The Application of Grassland Aboveground Biomass Estimating Model in Karst Mountainous Area

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In typical Karst mountain area, the complex and broken ground surface leads to serious mixed pixel phenomenon. In this paper, with Tuchang Town as the research area, we utilized linear spectral mixture analysis to estimate biomass of grassland from Landsat ETM+ image. The analysis indicated that the grassland component which extracted based on the linear mixed model has obvious lineal correlation with the field measured data. The grassland aboveground biomass estimation model can be expressed by the equation: Y=2451.158X-280.461, (R2=0.8012). The result of precision validation shows that the overall accuracy is above 85%. The grassland aboveground biomass estimation model which based on linear spectral mixture analysis is feasible.

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741-745

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January 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Dong Yongping, Wu Xinhong, Rong Yuping, et al. Grassland remote sensing monitoring techniques [M]. Beijing: Chemical Industry Press, (2005).

Google Scholar

[2] Cha yong, Jay Gao, Ni Shaoxiang. Most Recent Progress of International Research on Remote Sensing of Grassland Resources[J]. Progress in Geography, 2003, 22(6): 607-617.

Google Scholar

[3] Xu Bin, Yang X iuchun, Tao Weiguo, et al. Remote sensing monitoring upon the grass production in China[J]. Acta Ecologica Sinica, 2007, 27(2): 405-413.

DOI: 10.1016/s1872-2032(07)60012-2

Google Scholar

[4] Yang Xiuchun, Xu Bin , Zhu Xiaohua et al. Models of grass production based on remote sensing monitoring in northern agro-grazing ecotone[J]. Geographical Research, 2007, 26(2): 213-221.

Google Scholar

[5] Wang Jing, Guo Ni, Wang Zhenguo, et al. Mointoring Model of Aboveground Biomass in Gan- nan Grassland Based on Remote Sensing[J]. Journal of Arid Meteorology, 2010, 28(2): 128-133.

Google Scholar

[6] Xiong Kangning, Chen Yongbi, Chen Hu, et al. Touch graphite and turn it into diamond-the ecological techniques and models of controlling of Karst rocky desertification in Guizhou Province[M]. Guiyang: Guizhou Science&Technology Publishing House, (2011).

DOI: 10.17520/biods.2019351

Google Scholar

[7] Zhao Yingshi. Principles and methods of Remote Sensing Applications Analysis[M]. Beijing: Science Press, (2003).

Google Scholar

[8] Asner G, Lobell D. A biogeophysical approach for automated SWIR unmixing of soils and vegetation[J]. Remote Sensing of Environment, 2000, 74(1): 99-112.

DOI: 10.1016/s0034-4257(00)00126-7

Google Scholar

[9] Gilabert M A. An Atmospheric Correction Method for the Automatic Retrieval of Surface Reflectance from TM Images [J]. Int. J. Remote Sensing, 1994, 15(10): 2065-(2086).

DOI: 10.1080/01431169408954228

Google Scholar

[10] Li Xiaosong, Li Zengyuan, Wu Bo, et al. Retrieval of the Coverage of Artemisia ordosica Community in MuUs Sandland Based on Spectral Mixture Analysis(SMA)[J]. Journal of Remote Sensing, 2007, 11(6): 923-930.

Google Scholar

[11] Li Su, Li Wenzheng, Zhou Jianjun, et al. Hyperspectral Image Fusion by an Enhanced Gram Schmidt Spectral Transformation [J]. Geography and Geo-Information Science, 2007, 23(5): 35-38.

Google Scholar

[12] Liang Tiagang, Cui Xia, Feng Qisheng, et al. Remotely sensed dynamics monitoring of grassland aboveground biomass and carrying capacity during 2001- 2008 in Gannan pastoral area[J]. Acta Prataculturae Sinica, 2009, 18(6): 12-22.

Google Scholar