The understory plays a critical role in the disturbance dynamics of forest ecosystems, as it can influence wildfire behavior. Unfortunately, the 3D structure of understory fuels is often difficult to quantify and model due to vegetation and substrate heterogeneity. LiDAR remote sensing can measure changes in 3D forest structure more rapidly, comprehensively, and accurately than manual approaches, but a remote sensing approach is more frequently applied to the overstory compared to the understory. Here we evaluated the use of handheld mobile laser scanning (HMLS) to measure and detect changes in fine-scale surface fuels following wildfire and timber harvest in Northern Californian forests, USA. First, the ability of HMLS to quantify surface fuels was validated by destructively sampling vegetation below 1 m with a known occupied volume within a 3D frame and comparing destructive-based volumes with HMLS-based occupied volume estimates. There was a positive linear relationship (R² = 0.72) between volume estimates, and occupied volume estimated from 1-cm voxels had the best relationship with measured biomass (R² = 0.46, RMSE = 50.76 g, p < .0001) compared to larger voxel sizes. Next, HMLS was used to scan forest plots where wildfire or timber harvest had occurred, producing bi-temporal (pre and post) structural measurements. Plot scans were voxelized and the volume occupied by surface fuels was extracted and quantified. Changes in plot-level HMLS estimates of surface fuels were compared to data collected with a standardized manual field protocol to quantify plot-level dead and uprooted vegetation (Brown’s transects). Both HMLS and Brown’s transects detected a similar decrease in surface fuels post-wildfire. However, removal of ground voxels for the HMLS analysis revealed the regrowth of live vegetation one-year post-fire that was not captured by Brown’s transects. Neither remote sensing nor field approaches detected any changes in fine-scale surface fuels post-logging. HMLS can be a valuable tool for land stewards to rapidly quantify understory vegetation, especially following disturbance. An accurate assessment of understory vegetation is crucial for management plans to reduce wildfire risk and both live and dead fuels might not be captured fully post-wildfire using non-remote sensing approaches.

Data were collected in a few different ways. 3D frame data were collected by scanning a 3D frame with a handheld mobile laser scanner (HMLS) and then destructively sampling of the vegetation inside. The scans were processed by the scanner's software (GeoSLAM, SLAM algorithm), and the vegetation samples were oven dried to get dry mass measurements. Plot-level data were collected at 11.3 m radius circle plots at 2 locations across 3 time periods, lidar scans were taken with the HMLS and Brown's data were collected using the standard Brown's transect protocol. Brown's data were processed to extract estimates of fuel mass per area for each plot. All of the lidar scans taken with the HMLS (both frame and plot scans) were further processed in Lidar360, CloudCompare, and R with the lidR package to clip scans to the frame/plot boundary, height normalize, and voxelize the scans. Frame scans were voxelized at 4 different voxel sizes (1, 5, 10, and 25 cm), while plot scans were all voxelized at 1 cm voxel size.