Environmental monitoring via drone: development of algorithms for correcting background measurements

Remotely piloted aircraft systems, also called drones or Unmanned Aerial Systems (UAS), have considerable potential to radically improve environmental monitoring. UAS-mounted sensors offer an extraordinary opportunity to bridge the gap between field observations and traditional aerial remote sensing, providing high spatial detail over relatively large areas with significant cost and time savings. However, some recently published reviews on the use of UAS for environmental monitoring and in particular for landfill monitoring highlight that there are still some open issues that limit the use of such systems. Indeed, to fully exploit the potential of UAS-based approaches, these studies indicate that further research is needed on detection technologies, measurement protocols, post-processing techniques, correction algorithms, evaluation techniques and harmonized use protocols. This work focuses in particular on the development of algorithms for correcting measurement errors relating to methane concentrations in the atmosphere, measured through a laser spectrophotometer. The methodology followed was that of field testing. Firstly, several measurement tests were carried out in places where there are no methane emissions. The data obtained were analyzed to verify the erroneous measurements and the conditions under which these errors were obtained. We subsequently tested different correction algorithms to identify the protocol that limits the extent of this phenomenon in the post-processing phase.