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《Chinese Journal of Applied Ecology》 2006-06
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Hyperspectral remote sensing estimation models on vegetation coverage of natural grassland.

LIU Zhanyu1, HUANG Jingfeng1, WU Xinhong2, DONG Yongping2, WANG Fumin1, LIU Pengtao3 ( 1Institute of Agriculture Remote Sensing & Information System Application, Zhejiang University, Hangzhou 310029, China; 2Grassland Research Institute, Chinese Academy of Agricultural Science, Huhhot 010010, China; 3Department of Ecology and Environment Science, Inner Mongolia University, Huhhot 010021, China).  
By using ASD FieldSpec Pro FR TM spectroradiometer, the spectral measurement of natural grassland in Xilingole Leaguer of Inner Mongolia was performed, with the vegetation coverage of natural grassland calculated, and the correlation of 25 hyperspectral feature variables with the vegetation coverage of natural grassland was analyzed. The results showed that there were 17 variables correlated significantly with the vegetation coverage of natural grassland, among which, the correlation coefficient between vegetation coverage and the area of red edge peak calculated as the sum of the amplitudes between 680 nm and 780 nm (∑dr 680~780 nm) was the highest, with the value of 0.781. The basic experimental data including the vegetation coverage and canopy reflectance of natural grassland were classified into two groups. One group was used as the training sample to build the regression models with one-sample linear method, nonlinear method, and stepwise analysis method, while the other was used as the testing sample to test the precision of regression models. It was suggested that the variable of the area of red edge peak calculated as the sum of amplitudes between 680 nm and 780 nm (∑dr 680~780 nm) was the best one to univariate general linear model, with a standard deviation of 10.4% and an estimation precision of 83.99%, while the stepwise regression technique was not effective to estimate the grassland coverage with raw hyperspectral canopy reflectance.
【Fund】: 国家自然科学基金项目(30371018);; 国家科技部科技基础性工作资助项目(2003DEA2C010-13).
【CateGory Index】: S812
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