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A New Method for Estimating the Fractional Vegetation Cover Based on UVA Hyperspectrum |
FENG Hai-ying1, FENG Zhong-ke1*, FENG Hai-xia2 |
1. Beijing Key Laboratory of Precision Forestry in Beijing Forestry University,Beijing 100083, China
2. Shandong Jiaotong University, Ji’nan 250023, China |
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Abstract This paper analyzed the spectrum characteristic which is sensitive to the fractional vegetation cover (FVC). The red-edge slope(k) was set as the parameter of the FVC estimation model in the study. The relationship between vegetation cover and mixed spectrum was studied by controlling vegetation coverage of lawn with avafield-3 spectrometer (measuring range 300~2 500 nm). The result showed that the red edge region(680~760 nm) was most sensitive to the fractional vegetation cover and the correlation between the first derivative of red edge region’s spectrum and fractional vegetation cover was the highest (>0.98) which was steady at the same time. By referring to spectral misture analysis method for the classical inversion FVC model using NDVI as the parameter of the FVC estimation model, this paper established two new inversion models using red-edge slope instead of NDVI, improving the classical model. The accuracy of the models was verified by experiment using UVA hyperspectral data and vegetation coverage data measured in the study area. We calculated the slope between 680~760 nm of each pixel in hyperspectral image, extracted pure pixels by PPI, calculated the maximum spectral slope value of pure vegetational pixel and the minimum spectral slope value of pure soil pixel, and assessed the FVC by the two new models. The FVC of measured data were calculated by the method of photography after geometric correction and supervised classification. The result of the fitting analysis showed that the accuracy of two new red-edge slope models (R2=0.893 3, 0.892 7) were higher than the NDVI model (R2=0.839 9, 0.829 9). This model has higher physical and biologic meanings, application potentiality and promotion value.
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Received: 2016-11-23
Accepted: 2017-04-02
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Corresponding Authors:
FENG Zhong-ke
E-mail: fengzhongke@163.com
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