Date of Award

Fall 2025

Project Type

Thesis

Program or Major

Natural Resources and Environmental Studies

Degree Name

Master of Science

First Advisor

Mark J Ducey

Second Advisor

Benjamin T Fraser

Third Advisor

Olivia Fraser

Abstract

Understanding forest dynamics relies on forest inventories that assess stand- and tree-level characteristics. Traditionally, this process involves field-based measurements of tree attributes such as diameter at breast height (DBH), height, form, and crown metrics. In recent years, the integration of remote sensing technologies, particularly Light Detection and Ranging (LiDAR), has expanded the spatial and temporal reach of forest inventories. LiDAR is an active remote sensing device that generates three-dimensional representations of forest environments and can be used to derive structural forest metrics. More recently, mobile LIDAR, also referred to as mobile laser scanning (MLS) has emerged as a flexible and accessible technology offering high-resolution data and new perspectives for scanning forest. However, the performance of MLS across varying forest structures remains an area of active investigation. This study examined the effectiveness of MLS in detecting individual trees and estimating DBH in complex, mixed species forests of the Northeastern United States. Simultaneously, we evaluated how structural characteristics at both the tree and plot level influenced detection accuracy and DBH estimation. We observed an 84.5% tree detection rate, a 32.9% commission rate (false positives), and a DBH root mean square error (RMSE) of 2.09 cm. Among the variables assessed, individual tree DBH was the only significant predictor of detection probability; none of the stand-level covariates showed significant effects. These results suggest that while MLS demonstrates potential for precise DBH estimation when trees are correctly identified, further methodological improvements are needed to reduce false detections and enhance performance in structurally complex forests. Continued research into how forest structure influences MLS outcomes will be critical for its effective application in forest monitoring and inventory efforts.

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