Next Article in Journal
Effects of the Antiozonant Ethylenediurea (EDU) on Fraxinus ornus L.: The Role of Drought
Next Article in Special Issue
Improved Prediction of Stream Flow Based on Updating Land Cover Maps with Remotely Sensed Forest Change Detection
Previous Article in Journal
Improved Outbreak Prediction for Common Pine Sawfly (Diprion pini L.) by Analyzing Floating ‘Climatic Windows’ as Keys for Changes in Voltinism
Previous Article in Special Issue
Edyn: Dynamic Signaling of Changes to Forests Using Exponentially Weighted Moving Average Charts
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Forests 2017, 8(9), 322;

Prediction of Forest Canopy and Surface Fuels from Lidar and Satellite Time Series Data in a Bark Beetle-Affected Forest

Rocky Mountain Research Station, United States Forest Service, 1221 South Main Street, Moscow, ID 83843, USA
College of Natural Resources, University of Idaho, 875 Perimeter Drive MS 1142, Moscow, ID 83844, USA
Geosciences and Environmental Change Science Center, United States Geological Survey, P.O. Box 25046, MS 980, Denver, CO 80225, USA
College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, 114 Wilkinson Hall, Corvallis, OR 97331, USA
Author to whom correspondence should be addressed.
Received: 25 July 2017 / Revised: 24 August 2017 / Accepted: 27 August 2017 / Published: 30 August 2017
(This article belongs to the Special Issue Remote Sensing of Forest Disturbance)
Full-Text   |   PDF [9990 KB, uploaded 31 August 2017]   |  


Wildfire behavior depends on the type, quantity, and condition of fuels, and the effect that bark beetle outbreaks have on fuels is a topic of current research and debate. Remote sensing can provide estimates of fuels across landscapes, although few studies have estimated surface fuels from remote sensing data. Here we predicted and mapped field-measured canopy and surface fuels from light detection and ranging (lidar) and Landsat time series explanatory variables via random forest (RF) modeling across a coniferous montane forest in Colorado, USA, which was affected by mountain pine beetles (Dendroctonus ponderosae Hopkins) approximately six years prior. We examined relationships between mapped fuels and the severity of tree mortality with correlation tests. RF models explained 59%, 48%, 35%, and 70% of the variation in available canopy fuel, canopy bulk density, canopy base height, and canopy height, respectively (percent root-mean-square error (%RMSE) = 12–54%). Surface fuels were predicted less accurately, with models explaining 24%, 28%, 32%, and 30% of the variation in litter and duff, 1 to 100-h, 1000-h, and total surface fuels, respectively (%RMSE = 37–98%). Fuel metrics were negatively correlated with the severity of tree mortality, except canopy base height, which increased with greater tree mortality. Our results showed how bark beetle-caused tree mortality significantly reduced canopy fuels in our study area. We demonstrated that lidar and Landsat time series data contain substantial information about canopy and surface fuels and can be used for large-scale efforts to monitor and map fuel loads for fire behavior modeling at a landscape scale. View Full-Text
Keywords: canopy fuel; surface fuel; remote sensing; lidar; Landsat; time series analysis; bark beetle canopy fuel; surface fuel; remote sensing; lidar; Landsat; time series analysis; bark beetle

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Bright, B.C.; Hudak, A.T.; Meddens, A.J.H.; Hawbaker, T.J.; Briggs, J.S.; Kennedy, R.E. Prediction of Forest Canopy and Surface Fuels from Lidar and Satellite Time Series Data in a Bark Beetle-Affected Forest. Forests 2017, 8, 322.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Forests EISSN 1999-4907 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top