban Agglomeration. Huan Jing Ke Xue 2024, 45, 2828–2839. [Google Scholar] [CrossRef] [PubMed]
- Fang, C.L. Important Progress and Prospects of China’s Urbanization and Urban Agglomeration in the Past 40 Years of Reform and Opening-Up. Econ. Geogr 2018, 38, 1–9. [Google Scholar]
- Wang, Z.; Liu, M.; Liu, X.; Meng, Y.; Zhu, L.; Rong, Y. Spatio-Temporal Evolution of Surface Urban Heat Islands in the Chang-Zhu-Tan Urban Agglomeration. Phys. Chem. Earth Parts A/B/C 2020, 117, 102865. [Google Scholar] [CrossRef]
- Peng, S. 1 km Monthly Potential Evapotranspiration Dataset in China (1990–2021); National Tibetan Plateau Data Center: Beijing, China, 2022. [Google Scholar]
- Yang, J.; Huang, X. The 30 m Annual Land Cover Dataset and Its Dynamics in China from 1990 to 2019. Earth Syst. Sci. Data 2021, 13, 3907–3925. [Google Scholar] [CrossRef]
- Muñoz-Sabater, J.; Dutra, E.; Agustí-Panareda, A.; Albergel, C.; Arduini, G.; Balsamo, G.; Boussetta, S.; Choulga, M.; Harrigan, S.; Hersbach, H.; et al. ERA5-Land: A State-of-the-Art Global Reanalysis Dataset for Land Applications. Earth Syst. Sci. Data 2021, 13, 4349–4383. [Google Scholar] [CrossRef]
- Sharp, R.; Tallis, H.T.; Ricketts, T.; Guerry, A.D.; Wood, S.A.; Chaplin-Kramer, R.; Nelson, E.; Ennaanay, D.; Wolny, S.; Olwero, N.; et al. InVEST—Integrated Valuation of Ecosystem Services and Tradeoffs; Stanford University: Stanford, CA, USA, 2016. [Google Scholar]
- Zardo, L.; Geneletti, D.; Pérez-Soba, M.; Van Eupen, M. Estimating the Cooling Capacity of Green Infrastructures to Support Urban Planning. Ecosyst. Serv. 2017, 26, 225–235. [Google Scholar] [CrossRef]
- Allen, R.G.; Pereira, L.S.; Raes, D.; Smith, M. Crop Evapotranspiration: Guidelines for Computing Crop Requirements. FAO Irrig. Drain. Pap. 1998, 300, D05109. [Google Scholar]
- Hu, W.; Si, B. Technical Note: Improved Partial Wavelet Coherency for Understanding Scale-Specific and Localized Bivariate Relationships in Geosciences. Hydrol. Earth Syst. Sci. 2021, 25, 321–331. [Google Scholar] [CrossRef]
- Su, L.; Miao, C.; Duan, Q.; Lei, X.; Li, H. Multiple-Wavelet Coherence of World’s Large Rivers With Meteorological Factors and Ocean Signals. J. Geophys. Res. Atmos. 2019, 124, 4932–4954. [Google Scholar] [CrossRef]
- Koc, C.B.; Osmond, P.; Peters, A. Evaluating the Cooling Effects of Green Infrastructure: A Systematic Review of Methods, Indicators and Data Sources. Sol. Energy 2018, 166, 486–508. [Google Scholar] [CrossRef]
- McDonald, R.; Kroeger, T.; Boucher, T.; Longzhu, W.; Salem, R.; Adams, J.; Bassett, S.; Edgecomb, M.; Garg, S. Planting Healthy Air: A Global Analysis of the Role of Urban Trees in Addressing Particulate Matter Pollution and Extreme Heat; The Nature Conservancy: Arlington, VA, USA, 2016. [Google Scholar]
- Jamei, E.; Thirunavukkarasu, G.; Chau, H.W.; Seyedmahmoudian, M.; Stojcevski, A.; Mekhilef, S. Investigating the cooling effect of a green roof in Melbourne. Build. Environ. 2023, 246, 110965. [Google Scholar] [CrossRef]
- Kim, C.U.; Ryu, Y.H.; O, N.C.; Ri, J.N. A rainwater harvesting system in buildings with green roofs and a rooftop greenhouse in Pyongyang. Int. J. Environ. Sci. Technol. 2023, 20, 12295–12306. [Google Scholar] [CrossRef]
- Harun, Z.; Akkurt, N.; Khor, Y.K.; Abbas, A.A.; Lotfy, E.R. Urban heat island patterns in garden cities: Putrajaya as a case study. Indoor Built Environ. 2024, 1420326X241244459. [Google Scholar] [CrossRef]
Type | Dataset | Temporal Resolution | Time Range | Spatial Resolution |
---|---|---|---|---|
Evapotranspiration | 1 km monthly potential evapotranspiration dataset in China (1901–2022) [37] | Monthly | 1990–2020 | 1 km |
Land cover | China Land Cover Dataset [38] | Annual | 1990–2020 | 30 m |
Air temperature Precipitation wind speed | ERA5-Land Monthly Aggregated—ECMWF Climate Reanalysis [39] | Monthly | 1990–2020 | 11 km |
Wavelet Coherence | POSP (%) | AWC | ||||||
---|---|---|---|---|---|---|---|---|
All | <8 d | 8–16 d | >16 d | All | <8 d | 8–16 d | >16 d | |
HMI–PRE | 18.54 | 6.31 | 75.61 | 2.78 | 0.53 | 0.53 | 0.85 | 0.38 |
HMI–TEM | 19.39 | 5.52 | 79.22 | 3.96 | 0.55 | 0.57 | 0.86 | 0.35 |
HMI–WS | 17.54 | 11.65 | 61.69 | 1.51 | 0.55 | 0.58 | 0.79 | 0.39 |
Wavelet Coherence | POSP (%) | AWC | ||||||
---|---|---|---|---|---|---|---|---|
All | <8 d | 8–16 d | >16 d | All | <8 d | 8–16 d | >16 d | |
HMI–TEM–WS | 24.08 | 19.94 | 74.21 | 3.16 | 0.73 | 0.82 | 0.91 | 0.55 |
HMI–TEM–PRE | 24.05 | 19.58 | 75.32 | 2.88 | 0.72 | 0.79 | 0.89 | 0.55 |
HMI–WS–PRE | 17.79 | 9.04 | 62.3 | 4.63 | 0.69 | 0.72 | 0.88 | 0.56 |
HMI–WS–PRE–TEM | 21.52 | 16.01 | 69.2 | 3.29 | 0.82 | 0.88 | 0.93 | 0.69 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Gong, H.; Cao, Y.; Yao, J.; Xu, N.; Chang, H.; Wu, S.; Hu, L.; Liu, Z.; Liu, T.; Zhang, Z. Factors Influencing Spatiotemporal Changes in the Urban Blue-Green Space Cooling Effect in Beijing–Tianjin–Hebei Based on Multi-Source Remote Sensing Data. Land 2024, 13, 1423. https://doi.org/10.3390/land13091423
Gong H, Cao Y, Yao J, Xu N, Chang H, Wu S, Hu L, Liu Z, Liu T, Zhang Z. Factors Influencing Spatiotemporal Changes in the Urban Blue-Green Space Cooling Effect in Beijing–Tianjin–Hebei Based on Multi-Source Remote Sensing Data. Land. 2024; 13(9):1423. https://doi.org/10.3390/land13091423
Chicago/Turabian StyleGong, Haiying, Yongqiang Cao, Jiaqi Yao, Nan Xu, Huanyu Chang, Shuqi Wu, Liuru Hu, Zihua Liu, Tong Liu, and Zihao Zhang. 2024. "Factors Influencing Spatiotemporal Changes in the Urban Blue-Green Space Cooling Effect in Beijing–Tianjin–Hebei Based on Multi-Source Remote Sensing Data" Land 13, no. 9: 1423. https://doi.org/10.3390/land13091423