基于高光谱遥感数据提取森林结构参数的研究(英文)

Forest Structural Variables Retrieval Using EO-1 Hyperion Data in Combination with Linear Spectral Unmixing and an Inverted Geometric-Optical Model

以位于三峡库区的龙门河森林自然保护区为研究区,综合利用线性光谱混合模型和几何光学模型,基于高光谱遥感数据提取森林结构参数是本文研究的重点。在研究区地面调查数据的基础上,通过高光谱数据和混合光谱分解法,获得反演几何光学模型所需的四分量参数,根据背景光照分量与森林植被冠层各参数间的关系,反演得到森林冠层郁闭度及平均冠幅的定量分布图,并利用37个野外实测样本进行结果验证。

The potential of EO-1 Hyperion data combined with linear spectral unmixing and an inverted geometric-optical model for the retrieval of forest structural variables in the Longmenhe broadleaved forest natural reserve, located in the Three Gorges region（ China）, is studied in this paper. Based on the principle of Li-Strahler geometric-optical model, we derive the per-pixel reflectance as being a linear combination of four scene components（ sunlit canopy/sunlit background and shaded canopy/shaded background）. The fraction of each component is subsequently related to several forest structural attributes. With the advantage of having hyperspectral data, we use linear spectral unmixing to separate the above classes present in an atmospherically corrected Hyperion image with support of extensive in situ measurements. In addition, we include DEM derived parameters （ slope and aspect） and measured canopy structural parameters for different forest communities to invert the geometric-optical model and retrieve the pixel-based variables forest crown closure（CC） and crown diameter（CD）. In total 37 sample plots collected in the Longmenhe study region are used for validation, and the results of the above parameters show a good agreement （ e. g. , R^2CC =0. 61/RMSE =0. 046 ;R^2CD =0. 39/RMSE = 0. 984）.