Assessing UAV-collected image overlap influence on computation time and digital surface model accuracy in olive orchards

Abstract

Addressing the spatial and temporal variability of crops for agricultural management requires intensive and periodical information gathering from the crop fields. Unmanned Aerial Vehicle (UAV) photogrammetry is a quick and affordable method for information collecting; it provides spectral and spatial information when required with the added value of Digital Surface Models (DSMs) that reconstruct the crop structure in 3D using “structure from motion” techniques. In the full process from UAV flights to image analysis, DSM generation is one bottle-neck due to its high processing time. Despite its importance, the optimization of the required forward overlap for saving time in DSM generation has not yet been studied. UAV images were acquired at 50 and 100 m flight altitudes over two olive orchards with the aim of generating DSMs representing the tree crowns. Several DSMs created with different forward laps (in intervals of 5–6% from 58 to 97%) were evaluated in order to determine the optimal generation time according to the accuracy of tree crown measurements computed from each DSM. Based on our results, flying at 100 m altitude and with a 95% forward lap reported the best configuration. From the analysis derived from this configuration, tree volume was estimated with 95% accuracy. In addition, computing time was 85% lower in comparison to the maximum overlap studied (97%). It allowed computing the 3D features of 600 trees in a 3-ha parcel in a highly accurate and quick (a few hours after the UAV flights) manner by using a standard computer.