Development and Performance Evaluation of a Very Low-Cost UAV-Lidar System for Forestry Applications
Development and Performance Evaluation of a Very Low-Cost UAV-Lidar System for Forestry Applications
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用于林业应用的超低成本无人机激光雷达系统的开发和性能评估
DOI：
10.3390/rs13010077
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发表时间：
2021-01-01
期刊：
影响因子：
5
通讯作者：
Guo, Qinghua
中科院分区：
文献类型：
--
作者：
Hu, Tianyu;Sun, Xiliang;Guo, Qinghua
准确和重复的森林清查数据对于了解森林生态系统过程和管理森林资源至关重要。近年来，无人驾驶航空器（UAV）载光探测和测距（激光雷达）系统已被证明是有效的森林资源清查属性。然而，其高昂的成本在很大程度上阻碍了它们在大规模森林应用中的使用。在这里，我们开发了一种非常低成本的无人机激光雷达系统，该系统集成了最近出现的DJI Livox MID 40激光扫描仪（类似于600美元），并评估了其估计单个树木水平（即，树高）和地块级森林清查属性（即，冠层覆盖度、林隙比例和叶面积指数（LAI））。此外，还对开发的DJI Livox系统和其他四种配备高端激光扫描仪的无人机激光雷达系统（即，RIEGL VUX-1 UAV、RIEGL miniVUX-1 UAV、HESAI Pandar 40和Velodyne Puck LITE）。使用这些仪器，我们调查了针叶林网站和阔叶林网站，树木密度从500棵树/公顷到3000棵树/公顷，52无人机飞行在不同的飞行高度和速度组合。开发的DJI Livox MID 40系统有效地捕获了两个森林站点的上层冠层结构和地形表面信息。估计的单株树高与实地测量结果高度相关（针叶林站点：R-2 = 0.96，均方根误差/RMSE = 0.59 m;阔叶林站点：R-2 = 0.70，RMSE = 1.63 m）。冠层覆盖度，林隙分数，叶面积指数的图级估计对应以及来自高端RIEGL VUX-1无人机系统，但往往有系统的偏差，在中高郁闭度的地区。总体而言，DJI Livox MID 40系统在针叶林中对RIEGL miniVUX-1无人机，HESAI Pandar 40和Velodyne Puck LITE系统以及阔叶林中的Velodyne Puck LITE系统进行了测试。尽管其明显的缺点是对低强度回波的敏感性有限和视野狭窄，但我们相信，本研究开发的非常低成本的系统可以在很大程度上拓宽无人机激光雷达在森林资源清查应用中的潜在用途。该研究也为选择合适的无人机激光雷达系统和飞行规范进行森林研究和管理提供了指导。
Accurate and repeated forest inventory data are critical to understand forest ecosystem processes and manage forest resources. In recent years, unmanned aerial vehicle (UAV)-borne light detection and ranging (lidar) systems have demonstrated effectiveness at deriving forest inventory attributes. However, their high cost has largely prevented them from being used in large-scale forest applications. Here, we developed a very low-cost UAV lidar system that integrates a recently emerged DJI Livox MID40 laser scanner (similar to$600 USD) and evaluated its capability in estimating both individual tree-level (i.e., tree height) and plot-level forest inventory attributes (i.e., canopy cover, gap fraction, and leaf area index (LAI)). Moreover, a comprehensive comparison was conducted between the developed DJI Livox system and four other UAV lidar systems equipped with high-end laser scanners (i.e., RIEGL VUX-1 UAV, RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE). Using these instruments, we surveyed a coniferous forest site and a broadleaved forest site, with tree densities ranging from 500 trees/ha to 3000 trees/ha, with 52 UAV flights at different flying height and speed combinations. The developed DJI Livox MID40 system effectively captured the upper canopy structure and terrain surface information at both forest sites. The estimated individual tree height was highly correlated with field measurements (coniferous site: R-2 = 0.96, root mean squared error/RMSE = 0.59 m; broadleaved site: R-2 = 0.70, RMSE = 1.63 m). The plot-level estimates of canopy cover, gap fraction, and LAI corresponded well with those derived from the high-end RIEGL VUX-1 UAV system but tended to have systematic biases in areas with medium to high canopy densities. Overall, the DJI Livox MID40 system performed comparably to the RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE systems in the coniferous site and to the Velodyne Puck LITE system in the broadleaved forest. Despite its apparent weaknesses of limited sensitivity to low-intensity returns and narrow field of view, we believe that the very low-cost system developed by this study can largely broaden the potential use of UAV lidar in forest inventory applications. This study also provides guidance for the selection of the appropriate UAV lidar system and flight specifications for forest research and management.