夏热冬暖地区绿量构成对城市热环境的影响——以福州居住区为例

doi:10.13287/j.1001-9332.202307.026

应用生态学报 ›› 2023, Vol. 34 ›› Issue (7): 1932-1940.doi: 10.13287/j.1001-9332.202307.026

夏热冬暖地区绿量构成对城市热环境的影响——以福州居住区为例

邱瑶^1,2, 罗涛^1,2, 王琼^1*, 蒋思雨^1,2

¹福州大学建筑与城乡规划学院, 福州 350108;
²福建省国土空间分析与模拟数字技术重点实验室, 福州 350108

收稿日期:2023-02-21 接受日期:2023-05-22 出版日期:2023-07-15 发布日期:2024-01-15
通讯作者: *E-mail: wq00068@163.com
作者简介:邱瑶, 男, 1997年生, 硕士研究生。主要从事景观审美偏好和城市热环境研究。E-mail: qiuy413@163.com
基金资助:
国家重点研发计划项目(2022YFF1301303)

Influence of green biomass composition on the urban thermal environment in hot summer and warm winter regions: The example of Fuzhou residential area

QIU Yao^1,2, LUO Tao^1,2, WANG Qiong^1*, JIANG Siyu^1,2

¹College of Architecture and Urban Planning, Fuzhou University, Fuzhou 350108, China;
²Fujian Key Laboratory of Digital Technology for Territorial Space Analysis and Simulation, Fuzhou 350108, China

Received:2023-02-21 Accepted:2023-05-22 Online:2023-07-15 Published:2024-01-15

摘要/Abstract

摘要： 为探究绿量构成与热环境的关联特征及绿量的最优构成模式,选择城市居住区为研究样地,结合航拍影像和实地调研获取绿量数据并优化绿量测算方法,将一定空间范围内的总绿量分解为乔木和灌草两大类,研究不同空间范围内的绿量构成指标(灌草总绿量、乔木树冠总绿量、乔灌草总绿量、乔木单株树冠平均绿量和乔木株数)与环境温度和湿度的关系。结果表明: 不同绿量构成指标起降温增湿作用最显著的尺度和空间范围均为楼间尺度下50 m。乔木单株树冠平均绿量和乔木树冠总绿量分别是影响温度和湿度的关键因素,且反映在白天不同时段。在50 m空间范围内,当乔木树冠平均体积为211 m³、乔木株数为62棵时,乔木对于环境温度和湿度的调控效果更接近于人体的热舒适度需求。

关键词: 城市绿地, 三维绿量, 住区热环境, 影响范围, 时空变化

Abstract: The aim of this study was to investigate the relationship between green biomass composition and thermal environment, as well as their optimal composition pattern. We decomposed total green biomass in a certain spatial range into two categories: trees and shrubs-grasses, with urban residential areas as sampling sites and based on aerial photography and field research data of green biomass and optimized green biomass measurement method. We analyzed the correlation between the green biomass composition indicators (shrub and grass biomass, tree canopy biomass, green biomass, mean tree canopy biomass, number of trees) and ambient temperature and humidity in different spatial ranges. The results showed that the most significant cooling and humidifying effect of different green biomass composition indicators was at 50 m below the building scale. The mean tree canopy biomass and tree canopy biomass were the key factors affecting ambient temperature and humidity, respectively, in different time periods during the day. With an average canopy biomass of about 211 m³ and 62 trees in a 50 m space, the regulation effects of trees on ambient temperature and humidity were closer to the thermal comfort requirements of human body.

Key words: urban green space, three-dimensional (3D) green biomass, residential thermal environment, influencial range, spatiotemporal variation

邱瑶, 罗涛, 王琼, 蒋思雨. 夏热冬暖地区绿量构成对城市热环境的影响——以福州居住区为例[J]. 应用生态学报, 2023, 34(7): 1932-1940.

QIU Yao, LUO Tao, WANG Qiong, JIANG Siyu. Influence of green biomass composition on the urban thermal environment in hot summer and warm winter regions: The example of Fuzhou residential area[J]. Chinese Journal of Applied Ecology, 2023, 34(7): 1932-1940.

参考文献

[1] IPCC. Climate Change 2022: Impacts, Adaptation, and Vulnerability. Cambridge: Cambridge University Press, 2022
[2] United Nations, Department of Economic and Social Affairs, Population Division. World Urbanization Prospects[EB/OL]. (2018-12-12)[2023-02-10]. https: //population.un.org/wup/Download/
[3] 国家统计局. 中国统计年鉴. 北京: 中国统计出版社, 2021: 30-32
[4] 潘家华, 单菁菁, 武占云. 城市蓝皮书: 中国城市发展报告No.12. 北京: 社会科学文献出版社, 2019
[5] 杨旺明, 蒋冲, 喻小勇, 等. 气候变化背景下人为热估算和效应研究. 地理科学进展, 2014, 33(8): 1029-1038
[6] Zheng B, Myint SW, Fan C. Spatial configuration of anthropogenic land cover impacts on urban warming. Landscape & Urban Planning, 2014, 130: 104-111
[7] Rizwan AM, Dennis YCL, Liu C. A review on the generation, determination and mitigation of urban heat island. Journal of Environmental Sciences, 2008, 20: 120-128
[8] Tan PY, Hien WN, Tan CL, et al. A method to partition the relative effects of evaporative cooling and sha-ding on air temperature within vegetation canopy. Journal of Urban Ecology, 2018, 4: 1
[9] 殷实, Lang W, 肖毅强. 湿热地区传统骑楼街区夏季热环境研究. 南方建筑, 2019(4): 53-59
[10] Lin WQ, Yu T, Chang XQ, et al. Calculating cooling extents of green parks using remote sensing: Method and test. Landscape & Urban Planning, 2015, 134: 66-75
[11] Yu ZW, Guo XY, Jørgensen G, et al. How can urban green spaces be planned for climate adaptation in subtropical cities? Ecological Indicators, 2017, 82: 152-162
[12] 周坚华, 孙天纵. 三维绿色生物量的遥感模式研究与绿化环境效益估算. 环境遥感, 1995, 10(3): 162-174
[13] 周宏轩, 陶贵鑫, 炎欣烨, 等. 绿量的城市热环境效应研究现状与展望. 应用生态学报, 2020, 31(8): 2804-2816
[14] 谭瑛, 刘思. 基于温湿度实效的绿量测度算法对比研究. 中国园林, 2018, 34(3): 111-116
[15] Sun XY, Xu S, Hua WC, et al. Feasibility study on the estimation of the living vegetation volume of individual street trees using terrestrial laser scanning. Urban Fores-try & Urban Greening, 2022, 71: 127553
[16] Gourfi A, Taïbi AN, Salhi S, et al. The surface urban heat island and key mitigation factors in arid climate cities: Case of Marrakesh, Morocco. Remote Sensing, 2022, 14: 3935
[17] 应天玉. 城市森林资源结构与降温功能评价研究. 博士论文. 哈尔滨: 东北林业大学, 2009
[18] 秦俊. 绿地缓解城市居住区热环境效应的研究. 博士论文. 上海: 华东师范大学, 2014
[19] 高凯, 秦俊, 宋坤, 等. 城市居住区绿地斑块的降温效应及影响因素分析. 植物资源与环境学报, 2009, 18(3): 50-55
[20] 杨若, 敖祖锐, 张晶, 等. 中小尺度下植被冠层对屋顶表面温度的调控效应分析. 地球信息科学学报, 2019, 21(7): 1097-1108
[21] 邵钰涵, 刘滨谊. 城市街道空间小气候参数及其景观影响要素研究. 风景园林, 2016(10): 98-104
[22] 林中立, 徐涵秋. 近20年来新旧“火炉城市”热岛状况对比研究. 遥感技术与应用, 2019, 34(3): 521-530
[23] 姚远, 陈曦, 钱静. 城市地表热环境研究进展. 生态学报, 2018, 38(3): 1134-1147
[24] Morabito M, Crisci A, Georgiadis T, et al. Urban imperviousness effects on summer surface temperatures nearby residential buildings in different urban zones of Parma. Remote Sensing, 2017, 10: 26-42
[25] Lai DY, Liu WY, Gan TT, et al. A review of mitigating strategies to improve the thermal environment and thermal comfort in urban outdoor spaces. Science of the Total Environment, 2019, 661: 337-353
[26] 赵晓龙, 周烨, 赵冬琪, 等. 基于移动测量方法的哈尔滨兆麟公园健身步道春季微气候特征研究. 中国园林, 2018, 34(9): 58-62
[27] 陈荻, 李卫正, 孔文丽, 等. 基于低空高分辨影像的三维绿量计算方法——以南京林业大学校园为例. 中国园林, 2015, 31(9): 22-26
[28] 郭雪艳. 南京市常见园林植物的绿量研究. 硕士论文. 南京: 南京林业大学, 2009
[29] 何艺, 周小成, 黄洪宇, 等. 基于无人机遥感的亚热带森林林分株数提取. 遥感技术与应用, 2018, 33(1): 168-176
[30] 汪小钦, 王苗苗, 王绍强, 等. 基于可见光波段无人机遥感的植被信息提取. 农业工程学报, 2015, 31(5): 152-158
[31] Su W, Zhang MZ, Bian DH, et al. Phenotyping of corn plants using unmanned aerial vehicle (UAV) images. Remote Sensing, 2019, 11: 2021
[32] 季彪俊, 孙小芳, 孙依斌. 遥感在城镇绿化植物三维绿色生物量和生态环境效应估算中的应用. 福建农林大学学报: 自然科学版, 2005, 34(1): 102-107
[33] 李辉, 赵卫智. 北京5种草坪地被植物生态效益的研究. 中国园林, 1998, 14(4): 34-36
[34] Srivanit M, Hokao K. Evaluating the cooling effects of greening for improving the outdoor thermal environment at an institutional campus in the summer. Building and Environment, 2013, 66: 158-172
[35] Vuckovic M, Kiesel K, Mahdavi A. Studies in the assessment of vegetation impact in the urban context. Energy and Buildings, 2017, 145: 331-341
[36] Feyisa GL, Dons K, Meilby H. Efficiency of parks in mitigating urban heat island effect: An example from Addis Ababa. Landscape and Urban Planning, 2014, 123: 87-95
[37] Shi DC, Song JY, Huang JX, et al. Synergistic cooling effects (SCEs) of urban green-blue spaces on local thermal environment: A case study in Chongqing, China. Sustainable Cities and Society, 2020, 55: 102065
[38] 秦仲, 李湛东, 成仿云, 等. 夏季栾树群落冠层结构对其环境温湿度的调节作用. 应用生态学报, 2015, 26(6): 1634-1640
[39] Tong SS, Wong NH, Tan CL, et al. Impact of urban morphology on microclimate and thermal comfort in northern China. Solar Energy, 2017, 155: 212-213
[40] 陈慧梅, 张宇峰, 王进勇, 等. 我国湿热地区自然通风建筑夏季热舒适研究——以广州为例. 暖通空调, 2010, 40(2): 96-101

夏热冬暖地区绿量构成对城市热环境的影响——以福州居住区为例

Influence of green biomass composition on the urban thermal environment in hot summer and warm winter regions: The example of Fuzhou residential area

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