Index Catalog // ScholarsArchive@OSU

Creator:: Engler, Joseph D., Gorman, Mark, Cappaert, David, Stanton, Marek, Best, Lincoln R., and Jackson, August S.
Abstract:: The following taxonomic keys are adapted in part from the subgeneric and species descriptions and keys produced by McGinley (2003), Gibbs et al. (2013) and Gardner and Gibbs (2022). Additional identification information was provided by Jason Gibbs, Thilina Hettiarachchi (University of Manitoba), and Joel Gardner (Washington State University). While these...
Resource Type:: Technical Report
Full Text:: ) Joseph D. Engler1,3, Mark Gorman1, David Cappaert2, Marek Stanton1, Lincoln R. Best1,3, and August S

Creator:: Warren, Dana R., Loeb, Harper M., Betjemann, Peter, Munck, Isabel A., Keeton, William S., Shaw, David C., and Harvey, Eleanor J.
Abstract:: As we contemplate the future of forest landscapes under changing climate conditions and land-use demands, there is increasing value in studying historic forest conditions and how these landscapes have changed following past disturbances. Historic landscape paintings are a potential source of data on preindustrial forests with highly detailed, full-color depictions...
Resource Type:: Article
Full Text:: | Harper M. Loeb1 | Peter Betjemann2 | Isabel A. Munck3 | William S. Keeton4 | David C. Shaw5 | Eleanor J

Creator:: McGregor, Eric L., Green, Rebecca E., Purcell, Kathryn L., Tucker, Jody M., Matthews, Sean M., and Green, David S.
Abstract:: This dataset is designed to capture landscape changes brought on by a severe tree mortality episode in California's southern Sierra Nevada range, by mapping multiple land cover types in a continuous way at annual time-steps throughout the mortality event. By mapping multiple land cover types we can see not only...
Resource Type:: Map or Cartographic Material

Creator:: McGregor, Eric L., Purcell, Kathryn L., Green, Rebecca E., Matthews, Sean M., Green, David S., and Tucker, Jody M.
Abstract:: These high resolution (5x5-meter pixel) categorical models of land cover were created by training random forest classification models on RapidEye (Planet Labs Inc.) satellite spectral data for each year and then predicting those models across the landscape. Training data were created by visually interpreting random plots over high resolution NAIP...
Resource Type:: Map or Cartographic Material

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