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The National Innovation Center partnership development and science support has generated numerous publications over the last decade.

Filter Total Items: 30

A framework to facilitate development and testing of image-based river velocimetry algorithms

Image-based methods have compelling, demonstrated potential for characterizing flow fields in rivers, but algorithms like particle image velocimetry (PIV) must be further tested and improved to enable more effective use of these techniques. This paper presents a framework designed for this exact purpose: Simulating Hydraulics and Images for Velocimetry Evaluation and Refinement (SHIVER). The appro
Carl J. Legleiter, Paul J. Kinzel

Capturing patterns of evolutionary relatedness with reflectance spectra to model and monitor biodiversity

Biogeographic history can set initial conditions for vegetation community assemblages that determine their climate responses at broad extents that land surface models attempt to forecast. Numerous studies have indicated that evolutionarily conserved biochemical, structural, and other functional attributes of plant species are captured in visible-to-short wavelength infrared, 400 to 2,500 nm, refle
Daniel Mark Griffith, Kristin B. Byrd, Lee Anderegg, Elijah Allen, Demetrios Gatziolis, Dar A. Roberts, Rosie Yacoub, Ramakrishna Nemani

Variation in leaf reflectance spectra across the California flora partitioned by evolutionary history, geographic origin, and deep time

Evolutionary relatedness underlies patterns of functional diversity in the natural world. Hyperspectral remote sensing has the potential to detect these patterns in plants through inherited patterns of leaf reflectance spectra. We collected leaf reflectance data from across the California flora from plants grown in a common garden. Regions of the reflectance spectra vary in the depth and strength
Daniel M. Griffith, Kristin B. Byrd, Nicole Chin Taylor, Elijah Allan, Liz Bittner, Bart O'Brien, V. Thomas Parker, Michael C Vasey, Ryan Pavlick, Ramakrishna R. Nemani

From data to interpretable models: Machine learning for soil moisture forecasting

Soil moisture is critical to agricultural business, ecosystem health, and certain hydrologically driven natural disasters. Monitoring data, though, is prone to instrumental noise, wide ranging extrema, and nonstationary response to rainfall where ground conditions change. Furthermore, existing soil moisture models generally forecast poorly for time periods greater than a few hours. To improve such
Aniruddha Basak, Kevin M. Schmidt, Ole Mengshoel

Achieving sub-nanoTesla precision in multirotor UAV aeromagnetic surveys

An uncrewed aerial vehicle (UAV) multirotor aeromagnetic system using a 5-m sling load for a magnetic sensor system is described and characterized. Four magnetic surveys with identical flight lines were completed, at two nominal altitudes of 25 and 40 m. The surveys were used to assess the repeatability of data collected with the described UAV aeromagnetic system, and comparison with a ground surv
Geoffrey Phelps, Robert E. Bracken, John Spritzer, David S. White

In situ enhancement and isotopic labeling of biogenic coalbed methane

Subsurface microbial (biogenic) methane production is an important part of the global carbon cycle that has resulted in natural gas accumulations in many coal beds worldwide. Laboratory studies suggest that complex carbon-containing nutrients (e.g., yeast or algae extract) can stimulate methane production, yet the effectiveness of these nutrients within coal beds is unknown. Here, we use downhole
Elliott Barnhart, Leslie F. Ruppert, Randy Heibert, Heidi J. Smith, Hannah Schweitzer, Arthur Clark, Edwin Weeks, William H. Orem, Matthew S. Varonka, George A. Platt, Jenna L. Shelton, Katherine J Davis, Robert Hyatt, Jennifer C. McIntosh, Kilian Ashley, Shuhei Ono, Anna M. Martini, Keith Hackley, Robin Gerlach, Lee Spangler, Adrienne Phillips, Mark Barry, Alfred B. Cunningham, Matthew W. Fields

Poor relationships between NEON Airborne Observation Platform data and field-based vegetation traits at a mesic grassland

Understanding spatial and temporal variation in plant traits is needed to accurately predict how communities and ecosystems will respond to global change. The National Observatory Ecological Network (NEON) Airborne Observation Platform (AOP) provides hyperspectral images and associated data products at numerous field sites at 1 m spatial resolution, potentially allowing high-resolution trait mappi
Stephanie Pau, Jesse Nippert, Ryan Slapikas, Daniel Mark Griffith, Seton Bachle, Brent Helliker, Rory O’Connor, William J. Riley, Christopher J. Still, Marissa Zaricor

Characterization of bituminite in Kimmeridge Clay by confocal laser scanning and atomic force microscopy

This work investigates bituminite (amorphous sedimentary organic matter) in Upper Jurassic Kimmeridge Clay source rock via confocal laser scanning microscopy (CLSM) and atomic force microscopy (AFM). These petrographic tools were used to provide better understanding of the nature of bituminite, which has been historically difficult to identify and differentiate from similar organic matter types in
Paul C. Hackley, Jolanta Kus, João Graciano Mendonça Filho, Andrew D. Czaja, Angeles G. Borrego, Dragana Životić, Brett J. Valentine, Javin J. Hatcherian

Representing plant diversity in land models: An evolutionary approach to make ‘Functional Types’ more functional

Plants are critical mediators of terrestrial mass and energy fluxes, and their structural and functional traits have profound impacts on local and global climate, biogeochemistry, biodiversity, and hydrology. Yet Earth System Models (ESMs), our most powerful tools for predicting the effects of humans on the coupled biosphere-atmosphere system, simplify the incredible diversity of land plants into
Leander D.L. Anderegg, Daniel Mark Griffith, Jeannine Cavender-Bares, William J. Riley, Joseph A. Berry, Todd E. Dawson, Christopher J. Still

Experiences in LP-IoT: EnviSense deployment of remotely reprogrammable environmental sensors

The advent of Low Power Wide Area Networks (LPWAN) has improved the feasibility of wireless sensor networks for environmental sensing across wide areas. We have built EnviSense, an ultra-low power environmental sensing system, and deployed over a dozen of them across two locations in Northern California for hydrological monitoring applications with the U.S. Geological Survey (USGS). This paper det
Reese Grimsley, Mathieu D. Marineau, Robert A. Iannucci

Activity-based, genome-resolved metagenomics uncovers key populations and pathways involved in subsurface conversions of coal to methane

Microbial metabolisms and interactions that facilitate subsurface conversions of recalcitrant carbon to methane are poorly understood. We deployed an in situ enrichment device in a subsurface coal seam in the Powder River Basin (PRB), USA, and used BONCAT-FACS-Metagenomics to identify translationally active populations involved in methane generation from a variety of coal-derived aromatic hydrocar
Luke J. McKay, Heidi J. Smith, Elliott Barnhart, Hannah S. Schweitzer, Rex R. Malmstrom, Danielle Goudeau, Matthew W. Fields

Snow depth retrieval with an autonomous UAV-mounted software-defined radar

We present results from a field campaign to measure seasonal snow depth at Cameron Pass, Colorado, using a synthetic ultrawideband software-defined radar (SDRadar) implemented in commercially available Universal Software Radio Peripheral (USRP) software-defined radio hardware and flown on a small hexacopter unmanned aerial vehicle (UAV). We coherently synthesize an ultrawideband signal from steppe
S. Prager, Graham A. Sexstone, Daniel J McGrath, John Fulton, Mahta Moghaddam