Date of Award

Fall 2023

Project Type

Thesis

Program or Major

Natural Resources

Degree Name

Master of Science

First Advisor

Russell G. Congalton

Second Advisor

Mark J. Ducey

Third Advisor

Benjamin T. Fraser

Abstract

Knowing how to best conserve and sustain forests is of the utmost importance given the indispensable direct and indirect ecosystems services they provide. In order to make informed management decisions for better conservation and sustainability, forest change and growth (biometrics) must be quantified. Biometrics are typically made through forest inventorying, but this field-based procedure has spatial and temporal limitations. Given the vital role forests have, it is imperative that different technologies be explored for their potential to improve forest inventorying methodologies. Remote sensing technology, specifically unmanned aerial system (UAS) light detection and ranging technology (Lidar), has presented itself as a possible solution to traditional inventorying problems, as it offers the potential to estimate accurate forest biometrics from centimeter level structural analyses over large areas. Foresters in Canada, Sweden, Denmark, and Finland have relied on airborne lidar-based forest inventory biometrics for comprehensive stand attribute data for nearly twenty years. However, little research and few industry applications have explored the capability of UAS-Lidar to estimate biometrics in complex temperate forests like those of New Hampshire. This research project evaluated if UAS-Lidar can estimate forest biometrics on two forested University of New Hampshire properties, Dudley Lot and Burley Demeritt Farm (Lee, NH, USA). UAS-Lidar data were collected, processed, and analyzed to estimate individual tree and stand level biometrics, including basal area weighted-diameter average, Lorey’s tree height, trees per acre, and basal area per acre. The UAS-Lidar diameter estimates were determined from regression equations calculated from ground measured diameters plotted against segmentation polygon attributes, like crown area, tree height, and crown radius. The UAS-Lidar biometrics were compared to the ground collected data to determine if UAS-Lidar is an effective technology. A secondary goal of this project was to determine if UAS normal-color photogrammetry proved to be more effective for estimating the same biometrics. The UAS-Lidar biometric estimates proved to be comparable to UAS-SODA estimates when examined at the stand level.

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