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

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) b..., 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 ..., , # Data from: Using handheld mobile laser scanning to quantify fine-scale surface fuels and detect changes post-disturbance in Northern California forests

[https://doi.org/10.5061/dryad.sxksn038g](https://doi.org/10.5061/dryad.sxksn038g)

The dataset includes processed handheld lidar data and dry mass, from 3D frame and plot sampling. The lidar system used is a handheld mobile laser scanner (GeoSLAM's Zeb-REVO).

## Description of the data and file structure

Sheets within the Excel file are separated based on manuscript sections. '3D Frame' includes the data collected from lidar scans and destructive sampling which was collected to validate the use of handheld lidar for vegetation monitoring. 'Plot-level' contains the total occupied voxels from the processed plot scans taken in each survey/campaign. 'Brown's' is the mass per area calculated from Brown's transects collected at the plots and the predicted mass in grams as calculated from the voxelized plot scans. 'Point Density' contains...,