Please wait a minute...
img

官方微信

遥感技术与应用  2015, Vol. 30 Issue (2): 321-330    DOI: 10.11873/j.issn.1004-0323.2015.2.0321
图像与数据处理     
基于多端元混合光谱模型与Landsat影像的北京不透水层动态研究
张文婷1, 2,金可懿1,宋开山2,杭艳红1
(1.东北农业大学资源与环境学院,黑龙江 哈尔滨150030;
2.中国科学院东北地理与农业生态研究所,吉林 长春130102)
Impervious Surface Dynamic Quantification based on Multiple Endmember Spectral Mixture Analysis(MESMA)and Landsat Imagery Data:A Case Study in Beijing
Zhang Wenting1,2,Jin Keyi1,Song Kaishan2,Hang Yanhong1
(1.College of Resources and Environment,Northeast Agricultural University,Harbin 150030,China;
2.Northeast Institute of Geography and Agricultural Ecology,CAS,Changchun 130012,China)
 全文: PDF(11944 KB)  
摘要:

中国正在经历快速地城市化过程,及时又准确地掌握城市化过程对我国社会经济发展具有重要的实际意义。以Landsat\|TM和ETM+为主要数据源,通过多端元光谱混合分析法(MESMA)提取北京建成区不透水层的时空演变信息。在Ridd的V\|I\|S(植被—不透水层—土壤)概念模型框架下,基于最小噪音变换(MNF)将TM或ETM+的6个光谱波段转换成MNF空间,并定义4种端元光谱分别代表植被、高反射率地表、低反射率地表和土壤,同时构建北京建成区端元光谱数据库。然后在MATLAB软件包中实现MESMA模型程序,依次提取北京市6个时段的不透水层信息。研究结果表明:MESMA方法能够提高植被、土壤和不透水层提取精度,相对误差分别为14.6%、17.3%和11.9%。研究结论充分说明MESMA方法应用到一个时间序列的中分辨率多光谱遥感影像是非常有效的。MESMA光谱分解方法能高效实现北京城市动态变化和城市扩张的监测。

关键词: 北京光谱端元不透水层V-I-S模型多端元光谱混合分析    
Abstract:

China is experiencing rapid urbanization process,timely and accurate quantification of the urbanization process is pivotal for the currently social and economic development in China.This study used Multiple Endmember Spectral Mixture Analysis(MESMA)model to extract impervious surface information from a time series of Landsat TM and ETM+ images data under the framework of Ridd’s Vegetation\|Impervious Surface\|soil(V\|I\|S)model.For MESMA implementation,minimum noise fraction transform(MNF)was applied to transform the TM or ETM six spectral bands into the MNF space and four endmembers representing vegetation,high\|albedo surface,low\|albedo surface and soil were determined for images acquired over the Beijing City.The results show that MESMA yielded relative accurate estimate vegetation,soil and impervious surface for the Beijing city.Accuracy assessment indicates that MESMA resulted in the lowest RMSEs for impervious surface,vegetation and soil are 14.6%,17.3% and 11.9%,respectively.Further,the MESMA model generated the low Mean Absolute Error(MAE)value.This work demonstrates that applied MESMA to a time series of the moderate\|resolution multispectral remote sensing image can be an effective way to monitor the dynamics of urban environment variables dynamics and urban expansion,which has great potential for urbanization monitoring with MESMA modeling under the V\|I\|S framework.

Key words: Beijing    Endmember    Impervious    V-I-S model    MESMA
收稿日期: 2013-12-04 出版日期: 2015-05-08
:  TP 79  
基金资助:

国家自然科学基金重点项目(41030743)和中国科学院"百人计划"项目资助。

通讯作者: 宋开山(1974-),男,吉林靖宇人,博士,研究员,主要从事水体生物光学特性与水色遥感研究。Email:songks@neigae.ac.cn。    
作者简介: 张文婷(1990-),女,河南新乡人,硕士研究生,主要从事土地资源利用研究。Email:rainllyy@163.com。
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
张文婷
金可懿
宋开山
杭艳红

引用本文:

张文婷,金可懿,宋开山,杭艳红. 基于多端元混合光谱模型与Landsat影像的北京不透水层动态研究[J]. 遥感技术与应用, 2015, 30(2): 321-330.

Zhang Wenting,Jin Keyi,Song Kaishan,Hang Yanhong. Impervious Surface Dynamic Quantification based on Multiple Endmember Spectral Mixture Analysis(MESMA)and Landsat Imagery Data:A Case Study in Beijing. Remote Sensing Technology and Application, 2015, 30(2): 321-330.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2015.2.0321        http://www.rsta.ac.cn/CN/Y2015/V30/I2/321

[1]Turner II B L,Clark W C,Kates R W,et al.The Earth as Transformed by Human Action:Global and Regional Change in the Biosphere over the Past 300 Years[M].Cambridge:Cambridge University Press,1990:103-120.

[2]Voogt J.A,Oke T R.Thermal Remote Sensing of Urban Climates[J].Remote Sensing of Environment,2003,87(3):295-309.

[3]Shi Huichun,Liu Wei,He Jian,et al.An Evaluation of the Current Situation of City Ecological System Method and Its Application[J].Acta Ecologica Sinica,2012,12(17):5543-5544.[石惠春,刘伟,何剑,等.一种城市生态系统现状评价方法及其应用[J].生态学报,2012,12(17):5543-5544.]

[4]Forster B C.An Examination of Some Problems and Solutions in Monitoring Urban Areas from Satellite Platforms[J].International Journal of Remote Sensing,1985,6(1):139-151.

[5]Ridd M K.Exploring a V-I-S(Vegetation-Impervious Surface-Soil)Model for Urban Ecosystem Analysis through Remote Sensing:Comparative Anatomy for Cities[J].International Journal of Remote Sensing,1995,16(12):2165-2185.

[6]Mather P M.Land Cover Classification Revisited[C]//Advances in Remote Sensing and GIS Analysis,P.M.Atkinson and N.J Tate(Eds),New York:John Wiley & Sons.1999:7-16.

[7]Weng Q,Lu D.Landscape as a Continuum:An Examination of the Urban Landscape Structures and Dynamics of Indianapolis City,1991-2000,by Using Satellite Images[J].International Journal of Remote Sensing,2009,30(10):2547-2577.

[8]Small C.High Spatial Resolution Spectral Mixture Analysis of Urban Reflectance[J].Remote Sensing of Environment,2003,88(1-2):170-186.

[9]Ji M,Jensen J R.Effectiveness of Subpixel Analysis in Detecting and Quantifying Urban Imperviousness  from Landsat Thematic Mapper Imagery[J].Geocarto International,1999,14(4):31-41.

[10]Wu C.Normalized Spectral Mixture Analysis for Monitoring Urban Composition Using ETM+  Imagery[J].Remote Sensing of Environment,2004,3(4):480-492.

[11]Roberts D A,Gardner M,Church R,et al.Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models[J].Remote Sensing of Environment,1998,65(3):267-279.

[12]Rashed T,Weeks J R,Roberts D,et al.Measuring the Physical Composition of Urban Morphology Using Multiple Endmember Spectral Mixture Models[J].Photogrammetric Engineering and Remote Sensing,2003,69(9):1011-1020.

[13]State Council of China Office of Population Census.China Population Census[M].Beijing:Statistical Press of China,2010.

[14]Furby S L,Campbell N A.Calibrating Images from Different Dates to ‘Like Value’ Digital Counts[J].Remote Sensing of Environment,2001,77(2):186-196.[15]Souza C M,Roberts D,AmCochrane M A.Combining Spectral and Spatial Information to Map Canopy Damage from Selective Logging and Forest Fires[J].Remote Sensing of Environment,2005,98(2-3):329-343.

[16]Markham B L,Barker J L.Thematic Mapper Bandpass Solar Exoatmospheric Irradiances[J].International Journal of Remote Sensing,1987,8(3):517-523.

[17]Irish R.Chapter 11:Data Product[S].Landsat-7 Science Data User’s Handbook,1998:31-39.

[18]Foody G M.Status of Land Cover Classification Accuracy Assessment[J].Remote Sensing of Environment,2002,80(1):185-201.

[19]Luo Cailian,Chen Jie,Le Tongchao.The Correction of Atmospheric Landsat ETM+ Satellite Images based on FLAASH Model[J].Protction Forest Science and Technology,2008,5(86):46-47.[罗彩莲,陈杰,乐通潮.基于FLAASH模型的Landsat ETM+卫星影像大气校正[J].防护林科技,2008,5(86):46-47.]

[20]Zhang Xuexia,Ge Quansheng,Zheng Jingyun.The Application of the Study on Vegetation Phenology based on Remote Sensing Technology[J].Advances in Earth Science,2003,18(14):535-537.[张学霞,葛全胜,郑景云.遥感技术在植被物候研究中的应用综述[J].地球科学进展,2003,18(4):535-537.]

[21]Xu Qianxiang,Sheng Hui,Liao Mingsheng.Study on City Expansion Combined MNF with MAD[J].Remote Sensing for Land & Resources,2006,4(70):43-44.[徐前祥,盛辉,廖明生.MNF与MAD变换相结合的城市扩展研究[J].国土资源遥感,2006,4(70):43-44.]

[22]Yang Yetao,Gong Jianya,Zhou Qiming,et al.The Research of Effect of Land Use and Landscape Pattern on the Expansion of the City[J].Jouranl of Natural Resources,2010,25(2):320-321.[杨叶涛,龚健雅,周启鸣,等.土地利用景观格局对城市扩张影响研究[J].自然资源学报,2010,25(2):320-321.]

[23]Li Haitao,Gu Haiyan,Zhang Bing,et al.Research on Hyperspectral Remote Sensing Image Classification based on MNF and SVM[J].Remote Sensing Information,2007,(5):12-15.[李海涛,顾海燕,张兵,等.基于MNF 和SVM 的高光谱遥感影像分类研究[J].遥感信息,2007,(5):12-15.]


 

[1] 胡云锋,商令杰,张千力,王召海. 基于GEE平台的1990年以来北京市土地变化格局及驱动机制分析[J]. 遥感技术与应用, 2018, 33(4): 573-583.
[2] 史世莲,章文波,王国燕. 北京地区植被盖度提取及其分布变化研究[J]. 遥感技术与应用, 2014, 29(5): 866-872.
[3] 高利鹏,赵华亮,刘明翔,张华. 基于遥感影像不透水层估算的震后城区损坏面积评估[J]. 遥感技术与应用, 2013, 28(4): 582-587.
[4] 张道卫,郭华东,孙中昶. 超大城市地表特征参数估算及其对城市热环境的影响研究[J]. 遥感技术与应用, 2012, 27(1): 51-57.
[5] 周会珍, 汪爱华, 李 丽, 迟耀斌, 王智勇, 闫 军. 基于北京一号小卫星的北京及周边五大流域地表水资源监测与分析[J]. 遥感技术与应用, 2010, 25(2): 195-201.
[6] 夏俊士, 杜培军, 张海荣, 刘培. 基于遥感数据的城市地表温度与土地覆盖定量研究[J]. 遥感技术与应用, 2010, 25(1): 15-23.
[7] 陈涛, 牛瑞卿, 李平湘, 张良培. 基于人工神经网络的植被覆盖遥感反演方法研究[J]. 遥感技术与应用, 2010, 25(1): 24-30.
[8] 李俊杰,何隆华,戴锦芳,李杏朝,傅俏燕. 基于不透水地表比例的城市扩展研究[J]. 遥感技术与应用, 2008, 23(4): 424-427.
[9] 樊风雷. 基于线性光谱混合模型(LSMM)的两种不同端元值选取方法应用与评价——以广州市为例[J]. 遥感技术与应用, 2008, 23(3): 272-277.
[10] 王中挺,陈良富,张莹,韩冬,顾行发. 利用MODIS数据监测北京地区气溶胶[J]. 遥感技术与应用, 2008, 23(3): 284-288.
[11] 王 茜, 张增祥, 刘 斌, 牟凤云, 王 姣. “北京一号”小卫星数据在天津市土地利用动态变化监测中的应用[J]. 遥感技术与应用, 2006, 21(6): 502-506.
[12] 杨 娟, 陈洪滨, 王开存, 王振会. 利用MODIS卫星资料分析北京地区地表反照率时空分布及变化特征[J]. 遥感技术与应用, 2006, 21(5): 403-406.
[13] 张兆明, 何国金, 肖荣波, 王威, 欧阳志云. 利用TM6数据反演陆地表面温度新算法研究[J]. 遥感技术与应用, 2005, 20(6): 547-550.