Estimation of forage biomass and vegetation cover in grasslands using UAV imagery
Théau, J., Lauzier-Hudon, É., Aubé, L., Devillers, N. (2021). Estimation of forage biomass and vegetation cover in grasslands using UAV imagery. PLoS ONE, [online] 16(1 January), http://dx.doi.org/10.1371/journal.pone.0245784
Plain language summary
Grasslands are among the most widespread ecosystems on earth. They represent a key element for global food security, in addition to providing ecological services related to erosion protection, wildlife habitat support, carbon sequestration and water harvesting. Most of these ecosystems are in poor condition, mainly because of overgrazing. The tools and methods currently available for pasture monitoring rely mainly on field surveys that are time-consuming and difficult to generalize to the whole parcel.
This study aims to test and compare three approaches based on multispectral imagery acquired by unmanned aerial vehicles (UAV) to estimate forage biomass (FM) or vegetation cover in a pasture field over a complete growing season. Each method showed technical advantages and limitations that qualify them for different applications related to forage production, grassland monitoring or pasture management in a temperate climate.
The first approach generated a volumetric-based biomass estimation model This model is not very sensitive to low vegetation levels but is accurate for FM estimation greater than 0.5 kg/m2. However, it requires a reliable digital terrain model.
The second approach used the Green Normalized Difference Vegetation Index (GNDVI) to generate a regression biomass prediction model which accurately estimates levels of FM lower than 3 kg/m2. However, further validation would be needed to confirm its efficiency and accuracy in measuring small biomass variations related to grazing by animals and determine if it could be used for the estimation of their forage intake.
The last approach is based on a classification of vegetation cover from clustering of GNDVI values in four classes. This approach is more qualitative than the other ones but more robust and generalizable. It looks promising for monitoring the vegetation cover degradation and simple and adjustable enough to be used by producers in a commercial context.
Grasslands are among the most widespread ecosystems on Earth and among the most degraded. Their characterization and monitoring are generally based on field measurements, which are incomplete spatially and temporally. The recent advent of unmanned aerial vehicles (UAV) provides data at unprecedented spatial and temporal resolutions. This study aims to test and compare three approaches based on multispectral imagery acquired by UAV to estimate forage biomass or vegetation cover in grasslands. The study site is composed of 30 pasture plots (25 × 50 m), 5 bare soil plots (25 x 50), and 6 control plots (5 × 5 m) on a 14-ha field maintained at various biomass levels by grazing rotations and clipping over a complete growing season. A total of 14 flights were performed. A first approach based on structure from motion was used to generate a volumetric-based biomass estimation model (R2 of 0.93 and 0.94 for fresh biomass [FM] and dry biomass [DM], respectively). This approach is not very sensitive to low vegetation levels but is accurate for FM estimation greater than 0.5 kg/m2 (0.1 kg DM/m2). The Green Normalized Difference Vegetation Index (GNDVI) was selected to develop two additional approaches. One is based on a regression biomass prediction model (R2 of 0.80 and 0.66 for FM and DM, respectively) and leads to an accurate estimation at levels of FM lower than 3 kg/m2 (0.6 kg DM/m2). The other approach is based on a classification of vegetation cover from clustering of GNDVI values in four classes. This approach is more qualitative than the other ones but more robust and generalizable. These three approaches are relatively simple to use and applicable in an operational context. They are also complementary and can be adapted to specific applications in grassland characterization.