Canopy height measurements and non‐destructive biomass estimation of Lolium perenne swards using UAV imagery

Abstract

In perennial ryegrass breeding programmes, dry‐matter yield (DMY) of individual plots is monitored destructively at the different cuts or derived from non‐destructive canopy height measurements using devices like rising plate meters (RPM). These approaches both have constraints. Destructive sampling implies low temporal resolution, restraining the study of dry‐matter accumulation rates, while RPM measurements are influenced by the canopy structure and limit intra‐field variability identification. We present a phenotyping methodology, based on the use of an affordable RGB camera mounted on an unmanned aerial vehicle (UAV), to monitor the spatial and temporal evolution of canopy height and to estimate DMY. Weekly flights were carried out from April to October above a field comprising a diverse set of accessions. To test the capacity of UAV imagery to estimate canopy height, 8 ground control points and 28 artificial height references were placed at different locations. Accurate flights with an RMSE as low as 0.94 cm were achieved. In addition, canopy height was recorded using an RPM and destructive biomass samples were collected. Different models (linear, multiple linear, principal components, partial least squares regression and random forest) were used to predict DMY, and their performance was evaluated. The best estimations were obtained by combining variables including canopy height, vegetation indices and environmental data in a multiple linear regression (R2 = .81). All models built using UAV data obtained a lower RMSE than the one using RPM data. The approach presented is a possibility for breeders to incorporate new information in their selection process.

A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems

“…Some studies used spectral information [2,11,18,24,26,43,58,[60][61][62] and some structural information [1,8,22,23,28,34,48,50,55,[63][64][65]. Others used both [3][4][5][6]9,12,16,20,21,25,30,33,49,57,[66][67][68], while a few studies used spectral and structural metrics plus another data type [13,27,69] (Table A1). Within these categories, a wide range of species, study areas and methods are examined, demonstrating the applicability of UAS data to AGB estimation in agricultural and non-agricultural environments.…”

“…Mean height [3,9,12,13,15,16,[19][20][21]23,25,28,30,34,[48][49][50]57,58,63,65,[67][68][69][74][75][76] Maximum height [1,3,4,13,28,30,34,48,57,63,65,69] Minimum height [3,28,34,48,57,63,65,69] Median height [12,21,27,48,63,65,…”

“…[68] TIN-based structure, area, slope [13] Mean, median and maximum height metrics provide information on the vertical distribution of the vegetation canopy [3,69]. Maximum or median height appear to be particularly useful for trees.…”

“…Jiang et al [13] found that the CV of height outperformed all other height and spectral metrics tested in single variables models for rice biomass estimation (R 2 = 0.77) and CV of height was retained in the top multivariate model as well (R 2 = 0.86). The 50th percentile and CV of height were retained in the top multiple linear regression model (R 2 = 0.81), along with spectral and meteorological data, for ryegrass AGB estimation [69], outperforming mean and maximum height metrics. Variables representing the complexity and heterogeneity of vegetation canopies along vertical and horizontal axes appear to be especially useful when combined with other structural and/or spectral data in multivariate models for AGB estimation.…”

“…Several indices using all three RGB bands were found in top multivariate or ensemble models in more than one study, including VARI [3,5,12,16,20,27], Excess Green-Red Index [3,4,9,12], Excess Green Index [3,12,15], Excess Red Index [3,12,20], VEG [3][4][5], VDVI/GLI [3,5,12], RGBVI [12,66], Excess Blue Index [3,12] and red, green and blue ratio indices [3,20]. Finally, two studies found that using the SD of single-band or VI values within a unit area were the most important spectral variables in AGB estimation models [30,69], indicating that a measure of spectral variability may also be valuable for biomass modelling.…”