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Assessing the Value of UAV Photogrammetry for Characterizing Terrain in Complex Peatlands

机译:评估无人机摄影测量技术在复杂泥炭地地形表征中的价值

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Microtopographic variability in peatlands has a strong influence on greenhouse gas fluxes, but we lack the ability to characterize terrain in these environments efficiently over large areas. To address this, we assessed the capacity of photogrammetric data acquired from an unmanned aerial vehicle (UAV or drone) to reproduce ground elevations measured in the field. In particular, we set out to evaluate the role of (i) vegetation/surface complexity and (ii) supplementary LiDAR data on results. We compared remote-sensing observations to reference measurements acquired with survey grade GPS equipment at 678 sample points, distributed across a 61-hectare treed bog in northwestern Alberta, Canada. UAV photogrammetric data were found to capture elevation with accuracies, by root mean squares error, ranging from 14–42 cm, depending on the state of vegetation/surface complexity. We judge the technology to perform well under all but the most-complex conditions, where ground visibility is hindered by thick vegetation. Supplementary LiDAR data did not improve results significantly, nor did it perform well as a stand-alone technology at the low densities typically available to researchers.
机译:泥炭地的微观地形变化对温室气体通量有很大影响,但是我们缺乏在大面积上有效表征这些环境中地形的能力。为了解决这个问题,我们评估了从无人飞行器(UAV或无人驾驶飞机)获取的摄影测量数据再现现场测量的地面标高的能力。特别是,我们着手评估(i)植被/表面复杂性和(ii)补充LiDAR数据对结果的作用。我们将遥感观测结果与使用调查级GPS设备在678个采样点处获得的参考测量值进行了比较,这些测量值分布在加拿大艾伯塔省西北部61公顷的树木沼泽中。发现无人机摄影测量数据可以精确捕获海拔高度,其均方根误差范围为14-42 cm,具体取决于植被/表面复杂性的状态。我们认为该技术在最复杂的条件下(除了最复杂的条件下)都表现良好,在这些条件下,地面可见性受茂密的植被影响。补充的LiDAR数据并没有显着改善结果,也无法在研究人员通常可用的低密度下作为独立技术表现良好。

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