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Open AccessArticle

Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand

1
School of Earth Sciences and Environmental Sustainability, Northern Arizona University, 525 S Beaver St, Flagstaff, AZ 86011, USA
2
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, 1295 S Knoles Dr, Flagstaff, AZ 86011, USA
3
School of Forestry, Northern Arizona University, 200 E Pine Knoll Dr, Flagstaff, AZ 86011, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(8), 1266; https://doi.org/10.3390/rs10081266
Received: 19 July 2018 / Revised: 7 August 2018 / Accepted: 9 August 2018 / Published: 11 August 2018
(This article belongs to the Special Issue UAV Applications in Forestry)
Forests in the Southwestern United States are becoming increasingly susceptible to large wildfires. As a result, forest managers are conducting forest fuel reduction treatments for which spatial fuels and structure information are necessary. However, this information currently has coarse spatial resolution and variable accuracy. This study tested the feasibility of using unmanned aerial vehicle (UAV) imagery to estimate forest canopy fuels and structure in a southwestern ponderosa pine stand. UAV-based multispectral images and Structure-from-Motion point clouds were used to estimate canopy cover, canopy height, tree density, canopy base height, and canopy bulk density. Estimates were validated with field data from 57 plots and aerial photography from the U.S. Department of Agriculture National Agriculture Imaging Program. Results indicate that UAV imagery can be used to accurately estimate forest canopy cover (correlation coefficient (R2) = 0.82, root mean square error (RMSE) = 8.9%). Tree density estimates correctly detected 74% of field-mapped trees with a 16% commission error rate. Individual tree height estimates were strongly correlated with field measurements (R2 = 0.71, RMSE = 1.83 m), whereas canopy base height estimates had a weaker correlation (R2 = 0.34, RMSE = 2.52 m). Estimates of canopy bulk density were not correlated to field measurements. UAV-derived inputs resulted in drastically different estimates of potential crown fire behavior when compared with coarse resolution LANDFIRE data. Methods from this study provide additional data to supplement, or potentially substitute, traditional estimates of canopy fuel. View Full-Text
Keywords: unmanned aerial vehicle (UAV); drone; wildfire; fire behavior; structure-from-motion; SfM; lidar; base height; bulk density; cover unmanned aerial vehicle (UAV); drone; wildfire; fire behavior; structure-from-motion; SfM; lidar; base height; bulk density; cover
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MDPI and ACS Style

Shin, P.; Sankey, T.; Moore, M.M.; Thode, A.E. Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand. Remote Sens. 2018, 10, 1266.

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