Assessing the Cooling Effect of Four Urban Parks of Different Sizes in a Temperate Continental Climate Zone: Wroclaw (Poland)
Abstract
:1. Introduction
2. Study Area
3. Data and Methods
3.1. Data Sources
- 28 May 2017, 9:43 GMT, 27.4 °C,
- 3 August 2018, at 9:43 GMT, 32.8 °C,
- 3 June 2019, 9:44 GMT, 28.9 °C.
3.2. Image Pre-Processing and Retrieval of LST
- calculating the Top of Atmospheric Spectral Radiance,
- conversion of the Radiance to Sensor Temperature,
- calculating the NDVI,
- calculating the Land Surface Emissivity, and
- LST retrieval.
3.3. Urban Park Metrics
- Park perimeter (PP),
- Park area (PA),
- Landscape shape index (LSI), which was designed by Patton (1975) and describes the compactness of a patch shape [37], in our case compactness of a park shape (7),
- 4.
- Park land cover (PLC) is the structure of a park’s land cover types expressed as a percentage of the total park area.
3.4. Spatial Statistics
- 3.
- 4.
- 5.
- Extended Park Cooling Island (PCIe), which we propose as the difference between the mean LST in the buffer of maximum cooling area (TR) and the mean LST inside the park (TP) (10):
4. Results
4.1. Analysis of Urban Park Metrics
4.2. Spatial Statistics of the Urban Park Effect on LST
4.2.1. A Park
4.2.2. B Park
4.2.3. C Park
4.2.4. D Park
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Authors (Year of Publication) | Study Type | Source Data | Main Methods | Region, Climate |
---|---|---|---|---|
Jansson et al. (2007) [38] | (1, 2) | Ground based measurements | Temperature difference between the built-up area and the urban park, descriptive statistics | Stockholm (Sweden)/Continental |
Cao et al. (2010) [37] | (1, 3) | Aster and Ikonos | Vegetation and shape indexes | Nagoya (Japan)/ Temperate humid |
Hamada and Ohta (2010) [42] | (1) | Ground based measurements | Descriptive statistics, bivariate correlation | Nagoya (Japan)/ Temperate humid |
Oliveira et al. (2011) [39] | (2) | Ground based measurements | Park cooling intensity (maximum difference between the measured values inside and outside of green area), distance index, influence of the solar exposure | Lisbon (Portugal)/Mediterranean |
Mahmoud (2011) [51] | (2, 4) | Ground based measurements, Questionnaire surveys | Thermal comfort indices, regression analysis, descriptive statistics | Cairo (Egypt) /Desert |
Cohen et al. (2012) [52] | (4) | Ground based measurements | Physiological Equivalent Temperature, regression analysis, human thermal comfort | Tel Aviv (Israel)/Subtropical Mediterranean |
Choi et al. (2012) [43] | (1) | Landsat 7 | Kriging, spatial-autocorrelation, inverse distance squared weighting analysis | Seoul (Korea)/Humid continental |
Siti Nor Afzan Buyadi et al. (2013) [29] | (4) | Landsat 5 | LST transect profiles, Temperature distribution of land use types | Shah Alam, Selangor (Malaysia)/Tropical |
Ren et al. (2013) [47] | (3) | Landsat 5, SPOT | Descriptive statistics, correlation of Park Cooling Intensity and forest structure | Changchun, Jilin province (China)/Humid continental with monsoon influence |
Kong et al. (2014) [46] | (1, 3) | Landsat 5, Ikonos | Multiple linear regression analysis of vegetation influence on PCI intensity | Nan**g, Jiangsu Province (China)/Subtropical |
Chang and Li (2014) [62] | (4) | Ground based measurements | Classification and Regression Tree analysis, regression analysis | Taipei (Taiwan)/Subtropical monsoon |
Feyisa et al. (2014) [60] | (1) | Landsat 7, ground based measurements | Park cooling distance, and intensity, regression | Addis Ababa (Ethiopia)/Desert |
Skoulika et al. (2014) [11] | (1) | Ground based measurements | Nocturnal and daytime cool island intensity | Athens (Greece)/ Mediterranean |
Anjos and Lopes (2014) [61] | (4) | Ground based measurements | Cluster analysis | Aracaju (Brasil)/ Tropical/subtropical |
Doick et al. (2014) [44] | (1, 2) | Ground based measurements | Descriptive statistics, distance-temperature ratio, frequency distribution | London (UK)/Temperate oceanic |
Cheng et al. (2015) [41] | (1) | Landsat | Correlation of LST and park size | Shanghai, China /Subtropical monsoon |
Chen et al. (2015) [56] | (4) | Ground based measurements, Questionnaire surveys | Descriptive statistics, linear regression | Shanghai, China /Subtropical monsoon |
Monteiro et al. (2016) [55] | (1, 4) | Ground based measurements | Size metric, regression analysis | London (UK)/Temperate oceanic |
Bao et al. (2016) [48] | (1) | Landsat 5, 8 | Landscape metrics, park cooling distance and direction | Baotou, Inner Mongolia Province (China)/Continental |
Yang et al. (2016) [10] | (1) | Ground based measurements | Diurnal UHI index, frequency distribution of UHI index differences | Bei**g (China)/Humid continental |
Anguluri and Narayanan (2017) [57] | (4) | Geo-eye | Per capita and proportional green indexes, GIS | Kalaburagi, North Karnataka (India)/ Tropical/subtropical |
Du et al. (2017) [59] | (1, 3) | Landsat 8, Google Earth | Green cool indexes: range, amplitude of temperature drop, temperature gradient | Shanghai (China) Subtropical |
Park et al. (2017) [45] | (2, 4) | Ground based measurements | Descriptive statistics/ linear regression analysis | Seoul (Korea)/Humid continental climate |
Sun et al. (2017) [30] | (4) | Ground based measurements | Relationship between landscape parameters and thermal comfort, Numerical simulation modelling, bivariate regression | Bei**g (China)/Humid continental |
Xu et al. (2017) [49] | (4) | Landsat 5 QuickBird | Landscape structure index, woodland aggregation index, regression analysis | Bei**g (China)/Humid continental |
Yang et al. (2017) [12] | (3) | Landsat 8, | Area, perimeter, area to perimeter ratio, shape, total area and number of patches metrics | Changchun, Jilin Province (China)/Continental |
Yang et al. (2017) [13] | (1, 4) | Landsat 8, GF-2 | Urban park metrics (area, perimeter, shape, patch density), cooling effect extent | Changchun/Changchun, Jilin Province (China)/Continental |
Yu et al. (2017) [50] | (4) | Landsat 7, 8, SPOT 5, | PCI extent, intensity, efficiency, and TVoE | Fuzhou, Fujian Province (China)/Subtropical |
Yu et al. (2018) [28] | (4) | Landsat 7, 8, SPOT 5, Google Earth | Land cover change effect on LST distribution | Fuzhou, Fujian Province (China)/Subtropical |
Wang et al. (2018) [54] | (1, 4) | Landsat 8 | Temperature Drop Amplitude, Temperature Drop Range, Pearson correlation, regression analysis | Changzhou, Jiangsu Province (China)/Subtropical |
Algretawee et al. (2019) [5] | (2) | Ground measurements (handheld devices) | Park cooling magnitude and distance indexes | Melbourne (Australia)/temperate oceanic |
Li et al. (2020) [65] | (1, 2, 3) | Landsat 8 | Landscape metrics | Zhengzhou (China)/Humid subtropical |
Peng et al. (2020) [40] | (1, 3) | Landsat 8 | Four park cooling indexes: intensity, gradient, area and efficiency | Shenzen (China)/Subtropical |
Qiu and Jia, (2020) [58] | (1) | Landsat 8 | PCI range, amplitude of temperature difference, temperature gradient, regression analysis | Bei**g (China)/Humid continental |
Appendix B
Appendix C
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Value/Year | 2017 | 2018 | 2019 |
---|---|---|---|
Min. | 19.9 °C | 16.0 °C | 19.9 °C |
Max. | 43.9 °C | 39.2 °C | 43.1 °C |
Mean | 28.3 °C | 27.6 °C | 28.6 °C |
Park | Area (ha) | Perimeter (m) | LSI | PLC | Geometry (Not to Scale) |
---|---|---|---|---|---|
A Park | 8.84 | 1669 | 1.58 | F 7.8% G 71.4% W 7.6% O 13.2% | |
B Park | 27.57 | 2845 | 1.53 | F 63.6% G 19.5% W 5.3% O 11.6% | |
C Park | 45.23 | 3722 | 1.43 | F 73.2% G 19.3% W 0.0% O 7.5% | |
D Park | 76.87 | 6310 | 2.61 | F 92.1% G 7.7% W 0.0% O 0.2% | |
Statistics | Park | 2017 | 2018 | 2019 |
---|---|---|---|---|
Mean LST [°C] | A Park | 28.2 | 27.7 | 28.8 |
B Park | 25.8 | 26.7 | 26.9 | |
C Park | 25.6 | 25.2 | 26.3 | |
D Park | 25.7 | 25.9 | 26.4 | |
PCA [ha] | A Park | 101.6 | 89.9 | 78.8 |
B Park | 199.1 | 182.4 | 181.0 | |
C Park | 287.9 | 312.8 | 308.3 | |
D Park | 691.8 | 688.7 | 660.5 | |
PCE | A Park | 11.5 | 10.2 | 8.9 |
B Park | 7.2 | 6.6 | 6.6 | |
C Park | 5.3 | 5.8 | 5.7 | |
D Park | 9.4 | 9.4 | 9.0 | |
PCG [°C/100 m] | A Park | 1.3 | 1.0 | 1.8 |
B Park | 1.6 | 1.2 | 1.7 | |
C Park | 1.0 | 0.7 | 1.0 | |
D Park | 2.1 | 1.4 | 2.0 | |
PCI [°C] | A Park | 3.1 | 1.9 | 2.8 |
B Park | 3.6 | 2.1 | 3.0 | |
C Park | 3.4 | 2.2 | 3.0 | |
D Park | 2.9 | 2.0 | 2.2 | |
PCIe [°C] | A Park | 3.1 | 2.0 | 3.0 |
B Park | 3.6 | 2.1 | 3.1 | |
C Park | 3.4 | 2.3 | 3.1 | |
D Park | 3.1 | 2.1 | 2.3 |
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Blachowski, J.; Hajnrych, M. Assessing the Cooling Effect of Four Urban Parks of Different Sizes in a Temperate Continental Climate Zone: Wroclaw (Poland). Forests 2021, 12, 1136. https://doi.org/10.3390/f12081136
Blachowski J, Hajnrych M. Assessing the Cooling Effect of Four Urban Parks of Different Sizes in a Temperate Continental Climate Zone: Wroclaw (Poland). Forests. 2021; 12(8):1136. https://doi.org/10.3390/f12081136
Chicago/Turabian StyleBlachowski, Jan, and Monika Hajnrych. 2021. "Assessing the Cooling Effect of Four Urban Parks of Different Sizes in a Temperate Continental Climate Zone: Wroclaw (Poland)" Forests 12, no. 8: 1136. https://doi.org/10.3390/f12081136