Estimating Pasture Biomass and Canopy Height in Brazilian Savanna Using UAV Photogrammetry

Abstract

The Brazilian territory contains approximately 160 million hectares of pastures, and it is necessary to develop techniques to automate their management and increase their production. This technical note has two objectives: First, to estimate the canopy height using unmanned aerial vehicle (UAV) photogrammetry; second, to propose an equation for the estimation of biomass of Brazilian savanna (Cerrado) pastures based on UAV canopy height. Four experimental units of Panicum maximum cv. BRS Tamani were evaluated. Herbage mass sampling, height measurements, and UAV image collection were simultaneously performed. The UAVs were flown at a height of 50 m, and images were generated with a mean ground sample distance (GSD) of approximately 1.55 cm. The forage canopy height estimated by UAVs was calculated as the difference between the digital surface model (DSM) and the digital terrain model (DTM). The R2 between ruler height and UAV height was 0.80; between biomass (kg ha−1 GB—green biomass) and ruler height, 0.81; and between biomass (kg ha−1 GB) and UAV height, 0.74. UAV photogrammetry proved to be a potential technique to estimate height and biomass in Brazilian Panicum maximum cv. BRS Tamani pastures located in the endangered Brazilian savanna (Cerrado) biome.

Deep Learning Applied to Phenotyping of Biomass in Forages with UAV-Based RGB Imagery

“…Compared to the previous study that used 3D information to estimate the biomass for the same species [ 26 ], we achieved more accurate results. Batistoti et al [ 26 ] achieved an R2 of 0.74 considering a limited number of plots (four in total).…”

“…Compared to the previous study that used 3D information to estimate the biomass for the same species [ 26 ], we achieved more accurate results. Batistoti et al [ 26 ] achieved an R2 of 0.74 considering a limited number of plots (four in total). Another study [ 51 ] estimated yield with proximal sensing equipment in a heterogeneous sward structure of grasslands and applied a MLPSR (Multiple Partial Least Square Regression) approach, which returned an R2 of 0.69.…”

“…When comparing against other methods to estimate biomass, our approach differentiates from a methodological point-of-view. Up to the point, most approaches considered shallow learners (i.e., conventional machine learning methods) and RGB imagery combined with DSM and DTM models, LiDAR or even vegetation spectral indices from multi and hyperspectral data [ 1 , 24 , 25 , 26 , 27 , 28 , 50 ]. As an advantage of adopting deep neural networks is the oversimplification of remote sensing data variety necessary to conduct this estimation.…”

“…Batistoti et al. [ 26 ] also investigated CSM models to estimate biomass in pasture species, achieving satisfactory results.…”

“…Strategies based on plant height are limited because they require DTMs, which are 3D models of the terrain (i.e., without the plants covering the surface). The generation of a DTM can be performed using a GNSS (Global Navigation Satellite System)-based survey [ 26 ], which is time-consuming, especially in large areas, or flight-based approaches before initializing the planting process [ 1 ]. Although it is not possible to provide more accurate results compared to the CSMs approach, RGB VI-based approaches already demonstrated promising results, and Bendig et al.…”