Potential for unmanned aerial systems to inform ponderosa pine restoration: evaluation of horizontal and vertical complexity monitoring

Hanna, Laura, author
Tinkham, Wade T., advisor
Battaglia, Michael A., committee member
Vogeler, Jody C., committee member
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Over the last two decades, the restoration of dry conifer forests has increasingly prioritized the reintroduction of horizontal and vertical complexity. This emphasis has come from research showing that increased spatial complexity in forest structures is necessary to restore past ecological function and resilience to disturbance. However, most forest inventory and monitoring approaches lack the resolution, extent, or spatial explicitness required to describe within stand heterogeneity at a level adequate to inform forest management. Recently, Unmanned Aerial Systems (UAS) remote sensing has emerged with potential methods for bridging this gap. Specifically, photogrammetric Structure from Motion (SfM) algorithms have been shown as a cost-efficient way to characterize forest structure in 3-dimensions. Chapter 1 of this thesis reviews the relationship between forest heterogeneity and various ecological processes as well as methods and implications for restoring forest heterogeneity. Chapter 2 evaluates the accuracy of SfM-derived estimates of tree, clump, and stand horizontal and vertical heterogeneity metrics across 11 ponderosa pine-dominated stands treated with spatially-explicit silvicultural prescriptions. Specifically, we evaluated tree detection rates and extracted height and DBH error, analyzed stand-level density and canopy cover, and assessed UAS-derived derived distributions of individuals, clumps, and openings through metrics of the number of clump structures, percent of stand basal area, height CV, crown area, and distance to the nearest tree. UAS-derived metrics were compared to 1-ha stem maps located in each of the 11 stands. We found that tree detection was relatively high in all stands (F-scores of 0.64 to 0.89), with average F-scores over 0.8 for all but the shortest size class (<5 m). Average height and DBH errors of 0.34 m and -0.04 cm were produced, although DBH RSME was greatest for the tallest trees. Stand estimates of TPH were over by 53, with the greatest errors in the shortest size class, and metrics of basal area, QMD, and canopy cover all had errors of less than 10% compared to the stem map. Finally, UAS could successfully characterize and describe individuals, clumps, openings, and inter-clump characteristics like the percent of stand basal area and height CV through all clump size classes. These results indicate that in ponderosa pine forests, UAS can describe both large- and small-scale forest structure metrics to effectively inform spatially explicit management objectives.
2022 Summer.
Includes bibliographical references.
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