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Development of a predictive model for estimating forest surface fuel load in Australian eucalypt forests with LiDAR data
Affiliation:1. Authors are from Department of Wildland Resources and the Ecology Center, Utah State University, Logan, UT, 84322, USA;2. Department of Natural Resources and Environmental Science, University of Nevada, Reno, NV, 89557, USA;3. Shelby Law, Bureau of Land Management and the Great Basin Coordination Center, Salt Lake City, UT, 84116, USA.;1. AGRESTA Sociedad Cooperativa, c/Duque de Fernán Nuñez 2, 28012 Madrid, Spain;2. INIA, Forest Research Centre, Department of Silviculture and Forest Management, Forest Fire Laboratory, Crta. A Coruña Km 7.5, 28040 Madrid, Spain;3. Sustainable Forest Management Institute UVa-INIA, Crta. A Coruña Km 7.5, 28040 Madrid, Spain;1. Department of Geosciences, Fort Lewis College, 1000 Rim Drive, Durango, CO 81301, United States;2. Department of Geography, University of Utah, 332 South 1400 East, Salt Lake City, UT 84112, United States;3. Forest Sciences Laboratory, Rocky Mountain Research Station, USDA Forest Service, 1221 South Main Street, Moscow, ID 83843, United States;4. Missoula Fire Lab, Rocky Mountain Research Station, USDA Forest Service, 5775 Highway 10 West, Missoula, MT, 59808, United States
Abstract:
Keywords:Surface fuel load  Litter-bed fuel depth  Airborne LiDAR  Terrestrial LiDAR  Multiple regression
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