HIGH ACCURACY DIRECT GEOREFERENCING OF THE ALTUM MULTI-SPECTRAL UAV CAMERA AND ITS APPLICATION TO HIGH THROUGHPUT PLANT PHENOTYPING

• Main Authors: J. J. Hutton, G. Lipa, D. Baustian, J. Sulik, R. W. Bruce
• Format: Article
• Language: English
• Published: Copernicus Publications 2020-08-01
• Series: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Online Access: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B1-2020/451/2020/isprs-archives-XLIII-B1-2020-451-2020.pdf

With the appearance of cost effective, easy to fly Unmanned Aerial Vehicles (UAV), a new type of data collection has been enabled: super high resolution multi-spectral, precisely georeferenced imagery and point clouds, collected over high value targets. The high spatial resolution and precise georeferencing accuracy makes information extraction and advanced analytics possible both in the spatial and temporal domain at scales simply not possible to collect from manned aircraft, and at much greater efficiency than can be collected from the ground. One example of this is plant phenotyping for experimental research where a high-accuracy spatial reference needs to be assigned to each plot entry to enable accurate and efficient plot level statistics of plant phenotypic attributes. This paper presents results from an integration of the Trimble APX-15-EI UAV Direct Georeferencing system with the Micasense Altum multi-spectral camera to produce a highly accurate and efficient UAV based mapping solution for advanced spatial and temporal analytics without the use of Ground Control Points (GCP’s). Results from a series of flights over a test range outfitted with GNSS surveyed check points show an orthomap accuracy at the level of 3 cm RMSx,y horizontal can be achieved. The same system flown over a test field operated by researchers at the University of Guelph containing plots of soybean demonstrated pixel-level alignment of the directly georeferenced orthomosaic to the cm-level plot boundaries previously surveyed by the researchers, thus meeting the requirements for automated phenotyping.