Publication Date
12-2023
Date of Final Oral Examination (Defense)
November 2023
Type of Culminating Activity
Thesis
Degree Title
Master of Science in Biology
Department Filter
Biology
Department
Biological Sciences
Supervisory Committee Chair
Trevor Caughlin, Ph.D.
Supervisory Committee Co-Chair
Megan Cattau, Ph.D.
Supervisory Committee Member
Jennifer Forbey, Ph.D.
Abstract
Climate change is threatening rangeland ecosystems, including increasing frequency of extreme weather, wildfire, and drought. Identifying which native plants are likely to be resilient to these ongoing changes is crucial for developing climate-smart restoration plans. Sagebrush is a foundational species across western rangeland, with variation in flower phenology and success that may indicate a pattern of resilience for the shrub and associated species. Analyzing sagebrush resilience through flowering success will help us understand how vulnerable sagebrush populations and post-restoration sites will respond to extreme weather events. We applied high-resolution remotely sensed data to map flower stalk production in sagebrush plants along an elevation gradient. Using cost-effective unoccupied aerial vehicles (UAVs) we collected RGB imagery that enabled canopy segmentation to inform machine learning algorithms. We applied these data to quantify flower stalk production for individual plants across our 240-acre study site in Castle Rocks State Park, Idaho in 2021 and 2022. Individual plants represented three-sagebrush subspecies: Wyoming Big Sagebrush (Artemisia t. wyomingensis), Mountain Big Sagebrush (Artemisia t. vaseyana), and Basin Big Sagebrush (Artemisia t. tridentata). We found that high-resolution imagery has potential to predict flower stalk production, including an R2 of ~50%. Structural metrics, including height differences between June and September, canopy height, and edge-to-area ratio of plant crowns, were more important than spectral data for accurate predictions. Our work demonstrates the potential for UAV data collection to quantify how individual plants respond to weather events across landscape-scale environmental gradients, including an algorithm that can predict flower stalk production. Our goal is to apply these results to enable land managers to identify locally adapted sagebrush genotypes that are reproductively compatible and resilient in future climate regimes.
DOI
https://doi.org/10.18122/td.2206.boisestate
Recommended Citation
Wickersham, Ryan Scott, "Predicting Flower Stalk Production of a Native Shrub Using UAV Structure-from-Motion Photogrammetry" (2023). Boise State University Theses and Dissertations. 2206.
https://doi.org/10.18122/td.2206.boisestate