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Spatio-Temporal Change Monitoring of Outside Manure Piles Using Unmanned Aerial Vehicle Images

机译:使用无人空中车辆图像的外界粪便桩的时空变化监测

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Water quality deterioration due to outdoor loading of livestock manure requires efficient management of outside manure piles (OMPs). This study was designed to investigate OMPs using unmanned aerial vehicles (UAVs) for efficient management of non-point source pollution in agricultural areas. A UAV was used to acquire image data, and the distribution and cover installation status of OMPs were identified through ortho-images; the volumes of OMP were calculated using digital surface model (DSM). UAV- and terrestrial laser scanning (TLS)-derived DSMs were compared for identifying the accuracy of calculated volumes. The average volume accuracy was 92.45%. From April to October, excluding July, the monthly average volumes of OMPs in the study site ranged from 64.89 m~(3) to 149.69 m~(3). Among the 28 OMPs investigated, 18 were located near streams or agricultural waterways. Establishing priority management areas among the OMP sites distributed in a basin is possible using spatial analysis, and it is expected that the application of UAV technology will contribute to the efficient management of OMPs and other non-point source pollutants.
机译:牲畜粪便户外装载导致的水质恶化需要高效管理外部粪肥桩(OMP)。本研究旨在使用无人驾驶飞行器(无人机)来研究OMP,以便于农业区域的非点源污染的有效管理。 UAV用于获取图像数据,通过Ortho-Implicate识别OMP的分布和覆盖安装状态;使用数字表面模型(DSM)计算OMP的量。比较无人机和陆地激光扫描(TLS)的DSM,用于识别计算的体积的精度。平均体积精度为92.45%。从4月到10月,尤其是7月,学习网站的月平均常规的常委从64.89 m〜(3)到149.69 m〜(3)。在调查的28​​个OMP中,18位位于溪流或农业水道附近。使用空间分析,可以建立分布在盆地中的OMP网站之间的优先级管理区域,预计UAV技术的应用将有助于OMP和其他非点源污染物的有效管理。

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