Assessing the Feasibility of Low-Density LiDAR for Stand Inventory Attribute Predictions in Complex and Managed Forests of Northern Maine, USA
Problem Addressed: Applicability of using a low-density discrete-return LiDAR for forest management.
Goal(s)/Objective(s): Evaluate LiDAR for predicting maximum tree height, stem density, basal area, quadratic mean diameter, and total volume.
Key Findings: Low density LiDAR was able to capture variability but tended to underestimate maximum tree height and volume; accuracy of volume prediction was sensitive to the plot type.
Recommendation: Use of LiDAR-based inventory attribute predictions is a valuable option for achieving efficient and effective forest assessment.
Views: 2240
Downloads: 3
[mrp_rating_result no_rating_results_text="No ratings yet" before_count="(" after_count=" ratings)"]