Assessing forest structure with LiDAR: A method benchmark in the Rohrach Natural Forest Reserve
DOI:
https://doi.org/10.71911/cii-p3-nt-2025224Keywords:
forest structure analysis, Rohrach Natural Forest Reserve, method benchmarking, TLS, LiDAR, UAV, deadwood, large woody debris, growing stocks, beech forestAbstract
The Rohrach Natural Forest Reserve, located in the northern foothills of the Alps, serves as a long-term reference area for preserving and studying natural forest development. In this study, three methods for assessing forest structure—classical field-based inventory, terrestrial laser scanning (TLS), and drone-based LiDAR (UAV-LS)—were systematically compared. Building on an initial survey conducted in 1996, 44 sample plots were revisited and supplemented with high-resolution 3D measurements.
The results show that TLS provides highly accurate volume estimates that closely match those obtained through the classical inventory. The UAV-based approach enabled a comprehensive, area-wide survey of the 48-ha study site and yielded average growing stock values of 547 m³/ha—almost identical to those from the classical inventory (549 m³/ha). However, automated detection of lying deadwood using UAV data underestimated the volume by up to 50% compared to the line intersect method. Whether this discrepancy is due to the line intersect method’s assumption of randomly distributed logs being unsuitable for this site, or whether UAV-LS underestimates deadwood due to canopy shadowing or algorithmic omission of fine structures, requires further investigation.
The study also highlights persistent challenges in tree segmentation within steep or densely vegetated areas: overlapping crowns often result in misclassifications or undetected stems. While TLS continues to offer the highest geometric accuracy, UAV-LS provides the advantage of rapid, large-scale data acquisition with minimal disturbance to sensitive environments.
These findings underscore the importance of integrated methodological approaches for effective long-term monitoring in natural forest ecosystems.
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