Terrestrial Laser Scan Metrics Predict Surface Vegetation Biomass and Consumption in a Frequently Burned Southeastern U.S. Ecosystem

Abstract

Fire-prone landscapes found throughout the world are increasingly managed with prescribed fire for a variety of objectives. These frequent low-intensity fires directly impact lower forest strata, and thus estimating surface fuels or understory vegetation is essential for planning, evaluating, and monitoring management strategies and studying fire behavior and effects. Traditional fuel estimation methods can be applied to stand-level and canopy fuel loading; however, local-scale understory biomass remains challenging because of complex within-stand heterogeneity and fast recovery post-fire. Previous studies have demonstrated how single location terrestrial laser scanning (TLS) can be used to estimate plot-level vegetation characteristics and the impacts of prescribed fire. To build upon this methodology, co-located single TLS scans and physical biomass measurements were used to generate linear models for predicting understory vegetation and fuel biomass, as well as consumption by fire in a southeastern U.S. pineland. A variable selection method was used to select the six most important TLS-derived structural metrics for each linear model, where the model fit ranged in R2 from 0.61 to 0.74. This study highlights prospects for efficiently estimating vegetation and fuel characteristics that are relevant to prescribed burning via the integration of a single-scan TLS method that is adaptable by managers and relevant for coupled fire–atmosphere models.

Terrestrial 3D Laser Scanning for Ecosystem and Fire Effects Monitoring

“…The unit has an area of coverage that is 360° horizontally and 300° vertically, a range of accuracy of 4-mm at 10-m distance and 7-mm at 20-m distance, and a maximum range distance of 60 m (Table 1, Figure 2). The unit has multiple sampling density settings to control the number of points collected, which can help manage data collection speed and file size (Loudermilk and others 2023). For this monitoring protocol, the medium-density setting is required for compatibility with the automated analysis pipeline, which results in a scan time of less than 5 minutes.…”

“…In less than 5 minutes, the laser scans and collects a dense 3D point cloud (360,000 points per second) with a usable scan radius of 10–15 m. This monitoring protocol sets a ‘usable’ scan radius because of occlusion; while the scanner can capture data up to a 60-m distance, objects inherent to a forested environment (e.g., tree boles, foliage, debris) can occlude or block the scanner from capturing objects behind them, resulting in a shorter distance in which quality data can be captured. Experience and research show that a 10-to 15-m radius is ideal for plot monitoring purposes (Gallagher and others 2021, Loudermilk and others 2023, Pokswinski and others 2021). Despite this ‘cropping’ of the point-cloud, the output provides a significant amount of data.…”

“…Tier 3 sampling is performed as needed, or with specific objectives in mind; for instance, to measure surface fuel mass in clip plots (e.g., Loudermilk and others 2023), burn severity (e.g., Gallagher and others 2021), or woodland/rangeland woody cover via line intercept methods (e.g., Herrick and others 2005) to relate to the TLS output metrics. Other examples include additional tree measurements, biodiversity sampling, destructive harvesting, or qualitative information such as habitat quality.…”

“…As metrics are updated, previously collected TLS scans and field data can be reanalyzed for metric and data updates and consistency. Details on processing and early metric development are found in Gallagher and others (2021), Maxwell and others (2023), and Loudermilk and others (2023).…”

“…Loudermilk and others (2023) demonstrated TLS capabilities for predicting understory vegetation and fuel biomass as well as consumption by fire in frequently burned Southeastern United States’ pinelands. They describe 162 of the TLS metrics in detail, and illustrate how various fuel mass classes, such as fine fuels, fine woody debris, or simply total surface biomass pre- and post-burn, can be predicted using these TLS metrics.…”