Towards Spatially Explicit Quantification of Pre- and Postfire Fuels and Fuel Consumption from Traditional and Point Cloud Measurements. Issue 4 (22nd January 2020)

• Record Type: Journal Article
• Authors: Hudak, Andrew T; Kato, Akira; Bright, Benjamin C; Loudermilk, E Louise; Hawley, Christie; Restaino, Joseph C; Ottmar, Roger D; Prata, Gabriel A; Cabo, Carlos; Prichard, Susan J; Rowell, Eric M; Weise, David R
• Is Part Of: Forest science. Volume 66:Issue 4(2020)
• Page Start: 428
• Page End: 442
• Publication Date: 2020-01-22
• Subjects: airborne laser scanning -- Fort Jackson -- prescribed fire -- sparkleberry -- terrestrial laser scanning; Forests and forestry -- Periodicals; Forest management -- Periodicals; Forest policy -- United States -- Periodicals; Forest management; Forest policy; Forests and forestry; United States; Electronic journals; Periodicals; 634.9
• DOI: 10.1093/forsci/fxz085
• Languages: English
• ISSNs: 0015-749X

Abstract

Abstract: Methods to accurately estimate spatially explicit fuel consumption are needed because consumption relates directly to fire behavior, effects, and smoke emissions. Our objective was to quantify sparkleberry ( Vaccinium arboretum Marshall) shrub fuels before and after six experimental prescribed fires at Fort Jackson in South Carolina. We used a novel approach to characterize shrubs non-destructively from three-dimensional (3D) point cloud data collected with a terrestrial laser scanner. The point cloud data were reduced to 0.001 m –3 voxels that were either occupied to indicate fuel presence or empty to indicate fuel absence. The density of occupied voxels was related significantly by a logarithmic function to 3D fuel bulk density samples that were destructively harvested (adjusted R 2 = .32, P < .0001). Based on our findings, a survey-grade Global Navigation Satellite System may be necessary to accurately associate 3D point cloud data to 3D fuel bulk density measurements destructively collected in small (submeter) shrub plots. A recommendation for future research is to accurately geolocate and quantify the occupied volume of entire shrubs as 3D objects that can be used to train models to map shrub fuel bulk density from point cloud data binned to occupied 3D voxels.