LB 24 -
Remote Sensing And Image Analysis
Machine learning for the automated detection and classification of seabirds, waterfowl, and other marine wildlife from digital aerial imagery
Kyle Landolt, USGS UMESC;
Timothy White, BOEM;
Mark Koneff, USFWS;
Jennifer Dieck, USGS UMESC;
Travis Harrison, USGS UMESC;
Luke Fara, USGS UMESC;
Larry Robinson, USGS UMESC;
Enrika Hlavacek, USGS UMESC;
Brian Lubinski, USFWS;
Dave Fronczak, USFWS;
Lucas Spellman, University of Wisconsin- La Crosse;
Simon Wagner, University of Wisconsin- La Crosse;
Stella Yu, University of California- Berkeley;
Tsung-Wei Ke, University of California- Berkeley
Quantification of seagrass in St. Joseph Bay, FL with Landsat 8 and a machine learning classification algorithm
Marie Lebrasse, U.S. Environmental Protection Agency, North Carolina State University;
Blake A. Schaeffer, U.S. Environmental Protection Agency;
Megan M. Coffer, U.S. Environmental Protection Agency;
Peter Whitman, U.S. Environmental Protection Agency;
Richard Zimmerman, Old Dominion University;
Victoria Hill, Old Dominion University;
Kazi Islam, Old Dominion University;
Jiang Li, Old Dominion University;
Christopher L. Osburn, North Carolina State University
Amazonian forest canopy reflectance explains soil properties and understory species distribution and composition
Jasper Van doninck, Michigan State University, University of Turku;
Mirkka M. Jones, Aalto University;
Gabriela Zuquim, University of Turku;
Kalle Ruokolainen, University of Turku;
Gabriel Moulatlet, IKIAM University;
Anders Sirén, University of Turku;
Glenda Cárdenas, University of Turku;
Samuli Lehtonen, University of Turku;
Hanna Tuomisto, University of Turku
