基于背包式MLS与UAV遥感协同的单木-林分尺度森林地上生物量精细反演

Biomass is the kernel index for assessing the capacity of forest ecosystem for carbon storage, and estimation of its distribution has great significance for the associated domains, such as forest material production, climate regulation, and carbon cycle. However, researches indicated that lots of large-scale biomass estimation models may show giant errors, and for the whole field this results in the re-emphasizes of fine-scale measurement techniques as the technical bases. In fact, there are almost no efficient and flexible solutions for the gap between the demands of large-cover and high-accuracy, based on no matter the traditional forest inventory technology or the common remote sensing (RS) technology. Aimed at this issue, the project team are going to assume mobile terrestrial laser scanning (MLS), one of the newest terrestrial RS techniques appropriate for forest environment, and unmanned aerial vehicle (UAV) based imaging, one of the airborne RS techniques flexible for forest mapping. Specifically, the backpack MLS system and the UAV-based agricultural multispectral digital camera (ADC) RS system (UAV-ADC) developed by the project team will be applied. New allometric algorithms for estimating aboveground biomasses (AGB) of individual trees will be developed based on 3D point clouds. The feature parameters of individual trees will be extracted from both the point clouds and ADC images, and their correlation relationships will be exploited and deduced into new algorithms for AGB retrievals up-scaling. With the above frontier-oriented tasks implemented by using the team-developed facilities, a novel technique for accurate AGB estimation at the forest stand scales by collaborating backpack MLS and UAV-ADC can be validated. This also means some chances for the relevant communities to make breakthroughs involving forest biomass.

生物量是森林生态系统碳汇能力的重要指标，估测其分布对于森林物质生产、气候调节、碳循环等领域的研究均有重要意义。然而，研究发现诸多大尺度生物量模型的估测误差较大，这导致了目前该领域对小尺度精确估测这一技术基础的回归再重视。事实证明，传统的林业调查和遥感技术，存在一个无法灵活机动高效地实现大面积与高精度同时测量的技术空白带。针对此问题，课题团队拟引入地基遥感中适于林下环境的最新技术- - 移动地基激光扫描和航空遥感中满足灵活机动的最新技术- - 无人机成像，采用自主开发的背包式移动地基激光扫描系统和无人机农业多光谱数字成像系统，开发基于三维点云的单木地上生物量异速生长反演算法，同时提取点云与无人机图像的相关特征因子并开发尺度上推协同反演算法。通过立足国内而又同步国际的上述工作，藉由背包式激光扫描与无人机新型遥感技术的组合，建立一个林分尺度森林地上生物量精细估测的技术方案，推动相关领域的研究有所突破。

生物量是森林生态系统碳汇能力的重要指标，估测其分布对于森林物质生产、气候调节、碳循环等领域的研究均有重要意义。然而，研究发现诸多大尺度生物量模型的估测误差较大，这导致了目前该领域对小尺度精确估测这一技术基础的回归再重视。事实证明，传统的林业调查和遥感技术，存在一个无法灵活机动高效地实现大面积与高精度同时测量的技术空白带，这也是本领域国际上共同关注的前沿主题之一。.本研究引入地基遥感中适于林下环境的最新技术——移动地基激光扫描和航空遥感中满足灵活机动的最新技术——无人机成像，针对激光扫描与无人机成像反演森林AGB的关键环节问题展开研究，主要内容包括：（1）基于背包式MLS三维点云的单木识别、形态特征因子提取与AGB反演算法开发；（2）基于高分辨率UAV图像的单木识别及其形态、光谱、高程特征因子提取；（3）MLS-UAV特征因子相关关系探索、尺度上推协同反演算法开发及性能评价。. 在项目的支持下，我们提出了一系列原创性的解决方案和高效算法：（1）基于激光扫描的树种分类算法系统框架，涵盖点云分布统计、激光回波强度、树冠内部结构和树木外在形态四类特征参数；（2）基于激光扫描与UAV成像的树木形态结构特征提取算法，涵盖单树LAI、DBH、AGB等参数乃至整体形态表型；（3）延伸探讨影响森林AGB反演的复杂环境及其驱动机制，涵盖树木生长习性的遥感反演等；（4）基于激光扫描与UAV成像的森林AGB高效反演算法。. 项目组共发表SCI期刊论文16篇、中文核刊论文3篇、学术会议论文4篇，获批实用新型专利2项；项目支持下，课题组林沂研究员获得“高校GIS创新人物”奖、中国航海学会科学技术奖一等奖（排名第2）等学术奖励。