首页> 外文期刊>Limnologica >Modelling heights of sparse aquatic reed (Phragmites australis) using Structure from Motion point clouds derived from Rotary- and Fixed-Wing Unmanned Aerial Vehicle (UAV) data
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Modelling heights of sparse aquatic reed (Phragmites australis) using Structure from Motion point clouds derived from Rotary- and Fixed-Wing Unmanned Aerial Vehicle (UAV) data

机译:使用旋转翼无人驾驶飞行器(UAV)数据的运动点云的结构模拟稀疏水上芦苇(Phragmites Australis)的高度

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Aquatic reed beds consisting of Phragmites australis play an important role in lake ecosystems. Digital Elevation Models (DEM) provide essential information in identifying and quantifying these stocks. This study modelled sparse aquatic reed beds with aerial images collected from Rotary (RW) and Fixed Wing (FW) Unmanned Aerial Vehicles (UAV) by the same imaging system. Image processing was executed in a Structure from Motion (SfM) environment and based on bundle adjustment. The DEMs were referenced with Ground Control Points (GCPs) and validated with independent Reference Points (RPs) of heights from reed and flat surfaces. Root Mean Squared (RMS) reprojection error showed that imagery taken with FW could be better aligned than the RW dataset. Quality assessment proved that RW gathers sharper data and lowers image blur resulting in slightly more accurate DEM, while FW showed better area coverage. The results from both configurations proved the efficiency of the methodology in deriving diagnostic relevant features for monitoring sparse aquatic reed beds.
机译:水上芦苇床,包括芦苇芦苇在湖泊生态系统中发挥着重要作用。数字高度模型(DEM)提供识别和量化这些股票的基本信息。本研究采用了相同的成像系统模仿了与旋转(RW)和固定翼(FW)无人机(UAV)收集的空中图像的空中覆盖床。在来自运动(SFM)环境的结构中执行图像处理,并基于捆绑调整。使用地面控制点(GCP)引用DEM,并用来自簧片和平面的高度的独立参考点(RPS)验证。根均方平方(RMS)输注误差显示,使用FW拍摄的图像可以比RW DataSet更好地对齐。质量评估证明,RW收集了数据和降低图像模糊,导致稍微更准确的DEM,而FW显示出更好的区域覆盖范围。这两种配置的结果证明了在推导出诊断相关功能时,方法的效率是监控稀疏水上芦苇床的诊断相关功能。

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