在全球暖化的背景下，綠蔭能有效改善行人在戶外的熱不適。過去多以綠覆率評估綠化多寡，但這項指標並無法呈現一區域受綠蔭的程度；此外，都市植栽資訊取得不易，也造成進一步分析更精確都市熱輻射情況的阻礙。本研究以成功大學周邊與臺灣幾個城市為研究地點，首先找出人與建物分布較密集的建成區域，再從衛星影像分離出綠地與樹冠來計算與建成區之面積占比，取得都市數值模型，並以太陽輻射和長波環境輻照度幾何模型 (SOLWEIG)進行都市熱輻射模擬分析。
研究結果為以下三點，首先，從衛星影像中分離出的綠地與樹冠透過與ETH樹冠高度圖中的植栽幾何形狀來驗證樹冠分佈，經過對比結果達到了82.9%的準確率，為可接受且可靠之數據；此外，考慮到人的移動，以綠地可及性表示綠地服務都市空間的距離，使綠覆與綠蔭的評估更具實際意義。最後，在熱輻射模擬中觀察到，與空地相比，樹冠在夏季中午時能達到相當高的平均輻射溫度(Tmrt)降溫效益，最高可降16°C，當綠蔭率每增加10%，其Tmrt約可降低1.7°C。
本研究以經濟、快速、準確的方式評估一特定區域的綠化之配置情形，也透過大尺度之熱輻射模擬與綠蔭降溫模式分析，證實綠蔭率與輻射降溫之高度相關，隨著城市化的不斷發展，本研究的成果也將對未來城市規劃和氣候調適提供實用的參考。

Green shade provides relief from outdoor heat for pedestrians. Traditionally, greenery has been primarily assessed by greenery coverage ratio(GCR), but it does not fully capture the shading effect. Besides, obtaining a precise planting surface model is challenging, hindering accurate analysis of urban heat radiation.
This study focuses on National Cheng Kung University and seven other Taiwanese cities. It explores urban built-up areas, distinguishes green spaces and tree canopies from satellite images, and calculates their ratio to urban built-up areas. This process results in the creation of urban digital surface models, which are subsequently used to simulate urban heat radiation.
The results of this study can be summarized into three key points. Firstly, the separation of green spaces and tree canopies from satellite images achieved an accuracy of 82.9% when compared to the ETH canopy height model. Secondly, this study reevaluates the accessibility of urban green spaces, thereby making assessments of the GCR and tree canopy coverage ratio (TCR) more practical and meaningful. Lastly, heat radiation simulations revealed that tree canopies provide substantial cooling benefits compared to open spaces, with a maximum mean radiant temperature (Tmrt) reduction of up to 16°C. Furthermore, a 10% increase in TCR can lead to a Tmrt reduction of approximately 1.7°C during summer middays.
This study provides an economically viable, rapid, and accurate method for assessing greenery configuration in a specific area. Establishing a strong correlation between green coverage and radiation cooling through large-scale simulations and analysis offers practical insights for future urban planning

1. Akbari, H. (2002). Shade trees reduce building energy use and CO2 emissions from power plants. Environmental pollution, 116, S119-S126
2. Almagor, J., Omer, I., Omer, N., & Birenboim, A. (2024). How far will you go? From empirical findings to formalization of walking route distances. Computers, Environment and Urban Systems, 110, 102117.
3. Almeida, C. R. D., Teodoro, A. C., & Gonçalves, A. (2021). Study of the urban heat island (UHI) using remote sensing data/techniques: A systematic review. Environments, 8(10), 105.
4. Aleksandrowicz, O., & Pearlmutter, D. (2023). The significance of shade provision in reducing street-level summer heat stress in a hot Mediterranean climate. Landscape and Urban Planning, 229, 104588.
5. Aminipouri, M., Knudby, A., & Ho, H. C. (2016). Using multiple disparate data sources to map heat vulnerability: Vancouver case study. The Canadian Geographer/Le Géographe canadien, 60(3), 356-368.
6. Anniballe, R., Bonafoni, S., & Pichierri, M. (2014). Spatial and temporal trends of the surface and air heat island over Milan using MODIS data. Remote Sensing of Environment, 150, 163-171.
7. Bowler, D. E., Buyung-Ali, L., Knight, T. M., & Pullin, A. S. (2010). Urban greening to cool towns and cities: A systematic review of the empirical evidence. Landscape and urban planning, 97(3), 147-155.
8. Buo, I., Sagris, V., Jaagus, J., & Middel, A. (2023). High-resolution thermal exposure and shade maps for cool corridor planning. Sustainable Cities and Society, 93, 104499.
9. Chang, C. R., Li, M. H., & Chang, S. D. (2007). A preliminary study on the local cool-island intensity of Taipei city parks. Landscape and urban planning, 80(4), 386-395.
10. Chatzipoulka, C., Compagnon, R., Kaempf, J., & Nikolopoulou, M. (2018). Sky view factor as predictor of solar availability on building façades. Solar Energy, 170, 1026-1038.
11. Dimoudi, A., & Nikolopoulou, M. (2003). Vegetation in the urban environment: microclimatic analysis and benefits. Energy and buildings, 35(1), 69-76.
12. Donovan, G. H., & Butry, D. T. (2009). The value of shade: Estimating the effect of urban trees on summertime electricity use. Energy and Buildings, 41(6), 662-668.
13. García-Palomares, J. C., Gutiérrez, J., & Cardozo, O. D. (2013). Walking accessibility to public transport: an analysis based on microdata and GIS. Environment and Planning B: Planning and Design, 40(6), 1087-1102.
14. Grove, J. M., O’Neil-Dunne, J., Pelletier, K., Nowak, D., & Walton, J. (2006). A report on New York City’s present and possible urban tree canopy. United States Department of Agriculture, Forest Service, Northeastern Area, South Burlington, Vermont.
15. Guan, J., Wang, R., Van Berkel, D., & Liang, Z. (2023). How spatial patterns affect urban green space equity at different equity levels: A bayesian quantile regression approach. Landscape and Urban Planning, 233, 104709.
16. Guha, S., Govil, H., Dey, A., & Gill, N. (2018). Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy. European Journal of Remote Sensing, 51(1), 667-678.
17. Hamada, S., Tanaka, T., & Ohta, T. (2013). Impacts of land use and topography on the cooling effect of green areas on surrounding urban areas. Urban forestry & urban greening, 12(4), 426-434.
18. Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., ... & Thépaut, J. N. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999-2049.
19. Jamei, E., & Rajagopalan, P. (2017). Urban development and pedestrian thermal comfort in Melbourne. Solar Energy, 144, 681-698.
20. Jang, K. M., Kim, J., Lee, H. Y., Cho, H., & Kim, Y. (2020). Urban green accessibility index: A measure of pedestrian-centered accessibility to every Green Point in an urban area. ISPRS International Journal of Geo-Information, 9(10), 586.
21. Kaplan, R., & Kaplan, S. (1989). The experience of nature: A psychological perspective. Cambridge university press.
22. Kántor, N., & Unger, J. (2011). The most problematic variable in the course of human-biometeorological comfort assessment—the mean radiant temperature. Central European Journal of Geosciences, 3, 90-100.
23. Kim, K., Yi, C., & Lee, S. (2019). Impact of urban characteristics on cooling energy consumption before and after construction of an urban park: The case of Gyeongui line forest in Seoul. Energy and Buildings, 191, 42-51.
24. Kim, S. W., & Brown, R. D. (2022). Pedestrians' behavior based on outdoor thermal comfort and micro-scale thermal environments, Austin, TX. Science of the total environment, 808, 152143.
25. Klemm, W., Heusinkveld, B. G., Lenzholzer, S., & van Hove, B. (2015). Street greenery and its physical and psychological impact on thermal comfort. Landscape and urban planning, 138, 87-98.
26. Koc, C. B., Osmond, P., & Peters, A. (2018). Evaluating the cooling effects of green infrastructure: A systematic review of methods, indicators and data sources. Solar Energy, 166, 486-508.
27. Korhonen, L., Korhonen, K. T., Rautiainen, M., & Stenberg, P. (2006). Estimation of forest canopy cover: a comparison of field measurement techniques.
28. L.A.’s Green New Deal Sustainable City Plan Environment, L.A pLAn, U.S.A (2019), p120.
29. Langenheim, N., White, M., Tapper, N., Livesley, S. J., & Ramirez-Lovering, D. (2020). Right tree, right place, right time: A visual-functional design approach to select and place trees for optimal shade benefit to commuting pedestrians. Sustainable Cities and Society, 52, 101816.
30. Lau, K. K. L., Ren, C., Ho, J., & Ng, E. (2016). Numerical modelling of mean radiant temperature in high-density sub-tropical urban environment. Energy and buildings, 114, 80-86.
31. Lau, T. K., & Lin, T. P. (2024). Lowering the difficulty of mesoscale sky view factor mapping using satellite products. Remote Sensing Applications: Society and Environment, 101174.
32. Lang, N., Jetz, W., Schindler, K., & Wegner, J. D. (2023). A high-resolution canopy height model of the Earth. Nature Ecology & Evolution, 7(11), 1778-1789.
33. Lee, H., Holst, J., & Mayer, H. (2013). Modification of human-biometeorologically significant radiant flux densities by shading as local method to mitigate heat stress in summer within urban street canyons. Advances in Meteorology, 2013.
34. Liang, J., Gong, J., Zhang, J., Li, Y., Wu, D., & Zhang, G. (2020). GSV2SVF-an interactive GIS tool for sky, tree and building view factor estimation from street view photographs. Building and Environment, 168, 106475.
35. Lindberg, F., Holmer, B., & Thorsson, S. (2008). SOLWEIG 1.0–Modelling spatial variations of 3D radiant fluxes and mean radiant temperature in complex urban settings. International journal of biometeorology, 52, 697-713.
36. Lindberg, F., & Grimmond, C. S. B. (2011). Nature of vegetation and building morphology characteristics across a city: Influence on shadow patterns and mean radiant temperatures in London. Urban Ecosystems, 14, 617-634.
37. Lindberg F, Grimmond CSB, Gabey A, Huang B, Kent CW, Sun T, Theeuwes N, Järvi L, Ward H, Capel- Timms I, Chang YY, Jonsson P, Krave N, Liu D, Meyer D, Olofson F, Tan JG, Wästberg D, Xue L, Zhang Z (2018) Urban Multi-scale Environmental Predictor (UMEP) - An integrated tool for city-based climate services. Environmen tal Modelling and Software.99, 70-87
38. Liu, D., Kwan, M. P., & Kan, Z. (2021). Analysis of urban green space accessibility and distribution inequity in the City of Chicago. Urban Forestry & Urban Greening, 59, 127029.
39. Ma, B., Hauer, R. J., Östberg, J., Koeser, A. K., Wei, H., & Xu, C. (2021). A global basis of urban tree inventories: What comes first the inventory or the program. Urban Forestry & Urban Greening, 60, 127087.
40. Moreno, C., Allam, Z., Chabaud, D., Gall, C., & Pratlong, F. (2021). Introducing the “15-Minute City”: Sustainability, resilience and place identity in future post-pandemic cities. Smart cities, 4(1), 93-111.
41. Nazarian, N., Krayenhoff, E. S., Bechtel, B., Hondula, D. M., Paolini, R., Vanos, J., ... & Santamouris, M. (2022). Integrated assessment of urban overheating impacts on human life. Earths Future 10.
42. National Parks Board [NParks], A Natural Transformation Annual Report 2022 - 2023, (2023). p29.
43. Nielsen, A. B., Östberg, J., & Delshammar, T. (2014). Review of urban tree inventory methods used to collect data at single-tree level. Arboric. Urban For, 40, 96-111.
44. Norton, B. A., Coutts, A. M., Livesley, S. J., Harris, R. J., Hunter, A. M., & Williams, N. S. (2015). Planning for cooler cities: A framework to prioritise green infrastructure to mitigate high temperatures in urban landscapes. Landscape and urban planning, 134, 127-138.
45. Or Aleksandrowicz, David Pearlmutter. (2022) The significance of shade provision in reducing street-level summer heat stress in a hot Mediterranean climate
46. Pongracz, R., Bartholy, J., & Dezso, Z. (2006). Remotely sensed thermal information applied to urban climate analysis. Advances in Space Research, 37(12), 2191-2196.
47. Pozoukidou, G., & Chatziyiannaki, Z. (2021) 15-Minute City: Decomposing the new urban planning eutopia. Sustainability, 13(2), 928.
48. Santamouris, M. (2018). Minimizing energy consumption, energy poverty and global and local climate change in the built environment: innovating to zero: causalities and impacts in a zero concept world. Elsevier.
49. Van Der Hoeven, F., & Wandl, A. (2015). Amsterwarm: Mapping the landuse, health and energy-efficiency implications of the Amsterdam urban heat island. Building Services Engineering Research and Technology, 36(1), 67-88.
50. Tan, P. Y., Wang, J., & Sia, A. (2013). Perspectives on five decades of the urban greening of Singapore. Cities, 32, 24-32.
51. Thorsson, S., Rocklöv, J., Konarska, J., Lindberg, F., Holmer, B., Dousset, B., & Rayner, D. (2014). Mean radiant temperature–A predictor of heat related mortality. Urban Climate, 10, 332-345.
52. Tomlinson, C. J., Chapman, L., Thornes, J. E., & Baker, C. (2011). Remote sensing land surface temperature for meteorology and climatology: A review. Meteorological Applications, 18(3), 296-306.
53. Willberg, E., Fink, C., & Toivonen, T. (2023). The 15-minute city for all?–Measuring individual and temporal variations in walking accessibility. Journal of transport geography, 106, 103521.
54. Yang, Y., & Diez-Roux, A. V. (2012). Walking distance by trip purpose and population subgroups. American journal of preventive medicine, 43(1), 11-19.
55. Yee, A. T. K., Corlett, R. T., Liew, S. C., & Tan, H. T. (2011). The vegetation of Singapore—an updated map. Gardens’ Bulletin Singapore, 63(1&2), 205-212.
56. Zhang, P., Bounoua, L., Imhoff, M. L., Wolfe, R. E., & Thome, K. (2014). Comparison of MODIS land surface temperature and air temperature over the continental USA meteorological stations. Canadian Journal of Remote Sensing, 40(2), 110-122.
57. 土屋宰貴. (2009). わが国の 「都市化率. に関する事実整理と考察-地経済の視点から-」 日本銀行ワーキングペーパーシリーズ, (9-J), 4.
58. 行政院主計總處，1993，中華民國統計地區標準分類定義修正。
59. 行政院主計總處， 2010，「中華民國統計地區標準分類」停止適用新聞稿。
60. 臺北市都市發展局，2016，臺北市新建建築物綠化實施規則§3-2。
61. 內政部建築研究所，2023，綠建築評估手冊-基本型 p37。