全波形机载激光雷达修正森林聚集效应研究

Foliage clumping effect, which characterizes the non-uniform spatial distribution of leaves, is a key factor that restricts the accuracy of remote sensing retrieval of leaf area index. Traditional methods for correcting clumping effect mainly rely on fine gap distribution measured at ground scale, while its application is very limited at airborne or spaceborne scales. In previous research, the applicant has made a preliminary attempt to model the clumping effect based on path length distribution model and canopy height distribution acquired from three-dimensional point cloud of airborne lidar. However, the discrete point cloud information cannot characterize the lower envelope of the canopy, which restricts the completeness of clumping modeling and the retrieval of vertical leaf area distribution..In this study, full-waveform airborne lidar data will be introduced, with the particular focus on characterizing the lower envelope of the canopy, to model and correct the clumping effect for accurate leaf area index retrieval based on the three-dimensional envelope of the canopy. With the complete three-dimensional canopy envelope, the retrieval of three-dimensional spatial distribution of leaf area will be studied and the influence of spatial scale and vertical resolution will be analyzed. This study will eventually provide a method for modeling and correcting clumping effect using airborne lidar, with strong operability, clear mechanism and no dependence on ground regression, in order to provide more accurate leaf area index and vertical distribution of leaf area. It will further strengthen the ability of airborne lidar to monitor vegetation.

聚集效应表征叶片的非均匀空间分布特征，是制约叶面积指数遥感反演精度的关键因素。传统聚集效应修正方法集中在地面尺度，依赖于地面测量的精细间隙分布，在航空、航天遥感尺度的应用较为受限。在前期研究中，申请人基于机载激光雷达三维点云，利用冠层高度分布和路径长度分布模型初步实现了聚集效应建模。但同时发现，离散点云信息不足以获取树冠下部轮廓，限制了聚集效应建模的完整性和分层叶面积的反演。.针对这一问题，本研究将引入全波形机载激光雷达数据，突破树冠下部轮廓的获取技术，从冠层三维轮廓的角度，研究聚集效应建模与修正方法，实现叶面积指数的准确反演。并基于冠层完整三维轮廓约束，研究叶面积三维空间分布的获取方法，分析并确定最优空间尺度和垂直分辨率。最终形成一套可操作性强、机理清晰、不依赖地面回归的航空尺度聚集效应建模与修正方法，反演更为准确的叶面积指数并提供分层叶面积信息，进一步增强机载激光雷达的植被监测能力。

聚集效应表征叶片的非均匀空间分布特征，是制约叶面积指数遥感反演精度的关键因素。传统聚集效应修正方法集中在地面尺度，依赖于地面测量的精细间隙分布，在航空、航天遥感尺度的应用较为受限。在前期研究中，申请人基于机载激光雷达三维点云，利用冠层高度分布和路径长度分布模型初步实现了聚集效应建模。但同时发现，离散点云信息不足以获取树冠下部轮廓，限制了聚集效应建模的完整性和分层叶面积的反演。. 针对这一问题，本项目引入全波形机载激光雷达数据，通过波形分解和波形特征分析提取激光脉冲穿入和穿出树冠的位置，从而获取到较为准确的绝对路径长度分布来刻画树冠的三维轮廓和结构特征，在网格尺度应用路径长度分布模型，实现了聚集效应修正和叶面积指数反演，并在不同网格大小下开展反演并分析反演尺度对叶面积指数的影响。. 结果表明，基于全波形数据和路径长度分布法反演的真实叶面积指数和基于离散点云数据的反演结果在整个研究区域的分布趋势上基本一致；基于地面间接测量数据的验证表明，全波形数据的反演精度（RMSE=0.32）优于离散点云数据的反演精度（RMSE=0.41）。从5米到500米的不同网格大小反演结果的尺度效应分析表明，基于全波形数据在不同尺度反演的结果较为一致，受尺度影响较小。本项目利用全波形数据较为丰富的信息，实现了更为准确的树冠三维结构特征提取，形成了一套可操作性强、机理清晰、不依赖地面回归的航空尺度聚集效应建模与修正方法，并具备向航天尺度拓展的潜力，进一步增强激光雷达的大尺度植被监测能力。