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

Authors: Bright, Benjamin; Hudak, Andrew; Sánchez-López, Nuria; Hawley, Christie; Loudermilk, Louise; Rowell, Eric; O'Brien, Joseph; +2 Authors

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

Abstract

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.

Keywords

Georgia, biomass, weight, raster map, field observations, woodland, forest, destructive sampling, vegetation, understory, TLS, surface, RIEGL, terrestrial laser scanning, structure, three-dimensional point cloud, predicted biomass density, fuel, prescribed fire, lidar, pine, Pebble Hill Plantation, point cloud

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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Energy Research