Differences in canopy cover estimations from ALS data and their effect on fire predictions

Abstract

Canopy cover is a primary attribute used in empirical wildfire models for certain fuel types. Accurate estimation of canopy cover is key to ensuring accurate prediction of fire spread and behaviour in these fuels. Airborne Laser Scanning (ALS) is a promising active remote sensing technology for estimating canopy cover in natural eco-systems since it can penetrate and measure the vegetation canopy. Various methods have been developed to estimate canopy cover from ALS data. However, little attention has been given to the evaluation of algorithms used to calculate canopy cover and the subsequent influence these algorithms can have on wildfire behaviour models. In this study we evaluate the effect of using different algorithms to calculate canopy cover on the performance of the Australian Mallee-heath fire spread model. ALS data was used to derive five canopy cover models. Fire spread metrics including burned area, unburned area within the fire extent, and extent of fire were compared for different model run times. The results show that these metrics are strongly influenced by choice of algorithm used to calculate canopy cover. The results from this study may provide practical guidance for the optimal selection of estimation methods in canopy cover mapping.