Abstract
With the increased availability of low-cost, off-the-shelf drone platforms, drone data become easy to capture and are now a key component of environmental assessments and monitoring. Once the data are collected, there are many structure-from-motion (SfM) photogrammetry software options available to pre-process the data into digital elevation models (DEMs) and orthomosaics for further environmental analysis. However, not all software packages are created equal, nor are their outputs. Here, we evaluated the workflows and output products of four desktop SfM packages (AgiSoft Metashape, Correlator3D, Pix4Dmapper, WebODM), across five input datasets representing various ecosystems. We considered the processing times, output file characteristics, colour representation of orthomosaics, geographic shift, visual artefacts, and digital surface model (DSM) elevation values. No single software package was determined the “winner” across all metrics, but we hope our results help others demystify the differences between the options, allowing users to make an informed decision about which software and parameters to select for their specific application. Our comparisons highlight some of the challenges that may arise when comparing datasets that have been processed using different parameters and different software packages, thus demonstrating a need to provide metadata associated with processing workflows.
Highlights
Drone data use within environmental sciences has increased considerably over the past 20 y. This is due in part to the increased availability of drone platforms on the market, technological advances providing better sensors, a longer battery life, easier-to-use systems, and enhanced structure-from-motion (SfM) software that is able to process these datasets into orthomosaics and digital elevation models (DEMs) [1]
It is likely that the slow performance of Web Open Drone Map Version 2.6.4 (WebODM) for large datasets was due to it using the CPU for processing, while the other three packages are able to access the GPU for higher performance
The longer processing time for Pix4D is likely related to the additional processing steps requiring the software to generate a 3D mesh and automatically exporting the DEM and orthomosaic
Summary
Drone data use within environmental sciences has increased considerably over the past 20 y. Drone data have been captured to provide information across a range of environmental fields, predominantly to assess vegetation coverage, composition, and/or structure in the terrestrial environment (e.g., [4,5,6,7,8]). They have been used to study a range of other environments, including mangroves [9,10,11], oyster reefs [12], coral reefs [13,14], coastal dunes [15,16], and seagrass beds [17]. Drones are commonly used within agriculture (e.g., [24,25]), forestry [26], and urban settings (e.g., [27,28])
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