Drone Multiline Light Detection and Ranging Data Filtering in Coastal Salt Marshes Using Extreme Gradient Boosting Model
Drone Multiline Light Detection and Ranging Data Filtering in Coastal Salt Marshes Using Extreme Gradient Boosting Model
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DOI：
10.3390/drones8010013
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发表时间：
2024-01
期刊：
影响因子：
4.8
通讯作者：
Xixiu Wu;Kai Tan;Shuai Liu;Feng Wang;Pengjie Tao;Yanjun Wang;Xiaolong Cheng
中科院分区：
文献类型：
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作者：
Xixiu Wu;Kai Tan;Shuai Liu;Feng Wang;Pengjie Tao;Yanjun Wang;Xiaolong Cheng
定量表征滨海盐沼地形及其时空变化对于制定综合治理方案和阐明动态碳演变至关重要。多线激光雷达（LiDAR）具有穿透性强、扫描方式新颖等特点，在盐沼地形测量中具有很强的应用能力。获取高精度地形的前提条件是对多线LiDAR数据中的盐沼植被点和地面/滩涂植被点进行精确滤波。在这项研究中，提出了一种新的替代盐沼植被点云滤波方法，用于无人机多线LiDAR的基础上的极端梯度提升（即，XGBoost）模型。根据植被和地面表现出不同的几何和辐射特征的基本原理，XGBoost被构造成用一系列选定的基本几何和辐射度量（即，距离、扫描角度、仰角、法向矢量和强度），在缺少瞬时扫描几何形状的情况下（即，根据无人机多线激光雷达的扫描原理和点云空间分布特点，精确估计出每个点的距离和扫描角度。基于所构建的模型，所选特征的组合可以准确智能地预测每个点的类别。该方法在中国上海沿海盐沼进行了无人机16线激光雷达系统的测试。结果表明，该方法的平均AUC和G均值分别为0.9111和0.9063。所提出的方法具有增强的适用性和通用性，并优于传统的和其他机器学习方法，在不同的地形和植被生长状态的不同地区，这表明有前途的潜力点云过滤和分类，特别是在极端环境中的地形，土地覆盖，和点云分布是非常复杂的。
Quantitatively characterizing coastal salt-marsh terrains and the corresponding spatiotemporal changes are crucial for formulating comprehensive management plans and clarifying the dynamic carbon evolution. Multiline light detection and ranging (LiDAR) exhibits great capability for terrain measuring for salt marshes with strong penetration performance and a new scanning mode. The prerequisite to obtaining the high-precision terrain requires accurate filtering of the salt-marsh vegetation points from the ground/mudflat ones in the multiline LiDAR data. In this study, a new alternative salt-marsh vegetation point-cloud filtering method is proposed for drone multiline LiDAR based on the extreme gradient boosting (i.e., XGBoost) model. According to the basic principle that vegetation and the ground exhibit different geometric and radiometric characteristics, the XGBoost is constructed to model the relationships of point categories with a series of selected basic geometric and radiometric metrics (i.e., distance, scan angle, elevation, normal vectors, and intensity), where absent instantaneous scan geometry (i.e., distance and scan angle) for each point is accurately estimated according to the scanning principles and point-cloud spatial distribution characteristics of drone multiline LiDAR. Based on the constructed model, the combination of the selected features can accurately and intelligently predict the category of each point. The proposed method is tested in a coastal salt marsh in Shanghai, China by a drone 16-line LiDAR system. The results demonstrate that the averaged AUC and G-mean values of the proposed method are 0.9111 and 0.9063, respectively. The proposed method exhibits enhanced applicability and versatility and outperforms the traditional and other machine-learning methods in different areas with varying topography and vegetation-growth status, which shows promising potential for point-cloud filtering and classification, particularly in extreme environments where the terrains, land covers, and point-cloud distributions are highly complicated.