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Browsing by Author "Vogeler, Jody C., committee member"

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    Characterizing 30-years of conifer regeneration patterns in high-severity wildfires: a snow-cover remote sensing approach
    (Colorado State University. Libraries, 2023) Menick, Casey, author; Hoffman, Chad M., advisor; Tinkham, Wade T., advisor; Vanderhoof, Melanie K., committee member; Vogeler, Jody C., committee member
    The number of large, high-severity wildfires has been increasing across the Western United States. It is not fully understood how wildfire intensification may impact conifer forests of the West, whose resilience is dependent on successful seedling regeneration. It is important to understand how conifer-dominated forests are able to recolonize high-severity burn patches and subsequently respond to shifting disturbance regimes. The goal of our research is to characterize patterns of conifer recolonization within high-severity burn patches over a 30-year study period. We investigate 34 high-severity wildfire complexes that occurred between 1988 and 1991 in conifer-dominated ecosystems of the northern Rocky Mountains. Composite snow-cover Landsat imagery was utilized to isolate conifer-specific vegetation by diminishing spectral contributions from soil and deciduous vegetation. Conifer regeneration was determined to be detectable by Landsat 11-19 years post-fire across forest types and at >10% canopy cover using snow-cover imagery. The trajectory of snow-cover Landsat NDVI was utilized to project future recovery time to pre-fire conifer vegetation levels for lodgepole pine (29.5 years), Douglas-fir (36.9 years), and fir-spruce forests (48.7 years). The presence of conifer regeneration was then modeled at 3-year intervals post-fire to characterize the progression of recolonization. Conifer recolonization analysis showed that 65% of the total high-severity burn area was reforested after 30 years. Across all high-severity patches, median patch recolonization was 100% within lodgepole pine, 91.1% within Douglas-fir, and 41.3% within fir-spruce. Patch fragmentation occurred across all size classes and forest types, with the majority of the remaining unforested area in Douglas fir (76%), lodgepole pine (61%), and fir-spruce (50%) transitioning to smaller unforested patch size classes. While we identified overall patterns of conifer resilience, high-severity burn patches with lower likelihoods of 30-year conifer recovery had lower edge-densities, drier climates, steeper slopes, higher elevations, and fir-spruce forests. These findings have implications for climate change resilience and may be applied to support forest restoration decision-making following high-severity wildfire. Future analyses should be conducted using snow-cover remote sensing imagery to identify patterns of post-disturbance conifer recovery over a wider spatial and temporal extent.
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    Potential for unmanned aerial systems to inform ponderosa pine restoration: evaluation of horizontal and vertical complexity monitoring
    (Colorado State University. Libraries, 2022) Hanna, Laura, author; Tinkham, Wade T., advisor; Battaglia, Michael A., committee member; Vogeler, Jody C., committee member
    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.