A comparative study for obtaining effective Leaf Area Index from single Terrestrial Laser Scans by removal of wood material

Leaf Area Index (LAI) is a dimensionless parameter that has a significant impact on forestry applications. With conventional methods, LAI can be calculated with destructive sample collection or with a relatively new non-destructive method called hemispherical photography. With the engagement of surveying instruments in forestry, obtaining LAI value for large areas in a short time has recently become more prominent and possible with the use of Terrestrial Laser Scanners (TLS). Although promising, TLS data evaluation techniques for LAI calculation are still subject to development. This paper aims to make a comparative evaluation of existing novel techniques with newly proposed methods and incorporates the use of neural networks and connected component analysis for segmentation purposes. The in-situ measurements, as a case study, were conducted in Istanbul-University-Cerrahpasa research forest - a part of Belgrad forest - Istanbul, Turkey. The Results obtained from the study show that segmentation and removal of wood materials from forest point cloud data, by using neural network algorithms and connected component analysis methods, albeit time and resource consuming, have a promising future on the calculation of effective LAI values of large areas.

Automated summary

The study highlights the importance of Leaf Area Index (LAI) in forestry applications and discusses the current trends in calculating LAI values using TLS data and novel techniques. The case study conducted in Istanbul University-Cerrahpasa research forest demonstrates that segmentation and removal of wood materials from forest point cloud data using neural network algorithms and connected component analysis methods, despite being time and resource consuming, have a promising future in calculating effective LAI values for large areas.