Identification of surface thermal environment differentiation and driving factors in urban functional zones based on multisource data: a case study of Lanzhou, China

被引:0
|
作者
Wang, Yixuan [1 ,2 ]
Yang, Shuwen [1 ,3 ,4 ]
机构
[1] Lanzhou Jiaotong Univ, Fac Geomat, Lanzhou, Peoples R China
[2] Lanzhou Nonferrous Met Design & Res Inst Co Ltd, Lanzhou, Gansu, Peoples R China
[3] Natl Local Joint Engn Res Ctr Technol & Applicat N, Lanzhou, Peoples R China
[4] Lanzhou Jiaotong Univ, Key Lab Sci & Technol Surveying & Mapping, Lanzhou, Gansu, Peoples R China
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
surface thermal environment; urban functional zones; remote sensing; driving factors; Lanzhou city; HEAT; CLIMATE; AREAS; URBANIZATION; ISLANDS;
D O I
10.3389/fenvs.2024.1466542
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The urban functional zone, serving as a bridge to understanding the complex interactions between human spatial activities and surface thermal environmental changes, explores the driving force information of its internal temperature changes, which is crucial for improving the urban thermal environment. However, the impacts of the current urban functional zones on the thermal environment, based on the delineation of human activities, have yet to be sufficiently investigated. To address the issue, we constructed a two-factor weighted dominant function vector model of "population heat-land use scale" to identify urban functional zones. This model is based on multisource data and considers the perspective of urban functional supply and demand matching. We then analyzed the spatial differentiation and driving factors of the relationship between urban functional zones and the surface thermal environment using the random forest algorithm, bivariate spatial autocorrelation, geographical detectors, and geographically weighted regression models. The results showed that there are significant differences in the Land Surface Temperature among different urban functional zones in the central urban area of Lanzhou. Among these, the life service zone has the greatest impact on the surface thermal environment, followed by the industrial zone and catering service zone, while the green space zone has the least impact. The surface thermal environment exhibits high-high clusters in localized spatial clustering patterns with life service, industrial, catering service, and residential zones. In contrast, it tends to exhibit low-high clusters with green spaces. Significant spatial clustering and dependence exist between various functional zones and the surface thermal environment. The land cover types characterized by the Normalized Difference Bare Land and Building Index, the vegetation coverage represented by the Fraction of Vegetation Cover, and the density of industrial activities indicated by the Industrial POI Kernel Density Index are the main drivers of the surface thermal environment in the various functional zones of the central urban area of Lanzhou, and all exhibit significant spatial heterogeneity.
引用
收藏
页数:20
相关论文
共 50 条
  • [1] Refined Identification of Urban Functional Zones Integrating Multisource Data Features: A Case Study of Lanzhou, China
    Wang, Yixuan
    Yang, Shuwen
    Tang, Xianglong
    Ding, Zhiqi
    Li, Yikun
    SUSTAINABILITY, 2024, 16 (20)
  • [2] Analysis of Urban Thermal Environment Evolution and Mechanisms Based on Multisource Data: A Case Study of Hangzhou, China
    Li, Kaike
    Yang, Hongzhe
    Chen, Qianhu
    Chen, Tiantian
    Shen, Rusang
    JOURNAL OF URBAN PLANNING AND DEVELOPMENT, 2024, 150 (03)
  • [3] Identification of Urban Functional Area by Using Multisource Geographic Data: A Case Study of Zhengzhou, China
    Li, Jingzhong
    Xie, Xiao
    Zhao, Bingyu
    Xiao, Xiao
    Qiao, Jingxin
    Ren, Wanxia
    COMPLEXITY, 2021, 2021
  • [4] Identification and Portrait of Urban Functional Zones Based on Multisource Heterogeneous Data and Ensemble Learning
    Xu, Nan
    Luo, Jiancheng
    Wu, Tianjun
    Dong, Wen
    Liu, Wei
    Zhou, Nan
    REMOTE SENSING, 2021, 13 (03) : 1 - 20
  • [5] Spatio-Temporal Differentiation Characteristics and Driving Factors of Urban Thermal Environment: A Case Study in Shaanxi Province, China
    Feng, Xiaogang
    Zhou, Zaihui
    Somenahalli, Sekhar
    Li, Meng
    Li, Fengxia
    Wang, Yuan
    SUSTAINABILITY, 2023, 15 (17)
  • [6] Monitoring changes in the impervious surfaces of urban functional zones using multisource remote sensing data: a case study of Tianjin, China
    Cao, Shisong
    Hu, Deyong
    Zhao, Wenji
    Mo, You
    Yu, Chen
    Zhang, Yang
    GISCIENCE & REMOTE SENSING, 2019, 56 (07) : 967 - 987
  • [7] Geographical Detection of Urban Thermal Environment Based on the Local Climate Zones: A Case Study in Wuhan, China
    Wang, Renfeng
    Wang, Mengmeng
    Zhang, Zhengjia
    Hu, Tian
    Xing, Jiawen
    He, Zhanjun
    Liu, Xiuguo
    REMOTE SENSING, 2022, 14 (05)
  • [8] Seasonal urban surface thermal environment analysis based on local climate zones: A case study of Chongqing
    Wang, Rongxiang
    Lu, Tao
    He, Bo
    Wang, Fang
    Huang, Qiao
    Qian, Zihua
    Min, Jie
    Li, Yuechen
    SCIENCE OF THE TOTAL ENVIRONMENT, 2024, 954
  • [9] Spatio-temporal evolution of urban thermal environment and its driving factors: Case study of Nanjing, China
    Zhang, Menghan
    Dong, Suocheng
    Cheng, Hao
    Li, Fujia
    PLOS ONE, 2021, 16 (05):
  • [10] Analysis of human factors on urban heat island and simulation of urban thermal environment in Lanzhou city, China
    Pan, Jinghu
    JOURNAL OF APPLIED REMOTE SENSING, 2015, 9