地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (11): 1998-2012.doi: 10.12082/dqxxkx.2021.200747

• 地球信息科学理论与方法 • 上一篇    下一篇

基于百度街景的SVF计算及其在城市热岛研究中的应用

冯叶涵1,2(), 陈亮1,2,*(), 贺晓冬3,4   

  1. 1. 华东师范大学地理信息科学教育部重点实验室,上海200241
    2. 华东师范大学地理科学学院,上海200241
    3. 河北省气象与生态环境重点实验室,石家庄050021
    4. 清华海峡研究院,厦门361006
  • 收稿日期:2020-12-09 修回日期:2021-03-28 出版日期:2021-11-25 发布日期:2022-01-25
  • 通讯作者: *陈亮(1982— ),男,黑龙江哈尔滨人,副研究员,主要从事城市气候研究。E-mail: lchen@geo.ecnu.edu.cn
    *陈亮(1982— ),男,黑龙江哈尔滨人,副研究员,主要从事城市气候研究。E-mail: lchen@geo.ecnu.edu.cn
  • 作者简介:冯叶涵(1997— ),女,重庆人,硕士生,主要从事城市气候、遥感研究。E-mail: 51193901040@stu.ecnu.edu.cn
  • 基金资助:
    国家自然科学基金项目(41805089);上海市自然科学基金资助项目(18ZR1410700);上海市城市更新及其空间优化技术重点实验室开放课题资助(201830207);华东师范大学地理信息科学教育部重点实验室主任基金(KLGIS2019C01);河北省省级科技计划资助(18964201H)

Sky View Factor Calculation based on Baidu Street View Images and Its Application in Urban Heat Island Study

FENG Yehan1,2(), CHEN Liang1,2,*(), HE Xiaodong3,4   

  1. 1. Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
    2. School of Geographic Sciences, East China Normal University, Shanghai 200241, China
    3. Key Laboratory of Meteorology and Ecological Environment of Hebei Province, Shijiazhuang 050021, China
    4. Cross-strait Tsinghua Research Institute, Xiamen 361006, China;
  • Received:2020-12-09 Revised:2021-03-28 Online:2021-11-25 Published:2022-01-25
  • Contact: *CHEN Liang, E-mail: lchen@geo.ecnu.edu.cn
    *CHEN Liang, E-mail: lchen@geo.ecnu.edu.cn
  • Supported by:
    National Natural Science Foundation of China, No(41805089);Natural Science Foundation of Shanghai, No(18ZR1410700);Open Projects Fund of Key Laboratory of Shanghai Urban Renewal and Spatial Optimization Technology, No(201830207);Director's Fund of Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, No(KLGIS2019C01);S&T Program of Hebei, No(18964201H)

摘要:

SVF(Sky View Factor)是描述城市辐射和城市热环境的有效指标之一,是研究城市热岛的重要几何参数,如何快速准确地计算大规模的SVF对城市形态和城市气候研究具有重要意义。已有研究发现,SVF与热岛强度具有强烈关系,但以往研究存在争论和局限性。本研究采用百度全景静态图,基于深度学习,使用Deeplabv3+模型对天空范围进行探测,提出一种SVF自动计算方法,并用该方法计算上海市中心城区的SVF分布。本研究引入局地气候分区(Local Climate Zones, LCZ),将大规模、精确的SVF结合每个地块具体的土地利用和建筑情况进一步用于SVF与热岛强度的关系研究。实验结果表明,在不同场景下,Deeplabv3+模型都能对天空范围进行有效探测(MIOU=91.64%);本文方法计算的SVF与鱼眼照片计算的SVF具有令人满意的一致性(R2=0.8869);在不同区域,SVF与热岛强度的关系不同,对于LCZ5开敞中层建筑,最高相关系数为0.68,对于LCZ1紧凑高层建筑,最高相关系数为-0.79。本文SVF计算方法在上海市中心城区的成功应用,验证了在中国高密度和复杂的城市环境中使用街景图像计算大规模SVF的可行性,此外本文基于区域化研究思想进一步研究了SVF与城市热岛的关系,弥补了以往此类研究的不足。

关键词: 天空可视因子, 百度街景, 图像分割, 深度学习, 局地气候分区, 城市热岛, 街道峡谷, 上海市

Abstract:

The Sky View Factor (SVF) is one of the most important indicators to characterize urban radiation fluxes and urban thermal environment. Therefore, it is a key morphological parameter to study the Urban Heat Island (UHI) effect. Studies have shown that SVF has a strong relationship with UHI intensity. Nevertheless, the relationships found can be contradictory. This is primarily due to the fact that the cases studied are often in different regions with different climatic conditions. In addition, the influences of trees are sometimes ignored due to the lack of vegetation data or the limitation of calculating methods. How to calculate SVF quickly and accurately is important to urban climate research. SVF is typically calculated by four types of methods: fisheye photo methods, 3D GIS methods, GPS methods, and street view image methods. Compared with the other types of methods, calculating SVF using street view images has many advantages, such as widely available data, low cost, high efficiency, and the ability to consider the influences of trees and other obstacles. On the one hand, street view images provide the possibility for fast and accurate calculation of SVF in large-scale areas. On the other hand, the street view image method is still at its developing stage and more work needs to be done to verify its application in various urban environments. In this study, we proposed an automatic SVF calculation method using street view images and deep learning algorithms, and then applied the method to the UHI study in the city center of Shanghai. Baidu static panoramas and Deeplabv3+ were used to detect sky range while MATLAB code was written to calculate SVF. A Landsat-8 OLI / TIRS image was also used to retrieve land surface temperature at street level in the study area. Based on the Local Climate Zones (LCZ) scheme, we combined large-scale SVF value with the land use and building morphology to examine the relationship between SVF and UHI intensity. The results showed that Deeplabv3+ can detect the sky and non-sky range effectively in different scenarios (MIOU=91.64%). The SVF calculated using the proposed method was in good agreement with that calculated using fish-eye photos (R2=0.8869). The LCZ scheme provides new insights for the relationship between SVF and UHI. For LCZ5 and LCZ1, the highest correlation coefficients were 0.68 and -0.79, respectively. The proposed method was shown to be applicable in high-density and complex urban environments. In addition, the calculation of large-scale continuous SVF provides the possibility for zonal understandings of the UHI effect based on the LCZ scheme.

Key words: sky view factor, Baidu street view, image segmentation, deep learning, local climate zones, urban heat island, street canyon, Shanghai