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遥感技术与应用  2014, Vol. 29 Issue (1): 114-121    DOI: 10.11873/j.issn.1004-0323.2014.1.0114
遥感应用     
太湖湖滨敏感区的土地利用遥感分类研究
王少伟1,张晓祥1,杨晓英2
(1.河海大学地理信息科学与工程研究所,江苏 南京 210098;
2.复旦大学环境科学与工程系,上海 200433)
Land Use Classification based on Remote Sensing Image in Taihu Lake Lakeside Sensitive Area
Wang Shaowei1,Zhang Xiaoxiang1,Yang Xiaoying2
(1.Institute of Geographical Information Science and Engineering,Hohai University,Nanjing 210098,China;
2.Department of Environmental Science and Engineering,Fudan University,Shanghai 200433,China)
 全文: PDF(3760 KB)  
摘要:

近年来太湖流域水体污染日趋严重,土地利用是重要的环境变化影响因子,对太湖湖滨敏感区土地利用分类研究具有重要意义。研究基于2010年ALOS多光谱遥感影像,以太湖流域上游的武进港、直湖港流域为研究区,根据研究区实际状况和研究目的,建立太湖流域上游湖滨敏感区的土地利用/土地覆被分类系统,并用于该地区的面向对象遥感分类,研究通过影像的多尺度分割,获得不同层次的影像对象,在不同层次设置对应的分类规则,以充分利用影像中地物的光谱、纹理和不同层对象相互关系等信息,从而提高分类效果。研究表明:在面向对象多尺度影像分割的基础上,基于决策树建立多个分类规则的分类方法,能够有效提取建设用地、道路、水体等几类信息,分类总体精度达到88.00%;同时,该地区主要土地利用类型如耕地、农村居民点和城镇居民点的分类精度也较高,这也表明该分类方法对整个太湖流域以及其他平原河网地区的土地利用相关研究具有一定的实用价值。

关键词: 面向对象分类ALOS多尺度分割决策树土地利用湖滨敏感区    
Abstract:

In recent years,water pollution has made great crisis in Taihu Lake Watershed.Land use is seemed as an important factor for the environmental changes,so the land use classification study of Taihu Lake lakeside sensitive area has great significance.According to the local circumstance of land use and the research objectives in the study area,a novel land use/land cover classification schema is established for the Wujingang River & Zhihugang River Watershed in the upstream areas of Taihu Lake Watershed,then the reliable watershed land cover/land use information can be acquired by the means of images classification based on medium\|resolution remote sensing images.In this study,ALOS multi\|spectral remote sensing images in 2010 are used for the data sources,object\|oriented image classification are performed after the multi\|resolution image segmentation,then several classification rules based on decision tree methods are built to effectively extract the land use/ land cover information such an construction lands,road and water body.The classification results show that based on such an object\|oriented image classification method has a higher accuracy,and the overall accuracy is 88%.At the same time,the classification accuracy of the main land use types such as cropland,rural residence and urban residence are also higher.This study has some practical value to the watershed land use,and it also provides a methodological reference for land use classification of the whole Taihu Lake watershed or other plain river network regions.

Key words: Object-oriented classification    ALOS    Multi-scale segmentation    Decision trees;Land use    Lakeside sensitive areas
收稿日期: 2012-12-18 出版日期: 2014-05-14
:  TP 79  
基金资助:

河海大学中央高校基金项目(2009B00414),复旦大学“985工程”三期项目(2012SHKXQN009)。

作者简介: 王少伟(1987-),女,河南濮阳人,硕士研究生,主要从事地理信息系统和遥感研究。Email:wangshaowei@x\|gis.com.cn。
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引用本文:

王少伟,张晓祥,杨晓英. 太湖湖滨敏感区的土地利用遥感分类研究[J]. 遥感技术与应用, 2014, 29(1): 114-121.

Wang Shaowei,Zhang Xiaoxiang,Yang Xiaoying. Land Use Classification based on Remote Sensing Image in Taihu Lake Lakeside Sensitive Area. Remote Sensing Technology and Application, 2014, 29(1): 114-121.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2014.1.0114        http://www.rsta.ac.cn/CN/Y2014/V29/I1/114

[1]Wan Rongrong,Yang Guishan.Changes of Land Use and Landscape Pattern in Taihu Lake Basin[J].Chinese Journal of Applied Ecology,2005,16(3):475-480.[万荣荣,杨桂山.太湖流域土地利用与景观格局演变研究[J].应用生态学报,2005,16(3):475-480.]

[2]Li Bing,Bi Jun,Tian Ying.Effects of Land Use Change on Ecosystem Service Value in the Heavy Polluted Area in Taihu Lake Basin[J].Scientia Geographica Sinica,2012,32(4):471-476.[李冰,毕军,田颖.太湖流域重污染区土地利用变化对生态系统服务价值的影响[J].地理科学,2012,32(4):471-476.]

[3]Xia Lizhong,Yang Linzhang.Research on Non-point Source Pollution in Tai Lake Region[J].Resources and Environment in the Yangtze Basin,2003,12(1):45-49.[夏立忠,杨林章.太湖流域非点源污染研究与控制[J].长江流域资源与环境,2003,12(1):45-49.]

[4]Mishra A.Evaluation of Non-point Source N and P Loads in a Small Mixed Land Use Land Cover Watershed[J].Journal of Water Resource and Protection,2010,2(4):362-372.

[5]Shen Z Y,Hong Q,Hong Y,et al.Parameter Uncertainty Analysis of Non-point Source Pollution from Different Land Use Types[J].Science of the Total Environment,2010,408(8):1971-1978.

[6]Shen Z Y,Liao Q,Hong Q,et al.An Overview of Research on Agricultural Non-point Source Pollution Modeling in China[J].Separation and Purification Technology,2012,84:104-111.

[7]Chen Qiang,Hu Yong,Gong Cailan.Analysis of Satellite Remote Sensing Technology in the Evaluation of Agricultural Non-point Source Pollution[J].Remote Sensing for Land & Resources,2011,(4):1-5.[陈强,胡勇,巩彩兰.卫星遥感技术在农业非点源污染评价中的应用分析[J].国土资源遥感,2011,(4):1-5.]

[8]Lu D,Weng Q.A Survey of Image Classification Methods and Techniques for Improving Classification Performance[J].International Journal of Remote Sensing,2007,28(5):823-870.

[9]Defries R S,Chan J C.Multiple Criteria for Evaluating Machine Learning Algorithms for Land Cover Classification from Satellite Data[J].Remote Sensing of Environment,2000,74(3):503-515.

[10]Chen Q X,Luo J C,Zhou C H.Classification of Remotely Sensed Imagery Using Multi-features based Approach[J].Journal of Remote Sensing,2004,8(3):239-245.

[11]Cao M,Shi Z,Shen Q.On Selection of ALOS Image Optimum Band Combination in Land Cover Classification[J].Bulletin of Surveying and Mapping,2008,09:16-18.

[12]Wu Ruijuan,He Xiufeng,Yang Zhixiang.Land Cover Classification based on ALOS Panchromatic and Multi-spectral Images Fusion[J].Geospatial Information,2012,10(1):116-118.[吴瑞娟,何秀凤,杨智翔.ALOS全色与多光谱影像融合的土地覆盖分类[J].地理空间信息,2012,10(1):116-118.]

[13]Franklin S E,Wulder M A.Remote Sensing Methods in Medium Spatial Resolution Satellite Data Land Cover Classification of Large Areas[J].Progress in Physical Geography,2002,26(2):173-205.

[14]Guo Jian,Zhang Jixian,Zhang Yonghong,et al.Study of the Comparison of Land Cover Classification for Multitemporal MODIS Images[J].Acta Geodaetica et Cartographica Sinica,2009,38(1):88-92.[郭健,张继贤,张永红,等.多时相MODIS影像土地覆盖分类比较研究[J].测绘学报,2009,38(01):88-92.]

[15]Zhang Bin,Wang Jiyao,Lv Yihe,et al.Study on Land Cover Classification and Landscape Characteristics of ALOS Images[J].Computer Engineering and Applications,2012,48(24):216-221.[张斌,王继尧,吕一河,等.ALOS影像数据土地覆盖分类及景观特征研究[J].计算机工程与应用,2012,48(24):216-221.]

[16]Gao Y,Mas J F.A Comparison of the Performance of Pixel based and Object based Classifications over Images with Various Spatial Resolutions[J].Online Journal of Earth Sciences,2008,2(1):27-35.

[17]Wang Quanfang,Li Jiayong,Chen Baiming.Land Cover Classification System based on Spectrum in Poyang Lake Basin[J].Acta Geographica Sinica,2006,61(4):359-368.[汪权方,李家永,陈百明.基于地表覆盖物光谱特征的土地覆被分类系统——以鄱阳湖流域为例[J].地理学报,2006,61(4):359-368.]

[18]Canty M J.Boosting a Fast Neural Network for Supervised Land Cover Classification[J].Computers & Geosciences,2009,35(6):1280-1295.

[19]Peng Haitao,Ke Changqing.Study on Object-oriented Remote Sensing Image Classification based on Multi-levels Segmentation[J].Remote Sensing Technology and Application,2010,25(1):149-154.[彭海涛,柯长青.基于多层分割的面向对象遥感影像分类方法研究[J].遥感技术与应用,2010,25(1):149-154.]

[20]Li Shuguo,Ma Renhui.The Exploration of Land Use Classification System[J].China Land Science,2000,14(1):39-40.[李树国,马仁会.对我国土地利用分类体系的探讨[J].中国土地科学,2000,14(1):39-40.]

[21]Anderson J R,Hardy E E,Roach J T,et al.A Land Use and Land Cover Classification System for Use with Remote Sensor Data[M].America:USGS Professional Paper,1976,964.[22]Bazi Y,Melgani F.Toward an Optimal SVM Classification System for Hyper Spectral Remote Sensing Images[J].Geoscience and Remote Sensing,2006,44(11):3374-3385.

[23]Huang Huiping,Wu Bingfang.Analysis of the Multi-scale Characteristics with Objects Extraction[J].Remote Sensing Technology and Application,2003,18(5):276-281.[黄慧萍,吴炳方.地物提取的多尺度特征遥感应用分析[J].遥感技术与应用,2003,18(5):276-281.]

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

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