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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 4787054, 10 pages
Research Article

An Integrated Field and Hyperspectral Remote Sensing Method for the Estimation of Pigments Content of Stipa Purpurea in Shenzha, Tibet

1Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
2College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China

Correspondence should be addressed to Bing He

Received 28 November 2016; Revised 20 March 2017; Accepted 7 May 2017; Published 22 June 2017

Academic Editor: Huade Guan

Copyright © 2017 Bo Kong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Stipa purpurea is the representative type of alpine grassland in Tibet and the surviving and development material for herdsmen. This paper takes Shenzha County as the research area. Based on the analysis of typical hyperspectral variables sensitive to chlorophyll content of Stipa purpurea, 10 spectral variables with significant correlation with chlorophyll were extracted. The estimation model of chlorophyll was established. The photosynthetic pigment contents in the Shenzha area were calculated by using HJ-1A remote sensing images. The results show that there are significant correlations between chlorophyll content and spectral variables; in particular, the coefficient of Chlb in Stipa purpurea with RVI is the largest (0.728); 10 variables are correlated with chlorophyll, and the order of correlation is Chlb > Chla > Chls; for the estimation of Chla, the EVI is the best variable. RVI, NDVI, and VI2 are suitable for Chlb; RVI and NDVI are also suitable for the estimation of Chls; the mean estimated content of Chla in Stipa bungeana is about 4.88 times that of Chlb, while Cars is slightly more than Chlb; the distribution of Chla is opposite to Chlb and Chls content in water area.