EAGER: Scaling Up Plant Demographic Rates with Imagery from Unoccupied Aerial Systems

This award is funded in whole under the American Rescue Plan Act of 2021 (Public Law 117-2).Wildfires pose a growing threat to ecosystems in the American West, as climate change and invasive species promote larger and more frequent fires. Understanding how fast, if ever, native plant populations can recover from wildfire, and where recruitment of prior dominants is not occurring will aid efforts to restore degraded ecosystems. Plant population recovery is most often studied by marking and measuring individual plants in field plots or along 50 to 100 m transects. Logistical considerations limit the spatial coverage of these types of field measurements, resulting in a mismatch between multi-kilometer-scale wildfires and plot level responses. The research proposed here will use drones to develop new methods to measure plant population recovery over large areas. These methods will include computer algorithms to detect and distinguish individual plants in aerial imagery, and statistical models to quantify drone-detected plants' growth, survival, and reproduction. This quantitative framework will enable the research team to study how an ecologically important dominant plant species across the inter-mountain west is responding to wide spread fire and to large-scale environmental variation within the vast burned areas. The research will be applied to forecast population recovery of big sagebrush plants, a species with critical ecological importance. The results will aid land management across this vast region by advancing drone technology to monitor post-fire recovery. Novel remote sensing data from unoccupied aerial systems (UAS) could aid spatial models for plant demography by providing imagery with fine enough resolution to detect individual plants across large spatial extents. This project will develop a framework to infer plant demographic rates from UAS imagery by coupling computer vision techniques with hierarchical Bayesian models. The research will focus on spatial population dynamics of big sagebrush, Artemisia tridentata, a species with high conservation value in the American West. As wildfires decimate sagebrush habitat, landscape recovery depends on whether big sagebrush can recolonize disturbed areas. However, like most low-statured plants, our understanding of big sagebrush demography is almost entirely derived from meter-scale field plots, limiting extrapolation to large spatial scales. The research will develop a reproducible workflow to identify individual plants in UAS imagery, quantify demographic rates of plants while accounting for imperfect detection in aerial imagery, and determine the impacts of spatial covariates on sagebrush demography. This research will advance quantitative modeling approaches for spatial plant demography with immediate application to an imperiled ecosystem.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.