大气与环境光学学报 ›› 2023, Vol. 18 ›› Issue (2): 153-167.

• 光学遥感 • 上一篇    下一篇

基于卫星资料的合肥市热岛效应时空演变及其影响因子分析

赵强 1,2, 谭璐 1*, 方潜生 1,2, 刘常瑜 1, 马可 1, 朱曙光 1,2   

  1. 1 安徽建筑大学环境与能源工程学院, 安徽 合肥 230601; 2 安徽省建设领域碳达峰碳中和战略研究院, 安徽 合肥 230601
  • 收稿日期:2021-10-11 修回日期:2022-01-12 出版日期:2023-03-28 发布日期:2023-04-18
  • 通讯作者: E-mail: 18605654950@163.com E-mail:18605654950@163.com
  • 作者简介:赵 强 (1981 - ), 安徽合肥人, 博士, 教授, 硕士生导师, 主要从事环境遥感技术以及城市空间信息技术应用方面的研究。 E-mail: rommel99@163.com
  • 基金资助:
    安徽省教育厅重大项目 (KJ2017ZD41), 国家自然科学基金项目 (41005016), 安徽省高校优秀青年人才支持计划重点项目 (gxyqZD2020036), 安徽省高校优秀科研创新团队项目 (2022AH010018), 2021 年安徽省大学生创新训练项目

Analysis of spatiotemporal evolution and influencing factors of heat island effect in Hefei based on satellite data

ZHAO Qiang 1,2, TAN Lu 1*, FANG Qiansheng 1,2, LIU Changyu 1, MA Ke 1, ZHU Shuguang 1,2   

  1. 1 School of Environment and Energy Engineering, Anhui Jianzhu University, Hefei 230601, China; 2 Anhui Institute of Carbon Emission Peak and Carbon Neutrality in Urban-Rural Development, Hefei 230601, China
  • Received:2021-10-11 Revised:2022-01-12 Published:2023-03-28 Online:2023-04-18
  • Contact: Lu TAN E-mail:18605654950@163.com

摘要: 为研究合肥市近二十年的城市格局演变和热岛效应变化, 基于2005、2009、2015、2020 年每年10 月份的Landsat 卫星影像对合肥地区进行了土地分类以及地表温度反演, 并提取归一化差值裸地与建筑指数 (NDBBI)、植被覆盖 度 (FVC)、改进的归一化差异水体指数 (MNDWI) 以及人口密度进行了多元回归分析, 进而建立数学模型对合肥主城 区的热岛效应及影响因子进行了分析。结果表明: (1) 从2005 年到2020 年, 强热岛区增加了15.03 km²。热岛标准差 椭圆分布方向为东北—西南方向, 椭圆的范围逐年扩大, 热岛质心集中在蜀山经开区, 且81.90%的强热岛区为较高与 高核密度工业区。(2) 地理探测器分析结果表明各影响因子对地表温度的解释力从大到小为: NDBBI (0.542)、 MNDWI (0.409)、FVC (0.379) 和人口密度 (0.018)。(3) 岭回归处理后的多元线性模型 (R² = 0.654) 研究结果表明, 影响 地表温度的主要因子为NDBBI, 而人口密度的影响则较小。(4) 地理加权回归模型 (GWR) 的分析表明, 各点的相关 系数R²在0.489~0.667 之间, 建筑物与道路密集的城建区R²最高。NDBBI高值集中在经开区等地, 最高值达到0.9 以 上, 在GWR模型中人口的系数依然很小, FVC系数高值区集中在植被覆盖率高的区域, 而 MNDWI高值区则主要分 布于水域。

关键词: 地表温度, 热岛效应, 地理探测器, 岭回归, 多元回归模型

Abstract: In order to study the evolution of urban pattern and the change of heat island effect in Hefei in recent 20 years, land classification and land surface temperature inversion were carried out based on the Landsat satellite images of October 2005, October 2009, October 2015 and October 2020. The normalized difference between bare land and building index (NDBBI), fraction vegetation coverage (FVC), modified normalized difference water index (MNDWI) and population density were extracted for multiple regression analysis, and then a mathematical model was established to analyze the heat island effect and its influencing factors in the main urban area of Hefei. The results show that: (1) From 2005 to 2020, the strong heat island area has increased by 15.03 km². The distribution direction of standard deviation ellipse of heat island is from northeast to southwest, and the scope of the ellipse is expanding year by year. The mass center of heat island is concentrated in Shushan Economic Development Zone, and 81.90% of the strong heat island areas are high-density industrial areas, indicating a good corresponding relationship between the strong heat island areas and high-density industrial areas. (2) The analysis results of geographical detector show that the explanatory power of each influencing factor on land surface temperature from large to small is, NDBBI (0.542), MNDWI (0.409), FVC (0.379) and population density (0.018). (3) The results of multivariate linear model (R2 = 0.654) indicate that the main factors affecting land surface temperature is NDBBI, while the population density has little effect. (4) The analysis of geographical weighted regression (GWR) model shows that the R2 of each point is in the range of 0.489-0.667, and the R2 of urban construction area with dense buildings and roads is highest. The high value of NDBBI coefficient is concentrated in the economic development zone and other places, with the highest value reaching more than 0.9, the coefficient of population density is still very small, the high value areas of FVC coefficient are concentrated in areas with high vegetation coverage, while MNDWI high value areas are distributed in water areas.

Key words: surface temperature, heat island effect, geographic detector, ridge regression, multiple regression model

中图分类号: