Development and Testing of a UAV Laser Scanner and Multispectral Camera System for Eco-Geomorphic Applications.
Development and Testing of a UAV Laser Scanner and Multispectral Camera System for Eco-Geomorphic Applications.
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DOI：
10.3390/s21227719
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发表时间：
2021-11-19
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影响因子：
--
通讯作者：
Leyland J
中科院分区：
文献类型：
--
作者：
Tomsett C;Leyland J
虽然无人驾驶飞行器（UAV）系统和摄像传感器通常与运动结构（SfM）技术一起部署，以获得河流系统的3D模型，但在存在植被的情况下，这些技术容易产生很大的误差。这是因为植被结构的高度复杂性和处理技术无法识别植被地区的裸地点。此外，在生态地貌学应用中，在收集河流调查数据时，植被特征是一个重要目标，因此问题更加复杂，需要一种替代的调查方法。由于所收集数据的空间密度高以及某些系统能够提供双重（例如，第一次和最后一次）回报，激光扫描技术已被证明是一种适合于离散裸地和植被的技术。本文详细介绍了无人机安装激光雷达和多光谱相机系统的开发和测试以及处理流程，并应用于特定的河流现场位置，并可作为生态水力研究的一般参考。研究表明，该系统和数据处理流程能够检测裸露土地、植被结构和NDVI类型输出，这些输出优于单独的SfM输出，并且显示出更高的准确性和可重复性，检测水平低于0.1 m。这些已开发的传感器包和工作流程的特点为未来的生态地貌研究提供了巨大的潜力。
While Uncrewed Aerial Vehicle (UAV) systems and camera sensors are routinely deployed in conjunction with Structure from Motion (SfM) techniques to derive 3D models of fluvial systems, in the presence of vegetation these techniques are subject to large errors. This is because of the high structural complexity of vegetation and inability of processing techniques to identify bare earth points in vegetated areas. Furthermore, for eco-geomorphic applications where characterization of the vegetation is an important aim when collecting fluvial survey data, the issues are compounded, and an alternative survey method is required. Laser Scanning techniques have been shown to be a suitable technique for discretizing both bare earth and vegetation, owing to the high spatial density of collected data and the ability of some systems to deliver dual (e.g., first and last) returns. Herein we detail the development and testing of a UAV mounted LiDAR and Multispectral camera system and processing workflow, with application to a specific river field location and reference to eco-hydraulic research generally. We show that the system and data processing workflow has the ability to detect bare earth, vegetation structure and NDVI type outputs which are superior to SfM outputs alone, and which are shown to be more accurate and repeatable, with a level of detection of under 0.1 m. These characteristics of the developed sensor package and workflows offer great potential for future eco-geomorphic research.
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