Analysis of flight parameters and georeferencing of images with different control points obtained by RPA
¹Federal University of Lavras, Department of Agricultural Engineering, University Campus, BR37.200-000 Lavras-MG, Brazil
²Federal University of Lavras, Department of Engineering, University Campus, BR37.200-000 Lavras, Brazil
³University of Florence, Department of Agriculture, Food, Environment and Forestry (DAGRI), Via San Bonaventura, 13, IT50145 Florence, Italy
New techniques for analysing the earth’s surface have been explored, such as the use of remotely piloted aircraft (RPA) to obtain aerial images. However, one of the obstacles of photogrammetry is the reliability of the scenes, because in some cases, considerable geometric errors are generated, thus necessitating adjustments. Some parameters used in these adjustments are image overlaps and control points, which generate uncertainties about the amount and arrangement of these points in an area. The aim of this study was to test the potential of a commercial RPA for monitoring and its applicability in the management of and decision-making about coffee crops with two different overlaps and to evaluate geometric errors by applying four grids of georeferenced points. The study area is located in an experimental Arabica coffee plantation measuring 0.65 ha. To capture the images, the flight altitude was standardized to a 30 m altitude from the ground, and a constant travel speed of 3 m s-1 was used. The treatments studied were two combinations of image overlap, namely, 80/80% and 70/60%. Six points were tracked through Global Navigation Satellite System (GNSS) receivers and identified with signs, followed by an RPA flight for image collection. The obtained results indicated distinct residual error rates pointing to larger errors along Cartesian axis Y, demonstrating that the point distribution directly affects the residual errors. The use of control points is necessary for image adjustments, but to optimize their application, it is necessary to consider the shape of the area to be studied and to distribute the points in a non-biased way relative to the coordinate axes. It is concluded that the lower overlap can be recommended for use in the flight plan due to the high resolution of the orthomosaic and the shorter processing time.