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Measuring plant biomass remotely using drones in arid landscapes

Citation

McCann, Justin; Keith, David; Kingsford, Richard (2022), Measuring plant biomass remotely using drones in arid landscapes, Dryad, Dataset, https://doi.org/10.5061/dryad.xwdbrv1g1

Abstract

Measurement of variation in plant biomass is essential for answering many ecological and evolutionary questions. Quantitative estimates require plant destruction for laboratory analyses, while field studies use allometric approaches based on simple measurement of plant dimensions. We estimated the biomass of individual shrub-sized plants, using a low cost Unmanned Aerial System (drone), enabling rapid data collection and non-destructive sampling. We compared volume measurement (a surrogate for biomass) and sampling time, from the simple dimension measurements and drone, to accurate laboratory-derived biomass weights. We focused on three Australian plant species which are ecologically important to their floodplain and terrestrial ecosystems: porcupine grass Triodia scariosa, Queensland bluebush Chenopodium auricomum and lignum Duma florulenta.

Estimated volume from the drone was more accurate than simple dimension measurements for porcupine grass and Queensland bluebush, compared to estimates from laboratory analyses but, not for lignum. The latter had a sparse canopy, with thin branches, few vestigial leaves and a similar colour to the ground. Data collection and analysis consistently required more time for the drone method than the simple dimension measurements, but this would improve with automation. 

Methods

We estimated dry weight biomass by measuring volume with two field methods: a simple dimension measurement and a drone. Volume was not directly comparable between methods, as the drone method detected detailed structure, not simple dimensions, and so we harvested samples destructively to quantify dry weight biomass. We randomly stratified sampling using each size class and species’ combination, ensuring individuals (n=3) were under full sunlight and in good health, representative of most individuals in the field.

For simple dimension measurements, we measured height from ground level to the tallest plant part and crown circumference, using the longest horizontal dimension of the plant and its perpendicular axis to produce a 3D octahedron. This allowed estimation of volume. We then surveyed each individual plant, using a DJI Phantom 3 professional drone (DJI, Shenzhen, China) with its standard mounted camera (12 megapixel (MP) camera, fixed lens and focal length, mounted with a stabilising unit). Ground control points of known dimensions were placed for each plant, to generate two perpendicular scale constraints, increasing the accuracy of the resulting point cloud. We flew a manually-navigated grid pattern at 10 m above ground and within 3 hours of solar midday to minimise shadows, using a combination of downwards (nadir) and angled (non-nadir) images, with at least 70% overlap of each image. Where plants were close together, multiple plants were surveyed in one flight. The elevation provided about 40 high resolution images (<1 mm ground sample distance) for each plant, recorded as red, green and blue (RGB) jpeg files in the visible spectrum. 

After collecting field measurements, we destructively sampled each plant for laboratory measurements of dry and wet biomass by harvesting all above ground plant matter. Plants were stored in plastic bags with moist paper towels for transport. Subsequently, wet weight biomass of each plant was measured (stems and leaves amalgamated) before drying it in an oven (70 oC for at least 72 hours), after which dry biomass was weighed.

We used SfM (using Pix4Dmapper software, Pix4D SA, 2018) to generate a 3D model (point cloud) of each plant, allowing estimation of volume. Each plant point cloud was set with scale constraints from the ground control points to improve measurement precision. We manually selected each plant from point clouds using CloudCompare (V2.8.1, 2018), ensuring that nearby plants (e.g. grasses) were not included. We exported the point cloud for each plant into the R statistical software environment (R Core Team, 2018) and calculated the minimum convex hull of the plant using RLiDAR.

Usage Notes

Data collected for the drone method have been processed and the resulting volume estimates are presented in this table.

Funding

Bush Heritage Australia