城市热岛斑块遥感识别及空间扩张路径研究——以北京市为例

doi:10.13249/j.cnki.sgs.2022.08.017

地理科学 ›› 2022, Vol. 42 ›› Issue (8): 1492-1501.doi: 10.13249/j.cnki.sgs.2022.08.017

城市热岛斑块遥感识别及空间扩张路径研究——以北京市为例

乔治¹(), 卢应爽¹, 贺曈¹, 孙宗耀², 徐新良³, 杨俊⁴^,^*()

1.天津大学环境科学与工程学院，天津 300072
2.天津大学建筑学院，天津 300072
3.中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室，北京 100101
4.东北大学江河建筑学院，辽宁沈阳 110169

收稿日期:2021-06-28 修回日期:2021-10-11 出版日期:2022-08-25 发布日期:2022-10-11
通讯作者: 杨俊 E-mail:qiaozhi@tju.edu.cn;yangjun@lnnu.edu.cn
作者简介:乔治（1986-），男，山东滕州人，博士，副教授，研究方向为GIS和遥感应用、土地利用变化、城市热环境。E-mail: qiaozhi@tju.edu.cn
基金资助:
国家自然科学基金项目(41501472);国家自然科学基金项目(41971389);国家自然科学基金项目(41771178);天津市自然科学基金项目(21JCYBJC00390);国家高分辨率对地观测重大科技专项项目资助(05-Y30B01-9001-19/20-4)

Identifying Urban Heat Island Patches and Spatial Expansion Path Based on Remote Sensing Technology: A Case of Beijing City

Qiao Zhi¹(), Lu Yingshuang¹, He Tong¹, Sun Zongyao², Xu Xinliang³, Yang Jun⁴^,^*()

1. School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
2. School of Architecture, Tianjin University, Tianjin 300072, China
3. Institute of Geographic Sciences and Natural Resources Research, CAS, State Key Laboratory of Resources and Environmental Information Systems, Beijing 100101, China
4. Jiangho Architecture College, Northeastern University, Shenyang 110169, Liaoning, China

Received:2021-06-28 Revised:2021-10-11 Online:2022-08-25 Published:2022-10-11
Contact: Yang Jun E-mail:qiaozhi@tju.edu.cn;yangjun@lnnu.edu.cn
Supported by:
National Natural Science Foundation of China(41501472);National Natural Science Foundation of China(41971389);National Natural Science Foundation of China(41771178);Natural Science Foundation of Tianjin City(21JCYBJC00390);Major Projects of High Resolution Earth Observation Systems of National Science and Technology(05-Y30B01-9001-19/20-4)

摘要/Abstract

摘要：

研究利用MODIS地表温度数据产品，引入概率阈值算法提升城市热岛斑块空间识别精度。在此基础上，构建城市热岛扩张指数将城市热岛斑块时空变化过程分解为飞地型、边缘型、填充型扩张模式和缩减型模式。基于土地利用变化过程，开展城市热岛斑块空间扩张模式的路径分析，准确识别城市热岛斑块空间扩张路径的来源和归趋。研究表明：2020年北京市夏季昼夜城市热岛斑块总面积分别为3932 km²和2 266 km²，占北京市总面积比例23.96%和13.81%。2005―2020年夏季昼夜城市热岛斑块面积比例分别增加43.40%和24.44%。增长型城市热岛斑块面积大于缩减型城市热岛斑块面积。边缘型扩张模式是增长型城市热岛斑块最主要的模式。耕地、城市建设用地、农村居民点是增长型城市热岛斑块主要的空间扩张路径。研究结果为揭示城市热环境形成过程和精细化管理提供理论依据和数据支撑。

关键词: 城市热岛效应, 空间扩张模式, 空间扩张路径, 概率阈值, MODIS数据

Abstract:

With the MODIS surface temperature data products, the probabilistic threshold algorithm is introduced to improve the spatial recognition accuracy of urban heat island patches. On this basis, the urban heat island expansion index is constructed to decompose the spatio-temporal change process of urban heat island patches into leapfrogging, edge-expanding, infilling expansion model and reduced model. Based on the land use change process, the path analysis of the spatial expansion pattern of urban heat island plaque is carried out, and the source and trend of the spatial expansion path of urban heat island plaque are accurately identified. The results show that the total area of urban heat island patches in Beijing in summer day and night in 2020 was 3932 km² and 2266 km², accounting for 23.96% and 13.81% of the total area of Beijing. From the summer of 2005 to 2020, the proportion of urban heat island patches area had a 43.40% and 24.44% growth in the daytime and nighttime, respectively. The area of new heat urban island patches is larger than that of reduced patches. The edge-expanding model is the most important model for the new urban heat island patches. Cropland, urban land, and rural settlement area are the main spatial expansion paths of new urban heat island patches. The research results provide theoretical basis and data support for revealing the formation process and refined management of urban thermal environment.

Key words: urban heat island effect, spatial expansion model, spatial expansion path, probability threshold, MODIS

中图分类号:

乔治, 卢应爽, 贺曈, 孙宗耀, 徐新良, 杨俊. 城市热岛斑块遥感识别及空间扩张路径研究——以北京市为例[J]. 地理科学, 2022, 42(8): 1492-1501.

Qiao Zhi, Lu Yingshuang, He Tong, Sun Zongyao, Xu Xinliang, Yang Jun. Identifying Urban Heat Island Patches and Spatial Expansion Path Based on Remote Sensing Technology: A Case of Beijing City[J]. SCIENTIA GEOGRAPHICA SINICA, 2022, 42(8): 1492-1501.

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参考文献 30

[1]	Oke T R, Mills G, Christen A et al. Urban climates[M]. London: Cambridge University Press, 2017.
[2]	Howard L. The climate of London deduced from meteorological observations made in the metropolis and at various places around it (2nd Edition) [M]. London: Harvey and Darton, 1833: 1-9.
[3]	Zhou D C, Xiao J F, Bonafoni S et al. Satellite remote sensing of surface urban heat islands: Progress, challenges, and perspectives[J]. Remote Sensing, 2018, 11(1): 1-6. doi: 10.3390/rs11010001
[4]	黄群芳, 陆玉麒. 北京地区城市热岛强度长期变化特征及气候学影响机制[J]. 地理科学, 2018, 38(10): 1715-1523. doi: 10.13249/j.cnki.sgs.2018.10.016
	Huang Qunfang, Lu Yuqi. Long-term change characteristics of urban heat island intensity and climatological influence mechanism in Beijing area. Scientia Geographica Sinica, 2018, 38(10): 1715-1523. doi: 10.13249/j.cnki.sgs.2018.10.016
[5]	闫广华, 苏俊如, 管敦颐. 大连市中山区城市建筑垂直特征对城市热环境效应的影响[J]. 地理科学, 2019, 39(1): 125-130. doi: 10.13249/j.cnki.sgs.2019.01.014
	Yan Guanghua, Su Junru, Guan Dunyi. The influence of the vertical characteristics of urban buildings in Zhongshan District of Dalian on the effect of urban thermal environment. Scientia Geographica Sinica, 2019, 39(1): 125-130. doi: 10.13249/j.cnki.sgs.2019.01.014
[6]	乔治, 贺曈, 卢应爽, 等. 全球气候变化背景下基于土地利用的人类活动对城市热环境变化归因分析——以京津冀城市群为例[J]. 地理研究, 2022, 41(7): 1932-1947.
	Qiao Zhi, He Tong, Lu Yingshuang et al. Quantifying the contribution of land use change based on global climate change and human activities to urban thermal environment in the Beijing-Tianjin-Hebei urban agglomeration. Geographical Research. 2022, 41(7): 1932-1947.
[7]	Zhang X P, Yan F Y, Liu H J et al. Towards low carbon cities: A machine learning method for predicting urban blocks carbon emissions (UBCE) based on built environment factors (BEF) in Changxing City, China[J]. Sustainable Cities and Society, 2021, 69(6): 102875: 102888.
[8]	李元征, 尹科, 周宏轩, 等. 基于遥感监测的城市热岛研究进展[J]. 地理科学进展, 2016, 35(9): 1062-1074.
	Li Yuanzheng, Yin Ke, Zhou Hongxuan et al. Research progress of urban heat island based on remote sensing monitoring. Progress in Geographical Sciences, 2016, 35(9): 1062-1074.
[9]	Li Y Z, Wang L, Zhang L P et al. Monitoring intra-annual spatiotemporal changes in urban heat islands in 1449 cities in China based on remote sensing[J]. Chinese Geographical Science, 2019, 29(6): 90-91.
[10]	Zhou D C, Zhang L X, Lu H et al. Spatiotemporal trends of urban heat island effect along the urban development intensity gradient in China[J]. Science of the Total Environment, 2016, 544(5): 617-626.
[11]	占文凤, 陈云浩, 周纪, 等. 支持向量机的北京城市热岛模拟: 热岛强度空间格局曲面模拟及其应用[J]. 测绘学报, 2011, 40(1): 96-103.
	Zhan Wenfeng, Chen Yunhao, Zhou Ji et al. Support vector machine-based simulation of Beijing urban heat island: Surface simulation of heat island intensity spatial pattern and its application. Journal of Surveying and Mapping, 2011, 40(1): 96-103.
[12]	乔治, 田光进. 基于MODIS的2001年—2012年北京热岛足迹及容量动态监测[J]. 遥感学报, 2015, 19(3): 476-484.
	Qiao Zhi, Tian Guangjin. Dynamic monitoring of Beijing heat island footprint and capacity from 2001 to 2012 based on MODIS. Journal of Remote Sensing, 2015, 19(3): 476-484.
[13]	Qiao Z, Wu C, Zhao D Q et al. Determining the boundary and probability of surface urban heat island footprint based on a Logistic Model[J]. Remote Sensing, 2019, 11(11): 1368-1388. doi: 10.3390/rs11111368
[14]	乔治, 黄宁钰, 徐新良, 等. 2003—2017年北京市地表热力景观时空分异特征及演变规律[J]. 地理学报, 2019, 74(3): 475-489. doi: 10.11821/dlxb201903006
	Qiao Zhi, Huang Ningyu, Xu Xinliang et al. The temporal and spatial differentiation characteristics and evolution of the surface thermal landscape in Beijing from 2003 to 2017. Acta Geographica Sinica, 2019, 74(3): 475-489. doi: 10.11821/dlxb201903006
[15]	徐新良, 乔治, 田光进, 等. 城市热环境: 格局·机理·模拟[M]. 北京: 科学技术文献出版社, 2015.
	Xu Xinliang, Qiao Zhi, Tian Guangjin et al. Urban thermal environment: Pattern, mechanism and simulation. Beijing: Science and Technology Literature Press, 2015.
[16]	田振坤, 黄妙芬, 刘良云, 等. 使用单窗算法研究北京城区热岛效应[J]. 遥感信息, 2006, 4(1): 21-24. doi: 10.3969/j.issn.1000-3177.2006.01.006
	Tian Zhenkun, Huang Miaofen, Liu Liangyun et al. Using single window algorithm to study the heat island effect in Beijing urban area. Remote Sensing Information, 2006, 4(1): 21-24. doi: 10.3969/j.issn.1000-3177.2006.01.006
[17]	乔治, 田光进. 北京市热环境时空分异与区划[J]. 遥感学报, 2014, 18(3): 715-734. doi: 10.11834/jrs.20143030
	Qiao Zhi, Tian Guangjin. Spatial-temporal differentiation and regionalization of Beijing thermal environment. Journal of Remote Sensing, 2014, 18(3): 715-734. doi: 10.11834/jrs.20143030
[18]	Qiao Z, Liu L, Qin Y W et al. The impact of urban renewal on land surface temperature changes: A case study in the main city of Guangzhou, China[J]. Remote Sensing, 2020, 12(5): 794-809. doi: 10.3390/rs12050794
[19]	国家统计局. 中国统计年鉴[M]. 北京: 中国统计出版社, 2021.
	National Bureau of Statistics. China statistical yearbook. Beijing: China Statistics Press, 2021.
[20]	乔治, 孙宗耀, 孙希华, 等. 城市热环境风险预测及时空格局分析[J]. 生态学报, 2019, 39(2): 649-659.
	Qiao Zhi, Sun Zongyao, Sun Xihua et al. Analysis on time-space pattern of urban thermal environment risk prediction. Acta Ecologica Sinica, 2019, 39(2): 649-659.
[21]	于琛, 胡德勇, 张旸, 等. 近20年京津唐主体城区地表热场空间特征变化分析[J]. 地理科学, 2019, 39(6): 1016-1024. doi: 10.13249/j.cnki.sgs.2019.06.017
	Yu Chen, Hu Deyong, Zhang Yang et al. Analysis of changes in spatial characteristics of surface thermal fields in the main urban areas of Beijing, Tianjin and Tangshan in the past 20 years. Scientia Geographica Sinica, 2019, 39(6): 1016-1024. doi: 10.13249/j.cnki.sgs.2019.06.017
[22]	刘纪远, 宁佳, 匡文慧, 等. 2010-2015年中国土地利用变化的时空格局与新特征[J]. 地理学报, 2018, 73(5): 789-802. doi: 10.11821/dlxb201805001
	Liu Jiyuan, Ning Jia, Kuang Wenhui et al. The spatio-temporal pattern and new characteristics of land use change in China from 2010 to 2015. Acta Geographica Sinica, 2018, 73(5): 789-802. doi: 10.11821/dlxb201805001
[23]	刘小平, 黎夏, 陈逸敏, 等. 景观扩张指数及其在城市扩展分析中的应用[J]. 地理学报, 2009, 64(12): 1430-1438. doi: 10.3321/j.issn:0375-5444.2009.12.003
	Liu Xiaoping, Li Xia, Chen Yimin et al. Landscape expansion index and its application in urban expansion analysis. Acta Geographica Sinica, 2009, 64(12): 1430-1438. doi: 10.3321/j.issn:0375-5444.2009.12.003
[24]	金佳莉, 王成, 贾宝全. 北京平原造林后景观格局与热场环境的耦合分析[J]. 应用生态学报, 2018, 29(11): 3723-3734. doi: 10.13287/j.1001-9332.201811.011
	Jin Jiali, Wang Cheng, Jia Baoquan. Coupling analysis of landscape pattern and thermal environment after afforestation in Beijing Plain. The Journal of Applied Ecology, 2018, 29(11): 3723-3734. doi: 10.13287/j.1001-9332.201811.011
[25]	北京市昌平区人民政府, 昌平区, 2020年新一轮百万亩绿化造林工程正式启动[N/OL], http://www.bjchp.gov.cn/cpqzf/xxgk2671/zdxm/5257365/index.html,2021-06-08.
	The People’s Government of Changping District, Beijing, the new round of afforestation project of one million acres of greenery in Changping District officially launched in 2020. http://www.bjchp.gov.cn/cpqzf/xxgk2671/zdxm/5257365/index.html,2021-06-08.
[26]	Qiao Z, Xiao L, Tian G J. Diurnal and seasonal impacts of urbanization on the urban thermal environment: A case study of Beijing using MODIS data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 85(5): 93-101.
[27]	Mao K B, Yuan Z J, Zuo Z Y et al. Changes in global cloud cover based on remote sensing data from 2003 to 2012[J]. Science Press, 2019, 29(2): 306-315.
[28]	Lu Y S, He T, Xu X L et al. Investigation the robustness of standard classification methods for defining urban heat islands[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 11386-11394. doi: 10.1109/JSTARS.2021.3124558
[29]	匡文慧. 城市土地利用/覆盖变化与热环境生态调控研究进展与展望[J]. 地理科学, 2018, 38(10): 1643-1652. doi: 10.13249/j.cnki.sgs.2018.10.008
	Kuang Wenhui. Research progress and prospects of urban land use/cover change and thermal environment ecological regulation. Scientia Geographical Sinica, 2018, 38(10): 1643-1652. doi: 10.13249/j.cnki.sgs.2018.10.008
[30]	沈中健, 曾坚. 厦门市热岛强度与相关地表因素的空间关系研究[J]. 地理科学, 2020, 40(5): 842-852. doi: 10.13249/j.cnki.sgs.2020.05.019
	Shen Zhongjian, Zeng Jian. Research on the spatial relationship between the intensity of the heat island and related surface factors in Xiamen. Scientia Geographical Sinica, 2020, 40(5): 842-852. doi: 10.13249/j.cnki.sgs.2020.05.019

地表温度等级	分级标准
注：T_mean为某一时刻研究区域所有像元温度归一化后的平均值，T_n为某一像元地表温度的归一化值，STD为标准差。
低温	T_n＜T_mean-STD
次低温	T _mean-STD ≤ T_n ＜T_mean-0.5STD
中温	T_mean-0.5STD ≤ T_n＜T_mean+0.5STD
次高温	T_mean+0.5STD ≤ T_n ＜T_mean+STD
高温	T_n ≥ T_mean+STD

城市热岛斑块遥感识别及空间扩张路径研究——以北京市为例

Identifying Urban Heat Island Patches and Spatial Expansion Path Based on Remote Sensing Technology: A Case of Beijing City

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