Abstract
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 (1) 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); (2) 10 variables are correlated with chlorophyll, and the order of correlation is Chlb > Chla > Chls; (3) 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; (4) the mean estimated content of Chla in Stipa bungeana is about 4.88 times that of Chlb, while Cars is slightly more than Chlb; (5) the distribution of Chla is opposite to Chlb and Chls content in water area.
Highlights
Stipa purpurea, distributed in northern Tibet, is the most important and largest ecological system and the survival and development materials for herdsmen [1, 2]
The spectrum analysis of Stipa purpurea is the basis of remote sensing monitoring of forage resources in Tibetan plateau, and its spectral characteristics are the integrated responses of Stipa purpurea and its habitat conditions
The three spectral inversion models of chlorophyll a (Chla), chlorophyll b (Chlb), and Chls were established by using RVI, NDVI, EVI, RDVI, VI1, VI2, and VI3
Summary
Stipa purpurea, distributed in northern Tibet, is the most important and largest ecological system and the survival and development materials for herdsmen [1, 2]. Photosynthetic pigment content, as an effective plant health indicator for the detection of photosynthesis and disease pollution [5, 6], can be estimated and analyzed by remote sensing. This will be useful for analyzing the growth and health status of Stipa purpurea grassland, and for reflecting the succession of Stipa purpurea community.
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