地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (9): 1548-1558.doi: 10.12082/dqxxkx.2021.200757

• "人居环境健康与GIS" • 上一篇    下一篇

植被与不透水面的降温和增温效率分析方法

李玉1,2, 张友水1,2,*()   

  1. 1. 福建师范大学地理科学学院,福州 350007
    2. 福建师范大学地理研究所,福州 350007
  • 收稿日期:2020-12-15 出版日期:2021-09-25 发布日期:2021-11-25
  • 通讯作者: *张友水(1974— ),男,安徽含山人,教授,主要研究方向为资源与环境遥感。E-mail: yzha5553@163.com
  • 作者简介:李 玉(1995— ),女,山西太原人,硕士生,主要研究方向为资源与环境遥感。E-mail: liyuedu@foxmail.com
  • 基金资助:
    福建省公益类科研院所专项(2019R1102);福建省自然科学基金项目(2018J01739)

Cooling and Warming Efficiency of Vegetation and Impervious Surface

LI Yu1,2, ZHANG Youshui1,2,*()   

  1. 1. School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China
    2. Institute of Geography, Fujian Normal University, Fuzhou 350007, China
  • Received:2020-12-15 Online:2021-09-25 Published:2021-11-25
  • Supported by:
    Public Welfare Research Institutes of Fujian Province(2019R1102);Natural Science Foundation of Fujian Province(2018J01739)

摘要:

基于遥感的城市热环境研究通常通过分析植被、不透水面和地表温度(Land Surface Temperature, LST )的关系来进行。虽然植被的降温作用和不透水面的增温作用已受到普遍认可,但缺少针对降温和增温效率的定量研究,本研究采用地表降温率(Land Surface Cooling Rate, LSCR)和地表增温率(Land Surface Warming Rate, LSWR)量化植被降温效率和不透水面增温效率并对2017年江苏省南京市城市热环境进行分析。以Landsat 8 OLI 4期遥感影像为数据源,利用线性光谱混合分析法(Linear Spectral Mixture Analysis,LSMA)获取亚像元植被覆盖度(Fractional Vegetation Coverage, FVC)、不透水面覆盖度(Impervious Surface Percentage, ISP)并利用高分Google影像进行精度验证。结合地表温度(Land Surface Temperature, LST)反演结果计算各季总体LSCR和LSWR,分析不同LST对总体LSCR和LSWR的影响。最后,将FVC和ISP分别按照阈值平均划分为4个区间,计算各区间的LSCR和LSWR,并在此基础上分析不同区间LSCR和LSWR的变化情况。研究结果表明: ① LST与整体LSCR、LSWR正相关,夏季植被降温效应和不透水面增温效应最强,LSCR和LSWR分别为5.6%和5.1%;② 夏季各区间LSCR与FVC正相关,FVC为75%~100%时LSCR达到最大值7.5%;各区间LSWR与ISP负相关,ISP为75%~100%时LSWR达到最小值2.4%;③ 当FVC为0~25%,ISP为75%~100%时,可以充分发挥植被的降温效应,抑制不透水面的增温效应,是最佳的植被和不透水面组合方案。本研究采用的LSCR和LSWR分析方法可以从抑制地表温度上升的角度选择最佳的FVC和ISP区间,未来可基于此横向对比不同城市,并结合纬度、地形、气候、树种等因素对LSCR和LSWR的影响,进一步探索LSCR和LSWR的影响因子和变化规律。

关键词: 混合像元分解, 植被覆盖度, 不透水面, 地表降温率, 地表增温率, 地表温度反演, 城市热环境, 亚像元

Abstract:

Remote sensing based studies of urban thermal environment usually analyze the relationship among vegetation, impervious surface, and Land Surface Temperature (LST). Although the cooling effects of vegetation and warming effects of impervious surface have been widely recognized, quantitative studies on cooling and warming efficiencies are lacking. In this study, Land Surface Cooling Rate (LSCR) and Land Surface Warming Rate (LSWR) were used to quantify the cooling efficiency of vegetation and the warming efficiency of impervious surface, respectively. Taking the central urban area of Nanjing, Jiangsu Province in 2017 as the research area, Landsat 8 OLI remote sensing data of four dates were selected as the data source. Firstly, Linear Spectral Mixture Analysis (LSMA) was used to obtain Fractional Vegetation Coverage (FVC) and Impervious Surface Percentage (ISP). High-resolution Google earth images were used for precision verification. Then, with LST inversion results, the LSCR and LSWR of each season were calculated, and the influence of different LSTs on the LSCR and LSWR was analyzed. Finally, using a thresholding method, FVC and ISP were divided into four intervals of 0%~25%, 25%~50%, 50%~75% and 75%~100%. The LSCR and LSWR of each interval were calculated. On this basis, the changes of LSCR and LSWR of different intervals were analyzed. The results show that: (1) LST is positively correlated with the overall LSCR and LSWR. The cooling effect of vegetation and the warming effect of impervious layer are the strongest in summer, with LSCR being 5.6% and LSWR being 5.1%. (2) In summer, LSCR in every interval is positively correlated with FVC. When FVC is 75%~100%, LSCR reaches the maximum value of 7.5%. In addition, LSWR in every interval is negatively correlated with ISP in summer. When ISP is 75%~100%, LSWR reaches the minimum value of 2.4%. (3) In the future planning, the cooling effect of vegetation can best inhibit the warming effect of impervious surface when FVC is 0%~25% while ISP is 75%~100%, which is the best areal combination of vegetation and impermeable surface. The LSCR and LSWR analysis methods adopted in this study can select the best FVC and ISP intervals from the perspective of preventing the rise of surface temperature. Based on this, different cities can be compared with each other in the future. Considering the impacts of latitude, topography, climate, tree species, etc. on LSCR and LSWR, we can further explore the influencing factors and changing rules of LSCR and LSWR.

Key words: Key Words: mixed pixel decomposition, vegetation coverage, impervious surface, land surface cooling rate, land surface warming rate, land surface temperature retrieval, urban thermal environment, sub-pixel