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Matthew G. Gale, Geoffrey J. Cary, Albert I.J.M. van Dijk, Marta Yebra,
Untangling fuel, weather and management effects on fire severity: Insights from large-sample LiDAR remote sensing analysis of conditions preceding the 2019-20 Australian wildfires,
Journal of Environmental Management,
Volume 348,
2023,
119474,
ISSN 0301-4797, https://doi.org/10.1016/j.jenvman.2023.119474.
(https://www.sciencedirect.com/science/article/pii/S0301479723022624)
Abstract: Evaluation of fire severity reduction strategies requires the quantification of intervention outcomes and, more broadly, the extent to which fuel characteristics affect fire severity. However, investigations are currently limited by the availability of accurate data on fire severity predictors, particularly relating to fuel. Here, we used airborne LiDAR data collected before the 2019-20 Australian Black Summer fires to investigate the contribution of fuel structure to fire severity under a range of weather conditions. Fire severity was estimated using the Relative Burn Ratio calculated from Sentinel-2 optical remote sensing imagery. We modelled the effects of various fuel structure estimates and other environmental predictors using Random Forest models. In addition to variables estimated at each observation point, we investigated the influence of surrounding landscape characteristics using an innovative method to estimate fireline progression direction. Our models explained 63–76% of fire severity variance using parsimonious predictor sets. Fuel cover in the understorey and canopy, and vertical vegetation heterogeneity, were positively associated with fire severity. Up-fire burnt area and recent planned and unplanned fire reduced fire severity, whereby unplanned fire provided a longer-lasting reduction of fire severity (up to 15 years) than planned fire (up to 10 years). Although fuel structure and land management effects were important predictors, weather and canopy height effects were dominant. By mapping continuous interactions between weather and fuel-related variables, we found strong evidence of diminishing fuel effects below 20–40% relative air humidity. While our findings suggest that land management interventions can provide meaningful fire severity reduction, they also highlight the risk of warmer and drier future climates constraining these advantages.
Keywords: Airborne LiDAR; Fire severity; Fire behaviour; Remote sensing; Eucalypt forest; Fuel structure
The text was updated successfully, but these errors were encountered:
…ies v3.0 (#1209)
* Updates to DEA Waterbodies notebooks and tools for v3.0 (#1199)
* start editing, change modified date
* update v2 to v3
* update examples to v3
* add new image to DEA Waterbodies notebooks
* rerun prior to PR
* add @whatnick 's version support for v2 and v3
* change heading size for disclaimer text
* include back compatibility in waterbodies.py script and pt and propagate to waterbodies notebooks
* add header image and move disclaimer to info box
* update links to knowledge hub
* check knowledgehub links'
* rollback to develop to fix merge conflict
* reapply changes, and update knowledge hub links and GitHub references
* having to roll back again because of merge conflicts
* DEA Waterbodies notebook for v3, closes issue #1200 (#1207)
* add citation to usage.rst (#1208)
* add citation
closes#1179
* Update USAGE.rst
Co-authored-by: Robbi Bishop-Taylor <Robbi.BishopTaylor@ga.gov.au>
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Co-authored-by: Robbi Bishop-Taylor <Robbi.BishopTaylor@ga.gov.au>
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Co-authored-by: Robbi Bishop-Taylor <Robbi.BishopTaylor@ga.gov.au>
https://www.sciencedirect.com/science/article/pii/S0301479723022624
Matthew G. Gale, Geoffrey J. Cary, Albert I.J.M. van Dijk, Marta Yebra,
Untangling fuel, weather and management effects on fire severity: Insights from large-sample LiDAR remote sensing analysis of conditions preceding the 2019-20 Australian wildfires,
Journal of Environmental Management,
Volume 348,
2023,
119474,
ISSN 0301-4797,
https://doi.org/10.1016/j.jenvman.2023.119474.
(https://www.sciencedirect.com/science/article/pii/S0301479723022624)
Abstract: Evaluation of fire severity reduction strategies requires the quantification of intervention outcomes and, more broadly, the extent to which fuel characteristics affect fire severity. However, investigations are currently limited by the availability of accurate data on fire severity predictors, particularly relating to fuel. Here, we used airborne LiDAR data collected before the 2019-20 Australian Black Summer fires to investigate the contribution of fuel structure to fire severity under a range of weather conditions. Fire severity was estimated using the Relative Burn Ratio calculated from Sentinel-2 optical remote sensing imagery. We modelled the effects of various fuel structure estimates and other environmental predictors using Random Forest models. In addition to variables estimated at each observation point, we investigated the influence of surrounding landscape characteristics using an innovative method to estimate fireline progression direction. Our models explained 63–76% of fire severity variance using parsimonious predictor sets. Fuel cover in the understorey and canopy, and vertical vegetation heterogeneity, were positively associated with fire severity. Up-fire burnt area and recent planned and unplanned fire reduced fire severity, whereby unplanned fire provided a longer-lasting reduction of fire severity (up to 15 years) than planned fire (up to 10 years). Although fuel structure and land management effects were important predictors, weather and canopy height effects were dominant. By mapping continuous interactions between weather and fuel-related variables, we found strong evidence of diminishing fuel effects below 20–40% relative air humidity. While our findings suggest that land management interventions can provide meaningful fire severity reduction, they also highlight the risk of warmer and drier future climates constraining these advantages.
Keywords: Airborne LiDAR; Fire severity; Fire behaviour; Remote sensing; Eucalypt forest; Fuel structure
The text was updated successfully, but these errors were encountered: