Supporting Operational Tree Marking Activities through Airborne LiDAR Data in the Frame of Sustainable Forest Management

Abstract

Implementing adaptation and mitigation strategies in forest management constitutes a primary tool for climate change mitigation. To the best of our knowledge, very little research so far has examined light detection and ranging (LiDAR) technology as a decision tool for operational cut-tree marking. This study focused on investigating the potential of airborne LiDAR data in enhancing operational tree marking in a dense, multi-layered forest over complex terrain for actively supporting long-term sustainable forest management. A detailed tree registry and density maps were produced and evaluated for their accuracy using field data. The derived information was subsequently employed to estimate additional tree parameters (e.g., biomass and tree-sequestrated carbon). An integrated methodology was finally proposed using the developed products for supporting the time- and effort-efficient operational cut-tree marking. The results showcased the low detection ability (R2 = 0.15–0.20) of the trees with low DBH (i.e., regeneration and understory trees), while the dominant trees were accurately detected (R2 = 0.61). The stem biomass was accurately estimated, presenting an R2 of 0.67. Overall, despite some products’ low accuracy, their full and efficient exploitability within the aforementioned proposed methodology has been endeavored with the aim of actively contributing to long-term sustainable forest management.