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Browsing by Author "Tinkham, Wade T., advisor"

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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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    Constraints on mechanical fuel reduction treatments in USFS Wildfire Crisis Strategy priority landscapes
    (Colorado State University. Libraries, 2024) Woolsey, George, author; Hoffman, Chad M., advisor; Tinkham, Wade T., advisor; Battaglia, Mike A., committee member; Ross, Matthew R. V., committee member
    The US Forest Service recently launched a Wildfire Crisis Strategy outlining objectives to safeguard communities and other values at risk by substantially increasing the pace and scale of fuel reduction treatment. This analysis quantified layered operational constraints to mechanical fuel reduction treatments including existing vegetation, protected areas, steep slopes, and administrative boundaries in prioritized landscapes. A Google Earth Engine workflow was developed to analyze the area where mechanical treatment is allowed and operationally feasible under three scenarios representing a range of management alternatives under current standards. Results suggest that a business-as-usual approach to mechanical fuel reduction is unlikely in most landscapes to achieve the 20-40% of high-risk area treatment objective using mechanical methods alone. Increased monetary spending to overcome physical constraints to mechanical treatment (e.g., steep slopes and road access) opens sufficient acreage to meet treatment objectives in 18 of 21 priority landscapes. Achieving treatment objectives in the remaining landscapes will require both increased spending and navigating administrative complexities within reserved land allocations to implement fuels treatments at the pace and scale needed to moderate fire risk to communities. Broadening the land base available for treatment allows for flexibility to develop treatment plans that optimize across the multiple-dimensions of effective landscape-scale fuel treatment design. Spatial identification of the constraints to mechanical operability allows managers and policymakers to effectively prioritize mechanical and managed fire treatments.
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    Evaluation of UAS flight parameters for rapid monitoring of forest characteristics
    (Colorado State University. Libraries, 2020) Swayze, Neal, author; Tinkham, Wade T., advisor; Vogeler, Jody, committee member; Hudak, Andrew, committee member
    Forest managers are increasingly turning to finer spatial and temporal resolution data for monitoring forest structure in a rapidly changing world. Traditionally utilized networks of field plots for inventorying forest resources require significant time and financial investments; in response to this, remote sensing techniques have been investigated for providing inventory data across large extents. These methods, including light detection and ranging (LiDAR), require significant financial investment that limits the frequency of repeated surveys. Unmanned Aerial Systems (UAS) have emerged as potential alternatives for generating fine spatial and temporal resolution 2D and 3D data for modeling forest structure. The use of Structure from Motion (SfM) photogrammetry has made it possible to use UAS to collect aerial images and generate point clouds that can be used to model vertical forest structure information in a cost-effective way. Recent research has indicated that UAS-derived SfM point clouds are comparable to LiDAR point clouds for forest structure characterization through both area-based and individual tree observations. However, substantial knowledge gaps exist regarding the influence of UAS flight parameters on SfM-derived forest attributes. This thesis presents two studies to address these knowledge gaps. Specifically, Chapter 1 investigates the influence of UAS altitude and flight speed on modeling aboveground forest biomass through an area-based approach and Chapter 2 evaluates the influence of UAS altitude, camera angle, and flight pattern on extracted tree level and summarized plot and stand level attributes. Results show a strong positive relationship between flight altitude and plot-based aboveground biomass modeling, with UAS predictions increasingly outperforming (2-24% increased variance explained) contemporary LiDAR strategies as acquisition altitude increased from 80-120 m. When monitored at the individual tree level, UAS acquisitions conducted using a combination of crosshatch flight paths and off-nadir camera angles (20-30°) maximized tree detection rates (F-score of 0.77), correlations between stem mapped and extracted tree heights and DBHs (0.995 and 0.910, respectively), and estimates of stand and plot level basal area per hectare and TPH. These results indicate that UAS can be utilized to accurately summarize tree, plot, and stand level forest structure to assist in monitoring and planning of management prescriptions.
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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.
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    Unmanned aerial systems for forest structure mapping: assessments of area-based and individual tree monitoring
    (Colorado State University. Libraries, 2020) Creasy, Matthew, author; Tinkham, Wade T., advisor; Vogeler, Jody, committee member; Hoffman, Chad, committee member
    Characterization of forest structure is important for management-related decision making, especially in the wake of disturbance. Increasingly, observations of forest structure are needed at both finer resolution and across greater extents in order to support managers in meeting spatially explicit objectives. Current methods of acquiring forest measurements can be limited by a combination of time, expense, and either extent or temporal resolution. Drone or UAS-based photogrammetry provides an airborne method of forest structure data acquisition at a significantly lower cost and time commitment when compared to existing methods of such as airborne laser scanning (LiDAR). A growing body of literature confirms UAS-based photogrammetry models can be as detailed as conventional LiDAR models. However, there exists a knowledge gap in best practice for data acquisition parameters and assessment of accurate characterization within forest photogrammetry. The following two chapters utilize large stem mapped sites to fill that knowledge gap by 1) systematically testing the effects of UAS flight speed and altitude on plot-based aboveground biomass modeling through photogrammetry and 2) evaluating several algorithms for detecting individual tree locations and characterizing crown areas. Results show a strong positive relationship between flight altitude and aboveground biomass modeling, with all UAS flights evaluated above 80 m altitude, providing better results (2-24% more variance explained) than contemporary LiDAR modeling strategies. Additionally, results demonstrate that the probability of detecting individual trees decays moving from the dominant overstory to suppressed trees, corresponding to >97% at the top of the canopy and decreasing to 67% for trees in the understory. Our results indicate the potential for UAS photogrammetry to produce highly detailed maps of forest biomass, as well as capture variation of forest structure through the detection of trees and tree groups. Such high-resolution data has the potential to become a much-needed tool for monitoring forest structures to inform spatially explicit management objectives. Additionally, these studies reinforced how UAS photogrammetry can provide low-cost repeat monitoring of forest conditions.