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Comparing Remote Sensing and Field-Based Approaches to Estimate Ladder Fuels and Predict Wildfire Burn Severity

Forbes, B; Reilly, S; Clark, M; Ferrell, R; Kelly, A; Krause, P; Matley, C; ... Bentley, LP; + view all (2022) Comparing Remote Sensing and Field-Based Approaches to Estimate Ladder Fuels and Predict Wildfire Burn Severity. Frontiers in Forests and Global Change , 5 , Article 818713. 10.3389/ffgc.2022.818713. Green open access

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Abstract

While fire is an important ecological process, wildfire size and severity have increased as a result of climate change, historical fire suppression, and lack of adequate fuels management. Ladder fuels, which bridge the gap between the surface and canopy leading to more severe canopy fires, can inform management to reduce wildfire risk. Here, we compared remote sensing and field-based approaches to estimate ladder fuel density. We also determined if densities from different approaches could predict wildfire burn severity (Landsat-based Relativized delta Normalized Burn Ratio; RdNBR). Ladder fuel densities at 1-m strata and 4-m bins (1–4 m and 1–8 m) were collected remotely using a terrestrial laser scanner (TLS), a handheld-mobile laser scanner (HMLS), an unoccupied aerial system (UAS) with a multispectral camera and Structure from Motion (SfM) processing (UAS-SfM), and an airborne laser scanner (ALS) in 35 plots in oak woodlands in Sonoma County, California, United States prior to natural wildfires. Ladder fuels were also measured in the same plots using a photo banner. Linear relationships among ladder fuel densities estimated at broad strata (1–4 m, 1–8 m) were evaluated using Pearson’s correlation (r). From 1 to 4 m, most densities were significantly correlated across approaches. From 1 to 8 m, TLS densities were significantly correlated with HMLS, UAS-SfM and ALS densities and UAS-SfM and HMLS densities were moderately correlated with ALS densities. Including field-measured plot-level canopy base height (CBH) improved most correlations at medium and high CBH, especially those including UAS-SfM data. The most significant generalized linear model to predict RdNBR included interactions between CBH and ladder fuel densities at specific 1-m stratum collected using TLS, ALS, and HMLS approaches (R2 = 0.67, 0.66, and 0.44, respectively). Results imply that remote sensing approaches for ladder fuel density can be used interchangeably in oak woodlands, except UAS-SfM combined with the photo banner. Additionally, TLS, HMLS and ALS approaches can be used with CBH from 1 to 8 m to predict RdNBR. Future work should investigate how ladder fuel densities using our techniques can be validated with destructive sampling and incorporated into predictive models of wildfire severity and fire behavior at varying spatial scales.

Type: Article
Title: Comparing Remote Sensing and Field-Based Approaches to Estimate Ladder Fuels and Predict Wildfire Burn Severity
Open access status: An open access version is available from UCL Discovery
DOI: 10.3389/ffgc.2022.818713
Publisher version: https://doi.org/10.3389/ffgc.2022.818713
Language: English
Additional information: © 2022 Forbes, Reilly, Clark, Ferrell, Kelly, Krause, Matley, O’Neil, Villasenor, Disney, Wilkes and Bentley. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Keywords: ladder fuels, terrestrial laser scanner (TLS), handheld-mobile laser scanner (HMLS), unoccupied aerial system (UAS), airborne laser scanner (ALS), Structure from Motion (SfM), wildfire burn severity
UCL classification: UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Geography
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10148142
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