DOI: 10.5061/dryad.sxksn038g
title: Data from: Using handheld mobile laser scanning to quantify fine-scale surface fuels and detect changes post-disturbance in Northern California forests

description: 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
 &lt; .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.
description: 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.
description: # 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 all of the scan point densities and was used to compare point densities across the various surveys/campaigns/plots. Metadata has all of the variable names and their respective meanings, including units. Sheets are designed to be used separately and are optimized for data manipulation in R. For those wishing to use the plot-level and Brown's data, keep in mind that there are 2 study sites, one with 3 survey campaigns called Saddle Mountain ('c6', 'c7', 'c10') and the other with 2 called Jackson ('c3', 'c9'). The labeling came from a larger data collection effort by the Bentley Lab at Sonoma State University, hence the non-consecutive numbers.