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Ambient Electromagnetic Radiation As A Predictor Of Honey Bee (Apis Mellifera) Traffic In Linear And Non-Linear Regression: Numerical Stability, Physical Time And Energy Efficiency, Vladimir Kulyukin, Daniel Coster, Anastasiia Tkachenko, Daniel Hornberger, Aleksey V. Kulyukin
Ambient Electromagnetic Radiation As A Predictor Of Honey Bee (Apis Mellifera) Traffic In Linear And Non-Linear Regression: Numerical Stability, Physical Time And Energy Efficiency, Vladimir Kulyukin, Daniel Coster, Anastasiia Tkachenko, Daniel Hornberger, Aleksey V. Kulyukin
Computer Science Faculty and Staff Publications
Since bee traffic is a contributing factor to hive health and electromagnetic radiation has a growing presence in the urban milieu, we investigate ambient electromagnetic radiation as a predictor of bee traffic in the hive’s vicinity in an urban environment. To that end, we built two multi-sensor stations and deployed them for four and a half months at a private apiary in Logan, Utah, U.S.A. to record ambient weather and electromagnetic radiation. We placed two non-invasive video loggers on two hives at the apiary to extract omnidirectional bee motion counts from videos. The time-aligned datasets were used to evaluate 200 …
Innovation In Rangeland Monitoring: Annual, 30 M, Plant Functional Type Percent Cover Maps For U.S. Rangelands, 1984-2017, Matthew O. Jones, Brady W. Allred, David E. Naugle, Jeremy D. Maestas, Patrick Donnelly, Loretta J. Metz, Jason Karl, Rob Smith, Brandon Bestelmeyer, Chad Boyd, Jay D. Kerby, James D. Mciver
Innovation In Rangeland Monitoring: Annual, 30 M, Plant Functional Type Percent Cover Maps For U.S. Rangelands, 1984-2017, Matthew O. Jones, Brady W. Allred, David E. Naugle, Jeremy D. Maestas, Patrick Donnelly, Loretta J. Metz, Jason Karl, Rob Smith, Brandon Bestelmeyer, Chad Boyd, Jay D. Kerby, James D. Mciver
Articles
Innovations in machine learning and cloud‐based computing were merged with historical remote sensing and field data to provide the first moderate resolution, annual, percent cover maps of plant functional types across rangeland ecosystems to effectively and efficiently respond to pressing challenges facing conservation of biodiversity and ecosystem services. We utilized the historical Landsat satellite record, gridded meteorology, abiotic land surface data, and over 30,000 field plots within a Random Forests model to predict per‐pixel percent cover of annual forbs and grasses, perennial forbs and grasses, shrubs, and bare ground over the western United States from 1984 to 2017. Results were …