Evaluating unmanned aerial vehicle images for estimating forest canopy fuels in a ponderosa pine stand

Keyphrases

• Ponderosa pine 100%
• Pine Stands 100%
• Forest Canopy 100%
• Canopy Fuels 100%
• Root Mean Square Error 100%
• Unmanned Aerial Vehicle Images 100%
• Tree Density 66%
• Canopy Base Height 66%
• Canopy Bulk Density 66%
• UAV 66%
• Unmanned Aerial Vehicle (UAV) Imagery 66%
• Height Estimates 66%
• Southwestern United States 33%
• Strong Correlation 33%
• Weak Correlation 33%
• Spatial Resolution 33%
• Wildfire 33%
• Extreme Wildfire 33%
• Aerial Photography 33%
• Fuel Reduction Treatments 33%
• Tree Cover 33%
• Multispectral Image 33%
• Agriculture 33%
• Canopy Cover 33%
• Crown Fire Behaviour 33%
• Forest Managers 33%
• Canopy Structure 33%
• Forest Fuel 33%
• Error Rate 33%
• Density Estimates 33%
• Commission Errors 33%
• Correlation Coefficient 33%
• Individual Tree Level 33%
• U.S. Department of Agriculture 33%
• Canopy Height 33%
• Density Canopy 33%
• Coarse Resolution 33%
• Variable Accuracy 33%
• Spatial Variables 33%
• Vehicle-based 33%
• Multispectral Structures 33%
• Structure from Motion 33%
• Point Cloud 33%
• Structure Information 33%

Earth and Planetary Sciences

• Forest Canopy 100%
• Pilotless Aircraft 100%
• Root-Mean-Square Error 60%
• Bulk Density 40%
• United States 20%
• United States of America 20%
• Fire Behavior 20%
• Aerial Photography 20%
• Multispectral Image 20%
• Spatial Resolution 20%
• Correlation Coefficient 20%

Agricultural and Biological Sciences

• Pinus 100%
• Forest Canopy 100%
• Unmanned Aerial Vehicle 100%
• Bulk Density 40%
• Agricultural Science 40%
• Fire Behavior 20%
• Aerial Photography 20%