Estimating Methods of Forest Canopy Closure Based on Hyperion Data of Shangri-La

Use 2 methods to band selection and choose the band data feature extraction variables of the hyperspectral data. Using partial least squares to the Shangri-La main tree crown density remote sensing estimation model building, and its accuracy were presented and checked in the study. The results showed that R² of the model based on Hyperion characteristic band sensitive to forest crown closure was 0.837, the estimation precision was 82.09%; R² of the model based on Hyperion characteristic band via selection of segmented principal component was 0.764, the estimation precision was 78.4%. The accuracy and fitting effects of based on inventory data model were better than based on hyperspectral data model. Although the selected band characteristics from segmented principal component analysis contained more information, but many for continuous band or wavelength band. The highly correlative of bands which lead to modeling precision accuracy was lower than expected.