Abstract:

Leaf area index (LAI) is an important indicator of forest structural parameters. Remote sensing was considered to be an effective means of regional LAI inversion. Single variable curve estimate and linear regression was the dominant model in LAI inversion using remote sensing, but the model generality, modeling accuracy and vegetation index on the choice of the need to further discussed. In this paper, Hyperion data as a data source, through LAI-2000 instrument to obtain 130 samples (60 m x 60 m) leaf area index of ground measurements at Huangfengqiao forest farm in Youxian County. Extract NDVI, RVI and other 13 factors from Hyperion image, Variable Importance in the Projection (VIP), Proportion of Variance Explained and variable weights was used in variable selection, using partial least squares regression analysis(PLS) to establish vegetation index and measured plots of regression models, which to inversion the leaf area index and mapping in study area. The result as followed: PLS regression analysis has good prediction effect in LAI inversion, among all the fitting models, the effect of six vegetation factors atrial least-square regression was best with R2 coefficient of 0.91; LAI and vegetation index had a good linear relationship. The results showed that the sensitivity of ratio vegetation index (RVI) was highest among all the modeling factors. Residual analysis showed that it was reliable to build model using 4 to 6 independent variables, prediction accuracy of Partial least-square regression was highest.