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Abstract: Assessing the best modeling technique among linear, nonlinear, and artificial intelligence models for estimating the age of longleaf pine (Pinus palustris Mill.) trees from terrestrial laser scanning (AGU25)

Assessing the best modeling technique among linear, nonlinear, and artificial intelligence models for estimating the age of longleaf pine (Pinus palustris Mill.) trees from terrestrial laser scanning

Kody Brock1, Kleydson Diego Rocha2, Ana Paula Corte3, Ajay Sharma4, Jeffery Cannon5, Cesar Ivan Alvites Diaz2, Mauro Alessandro Karasinski6, Rodrigo Vieira Leite7, Monique Bohora Schlickmann2, Jinyi Xia8, Jason Vogel2, Jeff Atkins9, Aditya Sing10, Andrew T Hudak11, Carine Klauberg2, Andrew J. Sanchez Meador12, Carlos Alberto Silva2 and Adrián Cardil13, (1)University of Florida, School of Forest, Fisheries, and Geomatics Sciences, Gainsville, United States, (2)University of Florida, School of Forest, Fisheries and Geomatics Sciences, Gainesville, United States, (3)BIOFIX, Universidade Federal do Paraná, Avenida Prefeito Lothario Meissner, 632, Jardim Botânico, Curitiba, PR, BR 80210-170,, Curitiba, Brazil, (4)Auburn University, Auburn, United States, (5)Joseph W. Jones Ecological Research Center, Newton, United States, (6)University of Florida, School of Forest, Fisheries, and Geomatics Sciences, Gainesville, United States, (7)PUC Pontifical Catholic University of Rio de Janeiro, Rio De Janeiro, Brazil, (8)University of Florida, School of Forest, Fisheries and Geomatics Sciences, Ft Walton Beach, FL, United States, (9)Virginia Commonwealth University, Biology, Richmond, United States, (10)Caesar Kleberg Wildlife Research Institute, Kingsville, United States, (11)USDA Forest Service, Rocky Mountain Research Station, Moscow, United States, (12)Northern Arizona University, Ecological Restoration Institute and School of Forestry, Flagstaff, United States, (13)Technosylva, Fire Science, León, Spain
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