Using aerial imagery for assessing pasture vegetation coverage

Abstract

We aimed to assess pasture vegetation cover over two growing seasons using vegetation indices derived from RDB aerial images acquired by drones and multispectral satellite imagery. The study area was already divided into six grazing paddocks grown with forage grasses, three with Megathyrsus maximus cv. Mombaça and three with Urochloa brizantha cv. MG-4. Sampling was conducted during the January and July 2022/2023 growing season. The study adopted precision agriculture principles, generating customized sampling grids for each pasture, with an approximate density of four points per hectare. Field data were collected on pasture height, soil-exposed percentage, chlorophyll content, and pasture green biomass. RGB aerial imagery was acquired using a drone, while multispectral data was obtained from the Sentinel 2A satellite four times. Pasture vegetation cover (PVC) was estimated after calculating the vegetation indices Normalized Difference Vegetation Index (NDVI), Green-Red Vegetation Index (GRVI), and Green Leaf Index (GLI). PVC results indicate a slight degree of pasture degradation during the 2022/2023 growing season. Images captured by UAVs (unmanned aerial vehicles) enabled accurate identification of sparse areas prone to degradation, offering valuable insights to enhance pasture and forage management.