Structure from motion photogrammetry in ecology: Does the choice of software matter?

• Published in: Ecology and evolution 2019-12, Vol.9 (23), p.12964-12979
• Main Authors: Forsmoo, Joel, Anderson, Karen, Macleod, Christopher J. A., Wilkinson, Mark E., DeBell, Leon, Brazier, Richard E.
• Format: Article
• Language: English
• Subjects: Agronomy; Cameras; Computer programs; Data processing; Datasets; drone; Earth sciences; Ecology; Ecosystem management; Ecosystems; Elevation; elevation model; Experiments; Grassland management; Grasslands; Motion perception; Original Research; Outliers (statistics); Photogrammetry; Remote sensing; Reproducibility; Researchers; Software; structure from motion and multi‐view stereo; sward height; Vegetation; Wildlife conservation; Workflow
• ISSN: 2045-7758; 2045-7758
• DOI: 10.1002/ece3.5443

Image‐based modeling, and more precisely, Structure from Motion (SfM) and Multi‐View Stereo (MVS), is emerging as a flexible, self‐service, remote sensing tool for generating fine‐grained digital surface models (DSMs) in the Earth sciences and ecology. However, drone‐based SfM + MVS applications have developed at a rapid pace over the past decade and there are now many software options available for data processing. Consequently, understanding of reproducibility issues caused by variations in software choice and their influence on data quality is relatively poorly understood. This understanding is crucial for the development of SfM + MVS if it is to fulfill a role as a new quantitative remote sensing tool to inform management frameworks and species conservation schemes. To address this knowledge gap, a lightweight multirotor drone carrying a Ricoh GR II consumer‐grade camera was used to capture replicate, centimeter‐resolution image datasets of a temperate, intensively managed grassland ecosystem. These data allowed the exploration of method reproducibility and the impact of SfM + MVS software choice on derived vegetation canopy height measurement accuracy. The quality of DSM height measurements derived from four different, yet widely used SfM‐MVS software—Photoscan, Pix4D, 3DFlow Zephyr, and MICMAC, was compared with in situ data captured on the same day as image capture. We used both traditional agronomic techniques for measuring sward height, and a high accuracy and precision differential GPS survey to generate independent measurements of the underlying ground surface elevation. Using the same replicate image dataset (n = 3) as input, we demonstrate that there are 1.7, 2.0, and 2.5 cm differences in RMSE (excluding one outlier) between the outputs from different SfM + MVS software using High, Medium, and Low quality settings, respectively. Furthermore, we show that there can be a significant difference, although of small overall magnitude between replicate image datasets (n = 3) processed using the same SfM + MVS software, following the same workflow, with a variance in RMSE of up to 1.3, 1.5, and 2.7 cm (excluding one outlier) for “High,” “Medium,” and “Low” quality settings, respectively. We conclude that SfM + MVS software choice does matter, although the differences between products processed using “High” and “Medium” quality settings are of small overall magnitude. In this manuscript, we show that while centimetric resolution aerial photographic da