高光谱数据湿地植被类型信息提取

doi:10.3969/j.issn.1000-2006.2015.01.033

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南京林业大学学报（自然科学版） ›› 2015, Vol. 39 ›› Issue (01): 181-184.doi: 10.3969/j.issn.1000-2006.2015.01.033

• 研究简报 • 上一篇

高光谱数据湿地植被类型信息提取

柴颖,阮仁宗^*,傅巧妮

河海大学地球科学与工程学院,江苏南京 210098

出版日期:2015-01-31 发布日期:2015-01-31
基金资助:
收稿日期:2013-11-13 修回日期:2014-04-13
基金项目:中国科学院战略性先导科技专项(XDA05050106)
第一作者:柴颖,硕士生。*通信作者:阮仁宗,副教授。E-mail: ruanrenzong@163.com
引文格式:柴颖,阮仁宗,傅巧妮. 高光谱数据湿地植被类型信息提取[J]. 南京林业大学学报:自然科学版,2015,39(1):181-184.

Extraction of wetland vegetation information using hyperspectral image data

CHAI Ying, RUAN Renzong^*, FU Qiaoni

School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China

Online:2015-01-31 Published:2015-01-31

摘要/Abstract

摘要： 以美国Sacramento-San Joaquin三角洲为研究区,利用高光谱和高空间分辨率遥感影像HyMap数据,在光谱特征分析和实测数据的基础上,构造特征指数,建立决策树分类模型对湿地植被进行分类。研究结果表明,湿地植被在近红外波段(0.75～1.3 μm)上有明显的光谱特征差异,根据这些差异,可以构造合适的特征指数,实现湿地植被在物种水平上的识别。

Abstract: In this paper, we mapped wetland vegetation with 3 m spatial resolution,126-band HyMap image data in California’s Sacramento-San Joaquin delta. Specific vegetation indices were constructed and a decision tree model was used to identify wetland vegetation based on spectral analysis and field investigation. The result showed that we could construct suitable specific vegetation indices, according to the spectral difference obvious at near infrared bands(0.75-1.3 μm), which could be effective in distinguishing wetland vegetation and allowing for species-level detection necessary to map invasive species.

中图分类号:

TP75

柴颖,阮仁宗,傅巧妮. 高光谱数据湿地植被类型信息提取[J]. 南京林业大学学报（自然科学版）, 2015, 39(01): 181-184.

CHAI Ying, RUAN Renzong, FU Qiaoni. Extraction of wetland vegetation information using hyperspectral image data[J].Journal of Nanjing Forestry University (Natural Science Edition), 2015, 39(01): 181-184.DOI: 10.3969/j.issn.1000-2006.2015.01.033.

参考文献

[1] Marion L,Paillison J M. A mass balance assessment of the contribution of floating-leaved macrophytes in nutrient stocks in an eutrophic macrophyte-dominated lake[J]. Aquatic Botany,2003,75(3): 249-260.
[2] Wright C,Gallant A. Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data[J]. Remote Sensing of Environment,2007,107(4): 582-605.
[3] Davranche A,Lefebvre G,Poulin B. Wetland monitoring using classification trees and SPOT-5 seasonal time series[J]. Remote Sensing of Environment,2010,114(3): 552-562.
[4] Laba M,Blair B,Downs R,et al. Use of textural measurements to map invasive wetland plants in the Hudson River National Estuarine Research Reserve with IKONOS satellite imagery[J]. Remote Sensing of Environment,2010,114(4): 876-886.
[5] Laba M,Downs R,Smith S,et al. Mapping invasive wetland plants in the Hudson River National Estuarine Research Reserve using quickbird satellite imagery[J]. Remote Sensing of Environment,2008,112(1): 286-300.
[6] Zhou D M,Gong H L,Hu J M,et al. Application of satellite remote sensing technology to wetland research[J]. Remote Sensing Technology and Application,2006,21(6):577-583.
[7] Hestir E L,Khanna S,Andrew M E,et al. Identification of invasive vegetation using hyperspectral remote sensing in the California Delta ecosystem[J]. Remote Sensing of Environment,2008,112(11): 4034-4047.
[8] Wang C,Menenti M,Stoll M P,et al. Mapping mixed vegetation communities in salt marshes using airborne spectral data[J].Remote Sensing of Environment,2007,107(4): 559-570.
[9] Lawerence R L,Wood S D,Sheley R L. Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications(randomforest)[J].Remote Sensing of Environment,2006,100(3): 356-362.
[10] Jollineau M Y,Howarth P J. Mapping an inland wetland complex using hyperspectral imagery[J]. International Journal of Remote Sensing,2008,29(12): 3609-3631.
[11] Tong Q X,Zheng L F,Wang J N,et al. Study on imaging spectrometer remote sensing information for wetland vegetation[J]. Journal of Remote Sensing,1997,1(1): 50-57.
[12] Hestir E L, Khanna S. Identification of invasive vegetation using hyperspectral remote sensing in the California Delta ecosystem[J]. Remote Sensing of Environment,2008,112(11): 4034-4047.
[13] Jassby A D, Cloern J E. Organic matter sources and rehabilitation of the Sacramento-San Joaquin Delta(California, USA)[J]. Aquatic Conservation: Marine and Freshwater Ecosystems, 2000,10: 323-352.
[14] Maheu-Giroux M, Blois S D. Mapping the invasive species Phragmites australis in linear wetland corridors[J]. Aquatic Botany, 2005, 83: 310-320.
[15] Underwood E C, Mulitsch M J, Greenberg J A, et al. Mapping invasive aquatic vegetation in the Sacramento-San Joaquin Delta using Hyperspectral Imagery [J]. Environmental Monitoring and Assessment, 2006, 121: 47-64.
[16] Friedl M A, Brodley C E. Decision tree classification of land cover from remotely sensed data[J].Remote Sensing of Environment,1997,61(3): 399-409.
[17] Robert A. Schowengerdt. Remote Sensing: Models and Methods for Image Processing [M].NewYork:Academic Press,1997.
[18] Hansen M, Dubayah R, Defries R, Classification trees: An alternative to Traditional Land Cover Classifiers[J].Remote Sensing.1996,17(15):1075-1081.
[19] 童庆禧,张兵,郑兰芬.高光谱遥感的多学科应用[M].北京: 电子工业出版社,2006.

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高光谱数据湿地植被类型信息提取

Extraction of wetland vegetation information using hyperspectral image data

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