Abstract
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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Acknowledgements
The authors would like to thank Prof. Dr. Johannes Isselstein and Dr. Martin Komainda of the Institute of Grassland Science of the University of Göttingen for providing access to the long-term grazing experiment with in the BMBF-funded GreenGrass project.
Funding
Open Access funding is enabled and organized by Projekt DEAL. This work was funded by the Federal Ministry of Education and Research (BMBF) [Grant number 031B0734F] within the research initiative Agricultural Systems of the Future (www.agrarsysteme-der-zukunft.de) as part of the consortium research project “GreenGrass” (www.greengrass-project.de).
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Bareth, G., Hütt, C. Evaluation of Direct RTK-georeferenced UAV Images for Crop and Pasture Monitoring Using Polygon Grids. PFG 91, 471–483 (2023). https://doi.org/10.1007/s41064-023-00259-7
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DOI: https://doi.org/10.1007/s41064-023-00259-7