A Novel Framework to Automatically Fuse Multiplatform LiDAR Data in Forest Environments Based on Tree Locations
A Novel Framework to Automatically Fuse Multiplatform LiDAR Data in Forest Environments Based on Tree Locations
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一种基于树木位置自动融合森林环境中多平台 LiDAR 数据的新颖框架
DOI：
10.1109/tgrs.2019.2953654
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发表时间：
2020-03-01
影响因子：
8.2
通讯作者：
Guo, Qinghua
中科院分区：
文献类型：
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作者：
Guan, Hongcan;Su, Yanjun;Guo, Qinghua
新兴的近地表光探测和测距(LiDAR)平台[例如，陆地、背包、移动和无人驾驶飞行器(UAV)]在森林调查方面显示出巨大的潜力。然而，不同的LiDAR平台在数据覆盖或捕获树冠下信息方面都存在局限性。多平台激光雷达数据融合是解决这一问题的一种潜在方法。由于森林的复杂性和不规则性，以及林冠下定位信息的不准确，目前的多平台数据融合仍然需要大量的人工工作。在假设每个森林都有唯一的树木分布模式的基础上，我们提出了一个多平台的激光雷达数据自动配准框架。该框架包括五个步骤，即个体树分割、不规则三角网(TIN)生成、TIN匹配、粗配准和精细配准。TIN匹配作为从多平台LiDAR数据中寻找对应树对的关键步骤，使用了一种基于单个树位置组成的三角形的相似性的投票策略。通过融合背包和无人机激光雷达数据和针叶林多扫描陆地激光雷达数据对所提出的框架进行了验证。结果表明，两种配准实验均能达到令人满意的数据配准精度(水平均方根误差30 cm，垂直均方根误差20 cm)。此外，当个体树分割准确率高于80%时，该框架对个体树分割错误不敏感。我们相信，拟议的框架有可能提高在森林环境中准确登记多平台激光雷达数据的效率。
The emerging near-surface light detection and ranging (LiDAR) platforms [e.g., terrestrial, backpack, mobile, and unmanned aerial vehicle (UAV)] have shown great potential for forest inventory. However, different LiDAR platforms have limitations either in data coverage or in capturing undercanopy information. The fusion of multiplatform LiDAR data is a potential solution to this problem. Because of the complexity and irregularity of forests and the inaccurate positioning information under forest canopies, current multiplatform data fusion still involves substantial manual efforts. In this article, we proposed an automatic multiplatform LiDAR data registration framework based on the assumption that each forest has a unique tree distribution pattern. Five steps are included in the proposed framework, i.e., individual tree segmentation, triangulated irregular network (TIN) generation, TIN matching, coarse registration, and fine registration. TIN matching, as the essential step to find the corresponding tree pairs from multiplatform LiDAR data, uses a voting strategy based on the similarity of triangles composed of individual tree locations. The proposed framework was validated by fusing backpack and UAV LiDAR data and fusing multiscan terrestrial LiDAR data in coniferous forests. The results showed that both registration experiments could reach a satisfying data registration accuracy (horizontal root-mean-square error (RMSE) < 30 cm and vertical RMSE < 20 cm). Moreover, the proposed framework was insensitive to individual tree segmentation errors, when the individual tree segmentation accuracy was higher than 80%. We believe that the proposed framework has the potential to increase the efficiency of accurately registering multiplatform LiDAR data in forest environments.