Decomposing the Interactions between Fire Severity and Canopy Fuel Structure Using Multi-Temporal, Active, and Passive Remote Sensing Approaches

Abstract

Within the realms of both wildland and prescribed fire, an understanding of how fire severity and forest structure interact is critical for improving fuels treatment effectiveness, quantifying the ramifications of wildfires, and improving fire behavior modeling. We integrated high resolution estimates of fire severity with multi-temporal airborne laser scanning data to examine the role that various fuel loading, canopy shape, and other variables had on predicting fire severity for a complex of prescribed fires and one wildfire and how three-dimensional fuels changed as a result of these fires. Fuel loading characteristics were widely variable, and fires were ignited using a several techniques (heading, flanking, and backing), leading to a large amount of variability in fire behavior and subsequent fire effects. Through our analysis, we found that fire severity was linked explicitly to pre-fire fuel loading and structure, particularly in the three-dimensional distribution of fuels. Fire severity was also correlated with post-fire fuel loading, forest structural heterogeneity, and shifted the diversity and abundance of canopy classes within the landscape. This work demonstrates that the vertical distribution of fuel is an important factor and that subtle difference has defined effects on fire behavior and severity.

High-Resolution Estimates of Fire Severity—An Evaluation of UAS Image and LiDAR Mapping Approaches on a Sedgeland Forest Boundary in Tasmania, Australia

“…It was hypothesised that the improved vegetation representation from LiDAR would mean that predictor variables describing height or layer count differences between pre-fire and post-fire would be used in the prediction of severity, particularly in areas of forest. This would support Hu et al [43], Hoe et al [44] and Skowronski et al [46], who demonstrated the effectiveness of describing changes to structural characteristics such as profile area and LiDAR return proportions 2 m above ground, pre-fire 95% heights and pre-fire return proportions 2 m above ground. However, the structural variables generated in this research showed only a small change between pre-and post-fire (Table 14).…”

“…Furthermore, the area was captured both pre-and post-fire allowing for a two stage classification: firstly classifying land cover and secondly classifying the severity within each land cover type, providing a testbed to explore the changes resulting from fire. Prior work from fixed wing and satellite remote sensing platforms have demonstrated the utility of imagery and supervised classifications to estimate fire severity across an area [43,44,46,[68][69][70]79]. McKenna et al [24], Simpson et al [26] and Carvajal-Ramírez et al [27] demonstrated the utility of UAS SfM image-derived variables from pre-and post-fire point clouds to map fire severity at local scales across areas with limited structural diversity (open grassland, woodland and peatland).…”

“…Limited studies have investigated the link between multi-temporal vegetation structural characteristics and assessing fire severity from wildfire [43][44][45][46]. Prior research has shown the utility of UAS point clouds to measure disturbance, and UAS imagery to measure fire severity [24][25][26][27][28][29].…”

Development of a Field Deployable Firebrand Flux and Condition Measurement System

“…), and huckleberry (Gaylussacia spp.). For a detailed description of the species assemblages of the New Jersey Pine Barrens [20,21] and examples of experimental fire behaviour and firebrand production, see [8] and [22]. This section first discusses the measured fire behaviour to provide context for the firebrand measurements.…”

Integrating Remote Sensing Methods and Fire Simulation Models to Estimate Fire Hazard in a South-East Mediterranean Protected Area

“…Remote sensing data and methods are also widely employed for post-fire mapping of the burned area, as well as for the post-fire characterization of wildfires in terms of fire severity [41][42][43]. Furthermore, the integration of data acquired by active sensors allows for the mapping of the vertical characteristics of the complex forest structure and can assist the process of characterizing and mapping forest fuels and post-fire damage assessment [44].…”