Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data
Txomin Hermosilla A B D , Luis A. Ruiz A , Alexandra N. Kazakova C , Nicholas C. Coops B and L. Monika Moskal C
+ Author Affiliations
- Author Affiliations
A Geo-Environmental Cartography and Remote Sensing Group, Universitat Politècnica de València, Camino de Vera, s/n, E-46022 Valencia, Spain.
B Integrated Remote Sensing Studio, Department of Forest Resources Management, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada.
C Remote Sensing and Geospatial Analysis Laboratory and Precision Forestry Cooperative, School of Environmental and Forest Sciences, College of the Environment, University of Washington, Seattle, WA 98195-2100, USA.
D Corresponding author. Email: txomin.hermosilla@live.forestry.ubc.ca
International Journal of Wildland Fire 23(2) 224-233 https://doi.org/10.1071/WF13086
Submitted: 24 May 2013 Accepted: 19 August 2013 Published: 14 November 2013
Abstract
Precise knowledge of fuel conditions is important for predicting fire hazards and simulating fire growth and intensity across the landscape. We present a methodology to retrieve and map forest canopy fuel and other forest structural parameters using small-footprint full-waveform airborne light detection and ranging (LiDAR) data. Full-waveform LiDAR sensors register the complete returned backscattered signal through time and can describe physical properties of the intercepted objects. This study was undertaken in a mixed forest dominated by Douglas-fir, occasionally mixed with other conifers, in north-west Oregon (United States). We extracted two sets of LiDAR metrics using pulse detection and waveform modelling and then constructed several predictive models using forward stepwise multiple linear regression. The resulting models explained ~80% of the variability for many of the canopy fuel and forest structure parameters: aboveground biomass (R2 = 0.84), quadratic mean diameter (R2 = 0.82), canopy height (R2 = 0.79), canopy base height (R2 = 0.78) and canopy fuel load (R2 = 0.79). The lowest performing models included basal area (R2 = 0.76), stand volume (R2 = 0.73), canopy bulk density (R2 = 0.67) and stand density index (R2 = 0.66). Our results indicate that full-waveform LiDAR systems show promise in systematically characterising the structure and canopy fuel loads of forests, which may enable accurate fire behaviour forecasting that in turn supports the development of prevention and planning policies.
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