Hyperspectral Inversion of Leaf Area Index of Subtropical Vegetation in South China

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, CI_Green, CI_Red-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.