基于CA-Markov模型的海口市城市热岛模拟预测

王子安; 孟庆岩; 张琳琳; 胡蝶; 杨天梁

doi:10.7523/j.ucas.2021.0006

中国科学院大学学报 >

2022 , Vol. 39 >Issue 6: 742 - 753

DOI: https://doi.org/10.7523/j.ucas.2021.0006

环境科学与地理学

基于CA-Markov模型的海口市城市热岛模拟预测

王子安 ,
孟庆岩 ,
张琳琳 ,
胡蝶 ,
杨天梁

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1. 中国科学院空天信息创新研究院, 北京 100101;
2. 中国科学院大学, 北京 100049;
3. 中国科学院空天信息创新研究院海南研究院, 海南三亚 572029;
4. 三亚中科遥感研究所, 海南三亚 572029

收稿日期: 2020-09-04

修回日期: 2021-01-15

网络出版日期: 2021-01-15

基金资助

海南省重大科技计划项目(ZDKJ2017009)和国家高分辨率对地观测重大科技专项(05-Y30B01-9001-19/20-1)资助

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Simulation and prediction of urban heat island in Haikou City based on CA-Markov model

WANG Zi ,
MENG Qingyan ,
ZHANG Linlin ,
HU Die ,
YANG Tianliang

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1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. Hainan Research Institute, Aerospace Information Research Institute, Chinese Academy of Sciences, Sanya 572029, Hainan, China;
4. Sanya Institute of Remote Sensing, Sanya 572029, Hainan, China

Received date: 2020-09-04

Revised date: 2021-01-15

Online published: 2021-01-15

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摘要

随着城市化进程的快速发展，地表覆被类型发生着前所未有的变化，也加剧了热岛效应的发展。基于Landsat数据分析海口市城市热岛的空间变化，运用CA-Markov 模型模拟预测城市热岛的发展趋势及空间分布特征，并建立城市热岛与归一化差值植被指数(NDVI)、归一化差值建成指数(NDBI)的回归模型。结果表明：1)利用CA-Markov模型模拟预测2016年海口市热环境分布情况，各热岛强度等级平均误差较小，Kappa系数为80.49%，模拟精度较高，并得到2024年海口市热环境分布情况；2)2000—2016年间，海口市热岛效应愈发明显，主要向琼州海峡沿岸、南渡河西岸以及高铁周围延伸。强热岛范围增加11.60 km²，热岛范围减少2.26 km²，大致保持不变，绿岛范围增加38.64 km²，是16年中热岛强度转变最大的。预测2024年的热岛强度分布将有向东南方向移动的趋势；3)在多元线性回归分析中，NDVI指数每升高0.1，城乡地区地表温度差降0.22~0.45 ℃；而NDBI指数每升高0.1，将对城乡地区地表温度造成0.20~1.42 ℃的温度差。此研究成果可为缓解城市热岛及规划城市未来发展方向提供科学依据和参考。

关键词： 城市热岛效应; CA-Markov模型; 预测; 多元线性回归

本文引用格式

王子安 , 孟庆岩 , 张琳琳 , 胡蝶 , 杨天梁 . 基于CA-Markov模型的海口市城市热岛模拟预测[J]. 中国科学院大学学报, 2022 , 39(6) : 742 -753 . DOI: 10.7523/j.ucas.2021.0006

Abstract

With rapid development of urbanization, great changes of surface coverage have intensified urban heat island effect. Using Landsat data, we analyzed the spatial variation of urban heat island in Haikou City, and CA-Markov model was applied to simulate and predict the trending of spatial distribution characteristics of the urban heat island. Moreover, we constructed a regression model between the urban heat island and the normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI). The results show that: 1) By using the CA-Markov model to simulate and predict the distribution of thermal environment in Haikou in 2016, the average error of each urban heat island intensity level was small, and the Kappa coefficient was 80.49%. The simulation accuracy was high, and the thermal environment distribution of Haikou in 2024 was obtained. 2) From 2000 to 2016, the heat island effect in Haikou became increasingly obvious, mainly extending along the Qiongzhou Strait, the west bank of the Nandu River, and around the high-speed rail. The extent of the intense heat island has increased by 11.60 km², and the extent of the heat island has decreased by 2.26 km² and remained roughly unchanged. The extent of the green island has increased by 38.64 km², which was the largest change in the urban heat island intensity in 16 years. It is predicted that the distribution of urban heat island intensity in 2024 will move toward the southeast direction. 3) In the multiple linear regression analysis, every increase of 0.1 in the NDVI index will reduce the difference in surface temperature between urban and rural areas by 0.22-0.45 ℃. And every 0.1 increase in NDBI index will cause a temperature difference of 0.20-1.42 ℃ in urban and rural areas. The results can provide scientific basis and reference for alleviating urban heat island effect and planning the future development direction of the cities.

Key words： urban heat island effect; CA-Markov model; prediction; multiple linear regression

参考文献

[1] 冯晓刚, 石辉. 西安城市热环境格局的动态演变[J]. 生态学杂志, 2012, 31(11): 2921-2925.DOI:10.13292/j.1000-4890.2012.0463.
[2] 肖荣波, 欧阳志云, 李伟峰, 等. 城市热岛的生态环境效应[J]. 生态学报, 2005, 25(8): 2055-2060.DOI:10.3321/j.issn:1000-0933.2005.08.032.
[3] 张弛. 上海市城市热岛的时间多尺度分析与数值试验[D]. 上海: 华东师范大学, 2011.
[4] 孟丹, 王明玉, 李小娟, 等. 京沪穗三地近十年夜间热力景观格局演变对比研究[J]. 生态学报, 2013, 33(5): 1545-1558.DOI:10.5846/stxb201209121284.
[5] 陈命男, 张浩, 唐靖寅, 等. 上海城市地表热环境多时期遥感研究[J]. 中国环境科学, 2011, 31(7): 1143-1151.
[6] 唐菲, 徐涵秋. 城市不透水面与地表温度定量关系的遥感分析[J]. 吉林大学学报(地球科学版), 2013, 43(6): 1987-1996,2017.DOI:10.13278/j.cnki.jjuese.2013.06.030.
[7] 徐涵秋. 城市不透水面与相关城市生态要素关系的定量分析[J]. 生态学报, 2009, 29(5): 2456-2462.DOI:10.3321/j.issn:1000-0933.2009.05.032.
[8] Brazel A, Gober P, Lee S J, et al. Determinants of changes in the regional urban heat island in metropolitan Phoenix (Arizona, USA) between 1990 and 2004[J]. Climate Research, 2007, 33: 171-182.DOI:10.3354/cr033171.
[9] Giridharan R, Ganesan S, Lau S S Y. Daytime urban heat island effect in high-rise and high-density residential developments in Hong Kong[J]. Energy and Buildings, 2004, 36(6): 525-534.DOI:10.1016/j.enbuild.2013.12.016.
[10] 刘磊. 城市热力景观布局对热岛效应的影响:以南京为例[J]. 内蒙古气象, 2012(5): 9-12.DOI:10.14174/j.cnki.nmqx.2012.05.001.
[11] Buyantuyev A, Wu J G. Urban heat islands and landscape heterogeneity: linking spatiotemporal variations in surface temperatures to land-cover and socioeconomic patterns[J]. Landscape Ecology, 2010, 25(1): 17-33.DOI:10.1007/s10980-009-9402-4.
[12] 刘勇洪, 徐永明, 马京津, 等. 北京城市热岛的定量监测及规划模拟研究[J]. 生态环境学报, 2014, 23(7): 1156-1163.DOI:10.16258/j.cnki.1674-5906.2014.07.009.
[13] 万志红, 万志强. 沈阳市热岛动态及其影响因子分析[J]. 防灾科技学院学报, 2008, 10(4): 7-9,17.DOI:10.3969/j.issn.1673-8407.2008.04.002.
[14] 刘转年, 阴秀菊. 西安城市热岛效应及气象因素分析[J]. 干旱区资源与环境, 2008, 22(2): 87-90.DOI:10.3969/j.issn.1003-7578.2008.02.017.
[15] 何萍, 陈辉, 李宏波, 等. 云南高原楚雄市热岛效应因子的灰色分析[J]. 地理科学进展, 2009, 28(1): 25-32.
[16] 陈志, 俞炳丰, 胡汪洋, 等. 城市热岛效应的灰色评价与预测[J]. 西安交通大学学报, 2004, 38(9): 985-988.DOI:10.3321/j.issn:0253-987X.2004.09.025.
[17] 韦海东, 赵有益, 陈英. 兰州市城市热岛效应评价与灰色预测[J]. 中国沙漠, 2009, 29(3): 571-576.
[18] Tong H, Walton A, Sang J G, et al. Numerical simulation of the urban boundary layer over the complex terrain of Hong Kong[J]. Atmospheric Environment, 2005, 39(19): 3549-3563.DOI:10.1016/j.atmosenv.2005.02.045.
[19] Zehnder J A. Simple modifications to improve fifth-generation Pennsylvania State University-national center for atmospheric research mesoscale model performance for the Phoenix, Arizona, metropolitan area[J]. Journal of Applied Meteorology, 2002, 41(9): 971-979.DOI:10.1175/1520-0450(2002)041〈0971：SMTIFG〉2.0.co;2.
[20] 杨玉华, 徐祥德, 翁永辉. 北京城市边界层热岛的日变化周期模拟[J]. 应用气象学报, 2003, 14(1): 61-68.DOI:10.3969/j.issn.1001-7313.2003.01.007.
[21] 王耀斌, 赵永华, 韩磊, 等. 西安市景观格局与城市热岛效应的耦合关系[J]. 应用生态学报, 2017, 28(8): 2621-2628.DOI:10.13287/j.1001-9332.201708.022.
[22] 刘祎, 祝善友, 周洋, 等. 南京江北新区城市规划对区域热环境影响的WRF模拟研究[J]. 长江流域资源与环境, 2019, 28(2): 281-292.DOI:10.11870/cjlyzyyhj201902005.
[23] 西安市统计局，国家统计局西安调查队. 西安统计年鉴2012[M]. 北京: 中国统计出版社, 2012.
[24] 王翠云. 基于遥感和CFD技术的城市热环境分析与模拟: 以兰州市为例[D]. 兰州：兰州大学, 2008.
[25] 李小龙, 高敏华, 包茹. 基于CA-MARKOV模型的土地利用变化预测研究: 以新疆昌吉市为例[J]. 中国农学通报, 2014, 30(14): 315-320.
[26] 许小娟, 刘会玉, 林振山, 等. 基于CA-MARKOV模型的江苏沿海土地利用变化情景分析[J]. 水土保持研究, 2017, 24(1): 213-218,225.DOI：10.13869/j.cnki.rswc.2017.01.028.
[27] 余俞寒, 张文婷, 王加波, 等. 武汉城市群热岛效应对土地利用响应及其预测[J]. 环境科学与技术, 2018, 41(12): 158-168.DOI:10.19672/j.cnki.1003-6504.2018.12.023.
[28] Taha H, Akbari H, Rosenfeld A. Heat island and oasis effects of vegetative canopies: micro-meteorological field-measurements[J]. Theoretical and Applied Climatology, 1991, 44(2): 123-138.DOI:10.1007/BF00867999.
[29] Jauregui E. Influence of a large urban park on temperature and convective precipitation in a tropical city[J]. Energy and Buildings, 1990, 15(3/4): 457-463.DOI:10.1016/0378-7788(90)90021-A.
[30] 李舒婷, 周艺, 王世新, 等. 2001—2015年内蒙古NDVI时空变化及其对降水和气温的响应[J]. 中国科学院大学学报, 2019, 36(1): 48-55.DOI:10.7523/j.issn.2095-6134.2019.01.008.
[31] Gallo K P, McNab A L, Karl T R, et al. The use of NOAA AVHRR data for assessment of the urban heat island effect[J]. Journal of Applied Meteorology, 1993, 32(5): 899-908.DOI:10.1175/1520-0450(1993)032〈0899：tuonad〉2.0.co;2.
[32] Weng Q. A remote sensing-GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China[J]. International Journal of Remote Sensing, 2001, 22(10): 1999-2014.DOI:10.1080/713860788.
[33] Lu D S, Weng Q H. Spectral mixture analysis of ASTER images for examining the relationship between urban thermal features and biophysical descriptors in Indianapolis, Indiana, USA[J]. Remote Sensing of Environment, 2006, 104(2): 157-167.DOI:10.1016/j.rse.2005.11.015.
[34] Yuan F, Bauer M E. Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery[J]. Remote Sensing of Environment, 2007, 106(3): 375-386.DOI:10.1016/j.rse.2006.09.003.
[35] Montaner-Fernández D, Morales-Salinas L, Rodriguez J S, et al. Spatio-temporal variation of the urban heat island in Santiago, Chile during summers 2005-2017[J]. Remote Sensing, 2020, 12(20): 3345.DOI:10.3390/rs12203345.
[36] Athukorala D, Murayama Y. Spatial variation of land use/cover composition and impact on surface urban heat island in a tropical sub-Saharan City of Accra, Ghana[J]. Sustainability, 2020, 12(19): 7953.DOI:10.3390/su12197953.
[37] 覃志豪, 李文娟, 徐斌, 等. 利用Landsat TM6反演地表温度所需地表辐射率参数的估计方法[C]//第十四届全国遥感技术学术交流会论文选集. 青岛, 2003: 138-146.
[38] Oke T R. The energetic basis of the urban heat island[J]. Quarterly Journal of the Royal Meteorological Society, 1982, 108(455): 1-24.DOI:10.1002/qj.49710845502.
[39] 刘勇洪, 房小怡, 张硕, 等. 京津冀城市群热岛定量评估[J]. 生态学报, 2017, 37(17): 5818-5835.DOI:10.5846/stxb201606031074.
[40] 苏振华, 何报寅, 丁超, 等. 基于遥感分析土地利用变化对武汉城市热岛的影响[J]. 华中师范大学学报(自然科学版), 2015, 49(1): 139-146.DOI:10.19603/j.cnki.1000-1190.2005.01.024.
[41] 张瑜. 西安市城市热岛效应宏观动态监测和模拟预测模型研究[D]. 西安：长安大学, 2016.
[42] 边晓辉,刘燕,丁倩倩, 等. 浙江省湖州市土地利用和覆被变化对热岛效应的响应[J]. 水土保持通报, 2019, 39(3): 263-269,275.DOI:10.13961/j.cnki.stbctb.2019.03.043.
[43] Zhou D C, Zhao S Q, Zhang L X, et al. The footprint of urban heat island effect in China[J]. Scientific Reports, 2015, 5(1): 11160.DOI:10.1038/srep11160.
[44] 王靓, 孟庆岩, 吴俊, 等. 2005—2014年北京市主要城建区热岛强度时空格局分析[J]. 地球信息科学学报, 2015, 17(9): 1047-1054.DOI:10.3724/SP.J.1047.2015.01047.
[45] 景楠. 基于多智能体与GIS的城市人口分布预测研究: 以北京市区人口迁移为例[D]. 北京：中国科学院研究生院(广州地球化学研究所), 2007.
[46] 张显峰, 崔伟宏. 集成GIS和细胞自动机模型进行地理时空过程模拟与预测的新方法[J]. 测绘学报, 2001, 30(2): 148-155.DOI:10.3321/j.issn:1001-1595.2001.02.009.
[47] 韦春竹, 孟庆岩, 郑文锋, 等. 广州市地表温度反演与土地利用覆盖变化关系研究[J]. 遥感技术与应用, 2013, 28(6): 955-963.
[48] 李乐, 徐涵秋. 杭州市城市空间扩展及其热环境变化[J]. 遥感技术与应用, 2014, 29(2): 264-272.
[49] 布仁仓, 常禹, 胡远满, 等. 基于Kappa系数的景观变化测度:以辽宁省中部城市群为例[J]. 生态学报, 2005, 25(4): 778-784,945.DOI:10.3321/j.issn:1000-0933.2005.04.018.
[50] 郭继强, 潘洁. 基于CA-Markov模型的南京市热环境格局演变及预测分析[J]. 大气与环境光学学报, 2020, 15(2): 143-153.DOI:10.3969/j.issn.1673-6141.2020.02.008.
[51] Lu Y L, Feng X Z, Xiao P F, et al. Urban heat island in summer of Nanjing based on TM data[C]// 2009 Joint Urban Remote Sensing Event. May 20-22, 2009, Shanghai, China. IEEE, 2009: 1-5.DOI:10.1109/URS.2009.5137628.
[52] Ogashawara I, Bastos V. A quantitative approach for analyzing the relationship between urban heat islands and land cover[J]. Remote Sensing, 2012, 4(11):3596-3618.DOI:10.3390/rs4113596.
[53] Chen X L, Zhao H M, Li P X, et al. Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes[J]. Remote Sensing of Environment, 2006, 104(2): 133-146.DOI:10.1016/j.rse.2005.11.016.
[54] Baldi G, Nosetto M D, Aragón R, et al. Long-term satellite NDVI data sets: Evaluating their ability to detect ecosystem functional changes in south America[J]. Sensors (Basel, Switzerland), 2008, 8(9): 5397-5425.DOI:10.3390/s8095397.
[55] Botkin D B, Estes J E, MacDonald R M, et al. Studying the earth’s vegetation from space[J]. BioScience, 1984, 34(8): 508-514.DOI：10.2307/1309693.
[56] Mänd P, Hallik L, Peñuelasc J, et al. Responses of the reflectance indices PRI and NDVI to experimental warming and drought in European shrublands along a north-south climatic gradient[J]. Remote Sensing of Environment, 2010, 114(3):626-636.DOI:10.1016/j.rse.2019.11.003.
[57] Pettorelli N, Vik J O, Mysterud A, et al. Using the satellite-derived NDVI to assess ecological responses to environmental change[J]. Trends in Ecology & Evolution, 2005, 20(9): 503-510.DOI:10.1016/j.tree.2005.05.011.
[58] Liu S H, Zang Z F, Wang W C, et al. Spatial-temporal evolution of urban heat Island in Xi’an from 2006 to 2016[J]. Physics and Chemistry of the Earth, Parts A/B/C, 2019, 110:185-194.DOI:10.1016/j.pce.2018.11.007.
[59] Zha Y, Gao J, Ni S. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery[J]. International Journal of Remote Sensing, 2003, 24(3): 583-594.DOI:10.1080/01431160304987.
[60] 祝亚鹏, 王琳, 卫宝立. 发展中城市地表热环境与下垫面关系研究[J]. 环境科学与技术, 2018, 41(S1): 318-324.DOI:10.19672/j.cnki.1003-6504.2018.S1.057.
[61] 王昕, 张娜, 乐荣武, 等. 城市河岸带的斑块组成和空间分布对小气候的影响: 以北京永定河河岸带为例[J]. 中国科学院大学学报, 2020, 37(5): 606-618.DOI:10.7523/j.issn.2095-6134.2020.05.005.

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