One small step for a robot, one giant leap for habitat monitoring: A structural survey of EU forest habitats with Robotically-mounted Mobile Laser Scanning (RMLS)

EU States are mandated by the 92/43/EEC Habitats Directive to generate recurring reports on the conservation status and functionality of habitats at the national level. This assessment is based on their floristic and, especially for forest habitats, structural characterization. Currently, habitat field monitoring efforts are carried out only by trained human operators. The H2020 Project "Natural Intelligence for Robotic Monitoring of Habitats-NI" aims to develop quadrupedal robots able to move autonomously in the unstructured environment of forest habitats. In this work, we tested the locomotion performance, efficiency and the accuracy of a robot performing structural habitat monitoring, comparing it with traditional field survey methods inside selected stands of Luzulo-Fagetum beech forests (9110 Annex I Habitat). We used a quadrupedal robot equipped with a Mobile Laser Scanning system (MLS), implementing for the first time a structural monitoring of EU forest habitats with a Robotically-mounted Mobile Laser Scanning (RMLS) platform. Two different scanning trajectories were used to automatically map individual tree locations and extract tree diameter at breast height (DBH) from point clouds. Results were compared with field human measurements in terms of accuracy and efficiency of the survey. The robot was able to successfully execute both scanning trajectories, for which we obtained a tree detection rate of 100 %. Circular scanning trajectory performed better in terms of battery consumption, Root Mean Square Error (RMSE) of the extracted DBH (2.43 cm or 10.73 %) and prediction power (R 2 adj = 0.72, p < 0.001). The RMLS platform improved survey efficiency with 19.31 m 2 /min or 1.77 trees/min in comparison with 3.45 m 2 /min or 0.32 trees/ min of traditional survey. Finally, a processing script was developed to allow the repeatability of RMLS surveys in similar habitat monitoring missions. In the future, a human-robotic monitoring framework might represent an accurate support for those repetitive and time-consuming activities in habitat monitoring, offering a valuable benefit for biodiversity conservation.