在基于无人机的3D作物水应力指数应用中比较纳迪尔和斜热图像,用于精密葡萄种植,使用LiDAR验证
Thomas Buunk1, Sergio Vélez2, Mar Ariza-Sentís2
1Laboratory of Geo-Information Sciences and Remote Sensing, Wageningen University & Research, 6708 PB Wageningen, The Netherlands.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
无人机 (UAV) 热成像有助于优化精密农业的灌. 结合纳迪尔和斜飞行是3D作物水应力指数 (CWSI) 点云的最佳选择,提供更全面的树冠视图.
科学领域:
- 农业工程 农业工程
- 遥感 遥感 遥感 遥感
- 地理空间分析的研究.
背景情况:
- 精准农业越来越依赖无人机 (UAV) 的热成像来评估作物状况.
- 优化用水是非常重要的,因为水需求不断增加和资源有限.
- 现有的作物评估方法对不同无人机图像捕捉策略 (纳迪尔与斜视) 的影响缺乏清晰度.
研究的目的:
- 调查纳迪尔和斜面无人机图像在生成2D (地图) 和3D (点云) 农作物水应激指数 (CWSI) 产品中对木质作物的作用.
- 通过使用各种飞行配置,确定用于遥感产品生成的最有效的工作流程.
- 为了评估CWSI点云与LiDAR相比对几何表示的准确性.
主要方法:
- 无人机的热图像是使用点和斜飞行配置来捕获的.
- 使用Agisoft Metashape,ArcGIS Pro和CloudCompare生成了CWSI地图 (2D) 和CWSI点云 (3D).
- 使用线性回归和体积计算来分析飞行配置对产品准确性和几何表示的影响.
主要成果:
- 结合顶点和斜飞行角度是多余的,用于生成2D正方形产品.
- 3D CWSI点云是有效地使用点和斜图像生成的.
- 与LiDAR (R2 = 0.72) 相比,结合的点和斜飞行提供了最准确的CWSI点云 (R2 = 0.72),其次是点 (R2 = 0.68) 和斜 (R2 = 0.54).
结论:
- 3D CWSI点云提供了比2D产品更全面的作物树冠视图.
- 结合的点和斜飞行策略优化了准确的3D CWSI点云的生成,用于精确的葡萄种植.
- 这项研究为农场经理,技术人员和无人机飞行员提供了宝贵的见解,以提高无人机数据捕获策略,以达到特定的精准农业目标.
关键词:
3D点云是一个3D点云.卢维尔 (LWIR) 是一个非常有价值的人.葡萄酒 (Vitis vinifera) 是一种葡萄酒.无人机 无人机 无人机飞行配置 飞行配置多光谱的多光谱.这是一个ortho-mosaic.摄影计量 (摄影计量) 是一种摄影仪.精准农业 精准农业 精准农业葡萄园 葡萄园 葡萄园 葡萄园更多相关视频
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