The potential for LiDAR technology to map fire fuel hazard over large areas of Australian forest. (1st October 2016)

• Record Type: Journal Article
• Authors: Price, Owen F.; Gordon, Christopher E.
• Is Part Of: Journal of environmental management. Volume 181(2016)
• Page Start: 663
• Page End: 673
• Publication Date: 2016-10-01
• Subjects: Crown fire -- Fuel accumulation -- Australia -- Eucalypt forest -- Fuel profile; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2016.08.042
• Languages: English
• ISSNs: 0301-4797

Abstract

Abstract: Fuel load is a primary determinant of fire spread in Australian forests. In east Australian forests, litter and canopy fuel loads and hence fire hazard are thought to be highest at and beyond steady-state fuel loads 15–20 years post-fire. Current methods used to predict fuel loads often rely on course-scale vegetation maps and simple time-since-fire relationships which mask fine-scale processes influencing fuel loads. Here we use Light Detecting and Remote Sensing technology (LiDAR) and field surveys to quantify post-fire mid-story and crown canopy fuel accumulation and fire hazard in Dry Sclerophyll Forests of the Sydney Basin (Australia) at fine spatial-scales (20 × 20 m cell resolution). Fuel cover was quantified in three strata important for crown fire propagation (0.5–4 m, 4–15 m, >15 m) over a 144 km 2 area subject to varying fire fuel ages. Our results show that 1) LiDAR provided a precise measurement of fuel cover in each strata and a less precise but still useful predictor of surface fuels, 2) cover varied greatly within a mapped vegetation class of the same fuel age, particularly for elevated fuel, 3) time-since-fire was a poor predictor of fuel cover and crown fire hazard because fuel loads important for crown fire propagation were variable over a range of fire fuel ages between 2 and 38 years post-fire, and 4) fuel loads and fire hazard can be high in the years immediately following fire. Our results show the benefits of spatially and temporally specific in situ fuel sampling methods such as LiDAR, and are widely applicable for fire management actions which aim to decrease human and environmental losses due to wildfire. Highlights: We used freely available LiDAR data to map vegetation structure. LiDAR can map hazard both at fine scale and across broad regions. LiDAR quantifies the variation in fuel arrangements within a mapped vegetation patch. LiDAR is a better measure of hazard than is time-since-fire.