In continuous cover forestry, plenter silviculture is regarded as an elaborated system for optimi... more In continuous cover forestry, plenter silviculture is regarded as an elaborated system for optimizing the sustainable production of high-quality timber maintaining a constant but heterogeneous canopy. Its complexity necessitates high silvicultural expertise and a detailed assessment of forest stand structural variables. Terrestrial laser scanning (TLS) can offer reliable techniques for long-term tree mapping, volume calculation, and stand variables assessment in complex forest structures. We conducted surveys using both automated TLS and conventional manual methods (CMM) on two plots with contrasting silvicultural regimes within the Black Forest, Germany. Variations in automated tree detection and stand variables were greater between different TLS surveys than with CMM. TLS detected an average of 523 tree stems per hectare, while CMM counted 516. Approximately 9.6% of trees identified with TLS were commission errors, with 6.5% of CMM trees being omitted using TLS. Basal area per hectare was slightly higher in TLS (38.9 m 3) than in CMM (38.2 m 3). However, CMM recorded a greater standing volume (492.7 m 3) than TLS (440.5 m 3). The discrepancy in stand volume between methods was primarily due to TLS underestimating tree height, especially for taller trees. DBH bias was minor at 1 cm between methods. Repeated TLS inventories successfully matched an average of 424 tree positions per hectare. While TLS adequately characterizes complex plenter forest structures, we propose enhancing this methodology with personal laser scanning to optimize crown coverage and efficiency and direct volume measurements for increased accuracy of wood volume estimations. Additionally, utilizing 3D point cloud data-derived metrics, such as structural complexity indices, can further enhance plenter forest management. Keywords Terrestrial laser scanning • Conventional forest inventory • Plenter forest • Continuous cover forest • Automated tree mapping Communicated by Thomas Knoke.
Quercus serrata is a valuable deciduous oak native to East Asia. Despite its potential for produc... more Quercus serrata is a valuable deciduous oak native to East Asia. Despite its potential for producing high-quality wood, Quercus serrata remains underutilized in commercial forestry, mainly confined to traditional uses like shiitake mushroom cultivation. This study models its growth in Japan using Terrestrial Laser Scanning (TLS) data, applying crop tree models inspired by Central European practices on broadleaved species, and evaluating how varying automation levels in point cloud data processing affect model accuracy and forest management decisions. From data collected across 20 survey plots, we segmented 558 Quercus serrata trees, extracting parameters like diameter at breast height (at 1.3 m height, DBH), tree height (TH), crown diameter (CD), and branch-free bole length (BFBL) using semi-automated, automated, and manual measurements. We developed models for TH (semi-automated: pseudo R 2 = 0.65, automated: pseudo R 2 = 0.59), CD (semi-automated: pseudo R 2 = 0.75, automated: pseudo R 2 = 0.62), and BFBL (semi-automated: pseudo R 2 = 0.33, automated: pseudo R 2 = 0.27, manual: pseudo R 2 = 0.49). Quercus serrata exhibited typical traits of a lightdemanding oak, including early height growth and natural branch shedding. Using our models, we simulated forest management scenarios based on target DBH and annual radial increment (IR). We recommend adjusting tree spacing to address slow average IR (2.2 mm/yr) and implementing a twophase system to enhance wood quality. Refining algorithms for BFBL measurements is critical, as discrepancies significantly impact management decisions like thinning timing and harvest volume.
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Papers by Yannik W