Ricerc@Sapienza

Nowadays the proliferation of small unmanned aerial systems or vehicles (UAS/
Vs), formerly known as drones, coupled with an increasing interest in tools for environmental
monitoring, have led to an exponential use of these unmanned aerial
platforms for many applications in the most diverse fields of science. In particular,
ecologists require data collected at appropriate spatial and temporal resolutions to
describe ecological processes. For these reasons, we are witnessing the proliferation of

Nowadays, emerging technologies, such as long-range transmitters, increasingly miniaturized components for positioning, and enhanced imaging sensors, have led to an upsurge in the availability of new ecological applications for remote sensing based on unmanned aerial vehicles (UAVs), sometimes referred to as “drones”. In fact, structure-from-motion (SfM) photogrammetry coupled with imagery acquired by UAVs offers a rapid and inexpensive tool to produce high-resolution orthomosaics, giving ecologists a new way for responsive, timely, and cost-effective monitoring of ecological processes.

Timely weed mapping in crop post-emergence situations is a challenging task required for developing precision weed management solutions. It is necessary to discriminate the crop from the weeds and, if possible, to distinguish different weed species. The ability to map weeds using hyperspectral images acquired from an unmanned airborne vehicle (UAV) over a maize field was evaluated by comparing different classification strategies. The results were mainly affected by the variability in crop and weed spectral signatures.