DISSERTATION RESEARCH: Biomass estimation and uncertainty analysis: Integrating Bayesian modeling and small-footprint waveform LiDAR data

遥感被用来描述地球植被的空间变化及其随时间的变化并绘制地图。一种相对较新的机载激光传感器，称为激光雷达（光探测和测距），已被用来准确地描述三维植被结构，并测量植物生物量和碳含量。随着LiDAR数据可用性的增加，有必要开发开源工具来有效地处理这些数据，以获得植被和地形信息。采用目前的方法，在估计植被的数量和类型方面仍然存在很大的差异和不确定性。该博士论文改进补助金（DDIG）的支持将用于开发开源工具，以处理来自国家生态观测站网络（氖）的LiDAR数据，并探索其用于识别树种，估计植被数量和确定分析中的统计不确定性的潜力。该项目开发的方法将促进3D植被结构的创建，并使人们能够理解生态系统的模式和过程。对植被结构和数量的误差和不确定性进行量化，也有助于设计有效的可持续森林管理计划，并为生物地球化学模型提供准确的投入，以告知以科学为基础的政策。（1）开发LiDAR数据处理的算法和开源工具，（2）利用随机森林和贝叶斯机器学习方法，仅利用原始LiDAR数据分割树木个体并识别树种。（3）建立不同的模型（逐步、随机森林和分层贝叶斯模型）使用激光雷达指标和来自点云的变量估计森林生物量和碳储量，（4）量化不同处理阶段基于不同方法和模型参数的估计的不确定性。本提案中开发的算法和处理方法将为激光雷达波形处理提供开源工具，并提高氖数据的使用和价值。此外，这项研究的成果将有助于森林管理人员更好地管理宝贵的自然资源，并作出更明智的决定。

Remote sensing is used to characterize and map the spatial variation of Earth's vegetation and its changes over time. A relatively new airborne laser sensor called LiDAR (Light Detection And Ranging) has been used to accurately characterize the three-dimensional vegetation structure and to measure plant biomass and carbon content. With increased LiDAR data availability, there is a need to develop open source tools for processing these data effectively to obtain the vegetation and terrain information. With current methods, substantial variation and uncertainty remain in estimating the amount and type of vegetation. Support from this Doctoral Dissertation Improvement Grant (DDIG) will be used to develop open source tools to process LiDAR data from the National Ecological Observatory Network (NEON) and to explore their potential for use in identifying tree species, estimating vegetation amounts, and determining the statistical uncertainty in the analysis. The methods developed from this project will facilitate the creation of 3D-vegetation structure and enable the understanding of ecosystem patterns and processes. The quantification of errors and uncertainties of vegetation structure and amount are also conducive to designing effective plans for sustainable forest management and providing accurate inputs for biogeochemical models to inform science-based policy.This research will be accomplished through the following steps: (1) Develop algorithms and open source tools for LiDAR data processing, (2) Segment individual trees and identify tree species with raw LiDAR data alone using Random forests and Bayesian machine learning method, (3) Build different models (step-wise, Random forests, and hierarchical Bayesian models) to estimate forest biomass and carbon stocks using LiDAR metrics and variables derived from point clouds, and (4) Quantify the uncertainty of estimations in different processing stages based on different approaches and model parameters. The algorithms and processing methodologies developed in this proposal will provide open source tools for LiDAR waveform processing and enhance the use and value of NEON data. Moreover, the products of this research will assist forest managers to better manage precious natural resources and make more informed decisions.