Biomass field observations, terrestrial laser scanning point clouds, and fuel consumption maps for prescribed burns in Pebble Hill Plantation, Georgia, in 2018 and 2019

This data product contains 1) weights of destructively sampled biomass, 2) terrestrial laser scanning (TLS) point cloud data, and 3) maps of predicted biomass and consumption derived from field observations and TLS data. Data were acquired at three burn units within Pebble Hill Plantation, Georgia, in April 2018 and June 2019. Sampling was done in conjunction with prescribed burning, with the goal of quantifying biomass before and after prescribed fire. Field observation plots were sampled following the methodology of Hawley et al. (2018). In April 2018, 8 plots were measured before prescribed burning and 8 plots were measured after prescribed burning in unit PHP1. In June 2019, 20 plots were measured before prescribed burning, which occurred on 17 and 18 June, and 6 plots were measured directly after prescribed burning in burn units PHP1, PHP2, and PHP3. Only 6 of 20 planned plots were measured post-fire in 2019 because of a severe weather event with precipitation and winds that disturbed post-fire biomass. Following sampling in the field, destructively-sampled biomass was oven dried and weighed; thus these data represent dry biomass weights. Terrestrial laser scanning data were acquired both before and after prescribed burning. TLS point clouds coincident with field observation plots were extracted, and models predicting field-observed biomass from TLS point clouds were created. Models were then applied to TLS point clouds to map biomass before and after prescribed burning; consumption by prescribed fire was estimated by differencing pre- and post-fire biomass maps.

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Field Value
Last Updated July 4, 2025, 09:33 (UTC)
Created July 4, 2025, 09:33 (UTC)
Source https://wfsi-data.org/view/doi%3A10.60594/W4H01S
associated_parties Akira Kato, akiran@faculty.chiba-u.jp, Chiba University, Japan, contributor | Chad Hoffman, C.Hoffman@colostate.edu, Colorado State University, https://orcid.org/0000-0001-8715-937X, contributor | David Weise, david.weise@usda.gov, USDA Forest Service, Pacific Southwest Research Station, https://orcid.org/0000-0002-9671-7203, contributor | Kevin Hiers, john.hiers@ag.tamu.edu, United States Geological Survey, https://orcid.org/0000-0002-6813-8941, contributor
award Object-based aggregation of fuel structures, physics-based fire behavior and self-organizing smoke plumes for improved fuel, fire, and smoke management on military lands.
creators Benjamin Bright, benjamin.c.bright@usda.gov, USDA Forest Service, Rocky Mountain Research Station, https://orcid.org/0000-0002-8363-0803 | Andrew Hudak, andrew.hudak@usda.gov, USDA Forest Service, Rocky Mountain Research Station, https://orcid.org/0000-0001-7480-1458 | Nuria Sánchez-López, nuria.sl.im@gmail.com, USDA Forest Service, Rocky Mountain Research Station, https://orcid.org/0000-0002-0455-3951 | Christie Hawley, christie.m.hawley@usda.gov, USDA Forest Service, Southern Research Station, https://orcid.org/0000-0001-9105-2065 | Louise Loudermilk, eva.l.loudermilk@usda.gov, USDA Forest Service, Southern Research Station | Eric Rowell, California Air Resources Board, https://orcid.org/0000-0002-8905-3621 | Joseph O'Brien, joseph.j.obrien@usda.gov, USDA Forest Service, Southern Research Station, https://orcid.org/0000-0003-3446-6063 | Steven Flanagan, Steven.Flanagan@usda.gov, USDA Forest Service, Southern Research Station, https://orcid.org/0000-0001-5172-3530 | Kevin Robertson, krobertson@talltimbers.org, Tall Timbers Research Station, https://orcid.org/0000-0001-7266-6272
doi doi:10.60594/W4H01S
encoding utf8
funder U. S. Department of Defense (DoD), Strategic Environmental Research and Development Program (SERDP), http://dx.doi.org/10.13039/100013316
harvest_object_id ccd3a554-abcc-4d20-9550-8ff19ebc8958
harvest_source_id a2637971-af12-457f-ae4a-831d2202a539
harvest_source_title WIFIRE Commons
maintainor Benjamin Bright, benjamin.c.bright@usda.gov
method Field observations Field plots were collected using the methodology of Hawley et al. (2018). Plot locations were selected to best represent the fuel type in each burn unit. Plots were three dimensional; 0.5 m deep, 0.5 m wide, and 2 m tall. Biomass was measured within 0.1 m height strata between 0 and 1 m aboveground. Biomass between 1 and 2 m was measured as one height stratum. Samples were dried at 70 degrees Celsius for 48 hours and weighed immediately following drying. The locations of the northwestern corners of plots, marked with a conduit, were recorded via GNSS with the intent of relating biomass observations to coincident terrestrial laser scanning (TLS) data. These GNSS locations were later adjusted to match TLS point clouds by locating conduits within TLS point clouds. Locations given in this data product are these adjusted locations that are referenced to coincident TLS data. Plots are oriented so that plot sides run north-south and east-west. Locations of plots “PHP1Pre5_2018” and “PHP1Post5_2018” were not recorded. Hawley, C. M., Loudermilk, E. L., Rowell, E. M., & Pokswinski, S. (2018). A novel approach to fuel biomass sampling for 3D fuel characterization. MethodsX, 5, 1597-1604.
project Funding Award RC20-1346: Object-based aggregation of fuel structures, physics-based fire behavior and self-organizing smoke plumes for improved fuel, fire, and smoke management on military lands.
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temporal {"endTime": "2019-06-30", "startTime": "2018-04-01"}