Scaling Up Sagebrush Chemistry with Near-Infrared Spectroscopy and UAS-Acquired Hyperspectral Imagery

Sagebrush ecosystems (Artemisia spp.) face many threats including large wildfires and conversion to invasive annuals, and thus are the focus of intense restoration efforts across the western United States. Specific attention has been given to restoration of sagebrush systems for threatened herbivores, such as Greater Sage-Grouse (Centrocercus urophasianus) and pygmy rabbits (Brachylagus idahoensis), reliant on sagebrush as forage. Despite this, plant chemistry (e.g., crude protein, monoterpenes and phenolics) is rarely considered during reseeding efforts or when deciding which areas to conserve. Near-infrared spectroscopy (NIRS) has proven effective in predicting plant chemistry under laboratory conditions in a variety of ecosystems, including the sagebrush steppe. Our objectives were to demonstrate the scalability of these models from the laboratory to the field, and in the air with a hyperspectral sensor on an unoccupied aerial system (UAS). Sagebrush leaf samples were collected at a study site in eastern Idaho, USA. Plants were scanned with an ASD FieldSpec 4 spectroradiometer in the field and laboratory, and a subset of the same plants were imaged with a SteadiDrone Hexacopter UAS equipped with a Rikola hyperspectral sensor (HSI). All three sensors generated spectral patterns that were distinct among species and morphotypes of sagebrush at specific wavelengths. Lab-based NIRS was accurate for predicting crude protein and total monoterpenes (R2 = 0.7–0.8), but the same NIRS sensor in the field was unable to predict either crude protein or total monoterpenes (R2 \u3c 0.1). The hyperspectral sensor on the UAS was unable to predict most chemicals (R2 \u3c 0.2), likely due to a combination of too few bands in the Rikola HSI camera (16 bands), the range of wavelengths (500–900 nm), and small sample size of overlapping plants (n = 28–60). These results show both the potential for scaling NIRS from the lab to the field and the challenges in predicting complex plant chemistry with hyperspectral UAS. We conclude with recommendations for next steps in applying UAS to sagebrush ecosystems with a variety of new sensors

Spectral Fingerprints Predict Functional Chemistry of Native Plants Across Sagebrush-Steppe Landscapes

Landscapes are changing and under threat from anthropogenic activities, decreasing land cover, contaminated air and water quality, and climate change. These changes impact native communities and their functions at all spatial scales. A major functional trait being affected across these communities is nitrogen. Nitrogen supports plant nutrient cycling and growth, serves as an indicator for crude protein and productivity, and offers quality forage for wild and domestic herbivores. We need better ways to monitor nitrogen across space and time. Current monitoring is elaborate, time-consuming, and expensive. We propose drawing from agricultural methodologies to incorporate near-infrared spectroscopy as a technique in detecting and monitoring nitrogen concentrations across a threatened shrub-steppe ecosystem. We are currently developing calibration equations for nitrogen in sagebrush across four species (Artemisia tridentata wyomingensis, A. tripartita, A. arbuscula, A. nova), three study sites and two seasons. Preliminary results suggest that nitrogen can be accurately predicted across all sites, species, and seasons, explaining 75-90% of the variation in nitrogen. These results indicate that near infrared spectroscopy offers a rapid, noninvasive diagnostic tool for assessing nitrogen in wild systems. This advancing technology is important because it economizes the collection of ecological data in rapidly changing landscapes and provides land managers and researchers with valuable information about the health and sustainability of their lands

Remotely-Sensing Chemical Diversity and Function of Native Plants Across Sagebrush-Steppe Landscapes

Plant chemical diversity provides ecosystem services by supporting wildlife diversity and offering sources for novel medicines. Current mapping of phytochemicals can be expensive, time-intensive and provides only a snapshot of available diversity. To overcome this, I will use handheld and airborne instruments collecting near infrared spectra and hyperspectral imagery to remotely sense chemical diversity within plants and ecosystems. I hypothesize that greater plant chemical diversity will be correlated with greater habitat use by wildlife and greater bioactivity of plant extracts. This research provides a powerful tool to map chemical diversity, target wildlife conservation and direct the discovery of novel medicines

A First Overview of SnowEx Ground-Based Remote Sensing Activities During the Winter 2016–2017

NASA SnowEx\u27s goal is estimating how much water is stored in Earth\u27s terrestrial snow-covered regions. To that end, two fundamental questions drive the mission objectives: (a) What is the distribution of snow-water equivalent (SWE), and the snow energy balance, among different canopy and topographic situations?; and (b) What is the sensitivity and accuracy of different SWE sensing techniques among these different areas? In situ, ground-based and airborne remote sensing observations were collected during winter 2016-2017 in Colorado to provide the scientific community with data needed to work on these key questions. An intensive period of observations occurred in February 2017 during which over 30 remote sensing instruments were used. Their observations were coordinated with in situ measurements from snowpits (e.g. profiles of stratigraphy, density, grain size and type, specific surface area, temperature) and along transects (mainly for snow depth measurements). Both remote sensing and in situ data will be archived and publicly distributed by the National Snow and Ice Data Center at nsidc.org/data/snowex