Effectiveness of Airborne LiDAR Intensity for Identifying Surface Fire Burned Areas in Wildfires

Abstract. Wildfires induce significant changes in forest structure and the surface reflectance characteristics. This study evaluated the effectiveness of using airborne LiDAR Intensity data to delineate surface fire burn areas in wildfires. We extracted ground returns from both coniferous and deciduous forests and conducted qualitative assessment of Intensity through Intensity images, as well as statistical evaluation using the non-parametric Mann–Whitney U test to compare burned and unburned areas. We compared the median and standard deviation of Intensity at a 10-m mesh scale, calculating standard deviation at a finer 0.5-m mesh resolution. The results revealed significant differences between burned and unburned areas. The effect size r for the median in deciduous forests ranged from -0.55 to -0.84, while the effect size r for the standard deviation in coniferous forests ranged from -0.38 to -0.47. Both indicated a medium to large effect. These findings suggest that LiDAR Intensity can effectively identify surface fire burn areas even under heterogeneous forest floor conditions. The proposed method has the potential to contribute to enhancing post-fire monitoring using airborne LiDAR.