Next Article in Journal
Environmental Law Clinics in Collaboration with Environmental NGOs in China
Next Article in Special Issue
An Investigation of GIS Overlay and PCA Techniques for Urban Environmental Quality Assessment: A Case Study in Toronto, Ontario, Canada
Previous Article in Journal
Modifying Bananas: From Transgenics to Organics?
Previous Article in Special Issue
High Biodiversity of Green Infrastructure Does Not Contribute to Recreational Ecosystem Services
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Screening of Tree Species for Improving Outdoor Human Thermal Comfort in a Taiwanese City

Program of Landscape and Recreation, National Chung Hsing University, 145 Xingda Rd., Taichung 402, Taiwan
*
Author to whom correspondence should be addressed.
Sustainability 2017, 9(3), 340; https://doi.org/10.3390/su9030340
Submission received: 24 December 2016 / Revised: 21 February 2017 / Accepted: 21 February 2017 / Published: 24 February 2017
(This article belongs to the Special Issue Maintaining Ecosystem Services to Support Urban Needs)

Abstract

:
Tropical cities can use urban greening designs featuring trees that provide shade and cooling in hot outdoor environments. The cooling effect involves numerous tree characteristics that are not easy to control during planting design, such as the canopy size and the optical properties of leaves. Planting the appropriate tree species dominates the cooling effects and the human thermal environment. Based on environmental and plant data, including the tree species, crown diameter of trees, physiologically equivalent temperature (PET), and sky view factor (SVF) in an outdoor space, a series of hierarchical cluster analysis (HCA) procedures was implemented to identify the tree species that are appropriate for improving thermal comfort. The results indicated strong correlations between SVF, average crown diameter, and PET. SVF decreased as the average crown diameter increased. For the average crown diameter of trees in an area wider than 1.5 m, the cooling effect was especially dominated by the tree species. Therefore, 15 species were screened by HCA procedures, based on a similar cooling effect. These species had various cooling effects, and were divided into four categories. Tree species, such as Spathodea campanulata and Cinnamomum camphora, had the appropriate crown diameter and cooling effect for the most comfortable thermal environment.

1. Introduction

Because outdoor environments in tropical areas are hot, people who engage in outdoor daytime activities tend to prefer outdoor areas shaded by trees, to outdoor areas that are not shaded by trees [1]. A favorable tree planting design provides appropriate shade and cooling functions [2,3,4,5]. These functions not only influence microclimate modification, especially for tropical and subtropical regions [6,7,8], but also improve thermal comfort [9,10,11]. For example, the structures of tree crowns, and the shapes and colors of the leaves, influence the levels of visible light and solar infrared waves [6]. Small-leaved tree species tend to be more effective at cooling than larger-leaved species [12,13]. Therefore, the cooling effect and shading of plants is mainly determined by the canopy density, leaf arrangement, height, branch spread, bole height, solar radiation, and optical properties of the leaves [8,14,15].
Mature trees dominate the crown characteristics. Other tree characteristics, such as the leaf density, are either involved in complex processes or subject to considerable natural variability. The influence of different species on the outdoor thermal environment also differs by area [16]. A database of urban trees should be created to support landscape designers, and tree species should be evaluated for cities’ tree planting plans and the maintenance of an urban microclimate that does not impair humans [6,17]. Several human-biometeorological variables, such as the standard effective temperature, predicted mean vote, mean radiation temperature (Tmrt), and physiological equivalent temperature (PET), are used to evaluate human thermal comfort [18]. These variables for estimating outdoor thermal sensation are presented as functions of air temperature (Ta), wind speed, solar radiation intensity, and absolute humidity [19]. In European countries and Hong Kong, wind has a negative effect on thermal sensation, and high wind speed only becomes significant in thermal sensation in winter [19,20]. The sky view factor (SVF) describes the impact of canopy density on air circulation [8] and the irradiance reduction of plant canopies [9,10,21]. Because SVF can be measured conveniently, some studies have used SVF to discuss variations in shading levels and air temperature [22,23,24,25]. Areas with relatively dense broadleaf trees tend to have low SVF values [26]. High-SVF areas with thin broadleaf trees were shown to facilitate improved ventilation [27], but these areas also have low shading levels and are exposed to relatively high-air-temperature environments on summer days [28]. Therefore, SVF may be an integrated indicator to describe the crown characteristics. Hierarchical cluster analysis (HCA) is suitable for clustering targets with an unknown number of groups [29], and has been applied to identify pollution sources and assess rice production risks on the basis of the similarity of observations [30,31,32]. In addition, previous studies on the planting design of green spaces have investigated the visual effects, but not the functions, of plants.
The aim of this study was to improve outdoor human thermal environments through the appropriate choice of tree species for planting in open spaces, especially in the tropics. This study included a survey of environmental and planting parameters of outdoor spaces in Taiwan; parameters included the tree species, crown diameter, SVF, and meteorological and human-biometeorological variables. A database was created to support landscape designers. The relationships between these parameters were also discussed. Finally, HCA was used to choose appropriate tree species for tree planting in open spaces. This method could facilitate the improvement of thermal environmental comfort and climate adaptation in tropical urban areas.

2. Materials and Methods

2.1. Thermal Environment and Planting Design of Outdoor Space

Our study was conducted in the outdoor space of Chiayi Park (23.48°N, 120.47°E), which is located in southern Taiwan, and experiences subtropical and tropical climates (Figure 1). July is the hottest month, with a maximum air temperature of 33.1 °C and an average air temperature of 25.2 °C. The monthly average relative humidity (RH, %) is 75%–85% and the average vapor pressure during 2011–2015 was 27 hPa–32 hPa, indicating that this outdoor space experiences hot summers and high year-round humidity. Chiayi Park features a rich ecology and abundant relaxation facilities. Additionally, various shaded spaces provide park visitors with space for outdoor activities. In Figure 1, a total of 12 sites, including A1, A2, A3, B1, B2, B3, C1, C2, C3, D1, D2, and D3, can be seen, that could encourage more people to participate in activities and which have a low wind speed. A wind meter (R M Young 81000) was used to record the wind speed at a height of 1.1 m above the ground, and the average wind speed for each measurement was <0.5 m/s. For evaluating outdoor human thermal environments and reducing the interference of wind, the 12 sites were chosen as measurement sites. Environmental and planting parameters, such as plant characteristics, SVF, and meteorological and human-biometeorological variables, were measured at the 12 sites (Figure 1). Measurements were taken during the summer (July–October 2014) because this season witnesses the maximum use of shaded spaces.
Meteorological and human-biometeorological variables, including Ta, Tmrt, globe temperature (Tg), and PET, were measured to evaluate the outdoor thermal comfort [33]. RH, Ta, and Tg were sampled at a height of 1.1 m above the ground. A black globe thermometer was used to record Tg, and a Center 314 humidity and temperature meter was used to record the humidity and Ta. Measurements were recorded automatically and simultaneously at 1-min intervals, from 10:00 to 16:00, as the Ta of this period is hot; the temporal resolution of 2 h of recorded data will be adopted for further analysis. The recorded meteorological data per day was selected, according to similar weather conditions; for example, the recorded data was not selected for analysis when it rained during the day. Tmrt was calculated using Ta, Tg, and wind velocity, on the basis of ISO standard 7726 [34,35]. To obtain the values of PET [36,37], parameters including Ta, relative humidity, wind speed, and Tmrt, were entered into RayMan software package [38].
The studied area included 95 types of plant. Plant characteristics, such as tree species, tree height (m), crown diameter (m), and diameter at breast height (m), were recorded. Tree height, measured from the base to the tip of the tree, was measured using a Christen hypsometer [39]. The crown diameter of each tree was obtained by taking the arithmetic mean of the horizontal crown diameter measured on the north-south axis, and again on the east-west axis [40]. The diameter at breast height was obtained by directly measuring the diameter of the trunk with a tape. GPS was used to locate the plant which had been measured, and photographs of the plant were recorded. Photographs or leaves were collected and compared with the literature to confirm the tree species [41], and the data were entered into a geographic information system database. In addition, SVF was determined using a fish-eye lens at a height of 1.1 m above the ground and RayMan software package [6,38].

2.2. Screening of Appropriate Tree Species Using HCA

HCA is a popular method for discovering patterns in large observed data and clusters targets according to the similarity of observations (or variable) [42]. Groups of tree species with similar characteristics were identified using HCA. The distance between two tree species was defined as the Euclidean norm of the difference between the centered and scaled vectors of their characteristics. The dissimilarity between clusters, based on this distance, was defined using Ward’s method; the standardized m-space Euclidian distance is given by the following equation [43]:
d i j = k = 1 m ( X i k X j k ) 2 m
where Xik denotes the kth measured variable for observation i and Xjk is the kth measured variable for observation j. In the HCA dendrogram, a short distance indicates that the two observations are similar or “close together”, and vice versa. Detailed calculation processes of HCA can be found in a classical paper conducted by Punj and Stewart (1983) [44]. Some common decision rules, such as the R-squared (R2) and semipartial R-squared (SPR) rules, were recommended for screening the appropriate observations and evaluating the numbers of groups; the number of groups should be chosen such that a further reduction results in a substantial decrease in the SPR [45]. Within the analysis, when the rule values were bad, the inappropriate observations were removed, and the HCA was implemented progressively, until better rule values were obtained. In this study, the data were analyzed using SPSS 18 software.
Specifically, tree species were classified into the same group if they had a similar cooling effect; the variables, including crown diameter, SVF, and PET, were the inputs of HCA. The process was repeated to identify the appropriate tree species and determine the number of groups, according to the R2 and SPR rules. However, because trees with low heights did not cause shading and cooling effects, when they were located near other trees with higher heights, the HCA procedures for screening the appropriate tree species were conducted on the basis of the survey data for the trees near the SVF measurement sites that were higher than 2 m. In addition, the discovered patterns depended on the observations, climatic conditions, and geological environment.

3. Results and Discussion

3.1. Tree Database and the Relationships between Average Crown Diameter, Plant Height, Diameter at Breast Height, and SVF

The tree database comprised 614 trees that were surveyed in the study area, and Table 1 shows the classification of tree species according to the average crown diameter. Ten plant species were identified with crown diameters exceeding 10 m; twenty-five plants species were identified with crown diameters between 5 and 10 m; and twenty-one plants species were identified with crown diameters between 3 and 5 m.
Figure 2 shows the relationships between the crown diameters, diameters at breast height, and the plant heights of the trees. In Figure 2a, the trees with the longest diameters at breast height are the Norfolk Island pine and Mangifera indica; the tree with the shortest diameter at breast height was Zanthoxylum piperitum. The tree with the widest crown diameter was Delonix regia, and the tree with the narrowest crown diameter was Lantana camara. As expected, the low regression coefficient (R2 = 0.04) demonstrated no correlation between the tree diameter at breast height and the crown diameter. As shown in Figure 2b, the highest and lowest tree species were Roystonea regia and Lantana camara, respectively. Some trees with similar heights had different crown diameters, possibly because they were different tree species and had different soil environments. For example, specimens of Magnolia denudata and Chrysalidocarpus lutescens were both 4.8 m high, but they had crown diameters of 4.1 and 5.2 m, respectively. However, the high regression coefficient (R2 = 0.83) indicated a high correlation between the tree height and the crown diameter; the crown diameter increased with increasing tree height in the study area.
Table 2 shows the actual sky view situation (fish-eye lens), SVFs at 12 measuring sites (Figure 1), and the tree species near the SVF measurement sites. A total of 56 tree species were located within 15 m of the SVF measurement centers, and SVFs varied from 0.025 to 0.637. A low SVF value indicated a lower visible sky value and a high leaf density value; the highest and lowest SVF values were measured at sites A2 and D3, respectively. Because sites with low SVF values had high shaded area values, people tended to do activities there during hot conditions [1,28]. Figure 3 reveals the relationship between SVF and the average crown diameter of trees at the 12 measurement sites. The average crown diameters varied from 0.6 to 8.4 m. The high R2 indicated a significant correlation between SVF and the average crown diameter, and SVF decreased with increasing crown diameter in the study area. An inversion point was also observed. This revealed that SVF increased and the shaded area substantially decreased, when the average crown diameter of trees was less than 1.5 m.

3.2. Relationship between Crown Diameters of Plants, PET, and Tmrt

Figure 4 shows the relationship between the crown diameters of plants, PET, and Tmrt at the measurement sites; PET was strongly correlated with Tmrt. When the average crown diameter was shorter than approximately 1.5 m, PET was usually higher than 40 °C and SVF was larger than 0.411; thus, the corresponding outdoor thermal comfort feeling ranged from hot to very hot [46]. Comparatively, the trees with average crown diameters wider than 1.5 m, introduced cooling effects and resulted in a lower PET; however, the cooling level was not correlated with the average crown diameter when PET was between 20 °C and 40 °C. These results indicate that the tree species with average crown diameters narrower than 1.5 m result in a larger open sky, causing a hot thermal environment. When the average crown diameter of trees was wider than 1.5 m, the cooling effect was affected by other tree characteristics, in addition to the crown diameter, such as the optical properties of leaves. However, tree characteristics were not easy to control, in order to improve outdoor thermal comfort. Therefore, screening of the appropriate tree species is essential for improving outdoor thermal comfort.

3.3. Appropriate Tree Species for Improving Outdoor Thermal Comfort

After implementing a series of HCA procedures on the screening of appropriate tree species, the results suggested that 14 species had similar cooling effects (Figure 5). In addition, significant reductions in the SPR and the R2 from four groups to three groups, indicated that the four-group solution was the most appropriate, and the tree species for the four groups are shown in Table 3. Ficus microcarpa was the only tree species in Group A; the trees in Group B included Delonix regia and Terminalia catappa; the trees in Group C were Juniperus chinensis, Bischofia javanica Blume, and Chamaecyparis taiwanensis; and the trees in Group D included Michelia figo, Acer, Spathodea campanulata, Cinnamomum camphora, Liquidambar formosana, Araucaria cunninghamii, and Pistacia chinensis. The corresponding environmental conditions in which these 15 species exist, are shown in Figure 6; the thermal environment of the study area can be categorized as one of three sections, on the basis of the Taiwanese PET classification [46]: “Cool”, “Comfortable”, and “Warm to Hot”. The tree species in Groups A and B had wider crown diameters than those in the other groups, and they had superior cooling effects. Tree species in Group C had narrower crown diameters and an inferior cooling effect. The tree species in Group D had wider crown diameters than those in Group C, and a cooling effect that created the most comfortable thermal environment in the study area. The cooling characteristics of the species in Group D could provide designers with planting design references for thermal environments. In addition, the HCA procedure used in this study could serve as a useful tool for identifying appropriate species.

4. Conclusions

Taiwan experiences subtropical and tropical climates, and the thermal environments of outdoor spaces in urban areas are hot, especially at noon and during the afternoon. Improved planting design provides trees with shade and cooling functions that improve the thermal comfort of the outdoor space. The cooling effects involve complicated tree characteristics such as canopy size, tree height, and the optical properties of leaves. However, such tree characteristics are not easy to control, in order to improve outdoor thermal comfort. Therefore, planting the appropriate tree species dominates the cooling functions. In addition, the improvements made by different species to outdoor thermal environments varied greatly in different areas. Therefore, adopting a method to determine the appropriate tree species for planting design reference is essential. This study investigated environmental and plant data, including the tree species, crown diameter, meteorological and human-biometeorological variables, and SVF, in an outdoor space in Taiwan. A series of HCA procedures were applied to identify the appropriate trees species which could improve the thermal comfort of the outdoor space.
The results indicated a significant correlation between SVF and the average crown diameter, and they demonstrated that the SVF value decreased with the increasing average crown diameter of trees in the study area. When the average crown diameter was less than 1.5 m, the SVF value increased dramatically, and the shaded area substantially decreased. The trees with average crown diameters narrower than 1.5 m tended to have high SVF and a low canopy, and then the low irradiance reduction of the plant canopy would result in a high land surface temperature and hot environment. When the average crown diameter of the trees was wider than 1.5 m, the cooling effect was dominated by the other tree characteristics. This result indicated that planting appropriate tree species is the way to effectively improve outdoor thermal comfort in tropical climate areas. Fifteen species were identified by the HCA procedures with different cooling effects, and they were divided into four categories. The plants Ficus microcarpa, Delonix regia, and Terminalia catappa, had the most effective cooling effects. By contrast, Juniperus chinensis, Bischofia javanica Blume, and Chamaecyparis taiwanensis, had the least effective cooling effects. Other tree species had relatively large crown diameters and appropriate characteristics for cooling effects. Therefore, the HCA procedures are a useful tool for identifying the right tree species and improving the thermal comfort of open spaces. These results can serve as a reference for outdoor landscape planting designers in urban areas of the tropics. Planting the right trees in cities can also adapt those cities to climate change.

Acknowledgments

The authors would like to thank the National Science Council of Taiwan for the partial financial support of this research under Project NSC103-2221-E-507-005 and NSC104-2221-E-005-077. Chris Law is appreciated for his editorial assistance.

Author Contributions

For research articles with several authors, a short paragraph specifying their individual contributions must be provided. The following statements should be used “Yu-Hao Lin and Kang-Ting Tsai conceived and designed the experiments; Kang-Ting Tsai performed the experiments; Yu-Hao Lin and Kang-Ting Tsai analyzed the data; Yu-Hao Lin and Kang-Ting Tsai contributed reagents/materials/analysis tools; Yu-Hao Lin and Kang-Ting Tsai wrote the paper”. Authorship must be limited to those who have contributed substantially to the work reported.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Lin, T.-P.; Tsai, K.-T.; Hwang, R.-L.; Matzarakis, A. Quantification of the effect of thermal indices and sky view factor on park attendance. Landsc. Urban Plan. 2012, 107, 137–146. [Google Scholar] [CrossRef]
  2. Bowler, D.E.; Buyung-Ali, L.; Knight, T.M.; Pullin, A.S. Urban greening to cool towns and cities: A systematic review of the empirical evidence. Landsc. Urban Plan. 2010, 97, 147–155. [Google Scholar] [CrossRef]
  3. Holst, J.; Mayer, H. Impacts of street design parameters on human-biometeorological variables. Meteorol. Z. 2011, 20, 541–552. [Google Scholar]
  4. Mullaney, J.; Lucke, T.; Trueman, S.J. A review of benefits and challenges in growing street trees in paved urban environments. Landsc. Urban Plan. 2015, 134, 157–166. [Google Scholar] [CrossRef]
  5. Lee, H.; Mayer, H.; Chen, L. Contribution of trees and grasslands to the mitigation of human heat stress in a residential district of Freiburg, Southwest Germany. Landsc. Urban Plan. 2016, 148, 37–50. [Google Scholar] [CrossRef]
  6. De Abreu-Harbich, L.V.; Labaki, L.C.; Matzarakis, A. Effect of tree planting design and tree species on human thermal comfort in the tropics. Landsc. Urban Plan. 2015, 138, 99–109. [Google Scholar] [CrossRef]
  7. Sanusi, R.; Johnstone, D.; May, P.; Livesley, S.J. Street orientation and side of the street greatly influence the microclimatic benefits street trees can provide in summer. J. Environ. Qual. 2016, 45, 167–174. [Google Scholar] [CrossRef] [PubMed]
  8. Hsieh, C.-M.; Jan, F.-C.; Zhang, L. A simplified assessment of how tree allocation, wind environment, and shading affect human comfort. Urban For. Urban Green. 2016, 18, 126–137. [Google Scholar] [CrossRef]
  9. Lee, H.; Holst, J.; Mayer, H. Modification of Human-Biometeorologically Significant Radiant Flux Densities by Shading as Local Method to Mitigate Heat Stress in Summer within Urban Street Canyons. Adv. Meteorol. 2013, 2013, 312572. [Google Scholar] [CrossRef]
  10. Lee, H.; Mayer, H.; Schindler, D. Importance of 3-D radiant flux densities for outdoor human thermal comfort on clear-sky summer days in Freiburg, Southwest Germany. Meteorol. Z. 2014, 23, 315. [Google Scholar]
  11. Coutts, A.M.; White, E.C.; Tapper, N.J.; Beringer, J.; Livesley, S.J. Temperature and human thermal comfort effects of street trees across three contrasting street canyon environments. Theor. Appl. Climatol. 2016, 124, 55–68. [Google Scholar] [CrossRef]
  12. Doick, K.; Hutchings, T. Air temperature regulation by trees and wider green infrastructure in urban areas: The current state of knowledge; Research note 12 (FCRN012). For. Comm. 2013, 12, 1–10. [Google Scholar]
  13. Leuzinger, S.; Vogt, R.; Körner, C. Tree surface temperature in an urban environment. Agric. For. Meteorol. 2010, 150, 56–62. [Google Scholar] [CrossRef]
  14. Kong, F.; Yan, W.; Zheng, G.; Yin, H.; Cavan, G.; Zhan, W.; Zhang, N.; Cheng, L. Retrieval of three-dimensional tree canopy and shade using terrestrial laser scanning (TLS) data to analyze the cooling effect of vegetation. Agric. For. Meteorol. 2016, 217, 22–34. [Google Scholar] [CrossRef]
  15. Zhang, Z.; Lv, Y.; Pan, H. Cooling and humidifying effect of plant communities in subtropical urban parks. Urban For. Urban Green. 2013, 12, 323–329. [Google Scholar] [CrossRef]
  16. Zheng, S.; Zhao, L.; Li, Q. Numerical simulation of the impact of different vegetation species on the outdoor thermal environment. Urban For. Urban Green. 2016, 18, 138–150. [Google Scholar] [CrossRef]
  17. Tan, Z.; Lau, K.K.-L.; Ng, E. Urban tree design approaches for mitigating daytime urban heat island effects in a high-density urban environment. Energy Build. 2016, 114, 265–274. [Google Scholar] [CrossRef]
  18. Nastos, P.T.; Matzarakis, A. Weather impacts on respiratory infections in Athens, Greece. Int. J. Biometeorol. 2006, 50, 358–369. [Google Scholar] [CrossRef] [PubMed]
  19. Cheng, V.; Ng, E.; Chan, C.; Givoni, B. Outdoor thermal comfort study in a sub-tropical climate: A longitudinal study based in Hong Kong. Int. J. Biometeorol. 2012, 56, 43–56. [Google Scholar] [CrossRef] [PubMed]
  20. Nikolopoulou, M.; Lykoudis, S. Thermal comfort in outdoor urban spaces: Analysis across different European countries. Build. Environ. 2006, 41, 1455–1470. [Google Scholar] [CrossRef]
  21. Scott, K.I.; Simpson, J.R.; McPherson, E.G. Effects of tree cover on parking lot microclimate and vehicle emissions. J. Arboric. 1999, 25, 129–142. [Google Scholar]
  22. Dimoudi, A.; Nikolopoulou, M. Vegetation in the urban environment: Microclimatic analysis and benefits. Energy Build. 2003, 35, 69–76. [Google Scholar] [CrossRef]
  23. Emmanuel, R.; Johansson, E. Influence of urban morphology and sea breeze on hot humid microclimate: The case of Colombo, Sri Lanka. Clim. Res. 2006, 30, 189–200. [Google Scholar] [CrossRef]
  24. Ha, J.; Lee, S.; Park, C. Temporal Effects of Environmental Characteristics on Urban Air Temperature: The Influence of the Sky View Factor. Sustainability 2016, 8, 895. [Google Scholar] [CrossRef]
  25. Lai, A.; Maing, M.; Ng, E. Observational studies of mean radiant temperature across different outdoor spaces under shaded conditions in densely built environment. Build. Environ. 2017, 114, 397–409. [Google Scholar] [CrossRef]
  26. Jan, F.-C.; Hsieh, C.-M.; Ishikawa, M.; Sun, Y.-H. The influence of tree allocation and tree transpiration on the urban microclimate: An analysis of a subtropical urban park. Environ. Urban. Asia 2013, 4, 135–150. [Google Scholar] [CrossRef]
  27. Gál, T.; Unger, J. A new software tool for SVF calculations using building and tree-crown databases. Urban Clim. 2014, 10 Pt 3, 594–606. [Google Scholar] [CrossRef]
  28. Lin, T.-P.; Matzarakis, A.; Hwang, R.-L. Shading effect on long-term outdoor thermal comfort. Build. Environ. 2010, 45, 213–221. [Google Scholar] [CrossRef]
  29. Tsai, H.; Lin, Y.-H.; Yang, M.-D. Exploring Long Term Spatial Vegetation Trends in Taiwan from AVHRR NDVI3g Dataset Using RDA and HCA Analyses. Remote Sens. 2016, 8, 290. [Google Scholar] [CrossRef]
  30. Chen, H.-W.; Chen, W.-Y.; Chang, C.-N.; Chuang, Y.-H.; Lin, Y.-H. Identifying airborne metal particles sources near an optoelectronic and semiconductor industrial park. Atmos. Res. 2016, 174–175, 97–105. [Google Scholar] [CrossRef]
  31. Kelepertzis, E.; Argyraki, A. Mercury in the urban topsoil of Athens, Greece. Sustainability 2015, 7, 4049–4062. [Google Scholar] [CrossRef]
  32. Chen, Q.; Zhang, J.; Zhang, L. Risk Assessment, Partition and Economic Loss Estimation of Rice Production in China. Sustainability 2015, 7, 563–583. [Google Scholar] [CrossRef]
  33. Yang, F.; Qian, F.; Zhao, W. Towards a Climate-Responsive Vertical Pedestrian System: An Empirical Study on an Elevated Walkway in Shanghai China. Sustainability 2016, 8, 744. [Google Scholar] [CrossRef]
  34. International Standard Organization (ISO). International Standard 7726, Thermal Environment-Instruments and Method for Measuring Physical Quantities; International Standard Organization: Geneva, Switzerland, 1998. [Google Scholar]
  35. Thorsson, S.; Lindberg, F.; Eliasson, I.; Holmer, B. Different methods for estimating the mean radiant temperature in an outdoor urban setting. Int. J. Climatol. 2007, 27, 1983–1993. [Google Scholar] [CrossRef]
  36. Mayer, H.; Höppe, P. Thermal comfort of man in different urban environments. Theor. Appl. Climatol. 1987, 38, 43–49. [Google Scholar] [CrossRef]
  37. Mayer, H.; Holst, J.; Dostal, P.; Imbery, F.; Schindler, D. Human thermal comfort in summer within an urban street canyon in Central Europe. Meteorol. Z. 2008, 17, 241–250. [Google Scholar]
  38. Lee, H.; Mayer, H. Validation of the mean radiant temperature simulated by the RayMan software in urban environments. Int. J. Biometeorol. 2016, 60, 1775–1785. [Google Scholar] [CrossRef] [PubMed]
  39. Marliana, S.N.; Rühe, F. Post-reforestation vegetation development on abandoned highland fields in Java, Indonesia. For. Ecol. Manag. 2014, 328, 245–253. [Google Scholar] [CrossRef]
  40. Gering, L.R.; May, D.M. The relationship of diameter at breast height and crown diameter for four species groups in Hardin County, Tennessee. South. J. Appl. For. 1995, 19, 177–181. [Google Scholar]
  41. Lu, F.-Y.; Tasi, K.-H.; Jhuang, T.; Jhang, Y. Illustrated Commercial Wood in Taiwan; Council of Agriculture, & Chiayi Institute of Technology: Chiayi, Taiwan, 1998. [Google Scholar]
  42. Eriksson, L.; Byrne, T.; Johansson, E.; Trygg, J.; Vikström, C. Multi-and Megavariate Data Analysis Basic Principles and Applications; Umetrics Academy: Umeå, Sweden, 2013. [Google Scholar]
  43. Davis, J.C. Statistics and Data Analysis in Geology; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 1990; p. 656. [Google Scholar]
  44. Punj, G.; Stewart, D.W. Cluster analysis in marketing research: Review and suggestions for application. J. Mark. Res. 1983, 20, 134–148. [Google Scholar] [CrossRef]
  45. Milligan, G.W.; Cooper, M.C. An examination of procedures for determining the number of clusters in a data set. Psychometrika 1985, 50, 159–179. [Google Scholar] [CrossRef]
  46. Lin, T.-P.; Matzarakis, A. Tourism climate and thermal comfort in Sun Moon Lake, Taiwan. Int. J. Biometeorol. 2008, 52, 281–290. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Measurement sites of environmental parameters in the study area.
Figure 1. Measurement sites of environmental parameters in the study area.
Sustainability 09 00340 g001
Figure 2. Relationship between (a) crown diameter and diameter at breast height (b) crown diameter and plant height.
Figure 2. Relationship between (a) crown diameter and diameter at breast height (b) crown diameter and plant height.
Sustainability 09 00340 g002
Figure 3. Relationship between SVF and average crown diameter of trees around the measurement site.
Figure 3. Relationship between SVF and average crown diameter of trees around the measurement site.
Sustainability 09 00340 g003
Figure 4. Relationship between average crown diameter of plants, PET, and Tmrt. * The value is average of 2 h of recorded data.
Figure 4. Relationship between average crown diameter of plants, PET, and Tmrt. * The value is average of 2 h of recorded data.
Sustainability 09 00340 g004
Figure 5. Cluster dendrogram grouping tree species with similar cooling patterns.
Figure 5. Cluster dendrogram grouping tree species with similar cooling patterns.
Sustainability 09 00340 g005
Figure 6. Thermal comfort range of outdoor landscape plants.
Figure 6. Thermal comfort range of outdoor landscape plants.
Sustainability 09 00340 g006
Table 1. Plant species in the database.
Table 1. Plant species in the database.
Average Crown DiameterPlant Specie
Crown diameters exceeding 10 mTectona grandis, Peltophorum pterocarpum, Cerbera manghas, Hibiscus tiliaceus, Ficus microcarpa, Betula pendula, Delonix regia, Haematoxylum campechianum, Zelkova serrata, and Terminalia catappa
Crown diameters between 5 and 10 mRoystonea regia, Lagerstroemia speciosa, Terminalia catappa, Araucaria heterophylla, Terminalia mantaly, Adenanthera pavonina, Pongamia pinnata, Spathodea campanulata, Melaleuca leucadendra, Magnolia denudate, Pterocarpus indicus, Phoebe faberi, Michelia figo, Mangifera indica, Araucaria cunninghamii, Tabebuia impetiginosa, Machilus zuihoensis Hayata, Saraca asoca, Alstonia scholaris, Liquidambar formosana Hance, Acer, Cinnamomum camphora, Mangifera indica, Bauhinia blakeana, and Terminalia catappa
Crown diameters between 3 and 5 mMacaranga tanarius, Pithecellobium dulce, Cassia fistula, Chamaecyparis obtusa, Chorisia speciosa, Bischofia javanica Blume, Melia azedarach, Swietenia mahagoni, Calocedrus formosana, Hyophorbe verschaffeltii, Sterculia foetida, Pistacia chinensis, Elaeocarpus sylvestris, Pinus jeffreyi, Cocos nucifera, Syzygium samarangense, Juniperus chinensis, Dimocarpus longan, Barringtonia racemosa, Artocarpus altilis, and Codiaeum variegatum
Crown diameters smaller than 3 mLagerstroemia subcostata, Gordonia axillaris, Caryota urens, Carica papaya, Calocedrus formosana, Fraxinus griffithii, Hibiscus rosa-sinensis, Podocarpus nagi, Washingtonia robusta, Bambusa vulgaris Schrad, Duranta erecta, Salix babylonica, Ficus benjamina, Zanthoxylum piperitum, Cananga odorata, Strelitzia nicolai, Michelia compressa, Triadica sebifera, Hyophorbe lagenicaulis, Pachira aquatica, Lantana camara, Prunus mume, Sapindus, Lagerstroemia speciosa, Ficus religiosa, Aucuba japonica, Erythrina variegate, Chrysalidocarpus lutescens, Allamanda schottii, Broussonetia papyrifera, Garcinia subelliptica Merr., Plumeria alba, Aglaia odorata, Elaeocarpus serratus Linn., Agave americana, Phoenix roebelenii, and Euphorbia splendens
Table 2. Plant species, sky view, and SVF values near the measurement sites.
Table 2. Plant species, sky view, and SVF values near the measurement sites.
Site aFish-Eye LensSVFPlant Species *
A1 Sustainability 09 00340 i0010.611Ficus religiosa, Adenanthera pavonina, Roystonea regia, Pterocarpus indicus, Cinnamomum camphora, Artocarpus altilis, Juniperus chinensis, Liquidambar formosana Hance, and Lagerstroemia speciose
A2 Sustainability 09 00340 i0020.637Chorisia speciosa, Hibiscus tiliaceus, Allamanda schottii, Peltophorum pterocarpum, and Ficusmicrocarpa
A3 Sustainability 09 00340 i0030.544Cinnamomum camphora, Chamaecyparis obtusa, Juniperus chinensis, Lagerstroemia speciosa, and Swietenia mahagoni
B1 Sustainability 09 00340 i0040.419Bauhinia blakeana, Tabebuia impetiginosa, Alstonia scholaris, Delonix regia, Ficus microcarpa, Spathodea campanulata, and Terminalia catappa
B2 Sustainability 09 00340 i0050.420Garcinia subelliptica Merr., Ficus benjamina, Peltophorum pterocarpum, Liquidambar formosana Hance, Juniperus chinensis, Euphorbia splendens, Magnolia denudata, Ficus microcarpa, and Lagerstroemia speciose
B3 Sustainability 09 00340 i0060.411Pterocarpus indicus, Ficus microcarpa, Delonixregia, Calocedrus formosana, Roystonea regia, Fraxinus griffithii, Chorisia speciosa, Cinnamomum camphora, Spathodea campanulata, Adenanthera pavonina, Pterocarpus indicus, Magnolia denudata, and Cassia fistula
C1 Sustainability 09 00340 i0070.047Lagerstroemia speciosa, Cinnamomum camphora, Michelia figo, Acer, Alstonia scholaris, Cinnamomum camphora, Chamaecyparis obtusa, Juniperus chinensis, Spathodea campanulata, Adenanthera pavonina, and Pterocarpus indicus
C2 Sustainability 09 00340 i0080.075Lagerstroemia speciosa, Peltophorum pterocarpum, Acer, Delonix regia, Alstonia scholaris, Haematoxylum campechianum, Bauhinia blakeana, and Roystonea regia
C3 Sustainability 09 00340 i0090.065Pachira macrocarpa, Elaeocarpus serratus Linn., Terminalia catappa, Ficus religiosa, Lagerstroemia subcostata, Araucaria cunninghamii, Pithecellobium dulce, Chrysalidocarpus lutescens, Roystonea regia, Peltophorum pterocarpum, Caryota urens, Chrysalidocarpus lutescens, Liquidambar formosana Hance, Sapindus, Ficus microcarpa, Peltophorum pterocarpum, and Lagerstroemia speciose
D1 Sustainability 09 00340 i0100.025Tectona grandis, Delonix regia, Alstonia scholaris, Bischofia javanica Blume, Swietenia mahagoni, Pistacia chinensis, Elaeocarpus serratus Linn., and Terminalia catappa
D2 Sustainability 09 00340 i0110.059Chrysalidocarpus lutescens, Peltophorum pterocarpum, Ficus microcarpa, Michelia compressa, Mangifera indica, Pongamia pinnata, Araucaria heterophylla, Swietenia mahagoni, and Liquidambar formosana Hance
D3 Sustainability 09 00340 i0120.138Cassia fistula, Pithecellobium dulce, Pongamia pinnata, and Pistacia chinensis
a The site shown in Figure 1; * the trees height were higher than 2 m.
Table 3. Plant species in different groups.
Table 3. Plant species in different groups.
Group *Plant Species
AFicus microcarpa
BDelonix regia, and Terminalia catappa
CJuniperus chinensis, Bischofia javanica Blume, and Chamaecyparis taiwanensis
DMichelia figo, Acer, Spathodea campanulata, Cinnamomum camphora, Liquidambar formosana, Araucaria cunninghamii, and Pistacia chinensis
* Group screened by a series of HCA procedures.

Share and Cite

MDPI and ACS Style

Lin, Y.-H.; Tsai, K.-T. Screening of Tree Species for Improving Outdoor Human Thermal Comfort in a Taiwanese City. Sustainability 2017, 9, 340. https://doi.org/10.3390/su9030340

AMA Style

Lin Y-H, Tsai K-T. Screening of Tree Species for Improving Outdoor Human Thermal Comfort in a Taiwanese City. Sustainability. 2017; 9(3):340. https://doi.org/10.3390/su9030340

Chicago/Turabian Style

Lin, Yu-Hao, and Kang-Ting Tsai. 2017. "Screening of Tree Species for Improving Outdoor Human Thermal Comfort in a Taiwanese City" Sustainability 9, no. 3: 340. https://doi.org/10.3390/su9030340

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop