Drone‐based structure‐from‐motion photogrammetry captures grassland sward height variability

Abstract

Grasslands deliver a range of ecosystem services, including the provision of food and biodiversity, and regulation of soil carbon storage and hydrology. Monitoring schemes are needed to quantify spatial changes in these multiple functions alongside ecosystem degradation. Sward height is widely recognised as a key spatial variable in the provision of these services. Current manual monitoring approaches are labour intensive, and often fail to capture spatial patterns of important features, including sward height. Proximal sensing from small aerial drones carrying lightweight cameras can be transformed into surface height models using image‐based structure‐from‐motion and Multi‐View Stereo‐based approaches; this presents a new opportunity for monitoring the spatial structure of grassland sward height. We combined aerial photographs with field survey data and an open‐source image‐based modelling‐processing workflow to generate sward height measurements for a field comprising mainly Lolium perenne (perennial ryegrass) and Trifolium pratense (red clover). We compared the derived measurements with in situ data captured on the same day using traditional agronomic sward height techniques to determine the quality of the drone‐derived surface model product for sward characterisation. The SfM and Multi‐View Stereo‐based surface model had a mean absolute sward height measurement error of between 3.7 and 4.2 cm. To produce field observations with equivalent quality would require up to 550 sward height measurements for the study site (area: 8,059 m2), which is not feasible over larger extents required for conservation of key species or agronomic purposes. Synthesis and applications. We demonstrate how the collection of precise and detailed information on the spatial structure of grasslands can be made over management‐relevant extents. Aerial digital photographs can be transformed into surface models using an image‐based modelling approach: structure‐from‐motion and Multi‐View Stereo techniques. Image‐based measurements of sward heights were compared with manual sward height data captured on the same day. This novel source of vegetation spatial information could improve sward management for conservation and agronomy applications. The approach supports frequent surveys, at user‐controlled revisit times, and delivers data for spatial monitoring of key grassland functions and services.

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

“…However, it can provide a rough gauge for the quality of processing settings used—and conversely what one can expect following the workflow outlined herein. Image residual (pixels) is the mean local error in image alignment, as estimated by the bundle adjustment (Bogunovic et al, ; Forsmoo et al, ; James, Robson, & Smith, ). GCP residuals show the difference between measured coordinates and the corresponding coordinates within the SfM + MVS‐derived 3D model (James, Robson, d'Oleire‐Oltmanns, & Niethammer, ).…”

“…The amount of tilt will vary with, for example, flight speed, wind speed, platform attitude, position of camera mount, etc. Forsmoo et al () clearly show that these variations in tilt are enough to achieve centimeter accuracy. Having said that, the data do suggest that the results could (likely) consistently be improved by having included additional oblique image data.…”

“…An SfM + MVS workflow applies computer vision algorithms to images with a high degree of overlap to place the images taken in 3D space (Forsmoo et al, ; Remondino, Nocerino, Toschi, & Menna, ; Rupnik, Daakir, & Pierrot Deseilligny, ; Smith et al, ; Verhoeven & Vermeulen, ). These computer vision algorithms are implemented in numerous ways depending upon software choice, where the SfM + MVS workflows range from semi‐automatic, where each step such as identification of key points and camera calibration is called separately, to a fully automated workflow.…”

“…To reduce the influence of the “human factor,” the same location (pixel coordinates) of georeferenced high contrast markers in the aerial 2D images was used across the four different software. The citations given alongside indicate other literature examples that have utilized these software in ecology research: 3DFlow Zephyr Aerial (little evidence of use in ecology, though widely used in urban environments, e.g., Vassena & Clerici, ; Peel, Luo, Cohn, & Fuentes, ; Azzola, Cardaci, Mirabella Roberti, & Nannei, ). Agisoft Photoscan (Cunliffe et al, ; Dandois et al, ; Hoffmann et al, ; Javernick, Brasington, & Caruso, ; Lucieer, Turner, King, & Robinson, ; Obanawa & Hayakawa, ). Pix4D (Magtalas, Aves, & Blanco, ; Ouédraogo, Degré, Debouche, & Lisein, ; Raeva, Filipova, & Filipov, ). MICMAC (Forsmoo et al, ; Lisein, Pierrot‐Deseilligny, Bonnet, & Lejeune, ; Ouédraogo et al, ; Tournadre, Pierrot‐Deseilligny, & Faure, ; Tournadre, Pierrot‐Deseilligny, & Faure, ). …”

“…Structure from Motion (SfM) and Multi‐View Stereo (MVS) is a rapidly evolving technique for measuring surface structure in ecology (Dandois and Ellis, ; Forsmoo et al, ; Lucieer, Robinson, Turner, Harwin, & Kelcey, ; Remondino, Barazzetti, Nex, Scaioni, & Sarazzi, ; Tao, Lei, & Mooney, ; Turner, Lucieer, & Watson, ; Verhoeven & Vermeulen, ), and arguably, this offers the only realistic alternative to LiDAR for measuring the canopy structure of low‐sward systems (Forsmoo et al, ). The emergence of SfM + MVS‐based data analysis approaches has been complemented in recent times by an upsurge in drone‐based environmental monitoring (Anderson & Gaston, ).…”