基于体素特征的森林冠层叶绿素反演研究

项目名称： 基于体素特征的森林冠层叶绿素反演研究

项目编号： No.41501365

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 天文学、地球科学

项目作者： 佃袁勇

作者单位： 华中农业大学

项目金额： 20万元

中文摘要： 森林冠层叶绿素含量在森林生态系统光合作用中起着重要作用，叶绿素也可作为植物受到胁迫的指示性指标，衡量植被对环境的响应。高光谱遥感数据在反演大尺度范围植被叶绿素方面有着广泛的应用。但是由于缺少森林冠层垂直结构信息，群落垂直与水平结构分布的不均匀性对遥感反演冠层叶绿素影响的机制尚不明确。建立一种适应不同群落类型及冠层结构特点的冠层叶绿素遥感反演方法能为森林生态功能评价提供更有效的基础数据。本项目拟利用机载激光雷达与高光谱数据，对针叶林、阔叶林、针阔混交林三种具有不同垂直结构特性的森林群落类型，研究群落冠层三维结构“体素”划分与描述方法；利用野外测量数据及几何-光学辐射传输模型模拟数据，分析冠层“体素”结构特征对冠层光谱的响应规律，揭示群落类型及冠层结构差异对冠层叶绿素反演的影响；利用冠层“体素”特征构建顾及森林冠层垂直结构特点及群落类型的叶绿素反演方法，实现非均质森林覆盖区域冠层叶绿素反演。

中文关键词： 高光谱遥感；小光斑激光雷达；植被生物化学参数；植被生理生态参数；植被结构参数

英文摘要： Canopy Chlorophyll content in forested ecosystems plays important role in plant photosynthesis and can indicate vegetation stress which influenced by the environment. Hyperspectral remote sensing data has been widely used in inversion large area canopy chlorophyll content. However, due to lack of vegetation canopy vertical structure information, the mechanism was unclear when using canopy remote sensing data inverse canopy chlorophyll especially in non-uniformity vegetation areas. Therefore, the study on remote estimation of canopy chlorophyll resistant to complex vertical structure and variations in species is essential for evaluating forest ecology effect. In this study, three different forest community types such as coniferous, broad-leaved and mixed forest which contain different vertical structural characteristics was selected and the airborne hyperspectral and Light Detection and Ranging (LiDAR) data will using to inverse canopy chlorophyll. Firstly，the voxel was used to describe canopy three-dimensional structure, and then the 3D voxel characteristics were established by combining LiDAR and hyperspectral data. Second, the sensitive of canopy spectral responded with the canopy 3D structure was analysis and then the regularity of how canopy 3D structure parameters influenced on canopy chlorophyll inversion was researched based on the model simulated data and in-situ measurements. Third, the regularity between canopy chlorophyll and canopy 3-D structure in community level was applied to airborne hyperspectral and LiDAR data, and canopy chlorophyll content inversion framework considering the vegetation type and canopy 3D structure were established based on these canopy voxels.

英文关键词： hyperspectral remote sensing;small footprint LiDAR;vegetation biochemical parameters;vegetation biophysical parameters;vegetation structural parameters