"Predicting Flower Stalk Production of a Native Shrub Using UAV Structu" by Ryan Scott Wickersham

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

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