Methodological Approaches for Estimating the Biomass of Natural Pastures in the Lucanian Hills Using UAV Remote Sensing

Grazing is essential for extensive farming; effective management faces challenges from competition between fodder and energy crops, land degradation, and the variability in yields due to climate change. The amount of biomass in pastures can significantly influence the number of livestock that can be supported. Nowadays, seasonal variability, sustainable pasture management, and animal feed requirements ask for a simple method to estimate the amount of available biomass. Using sensors, unmanned aerial vehicles (UAVs), and information technologies has streamlined farm management. Key parameters for assessing pasture quality and animal nutrition are dry matter (DM) and biomass. The project has two objec-tives: (i) to compare and evaluate two methods for assessing the natural pasture biomass; (ii) to calculate an equation to be applied to estimate the biomass of natural pastures using surveys made only by UAVs. In this paper, we focused on the first objective. The methods compared were pasture biomass monitoring using field sampling versus UAV surveys. Both methods were applied on the same day: the first more laborious and demanding, provided accurate vegetative data; the second proved to be a more effective approach for large-scale vegetation monitor-ing. Remote sensing was performed using a drone equipped with a multispectral camera. This approach is being used for the first time on natural Mediterranean pastures. Results indicated a significant and positive correlation between the two methods. These promising findings will be tested in the spring-summer period, and a calibration equation will be created to convert NDVI values into kg DM ha-1 of pasture.