| 光谱学与光谱分析 |
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| Analysis on Vegetations Spectral Characteristics along the Altitudinal Gradients in South-Facing Slope of Dangxiong Valley |
| JIAO Quan-jun1, ZHANG Bing1*, LIU Liang-yun1, HE Yong-tao2, HU Yong1 |
1. Key Laboratory of Digital Earth Sciences, Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
2. Lhasa Station, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China |
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Abstract The present study focused on variation of vegetation types and canopy spectra along the altitudinal gradients in south-facing slope of Dangxiong valley in Tibet. Spectral extraction methods including red edge analysis and vegetation indices were used for vegetation spectral characteristics analysis. Through the hierarchical clustering analysis based on the vegetation spectral features, the feasibility of remote sensing classification of vegetation types along the elevation gradients in the experimental area was evaluated. The experimental results showed that: there were significant differences in spectral features including water index (WI), red edge POSITION (REP), and normalized difference vegetation index (NDVI) in different plots along elevation gradients in the study area, and there were strong correlations between WI and leaf water content, REP and dry biomass, NDVI and vegetation coverage. The hierarchical clustering analysis result of 12 vegetation samples along the altitudinal gradients is consistent with the ground survey, which shows that the selected vegetation spectral features can characterize the vertical distribution of vegetation types in the experimental area. The vegetation spectral analysis in this study can provide the priori knowledge support of spectral characteristics for the vegetation vertical distribution information extraction in the Tibet Plateau.
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Received: 2012-04-24
Accepted: 2012-07-25
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Corresponding Authors:
ZHANG Bing
E-mail: zhangbing@ceode.ac.cn
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