Estimation of forage biomass and vegetation cover in grasslands using UAV imagery

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.