Effect of Center Determination Method, Point Cloud Filtering and Cross-Section Type on the Accuracy of Tree Stem...

Area of Science:

• Forestry and Wood Science
• Geomatics and Remote Sensing

Background:

• Static laser scanning offers advanced timber volume estimation, rivaling traditional forestry methods.
• Accurate volume data is crucial for effective woodworking management.

Purpose of the Study:

• To evaluate the impact of stem surface condition, cross-sectional area calculation methods, point cloud filtering, and stem center determination on static laser scanning accuracy for timber volume estimation.
• To identify optimal methodologies for improving timber volume estimation using static laser scanning.

Main Methods:

• Point cloud data acquisition using static laser scanning.
• Comparative analysis of different cross-sectional area calculation methods (squared diameter, quadratic mean diameter) on stems with and without bark.
• Evaluation of point cloud filtering techniques and stem center determination algorithms.
• Validation against reference values, including computed tomography.

Main Results:

• Stem surface condition (bark presence) fundamentally dictates systematic errors in volume estimation.
• For stems with bark, the squared diameter method caused underestimation, while the quadratic mean diameter led to overestimation, significantly reduced by filtering.
• For debarked stems, filtering mitigated overestimation from the squared diameter method but not the quadratic mean diameter method.

Conclusions:

• Volume estimation accuracy is primarily governed by cross-sectional area formulation and stem surface condition, not the volumetric method itself.
• Static laser scanning shows high potential for timber volume estimation, but requires point cloud-adapted computational models for optimal accuracy.
• Further research should focus on developing and refining these adapted models to address systematic errors related to bark and calculation methods.