Please wait a minute...
img

官方微信

遥感技术与应用  2010, Vol. 25 Issue (5): 687-694    DOI: 10.11873/j.issn.1004-0323.2010.5.687
研究与应用     
基于纹理的乌兰布和沙漠地区植被信息提取
邵晓敏,刘勇
(兰州大学遥感与地理信息系统研究所,甘肃 兰州730000)
Deriving Vegetation Information in Ulan Buh Desert Based on Texture
SHAO Xiao-min,LIU Yong
(Institute of Remote Sensing and Geographic Information System,
Lanzhou University,Lanzhou 730000,China)
 全文: PDF(886 KB)  
摘要:

乌兰布和沙漠是我国主要的沙漠之一,近年来,其快速扩张已严重影响当地的生态安全。荒漠植被是该地区最重要的生态防护屏障,准确掌握植被分布状况具有重要意义。以乌兰布和沙漠的典型地区为研究对象,通过NDVI计算、主成分分析以及基于灰度共生矩阵纹理特征相结合的方法,对ALOS多光谱影像进行分析,综合NDVI和均值纹理作为分类指标,确定合适的阈值范围,采用决策树分类方法进行植被信息提取。研究表明,决策树分类可有效运用纹理等辅助信息,与传统分类方法相比能够取得更好的分类效果。

关键词: NDVI主成分分析灰度共生矩阵乌兰布和沙漠荒漠植被决策树分类    
Abstract:

Ulan Buh Desert is one of Chinas major deserts.In recent years its rapid expansion has seriously affected the local ecological security.Desert vegetation is the most important ecological protection barrier in this region.Gaining the knowledge accurately of the distribution of vegetation is important.Calculated NDVI,and integrated principal component analysis combined with Gray Level Co\|occurrence Matrix texture analysis to analysis the ALOS image in the reserch area.Using NDVI and mean texture as the classification indices,the article determined the appropriate threshold range,and abstracted the vegetation information by using the decision tree method.The result shows that the decision tree method could use texture and other auxiliary information effectively,and achieve better classification results compared with traditional classification method.

Key words: NDV    ;Principal component analysis    Gray level co-occurrence matrix    Ulan Buh Desert    Desert vegetation    Decision tree classification
收稿日期: 2010-04-28 出版日期: 2013-10-30
基金资助:

阿拉善SEE生态协会项目(SEEA0905YWL001)资助。

作者简介: 邵晓敏(1985-),女,硕士研究生,主要从事环境遥感研究。E-mail:xiaomin860207@yahoo.com.cn。
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  
邵晓敏
刘勇

引用本文:

邵晓敏, 刘勇. 基于纹理的乌兰布和沙漠地区植被信息提取[J]. 遥感技术与应用, 2010, 25(5): 687-694.

SHAO Xiao-Min, LIU Yong. Deriving Vegetation Information in Ulan Buh Desert Based on Texture. Remote Sensing Technology and Application, 2010, 25(5): 687-694.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2010.5.687        http://www.rsta.ac.cn/CN/Y2010/V25/I5/687

[1]Zhao Gengxing,Dou Yixiang,Tian Wenxin,et al.Study on Automatic Abstraction Methods of Cultivated Land Information from Satellite Remote Sensing Images[J].Scientia Geographica Sinica,2001,21(3):224-229.[赵庚星,窦益湘,田文新,等.卫星遥感影像中耕地信息的自动提取方法研究[J] .地理科学,2001,21(3):224-229.]
[2]Zhao Rui.Monitoring and Yield Estimation of Rice Using Remote Sensing in China[M].Beijing:Science and Technology of China Press,1996.[赵锐.中国水稻遥感动态监测与估产[M].北京:中国科学技术出版社,1996.]
[3]Deng Kun,Chang Qingrui,Wei Lin,et al.Extracting Land Use Information of TM Image in Wind-water Erosion Interlaced Region Based on Texture[J].Journal of Northwest A &F University (Natural Science Edition),2009,37(1):91-98.[邓锟,常庆瑞,蔚霖,等.基于纹理的风蚀水蚀过渡区TM影像土地利用信息提取[J].西北农林科技大学学报(自然科学版),2009,37(1):91-98.]
[4]Yan Meichun,Zhang Youjing,Bao Yansong.Deriving Bamboos from IKONOS Image by Texture Information[J].Remote Sensing Information,2004,(2):31-35.[颜梅春,张友静,鲍艳松.基于灰度共生矩阵法的IKONOS 影像中竹林信息提取[J].遥感信息,2004,(2):31-35.]
[5]Zhao Yingshi.Principles and Methods of Analysis of Remote Sensing Applications[M].Beijing:Science Press,2003,374-375.[赵英时.遥感应用分析原理与方法[M].北京:科学出版社,2003,374-375.]
[6]Jensen J R.Trans,Chen Xiaoling,et al.Introductory Digital Image Processing (the Third Version of the Original Book)[M].Beijing:Machinery Industry Press,2007,281-285.[(美)延森(Jensen J R)著,陈晓玲等译.遥感数字影像处理导论(原书第3版)[M].北京:机械工业出版社,2007,281-285.]
[7]Wang Hui,Wang Keqi,Bai Xuebing.The Research on Wood Texture Classification in Noise Environment[J].Forestry Machinery & Wood Working Equipment,2006,34(10):13-15.[王辉,王克奇,白雪冰.噪声环境下木材纹理分类的研究[J].林业机械与木工设备,2006,34(10):13-15.]
[8]Xia Deshen.Modern Image Processing Technology and Application[M].Nanjing:Southeast University Press,2001.[夏德深.现代图像处理技术与应用[M].南京:东南大学出版社,2001.]
[9]Cui Linli.Integrative Analysis and Evaluation of the Interpretation Features in Remote Sensing Image[D].Beijing:Institute of Remote Sensing Applications,Chinese Academy of Sciences,2005.[崔林丽.遥感影像解译特征的综合分析与评价[D].北京:中国科学院遥感应用研究所,2005.]
[10]Shen Wenming,Wang Wenjie,Luo Haijiang,et al.Classification Methods of Remote Sensing Image Based on Decision Tree Technologies[J].Remote Sensing Technology and Application,2007,22(3):333-338.[申文明,王文杰,罗海江,等.基于决策树分类技术的遥感影像分类方法研究[J].遥感技术与应用,2007,22(3):333-338.]
[11]Wei Yuchun,Tang Guoan,Yang Xin,et al.Remote Sensing Digital Image Processing Tutorial[M].Beijing:Science Press,2007,244-247.[韦玉春,汤国安,杨昕,等.遥感数字图像处理教程[M].北京:科学出版社,2007,244-247.]

 

[1] 张滔,唐宏. 基于Google Earth Engine的京津冀2001~2015年植被覆盖变化与城镇扩张研究[J]. 遥感技术与应用, 2018, 33(4): 593-599.
[2] 汪航,师茁. 基于MODIS时间序列数据的春尺蠖虫害遥感监测方法研究—以新疆巴楚胡杨为例[J]. 遥感技术与应用, 2018, 33(4): 686-695.
[3] 苗茜,王昭生,王荣,黄玫,孙佳丽. 基于NDVI数据评估O3污染对华北地区夏季植被生长的影响[J]. 遥感技术与应用, 2018, 33(4): 696-702.
[4] 周玉科,刘建文. 基于MODIS NDVI和多方法的青藏高原植被物候时空特征分析[J]. 遥感技术与应用, 2018, 33(3): 486-498.
[5] 杨朦朦,汪汇兵,欧阳斯达,范奎奎,戚凯丽. 基于双树复小波分解的BP神经网络遥感影像分类[J]. 遥感技术与应用, 2018, 33(2): 313-320.
[6] 王佳鹏,施润和,张超,刘浦东,曾毓燕. 基于光谱分析的长江口湿地互花米草叶片叶绿素含量反演研究[J]. 遥感技术与应用, 2017, 32(6): 1056-1063.
[7] 杨涛,黄法融,李倩,白磊,李兰海. 新疆北部植被生长季NDVI时空变化及其与冬季降雪的关系[J]. 遥感技术与应用, 2017, 32(6): 1132-1140.
[8] 李玉琴,苏程,王习之,黄智才,章孝灿. 菲律宾吕宋岛斑岩铜金矿遥感找矿模型[J]. 遥感技术与应用, 2017, 32(6): 1151-1160.
[9] 孙晓,吴孟泉,何福红,张安定,赵德恒,李勃 . 2015年黄海海域浒苔时空分布及台风“灿鸿”影响研究[J]. 遥感技术与应用, 2017, 32(5): 921-930.
[10] 周金霖,马明国,肖青,闻建光. 西南地区植被覆盖动态及其与气候因子的关系[J]. 遥感技术与应用, 2017, 32(5): 966-972.
[11] 方雨晨,王培燕,田庆久. 不同覆盖度下小麦农田土壤对NDVI影响模拟分析[J]. 遥感技术与应用, 2017, 32(4): 660-666.
[12] 姜涛,朱文泉,詹培,唐珂,崔雪锋,张天一. 一种抗时序数据噪声的冬小麦识别方法研究[J]. 遥感技术与应用, 2017, 32(4): 698-708.
[13] 葛美香,赵军,仲波,杨爱霞. FY-3/VIRR及MERSI与EOS/MODIS植被指数比较与差异原因分析[J]. 遥感技术与应用, 2017, 32(2): 262-273.
[14] 郝泷,陈永富,刘华,朱雪林,达哇扎西,李伟娜. 基于纹理信息CART决策树的林芝县森林植被面向对象分类[J]. 遥感技术与应用, 2017, 32(2): 386-394.
[15] 李洛晞,沈润平,李鑫慧,郭佳. 基于MODIS时间序列森林扰动监测指数比较研究[J]. 遥感技术与应用, 2016, 31(6): 1083-1090.