基于Hyperion数据的香格里拉森林郁闭度遥感估测研究

基于Hyperion高光谱数据，采用2种方法进行波段选取，将选择的波段数据进行特征提取变量，采用偏最小二乘法分别对2种方法选择的特征变量建立香格里拉主要树种郁闭度遥感估测模型，并进行精度检验评价。结果表明：基于实测样地郁闭度差异特征分析选择的Hyperion特征波段建立的模型R²为0.837、估测精度为82.09%，基于遥感影像进行分段主成分分析选择的Hyperion特征波段建立的模型R²为0.764、估测精度为78.4%，基于样地数据郁闭度变化敏感性分析模型优于基于Hyperion影像的分段主成分分析模型；分段主成分分析法所选出的特征波段虽然包含了较多的波段信息，但是很多为连续波段或者波长较近的波段，波段之间的相关性较高，导致建模精度不如预期。

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.