Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

Predicting Flower Stalk Production of a Native Shrub Using UAV Structure-from-Motion Photogrammetry

View through CrossRef
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.
Boise State University, Albertsons Library
Title: Predicting Flower Stalk Production of a Native Shrub Using UAV Structure-from-Motion Photogrammetry
Description:
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.

Related Results

Physiological Influence of Stalk Rot on Maize Lodging after Physiological Maturity
Physiological Influence of Stalk Rot on Maize Lodging after Physiological Maturity
The stalk lodging caused by stalk rot after physiological maturity (PM) is a major factor restricting further development of mechanical grain harvesting in China. The physiological...
Productivity of Arenga Pinnata Merr Male Flower Stalks in South Tapanuli Regency Referring to Sustainable Agricultural Systems
Productivity of Arenga Pinnata Merr Male Flower Stalks in South Tapanuli Regency Referring to Sustainable Agricultural Systems
This study aims to determine and complete the database related to the productivity level of male palm flower stalks in producing sap in wild populations of natural habitats in the ...
The 2009 pandemic H1N1 hemagglutinin stalk remained antigenically stable after circulating in humans for a decade
The 2009 pandemic H1N1 hemagglutinin stalk remained antigenically stable after circulating in humans for a decade
Abstract An H1N1 influenza virus caused a pandemic in 2009 and descendants of this virus continue to circulate seasonally in humans. Upon infection with the 2009 H1...
Impacts of man-made structures on marine biodiversity and species status - native & non-native species
Impacts of man-made structures on marine biodiversity and species status - native & non-native species
<p>Coastal environments are exposed to anthropogenic activities such as frequent marine traffic and restructuring, i.e., addition, removal or replacing with man-made structur...
Mixed-reality for unmanned aerial vehicle operations in near earth environments
Mixed-reality for unmanned aerial vehicle operations in near earth environments
Future applications will bring unmanned aerial vehicles (UAVs) to near Earth environments such as urban areas, causing a change in the way UAVs are currently operated. Of concern i...
Automatic Detection of UAV GCP Targets Using Line-Based Approach
Automatic Detection of UAV GCP Targets Using Line-Based Approach
With the advent and development of UAV technologies, UAV images are widely used in various fields since UAV photogrammetry has many advantages in terms of cost and accessibility. I...
Tethered UAV-active defense against intelligent cluster
Tethered UAV-active defense against intelligent cluster
Purpose With the development of wireless networks and artificial intelligence technology, unmanned aerial vehicle (UAV) clusters are widely used in various fields...
Mixed-Reality Geotechnical Cell Mapping for Slope Stability Assessment at Rio Tinto Bingham Canyon Mine
Mixed-Reality Geotechnical Cell Mapping for Slope Stability Assessment at Rio Tinto Bingham Canyon Mine
ABSTRACT: In this paper, the applicability of Mixed Reality (MR) in combination with Uncrewed Aerial Vehicle (UAV) photogrammetry data is investigated to aid the ...

Back to Top