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.
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- Hayashi, Rei
School of Forest Resources, University of Maine
rei.hayashi@maine.edu - Weiskittel, Aaron
School of Forest Resources, University of Maine - Sader, Steven
School of Forest Resources, University of Maine
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