Bayesian decomposition of full waveform LiDAR data with uncertainty analysis
Bayesian decomposition of full waveform LiDAR data with uncertainty analysis
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DOI：
10.1016/j.rse.2017.08.012
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发表时间：
2017-10
影响因子：
13.5
通讯作者：
Tan Zhou;S. Popescu
中科院分区：
文献类型：
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作者：
Tan Zhou;S. Popescu
全面了解全波形（FW）激光雷达数据处理和相关的不确定性是植被应用，如检索森林结构变量和估计森林生物量的关键。本文应用贝叶斯非线性建模的概念来处理小足迹FW激光雷达数据（贝叶斯分解）收集在一个研究地点的国家生态观测网络（氖）的波形分解和不确定性估计的潜力。具体而言，在贝叶斯框架内评估了几种适合拟合波形的可能模型，并选择高斯模型进行贝叶斯分解。随后，我们进行了性能评估和不确定性分析，在参数，衍生点云和表面模型的水平。模型合理性分析结果表明，高斯模型在不确定性、物理意义和处理效率等方面均优于其他模型，具有上级优势。在将波形转换为离散点之后，模型比较表明贝叶斯分解可以用于FW LiDAR数据处理，其结果与直接分解（DD）、Gold和RL（Richardson-Lucy）方法在基于波形的点云与参考点云之间的点距离的均方根误差（RMSE < 0.93 m）方面相当。此外，更多的点可以提取从FW激光雷达数据与这些方法比离散返回激光雷达数据，特别是在中间层的植被的高度箱，百分位高度和冠层激光雷达密度的结果在单个树的水平。此外，贝叶斯方法的不确定性估计在概率意义上增强了分解结果的可信度，以捕获估计的真实误差并跟踪沿着处理步骤的不确定性传播。例如，与分布更紧凑的分位数点云相比，表面模型的结果产生更大的RMSE值（1.38 m对0.65 m），具有更宽的可信区间。与通常使用的确定性方法相比，贝叶斯分解方法可以通过马尔可夫链蒙特卡罗（MCMC）抽样从模型参数的后验分布中产生具有概率的合理参数估计的集合。可以查询这些参数估计值和相应的衍生产品，以提供对结果和相关不确定性的有意义的解释。平坦先验和经验先验都能获得较好的分解性能，而经验先验能显著加快模型的收敛速度。贝叶斯方法也呈现出一个重要的洞察到模型性能评估的不确定性，使用现场数据生成合理的预测区间，以减少现场测量的固有误差。
A thorough understanding of full waveform (FW) LiDAR data processing and associated uncertainty is critical to vegetation applications such as retrieving forest structure variables and estimating forest biomass. This paper applies the Bayesian non-linear modeling concept to process small-footprint FW LiDAR data (the Bayesian decomposition) collected at a study site of the National Ecological Observatory Network (NEON) to investigate its potential for waveform decomposition and uncertainty estimation. Specifically, several possible models suitable for fitting waveforms were assessed within the Bayesian framework, and the Gaussian model was selected to perform the Bayesian decomposition. Subsequently, we conducted performance evaluation and uncertainty analysis at the parameter, derived point cloud and surface model levels. Results of the model reasonableness show that the Gaussian model is superior to alternative models with respect to uncertainty, physical meaning and processing efficiency. After converting waveforms to discrete points, the model comparisons demonstrate that the Bayesian decomposition can be utilized for FW LiDAR data processing, and its results are comparable to the direct decomposition (DD), Gold and RL (Richardson–Lucy) approaches in terms of the root mean squared error (RMSE < 0.93 m) of the point distances between the waveform-based point cloud and the reference point cloud. Additionally, more points can be extracted from FW LiDAR data with these methods than discrete-return LiDAR data, especially at the mid-story of vegetation based on the results of height bins, percentile heights and canopy LiDAR density at the individual tree level. Moreover, uncertainty estimates from the Bayesian method enhance the credibility of decomposition results in a probabilistic sense to capture the true error of estimates and trace the uncertainty propagation along the processing steps. For example, results of the surface model yield larger RMSE values (1.38 m vs. 0.65 m) with a wider credible interval than quantile point clouds with a more compact distribution. In contrast to commonly used deterministic approaches, the Bayesian decomposition method can produce an ensemble of reasonable parameter estimates with probability through Markov Chain Monte Carlo (MCMC) sampling from the posterior distribution of model parameters. These parameter estimates and corresponding derived products can be queried to provide meaningful interpretation of results and associated uncertainty. Both the flat priors and empirical priors can achieve good performance of the decomposition while the empirical priors tend to significantly speed up the model convergence. The Bayesian approach also renders an important insight into the uncertainty of the model performance evaluation using field data by generating reasonable prediction intervals to reduce inherent errors of field measurements.