基于多边形网格的直接rtk -地理参考无人机作物和牧场监测图像评价

IF 2.1 4区 地球科学 Q3 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY
Georg Bareth, Christoph Hütt
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引用次数: 0

摘要

利用无人机(uav)的遥感方法已成为监测农业系统的既定方法。它们可以通过多光谱或高光谱、RGB或激光雷达传感器进行数据采集。基于运动结构和多视角立体视觉(SfM/MVS)的摄影测量分析为作物或草地性状的无损估计开辟了一个新的研究领域。SfM/MVS分析能够监测植物高度和植物生长,从而确定生物量等。SfM/MVS分析工作流程的一个缺点是它需要地面控制点(gcp),这使得它不适合监测通常大于1公顷的管理油田。因此,准确的地理参考图像数据采集将是有益的,因为它可以在没有gcp的情况下进行数据分析。在过去的十年中,在无人机集成实时运动学(RTK)定位方面取得了实质性进展,可以提供厘米范围内所需的精度。因此,为了评估作物和草地高度分析的准确性,我们研究了rtk标记无人机数据的两种SfM/MVS工作流程,(I)不使用gcp和(II)使用gcp。结果清楚地表明,与在SfM/MVS分析中包含gcp (Z的RMSE为1.7 cm)的工作流(II)中的有效性相比,直接rtk -地理参考无人机数据在不使用任何gcp (Z的RMSE为2.8 cm)的工作流(I)中表现良好。两个数据集的地面采样距离(GSD)相同,均为2.46 cm。我们得出的结论是,配备rtk的无人机能够监测大于3厘米的作物和草地生长。在植物高度差异较大的情况下,监测明显更加准确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Evaluation of Direct RTK-georeferenced UAV Images for Crop and Pasture Monitoring Using Polygon Grids

Evaluation of Direct RTK-georeferenced UAV Images for Crop and Pasture Monitoring Using Polygon Grids

Remote sensing approaches using Unmanned Aerial Vehicles (UAVs) have become an established method to monitor agricultural systems. They enable data acquisition with multi- or hyperspectral, RGB, or LiDAR sensors. For non-destructive estimation of crop or sward traits, photogrammetric analysis using Structure from Motion and Multiview Stereopsis (SfM/MVS) has opened a new research field. SfM/MVS analysis enables the monitoring of plant height and plant growth to determine, e.g., biomass. A drawback in the SfM/MVS analysis workflow is that it requires ground control points (GCPs), making it unsuitable for monitoring managed fields which are typically larger than 1 ha. Consequently, accurately georeferenced image data acquisition would be beneficial as it would enable data analysis without GCPs. In the last decade, substantial progress has been achieved in integrating real-time kinematic (RTK) positioning in UAVs, which can potentially provide the desired accuracy in cm range. Therefore, to evaluate the accuracy of crop and sward height analysis, we investigated two SfM/MVS workflows for RTK-tagged UAV data, (I) without and (II) with GCPs. The results clearly indicate that direct RTK-georeferenced UAV data perform well in workflow (I) without using any GCPs (RMSE for Z is 2.8 cm) compared to the effectiveness in workflow (II), which included the GCPs in the SfM/MVS analysis (RMSE for Z is 1.7 cm). Both data sets have the same Ground Sampling Distance (GSD) of 2.46 cm. We conclude that RTK-equipped UAVs enable the monitoring of crop and sward growth greater than 3 cm. At greater plant height differences, the monitoring is significantly more accurate.

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来源期刊
CiteScore
8.20
自引率
2.40%
发文量
38
期刊介绍: PFG is an international scholarly journal covering the progress and application of photogrammetric methods, remote sensing technology and the interconnected field of geoinformation science. It places special editorial emphasis on the communication of new methodologies in data acquisition and new approaches to optimized processing and interpretation of all types of data which were acquired by photogrammetric methods, remote sensing, image processing and the computer-aided interpretation of such data in general. The journal hence addresses both researchers and students of these disciplines at academic institutions and universities as well as the downstream users in both the private sector and public administration. Founded in 1926 under the former name Bildmessung und Luftbildwesen, PFG is worldwide the oldest journal on photogrammetry. It is the official journal of the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF).
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