DOI: 10.5061/dryad.xwdbrv1g1
title: Measuring plant biomass remotely using drones in arid landscapes

description: 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. 
description: 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 (&lt;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.
description: Data collected for the drone method have been processed and the
 resulting volume estimates are presented in this table.