Comparative analysis of SLAM and TLS LiDAR technologies for biodiversity relevant information extraction over two Natura 2000 Sites in Greece

Biodiversity monitoring is a critical global issue, requiring reliable and precise information on forest and tree attributes to ensure sustainable management and biodiversity conservation. Remote sensing (RS) technology has emerged as a powerful tool for forest monitoring, offering significant advantages over traditional methods. The advent of advanced LiDAR technologies has revolutionized the field, enabling high-resolution 3D data collection and capturing intricate forest structures. Despite advancements, efficiently monitoring dense and complex forest environments in three dimensions remains a challenging task. This study in processing and analysing Simultaneous Localization and Mapping (SLAM) and Terrestrial Laser Scanning (TLS) LiDAR datasets to estimate biodiversity relevant attributes in Greek Natura 2000 (N2K) forested areas. The study is implemented as part of the hELlenic BIOodiversity Information System (EL-BIOS). The EL-BIOS is the first national-scale EODC infrastructure, with the aim of advancing EO data and products use for biodiversity management and conservation over Greece. This research encompasses three 0.1 ha plots, distributed  in two distinctive protected areas: the Kotychi–Strofilia National Park  in south Greece and the Northern Pindos National Park in north Greece. Open-source tools such as LAStools and 3D-Forest were utilized for individual tree segmentation and the calculation of key forestry parameters. Optimal algorithm configurations and functional tools were explored to compute structural attributes such as tree height, diameter at breast height (DBH), and crown metrics. To evaluate the performance and accuracy of the SLAM and TLS datasets, the automatically derived parameters were compared against traditionally collected in-situ data using classification metrics, accuracy statistics, and precision measures. The findings indicate that both SLAM and TLS effectively captured detailed 3D point cloud data of the forested plots, albeit with differences in accuracy, resolution, and acquisition time. TLS consistently delivered higher-resolution data but required extended processing times, stationary positioning, and manual repositioning within the plot area. Conversely, SLAM offered greater mobility and efficiency, albeit with slightly lower resolution. TLS achieved an accuracy of approximately 75% in tree detection, while SLAM ranged between 60% and 65%, demonstrating its operational viability despite a slight reduction in precision. Overall, this study underscores the potential of advanced 3D modelling techniques and efficient parameter extraction methods for biodiversity relevant information extraction over forest protected areas.