Home > Archive>Volume 26, Issue 4, 2018 >323-334. DOI:10.11926/jtsb.3840
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Hyperspectral Inversion of Leaf Area Index of Subtropical Vegetation in South China
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    Abstract:

    In order to determine the optimal hyperspectral characteristic variables of subtropical tree species and construct the estimation model of leaf area index (LAI), the leaf reflectance and LAI of 50 tree species in the campus of South China Agricultural University (SCAU) were measured. At the same time, the relationship model of LAI with the six hyperspectral characteristic variables, including NDVI, RVI, FREP, CIGreen, CIRed-edge and MSAVI2, were constructed through statistical analysis, respectively. The results showed that there were significant correlations between the six hyperspectral characteristic variables and LAI of tree species. The R2 of fitting equations between LAI with red edge position reflectivity (FREP) and the ratio vegetation index RVI were more than 0.8 with correlation coefficients for 0.820 and 0.811, respectively. The root mean square error (RMSE) of FREP estimation is only 0.13, so the regression model is the best model for estimating the LAI of typical subtropical tree species. Combining subtropical vegetation community structure and hyperspectral remote sensing, the regression model between red edge position reflectivity and leaf area index generally has a high fitting degree. Therefore, using hyperspectral characteristic variables inverted subtropical leaves of the leaf area index and other vegetation parameters had better application prospects.

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汪清泓,刘振华,胡月明,宋英强.华南地区亚热带树木叶面积指数的高光谱反演研究[J].热带亚热带植物学报,2018,26(4):323~334

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History
  • Received:October 19,2017
  • Revised:January 09,2018
  • Online: July 20,2018
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