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摘要： 天然森林具有冠层和林下植被(即灌丛、草地)的垂直立体结构，准确、定量地分离林下植被对于改善森林冠层叶面积指数反演精度具有重要的科学意义和实用价值。传统被动光学遥感数据由于在直接获取三维信息方面存在局限性，联合主被动的航空激光雷达(ALS)和高光谱数据(HyMap)，以美国华盛顿州植物园为重点研究区，首先在单木分割的基础上实现了森林的垂直分层(即森林冠层和林下植被层)。在此基础上，利用森林冠层激光点云数据对光学影像数据进行林下植被信息剔除。通过对比利用航空光学影像和地面实测得到的森林有效叶面积指数结果发现：①森林郁闭度对于ALS数据的穿透性具有显著影响；②去除林下植被信息能够有效改善森林冠层有效叶面积指数(LAIe)估算精度。通过剔除林下植被信息，植被指数(NDVI)与地面实测有效叶面积指数的相关性由0.087提升到0.591。此外，基于剔除林下植被信息的光学遥感影像，与简单比值植被指数(SR)(相关性由0.209提升到0.559)和简化简单比例植被指数(RSR)(相关性由0.147提升到0.358)相比，归一化植被指数(NDVI)对冠层叶面积指数的变化最为敏感(相关性提高0.5)。本研究所提出的联合主被动遥感数据定量分离林下植被的方法能够有效地改善森林冠层叶面积指数的反演精度，为准确定量地估算森林生物物理参数和研究碳、水循环过程提供坚实的基础。   关键词： 单木分割;  林下植被;  叶面积指数(LAI);  激光雷达;  植被指数     Abstract: Natural forests have the vertical three\|dimensional structure of canopy and understory vegetation (shrubs，grasslands，and bare soil).Accurate and quantitative separation of understory vegetation is of great scientific significance and practicality on improving the precision of inversion of forest canopy leaf area index.value.Due to the limitations of traditional passive optical remote sensing data on directly acquiring 3D information，this study intends to combine active and passive ALS and HyperMap data with the Washington Botanic Garden as the key research area.On the basis of individual tree segmentation，the vertical stratification of the forest (forest canopy and undergrowth vegetation layer) is achieved.On this basis，the forest canopy laser point cloud data was used to remove the understory information from the optical image data.By comparing the results of the forest effective leaf area index obtained from aerial optical images and ground measurements，it was found that：(1) forest canopy density has a significant impact on the penetration of ALS data；(2) removal of understory information can effectively improve the forest crown accuracy of LAIe estimated.The correlation between Normalized Difference Vegetation Index (NDVI) and ground surface measured effective leaf area index increased from 0.087 to 0.591.In addition，the optical remote sensing image based on the removal of understory vegetation information was compared with the Simple Ratio vegetation index (SR) (the correlation increased from 0.209 to 0.559) and the simplified simple Ratio vegetation index (RSR) (the correlation increased from 0.147 to 0.358).The NDVI was most sensitive to changes in canopy leaf area index (correlation increased by 0.5).The method of quantitatively separating understory vegetation with the combined active and passive remote sensing data proposed in this study can effectively improve the accuracy of inversion of forest canopy leaf area index，and provide a solid foundation for quantitative and accurate estimate of forest biophysical parameters and study of carbon and water cycle processes.

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