Using handheld mobile laser scanning to quantify fine-scale surface fuels and detect changes post-disturbance in northern California forests

• Handheld mobile laser scanning (HMLS) can measure fine-scale surface fuels. • HMLS is a rapid approach to detect change in fine-scale fuels post-disturbance. • HMLS and Brown’s transects detect similar changes in surface fuels post-disturbance. • Vegetation recovery post-wildfire was detected via HMLS but not Brown’s transects. The understory plays a critical role in the disturbance dynamics of forest, as it influences wildfire behavior. Unfortunately, the structure of understory fuels is difficult to quantify due to heterogeneity. LiDAR can measure changes in forest structure more rapidly, comprehensively, and accurately than manual approaches, but remote sensing is more frequently applied to the overstory. We evaluated the use of handheld mobile laser scanning (HMLS) to measure changes in fine-scale surface fuels following wildfire and timber harvest in Northern Californian forests, USA. The ability of HMLS to quantify surface fuels was validated by destructively sampling vegetation within a 3D frame and comparing destructive-based volumes with HMLS-based occupied volume estimates. There was a positive linear relationship between volume estimates, and occupied volume estimated from 1-cm voxels had the best relationship with measured biomass compared to larger voxel sizes. Next, HMLS was used to scan forest plots where wildfire or timber harvest occurred, producing bi-temporal structural measurements. Plot level HMLS estimates without ground voxels revealed regrowth of live vegetation one-year post-fire that was not apparent from field measurements collected via Brown’s transects. Comparison between Brown’s transects and HMLS estimate showed similar decreases in surface fuels post-wildfire, further indicating that the increase in estimated volume one-year post-fire comes from vegetation regrowth rather than dead fuel accumulation. HMLS can be a valuable tool for land stewards to rapidly quantify understory vegetation, especially following disturbance. Assessing understory vegetation is crucial for reducing wildfire risk and fuels might not be captured fully post-wildfire using traditional approaches.