Please wait a minute...
img

官方微信

遥感技术与应用  2014, Vol. 29 Issue (5): 823-832    DOI: 10.11873/j.issn.10040323.2014.5.0823
遥感应用     
基于混合像元分解的喀斯特石漠化地物丰度估测
杨苏新1,2,张霞1,帅通1,林卉2
(1.中国科学院遥感与数字地球研究所,北京100101;
2.江苏师范大学城乡规划设计研究院,江苏 徐州221116)
Estimating Karst Rocky Desertification Feature Abundance by Pixel Unmixing
Yang Suxin1,2,Zhang Xia1,Shuai Tong1,Lin Hui2
(1.Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences,Beijing 100101,China; 
2.Jiangsu Normal University,Xuzhou 221116,China)
 全文: PDF(12114 KB)  
摘要:

我国西南喀斯特地区长期存在以石漠化为特征的土地退化问题,是我国三大生态问题之一。喀斯特地区地表复杂度高,具有高度时空异质性,像元混合现象严重,植被、裸岩和裸土为喀斯特地区典型地物,使得评价喀斯特石漠化的关键指标(如裸岩率、植被覆盖度)获取比较困难,高光谱遥感在混合像元分解方面有独特优势,可以获取地物端元的丰度。通过地面试验表明光谱指数能够表征地物覆盖度,进而以Hyperion高光谱影像为数据源,利用连续最大角凸锥方法从影像中提取这3类地物的端元,运用半约束和全约束线性光谱分解方法估算其丰度。研究表明:半约束线性分解得到的丰度优于全约束分解结果,其反演的植被、裸土和裸岩的丰度与相应的光谱指数间具有显著线性相关性,确定系数R2分别为0.92、0.66与0.84,表明地物丰度能够表征其覆盖度。因此,通过混合像元分解算法反演地物丰度来提取喀斯特石漠化因子具有一定的可行性,这为高光谱遥感在喀斯特石漠化中的评价和监测奠定了理论和算法基础。

关键词: 高光谱混合像元分解喀斯特石漠化光谱指数地物丰度    
Abstract:

In recent years,land degradation characterized by rocky desertification in karst areas of southwestern China becomes one of China’s three major ecological problems.Karst areas are areas of high complexity surface,and with high spatial and temporal heterogeneity and seriously mixed pixels.Vegetation,bare rock,bare soil are typical features of Karst regions.Thus it makes it more difficult to extract the key indicators like the fractional cover of vegetation and exposed bedrock which is required to evaluate how serious the rocky desertification is.Hyperspectral remote sensing has unique advantages on unmixing,and can get the abundance of surface features endmember.First,ground tests showed the spectral indices could characterize the feature coverage.Secondly,based on the data of Hyperion hyperspectral images,the study proposed endmember extraction of three types of classes feature from Hyperspectral Images,and estimate abundance by semi\|constrained and fully constrained linear spectral decomposition method.Results showed that:semi\|constrained linear decomposition method was better than fully constrained,and its abundance of inversed vegetation,bare soil,bare rock was much more suited for spectral indices.The proposed feature abundance was able to characterize its coverage with R20.92,0.66 and 0.84,respectively.The linear unmixing method inversed the feature abundance to extract the Karst indicator was feasible.This study indicates that hyperspectral remote sensing laid the foundation for the karst rocky desertification assessment and monitoring.

Key words: Hyperspectral remote sensing    Spectral unmixing    Karst rocky desertification    Spectral index    Abundance of typical object
收稿日期: 2013-08-08 出版日期: 2014-11-10
:  TP 79  
基金资助:

国家自然科学基金项目(40971205,41371359)。

通讯作者: 张霞(1972-),女,山东乳山人,研究员,博士,主要从事光谱响应机理、图像处理与信息提取研究。Email:zx@irsa.ac.cn。    
作者简介: 杨苏新(1987-),女,山东济宁人,硕士研究生,主要从事遥感信息提取方面的研究。Email:xiaoT881231@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  
杨苏新
张霞
帅通
林卉

引用本文:

杨苏新,张霞,帅通,林卉. 基于混合像元分解的喀斯特石漠化地物丰度估测[J]. 遥感技术与应用, 2014, 29(5): 823-832.

Yang Suxin,Zhang Xia,Shuai Tong,Lin Hui. Estimating Karst Rocky Desertification Feature Abundance by Pixel Unmixing. Remote Sensing Technology and Application, 2014, 29(5): 823-832.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.10040323.2014.5.0823        http://www.rsta.ac.cn/CN/Y2014/V29/I5/823

[1]Liu Bo,Yue Yuemin,Li Ru,et al.Study on the Relation between Fraction Cover and Mixed Spectra in Karst Environment[J].Spectroscopy and Spectral


Analysis,2010,30(9):2470-2473.[刘波,岳跃民,李儒,等.喀斯特典型地物混合光谱与复合覆盖度关系研究[J].光谱学与光谱分析,2010,30(9):2470-2473.]

[2]Li Ruiling,Wang Shijie,Xiong Kangning,et al.Study on Rocky Desertification Evaluation Index System——A Case Study of Guizhou Province[J].Tropical


Geography,2004,24(2):145-149.[李瑞玲,王世杰,熊康宁,等.喀斯特石漠化评价指标体现探讨——以贵州省为例[J].热带地理,2004,24(2):145-149.]

[3]Hu Juan,Xiong Kangning,An Yulun.The Application of Application of CBERS-02 Image in Remote Sensing Interpretation for Karst Rock Desertifacation-with a


Special Reference of Qiannan Prefecture,Guizhou Province[J].Journal of Guizhou Normal University(Natural Sciences),2008,(2):39-41.[胡娟,熊康宁,安裕


伦.CBERS-02数据在喀斯特石漠化遥感调查中的应用——以贵州黔南布依族自治州为例[J].贵州师范大学学报(自然科学版),2008,(2):39-41.]

[4]Hu Shunguang,Zhang Zengxiang,Xia Kuiju.Information Extraction of Karst Rocky Desertification Using Remote Sensing[J].Journal of Geo-Information


Science,2010,(6):870-879.[胡顺光,张增祥,夏奎菊.遥感石漠化信息的提取[J].地球信息科学学报,2010,(6):870-879.]

[5]Ye Jiaolong,He Zhengwei,Weng Zhongyin,et al.Application of NDVI Pixel Binary Model in Extraction of Rocky Desertification in Karst Areas[J].Geo-


spatial Information,2012,10(4):134-136.[叶娇珑,何政伟,翁中银,等.NDVI像元二分模型在喀斯特地区提取石漠化中的应用[J].地理空间信息,2012,10(4):134-136.]

[6]Li Xiaoshong,Li Zengyuan,Gao Zhihai,et al.Estimation of Sparse Vegetation Cover in Arid Regions based on Vegetation In〖HJ2.19mm〗dices Derived from


Hyperion Data[J].Journal of  Beijing Forestry University,2010,32(3):195-198.[李晓松,李增元,高志海,等.基于Hyperion 植被指数的干旱地区稀疏植被覆盖度估测[J


].北京林业大学学报,2010,32(3):195-198.]

[7]Goodenough D G,Dyk A,Niemann K O,et al.Processing Hyperion and ALI for Forest Classification[J].IEEE Transactions on Geoscience and Remote


Sensing,2003,41(6):1321-1331.

[8]Tan Bingxiang,Li Zengyuan,Chen Eerxue,et al.Preprocessing of EO-1 Hyperion Hyperspectral Data[J].Remote Sensing Information,2005,(6):36-41.[谭炳


香,李增元,陈尔学,等.EO-1 Hyperion高光谱数据的预处理[J].遥感信息,2005,(6):36-41.]

[9]Yue Yuemin,Zhang Bing,Wang Kelin,et al.Remote Sensing of Indicators for Evaluating Karst Rocky Desertification[J].Journal of Remote


Sensing,2011,15(4):722-734.[ 岳跃民,张兵,王克林,等.石漠化遥感评价因子提取研究[J].遥感学报,2011,15(4):722-734.]

[10]Chen Xueyang,Meng Jihua,Zhu Jianjun,et al.Hyperspectral Characteristics and Estimating Models about Physiological Ecological Parameters of Winter


Wheat[J].Science of Surveying and Mapping,2012,37(5):141-144.[陈雪洋,蒙继华,朱建军,等.冬小麦叶面积指数的高光谱估算模型研究[J].测绘科学,2012,37(5):141-


144.]

[11]Daughtry C S T,Walthall C L,Kim M S,et al.Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance[J].Remote Sensing of


Environment,2000,74:229-239.

[12]Rouse J W,Haas R H,Schell J A,et al.Monitoring Vegetation Systems in the Great Palins with ERTS[C]//Proceedings 3rd Earth Resource


Technology Satellite (ERTS) Symposium,1974,1:48-62.

[13]Huete A R.A Soil-adjusted Vegetation Index (SAVI)[J].Remote Sensing of Environment,1988,25:295-309.

[14]Yue Y M,Zhang B,Wang K L,et al.Spectral Indices for Estimating Ecological Indicators of Karst Rocky Desertification[J].International Journal of


Remote Sensing,2010,31(8):2115-2122.

[15]Deng Shubin.Remote Sensing Image Processing Method of ENVI[M].Beijing:Sciences Press,2010:349-350.[邓书斌.ENVI遥感图像处理方法[M].北京:科学出版


社,2010:349-350.]

[1] 汪子豪,秦其明,孙元亨. 基于BP神经网络的地表温度空间降尺度方法[J]. 遥感技术与应用, 2018, 33(5): 793-802.
[2] 陈伟民,张凌,宋冬梅,王斌,丁亚雄,许明明,崔建勇. 基于AdaBoost改进随机森林的高光谱图像地物分类方法研究[J]. 遥感技术与应用, 2018, 33(4): 612-620.
[3] 苏阳,祁元,王建华,徐菲楠,张金龙. 基于航空高光谱影像的额济纳绿洲土地覆被提取[J]. 遥感技术与应用, 2018, 33(2): 202-211.
[4] 秦振涛,杨茹,张靖,杨武年. 基于聚类结构自适应稀疏表示的高光谱遥感图像修复研究[J]. 遥感技术与应用, 2018, 33(2): 212-215.
[5] 郭宇柏,卓莉,陶海燕,曹晶晶,王芳. 基于空谱初始化的非负矩阵光谱混合像元盲分解[J]. 遥感技术与应用, 2018, 33(2): 216-226.
[6] 刘爱林,郭宝平,李岩山 . 基于离散粒子群算法的凸多模态高光谱图像端元提取研究[J]. 遥感技术与应用, 2018, 33(2): 227-232.
[7] 吴兴,张霞,孙雪剑,张立福,戚文超. SPARK卫星高光谱数据辐射质量评价[J]. 遥感技术与应用, 2018, 33(2): 233-240.
[8] 王光镇,王静璞,邹学勇,韩柳,宗敏. 遥感技术估算非光合植被覆盖度研究综述[J]. 遥感技术与应用, 2018, 33(1): 1-9.
[9] 宋婷婷,付秀丽,陈玉,魏永明,王钦军,程先锋. 云南个旧矿区土壤锌污染遥感反演研究[J]. 遥感技术与应用, 2018, 33(1): 88-95.
[10] 刘慧珺,苏红军,赵-波. 基于改进萤火虫算法的高光谱遥感多特征优化方法[J]. 遥感技术与应用, 2018, 33(1): 110-118.
[11] 王凯,赵军,朱国锋. 基于GF-1遥感数据决策树与混合像元分解模型的冬小麦种植面积早期估算[J]. 遥感技术与应用, 2018, 33(1): 158-167.
[12] 李伟娜,韦玮,张怀清,刘华. 基于多角度高光谱数据的高寒沼泽湿地植被生物量估算[J]. 遥感技术与应用, 2017, 32(5): 809-817.
[13] 肖昊,王杰. 基于IDL和MATLAB混合编程的两种光谱混合分析方法比较[J]. 遥感技术与应用, 2017, 32(5): 858-865.
[14] 李颖,陈怀亮,李耀辉. 利用夏玉米端元丰度估算夏玉米种植面积的研究[J]. 遥感技术与应用, 2017, 32(5): 913-920.
[15] 唐超,邵龙义. 高光谱遥感地物目标识别算法及其在岩性特征提取中的应用[J]. 遥感技术与应用, 2017, 32(4): 691-697.