无人机-激光雷达在植被地形中的精度

IF 1.3 Q3 REMOTE SENSING
Maja Kucharczyk, C. Hugenholtz, Xueyang Zou
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引用次数: 7

摘要

利用无人机对6种植被类型(针叶树、落叶树、矮草(0 ~ 0.3 m高度)、高草(> ~ 0.3 m高度)、矮灌木(0 ~ 1 m高度)和高灌木(> ~ 1 m高度)的LiDAR数据进行水平和垂直精度分析。目的是在数字制图标准的背景下评估地面的定位精度,并确定不同植被类型如何影响垂直精度。数据是从配备Riegl vux -1无人机激光扫描仪、KVH Industries 1750 IMU和双NovAtel GNSS接收器的单旋翼垂直起降无人机获得的。地面高程参考测量采用常规野外测量技术。使用2015年美国摄影测量与遥感学会(ASPRS)数字地理空间数据定位精度标准中的方法对精度进行了评估。结果表明,在95%置信水平下,水平精度为0.05 m,垂直精度为0.24 m。在15个植被类型对中,有10个的垂直误差中位数存在显著差异,这凸显了考虑植被结构变化的必要性。根据2015年ASPRS标准,报告的误差分别满足2 cm和8 cm水平和垂直类水平的制图要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
UAV–LiDAR accuracy in vegetated terrain
We examined the horizontal and vertical accuracy of LiDAR data acquired from an unmanned aerial vehicle (UAV) at a field site with six vegetation types: coniferous trees, deciduous trees, short grass (0–0.3 m height), tall grass (>0.3 m height), short shrubs (0–1 m height), and tall shrubs (>1 m height). The objective was to assess positional accuracy of the ground surface in the context of digital mapping standards, and to determine how different vegetation types affect vertical accuracy. The data were acquired from a single-rotor vertical takeoff and landing UAV equipped with a Riegl VUX-1UAV laser scanner, KVH Industries 1750 IMU, and dual NovAtel GNSS receivers. Reference measurements of ground surface elevation were acquired with conventional field surveying techniques. Accuracy was evaluated using methods in the 2015 American Society for Photogrammetry and Remote Sensing (ASPRS) Positional Accuracy Standards for Digital Geospatial Data. Results show that horizontal accuracy and vegetated vertical accuracy at the 95% confidence level were 0.05 and 0.24 m, respectively. Median vertical errors significantly differed among 10 of 15 vegetation type pairs, highlighting the need to account for variations of vegetation structure. According to the 2015 ASPRS standards, the reported errors fulfill the requirements for mapping at the 2 and 8 cm horizontal and vertical class levels, respectively.
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来源期刊
CiteScore
5.30
自引率
0.00%
发文量
2
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