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High-severity wildfires in temperate Australian forests have increased in extent and aggregation in recent decades


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Wildfires have increased in size and frequency in recent decades in many biomes, but have they also become more severe? This question remains under-examined despite fire severity being a critical aspect of fire regimes that indicates fire impacts on ecosystem attributes and associated post-fire recovery. We conducted a retrospective analysis of wildfires larger than 1000 ha in southeastern Australia to examine the extent and spatial pattern of high-severity burned areas between 1987 and 2017. High-severity maps were generated from Landsat remote sensing imagery. Total and proportional high-severity burned area increased through time. The number of high-severity patches per year remained unchanged but variability in patch size increased, and patches became more aggregated and more irregular in shape. Our results confirm that wildfires in southern Australia have become more severe. This shift in fire regime may have critical consequences for ecosystem dynamics, as fire-adapted temperate forests are more likely to be burned at high severities relative to historical ranges, a trend that seems set to continue under projections of a hotter, drier climate in southeastern Australia.
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High-severity wildfires in temperate
Australian forests have increased in extent
and aggregation in recent decades
Bang Nguyen TranID
*, Mihai A. Tanase
, Lauren T. Bennett
, Cristina Aponte
1School of Ecosystem and Forest Sciences, University of Melbourne, Richmond, Victoria, Australia,
2Faculty of Environment, Vietnam National University of Agriculture, Trauquy, Gialam, Hanoi, Vietnam,
3Department of Geology, Geography and Environment, University of Alcala, Alcala de Henares, Spain,
4School of Ecosystem and Forest Sciences, The University of Melbourne, Creswick, Victoria, Australia,
5National Institute for Research and Development in Forestry “Marin Dracea”, Voluntari, Ilfov, Romania
Wildfires have increased in size and frequency in recent decades in many biomes, but have
they also become more severe? This question remains under-examined despite fire severity
being a critical aspect of fire regimes that indicates fire impacts on ecosystem attributes and
associated post-fire recovery. We conducted a retrospective analysis of wildfires larger than
1000 ha in south-eastern Australia to examine the extent and spatial pattern of high-severity
burned areas between 1987 and 2017. High-severity maps were generated from Landsat
remote sensing imagery. Total and proportional high-severity burned area increased
through time. The number of high-severity patches per year remained unchanged but vari-
ability in patch size increased, and patches became more aggregated and more irregular in
shape. Our results confirm that wildfires in southern Australia have become more severe.
This shift in fire regime may have critical consequences for ecosystem dynamics, as fire-
adapted temperate forests are more likely to be burned at high severities relative to historical
ranges, a trend that seems set to continue under projections of a hotter, drier climate in
south-eastern Australia.
Wildfire shapes landscape patterns and ecosystem processes as it determines both vegetation
distribution and structure [1,2]. Changes in wildfire activity may alter mortality and regenera-
tion patterns, initiating new successional pathways that ultimately lead to shifts in vegetation
composition and landscape attributes [3]. Many studies over the past decades have reported a
change in wildfire activity including increases in the frequency, size, and duration of wildfires,
as well as the length of the fire season [48]. Such increases have been linked to climate change,
which influences key fire drivers like fuel accumulation and availability [911]. Models based
on climate change projections suggest that this trend in increasing fire activity will continue
into the future [3,1215] posing threats to forest resilience, including shifts to lower density
forests or non-forest states [1618].
PLOS ONE | November 18, 2020 1 / 17
Citation: Tran BN, Tanase MA, Bennett LT, Aponte
C (2020) High-severity wildfires in temperate
Australian forests have increased in extent and
aggregation in recent decades. PLoS ONE 15(11):
Editor: Krishna Prasad Vadrevu, University of
Maryland at College Park, UNITED STATES
Received: April 18, 2020
Accepted: November 3, 2020
Published: November 18, 2020
Peer Review History: PLOS recognizes the
benefits of transparency in the peer review
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Copyright: ©2020 Tran et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the manuscript and its Supporting
Information files
Fire severity is a wildfire attribute that quantifies the degree of environmental change
caused by fire including immediate fuel consumption and carbon emissions and longer-term
impacts on vegetation mortality, successional pathways, and soil substrate [19]. Wildfire sever-
ity is spatially heterogeneous and can range from partial litter consumption and light scorching
of understorey vegetation to near complete mortality of canopy trees [1921]. Fire severity and
the spatial configuration of severity classes have critical implications for fire-related resilience
and potential degradation of ecosystems [2125]. Wildfire severity is related to fire intensity,
which is driven by fuel, climate, and weather [2629]. As such, fire severity, as for other com-
ponents of fire regimes, has likely been affected by changing climates in recent decades [30,
31]. In contrast to the large number of studies that have documented recent increases in wild-
fire area and frequency [9,3234], comparatively fewer studies, mostly focused on North
America forests, have investigated trends in fire severity, some indicating increases while oth-
ers indicating no change or decreases [3537]. Changes in wildfire severity can influence eco-
logical processes by affecting the trajectory of postfire vegetation succession, leading to
reductions in forest cover and even conversions to non-forested vegetation [38,39]. A better
understanding of changes in fire severity is crucial to foresee the future pathways of forest sys-
tems [4044].
Australia is one of the most fire-prone countries worldwide [45,46] with 30.4 million hect-
ares burned across Australia in 2019–2020 alone [47]. Studies have highlighted how climate
change has and will continue to impact Australian fire weather and fire activity [31,48,49]
with fires predicted to become larger and more frequent [5052]. Whether fires have also
become more severe remains largely undocumented. This study’s principal objective was to
examine patterns in high-severity fires in temperate forests of the state of Victoria, south-east-
ern Australia over the last three decades. Specifically, we addressed three questions: 1) Has the
area burnt by high- severity fire in temperate forests of Victoria increased in the last 30 years?;
2) Has the spatial configuration of high-severity patches in the landscape changed in the last
30 years; and 3) Are the observed trends consistent across bioclimatic regions?
Materials and methods
Study area and forest types
This study was conducted across the state of Victoria, south-eastern Australia, an area that
encompasses 237,659 km
, ranges from 0 to 1986 m a.s.l in elevation and comprises several
geographical bioregions with differing geology, soils, climate, and predominant vegetation
(Table 1 and Fig 1) [53]. Climate across Victoria is temperate with warm to hot summers
(average maximum temperature between 16˚C and 30˚C; [54]). The annual mean temperature
ranges from 12.6˚C in the south-east region to 14.7˚C in the north and north-west regions of
the state [55]. The mean annual precipitation varies from 500 to 2,200 mm, with precipitation
over 1000 mm in the mountainous areas of the Great Dividing Range [56]. Over the past few
decades, Victoria has become warmer and drier, consistent with global trends, and these
trends are likely to continue [5759].
Vegetation affected by the studied wildfires was predominantly comprised of a range of
Eucalyptus forests of varying composition, structure and post-fire regeneration strategies [60]
(Table 1). These included Mallee, with low canopy height (7 m) and sparse canopy cover
(25%), Woodlands with medium canopy height (15 m) and sparse canopy cover, Open forests,
with medium to tall canopy height (10–30 m) and mid-dense canopy cover (30–70%) and
Closed forests, with tall canopy height (30 m) and dense canopy cover (70–100%) [61]. Obli-
gate seeder tree species are dominant in Closed forest whereas resprouter eucalypts (basal or
epicormic) are dominant in all other forest types [60,62,63].
Spatial pattern changes of high-severity wildfires in temperate Australian forests
PLOS ONE | November 18, 2020 2 / 17
Funding: The authors would like to acknowledge
the financial support of the Melbourne Research
Scholarship program, the Vietnam International
Education Cooperation Department (VIED)
scholarship, and the Integrated Forest Ecosystem
Research program, supported by the Victorian
Department of Environment, Land, Water and
Planning. The funders had no role in study design,
data collection and analysis, decision to publish, or
preparation of the manuscript.
Competing interests: The authors have declared
that no competing interests exist
Fire history dataset
We used the wildfire history data available from the Victorian Department of Environment,
Land, Water & Planning (‘DELWP’; [64]). Data contained the spatial extent of wildfires since
1926 and, for the most recent fires (from 1998 onward), the start date of the fire. For this study
we selected the subset of wildfires that occurred between 1987 and 2017 and that had a mini-
mum burned area of 1000 ha to ensure the fire size was sufficient to include multiple fire-
severity levels. That amounted to 211 wildfires that were used to assess changes in the number
of fires per year and mean fire size between 1987 and 2017. Each fire was classified according
to its dominant bioregion [53]. For the purpose of assessing changes in fire severity, 32 of the
211 wildfires were discarded because pre- or post-fire remote sensing images were unavailable,
and 11 were discarded because clouds covered more than 25% of the fire affected area, which
may affect the spatial metrics assessed in our study. In total, a subset of 162 wildfires, with at
least two fires per year over the past three decades, was used to generate fire-severity maps and
analyse changes in severity patterns.
Remote sensing dataset and spectral indices
Wildfire severity of the selected 162 fires was mapped using Landsat TM, ETM+ and Landsat 8
imagery (30 m spatial resolution, all from Landsat Collection 1, Tier 1). Pre- and post-fire
Table 1. Characteristics of the bioregions in the study area affected by the selected 162 fires.
Bioregion Major forest
Foliage Cover
No of
Total burnt
area (ha)
Total high-
severity burnt
area (ha)
AA Australian
High Altitude
15 10–30 R 844–1996 4.5–12.6 712–1996 9 1,426,791 290,073
15 10–30 R
MDD Murray
Lowan Mallee 7 10–30 R 265–690 12.8–17.2 265–702 52 514,689 358,238
15 10–30 R
SCP South East
Coastal Plain
15 10–30 R 492–1260 11.4–14.9 494–1306 10 40,375 8,745
SEC South East
Moist Forest 30 70–100 S 664–1184 7.3–15.2 656–1292 17 170,045 18,700
15 10–30 R
SEH South Eastern
Dry Forest
10–30 10–30 R 681–1922 6.6–14.8 645–1942 17 995,133 170,452
Moist Forest 30 70–100 S
VM Victorian
Forby Forest 15–30 30–70 R 418–1411 8.5–15.3 418–1490 46 404,363 156,083
VVP Victorian
Volcanic Plain
Moist Forest 30 70–100 S 477–1026 11–14.9 476–1026 11 165,003 79,022
Bioregion name and acronym [53], major forest types in each bioregion affected by the selected wildfires, height, projective foliage cover and regeneration strategy of the
dominant species in each forest type, elevation range, mean annual temperature (MAT) and annual precipitation (MAP) range [65]; Number of wildfires included in
this study (i.e. 162 wildfires greater than 1000 ha, occurred between 1987 and 2017 and with available Landsat imagery) and their cumulative total [64] and high-severity
burnt area (as estimated in this study).
Major forest types were adopted from EVD names and associated structural data [66]. Dominant tree species were derived from the Ecological Vegetation Classes
(EVC) benchmarks database [67];
R: resprouter; S: obligate seeder, classifications based on predominant fire-response traits of dominant tree species [62,68,69].
Spatial pattern changes of high-severity wildfires in temperate Australian forests
PLOS ONE | November 18, 2020 3 / 17
images were selected for each wildfire based on the recorded fire start dates, which were pre-
dominantly in the summer months (December to February). Images were selected within two
months before and after the fire to minimise differences in forest phenology and general atmo-
spheric conditions at the time of acquisition. When only the fire year but not start date was
recorded (~13% of the fires), we conducted a visual inspection of all images available for the
fire season, identified the image where the fire scar was first visible and selected that image and
the previous one as post- and pre-fire images respectively for that event. A total of 347 Landsat
images including 228 scenes of Landsat 5 (TM), 36 scenes of Landsat 7 (ETM+), and 83 scenes
of Landsat 8 (OLI/TIRS) were selected and obtained through the US Geological Survey
(USGS) EarthExplorer at as higher level surface reflectance prod-
ucts for each fire. The images were masked for clouds and shadows using the Fmask algorithm
[70], which has an accuracy of about 96% [71].
Four spectral indices, namely NBR, NDVI, NDWI, and MSAVI, and their temporal dif-
ferences (i.e. delta versions, which calculate the change between pre-fire and post-fire spec-
tral index values) were computed for each of the 162 wildfires. These indices are
commonly used to assess fire severity [7276] and were identified by the authors, in a pre-
vious study, as the optimal spectral indices for mapping fire severity in the forest types of
the study area [77].
Fig 1. Map of study area. (i) Victoria highlighted (grey) in the map of Australia; (ii) Locations of study areas within the state of Victoria in south-eastern
Australia. Red points rrepresent the centroids of the 162 wildfires investigated in this study. Colours relate to bioregions (Acronyms are defined in Table 1).
Spatial pattern changes of high-severity wildfires in temperate Australian forests
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Fire severity mapping
Severity of the wildfires in Victoria has not been consistently recorded, with historic fire sever-
ity mapping only available for nine years in the period between 1998 and 2014 [78]. To gener-
ate fire severity maps for the 162 selected wildfires ensuring the consistency of the
classification we used a Random Forest model based on spectral indices that had been previ-
ously trained and validated by the authors for the same study area [61]. The reference fire-
severity dataset used for training and validation was comprised of 3730 plots from eight large
wildfires (>5,000 ha) that occurred between 1998 and 2009 and covered 13 forest types differ-
ing in species composition, canopy cover, canopy height and regeneration strategy. These for-
est types match those affected by the 162 wildfires of this study. Fire severity of the 3730
reference plots had been assessed in situ or visually interpreted on very high resolution ortho-
photos by the Department of Environment, Water & Planning (DELWP) [78]. Severity was
classified as Unburnt: less than 1% of eucalypt and non-eucalypt crowns scorched; Low sever-
ity: light scorch of 1–35% of eucalypt and non-eucalypt crowns; Moderate severity: 30–65% of
eucalypt and non-eucalypt crowns scorched; or High severity: 70–100% of eucalypt and non-
eucalypt crowns burnt [79]. Overall, the reference data included a minimum of 20 plots for
each forest type and fire-severity class combination. The Random Forest model was trained
with 60% of the data and used 12 predictor variables, which included the four optimal SI indi-
ces (dNBR, dNDVI, dNDWI, and dMSAVI) and their pre- and post- fire values. Model accu-
racy was tested on the remaining 40% of the data that had been left for model validation.
Accuracy for high-severity mapping was very high, with a commission error (plots wrongly
attributed to high severity) of 0.06 and an omission error (high severity plots incorrectly classi-
fied) of 0.18.
Metrics of high-severity fire
Based on the high-severity maps of each of the 162 wildfires, we calculated eight landscape
metrics to characterize the extent and spatial configuration of the high-severity burned area.
Extent metrics included total and proportional high-severity burned area. Spatial configura-
tion metrics were calculated at the patch level, i.e. areas of high-severity fire surrounded by dif-
ferent severities within the wildfire boundary. Spatial configuration metrics included two
patch size metrics (mean patch size, coefficient of variation of patch size), two fragmentation
metrics (number of patches, and edge density—a measure of shape complexity) and two aggre-
gation metrics (clumpiness and normalized landscape shape index–NLSI, S1 Table of S1 File).
Edge density is the ratio between the total length (m) of the edges of the high-severity patches
and the fire size (i.e. total wildfire area burnt at any severity; ha). Low edge density values rep-
resent simple shape (e.g. circular) and/or large patches, while large values indicate irregular
and/or less continuous patches [80]. Clumpiness and NLSI, both unitless, quantify patch
aggregation. The former is based on the likelihood of adjacent pixels belonging to the same
class, whereas the later measures the deviation from the hypothetical minimum edge length of
the class. Increasing levels of aggregation (i.e. increasing clumsiness and decreasing NLSI) rep-
resent more compact and simpler-shaped patches [80,81]. These metrics describe different
aspects of landscape configuration but were not completely independent and therefore should
be interpreted jointly (S1 Table of S1 File). Spatial pattern metrics were obtained using the
‘landscapemetric’ package [82] in the R statistical software [83].
Data analysis
Linear regression models were used to evaluate the trends in high-severity fire metrics from
1987 to 2017, with individual fires as the sampling unit. We built two groups of models, a
Spatial pattern changes of high-severity wildfires in temperate Australian forests
PLOS ONE | November 18, 2020 5 / 17
state-wide model (n = 162 fires) and separate bioregion models. The response variables for
both groups of models were the extent or landscape configuration metrics of the high-severity
burned area. Predictor variables included year and fire size (i.e. total wildfire area, ha) as fixed
effects and bioregion as a random effect, which was only included in the state-wide mixed
effects models. Fire size was included as covariate in all models as it can be related to burn pat-
terns [27] and was not correlated with fire year (Pearson’s r = -0.01). Data were transformed
when needed to meet assumptions of normality (S1 Table of S1 File). All statistical tests were
conducted in the statistical programming language R [83].
Changes in area and proportion of high-severity fire over time
Based on the fire history dataset (n = 211), the number of wildfires per year larger than 1000
ha between 1987 and 2017 increased significantly (P= 0.012), a trend that was mostly due to
an increase since 2000 (Fig 2). In contrast, we detected no significant change in total fire size
(i.e. all fire severities combined) over that period.
Between 1987 and 2017 the area burnt by high-severity fire increased significantly (P
<0.001) even when accounting for total fire size (P
Fire size
<0.001; Fig 3 and S1 Fig of S1
File). The same trend was observed for the proportion of the area burnt by high-severity fire
<0.001; Fig 3). Estimated changes in the area and the proportion of area burnt by
high-severity fire over time by bioregions were positive and significant (or marginally sig-
nificant 0.05 <P<0.1) in all cases (Fig 3 and S2-S3 Figs of S1 File). The studied bioregions
supported quite distinct forest types, from wet, tall, and highly productive to dry, open, and
less productive. This suggests that the observed increases in the area burnt by high-severity
fire was ubiquitous across regions and did not depend on local environmental conditions or
forest types.
Fig 2. Changes in the number of fires per year and fire size between 1987 and 2017. Data includes all wildfires 1000 ha from DEWLP fire history dataset
(n = 211) [64]. Solid black line indicates significant relationship (P<0.05), dashed grey line indicates no significant relationship.
Spatial pattern changes of high-severity wildfires in temperate Australian forests
PLOS ONE | November 18, 2020 6 / 17
Changes in spatial patterns of high-severity fire
We detected no changes in fragmentation of wildfires between 1987 and 2017 as evidenced by no
significant increases in the number of high-severity patches, a result that was consistent across all
bioregions (Figs 4and 5and S4 Fig of S1 File). In contrast, edge density, which is related to patch
shape complexity, increased over time across Victoria (P
= 0.006), although this trend was
only (marginally) significant for the SEC, VM, VVP bioregions (0.05 <P
<0.1; Fig 5 and S5
Fig 3. Changes in the area and proportional area of high-severity fire from 1987 to 2017. Left panels: Area and proportional area burnt by high-severity fire
in each of 162 wildfires (line represents significant relationship between variables). Right panels: Standardized coefficients for high-severity area (top, log
transformed) and the proportion high-severity area (bottom, arcsine transformed) indicating the relationship between area burnt and time. Each panel displays
results for a single model for all regions (“Victoria”) and for individual bioregions (Acronyms of bioregions are defined in Table 1); Dot points represent mean
estimated coefficient along with the 90
(solid line) and 95
(dashed line) percentile intervals. Coefficients denote significant changes when interval does not
include zero.
Spatial pattern changes of high-severity wildfires in temperate Australian forests
PLOS ONE | November 18, 2020 7 / 17
Fig of S1 File). While mean high-severity patch size did not change significantly, the coefficient of
variation of patch size, which was related to fire size, increased in all models (P
<0.05 and P
<0.001; Figs 4and 5and S6-S7 Figs of S1 File). Accordingly, we detected an increase in the size
of the largest patch (P
= 0.005; S8 and S9 Figs of S1 File). The level of patch aggregation mea-
sured through increased clumpiness and/or decreased Normalized Landscape Shape Index
(NLSI), also increased from 1987 to 2017 (Figs 4and 5and S10 and S11 Figs of S1 File). This
trend, which was significant both at the state and bioregion level, suggests the patterns in high-
severity fire changed from a more random, highly-dispersed distribution of patches towards
fewer, larger patches of irregular shape that were more aggregated within the fire boundaries.
Our study assessed for the first-time changes in high-fire severity patterns since 1987 in Victo-
ria, south-eastern Australia. We detected an increase in the area burnt at high-severity during
that period and a shift in the landscape configuration of high-severity patches, which was con-
sistent across most bioregions, encompassing a broad range of forest types.
The area of high-severity fire has increased
Our results showed an increasing trend in both total and proportion of high-severity burned
area between 1987 and 2017 across various temperate forests types in south-eastern Australia.
Fig 4. Changes in high-severity spatial metrics over time. Each subplot displays a scatterplot between the Year of the fire and the defined high-severity spatial
metric. Dots represent each of the 162 wildfires. Values are the results for single mixed effects models where Year and Fire size are fixed effects and Bioregion is
a random effect. Lines represent significant (solid black) or not significant (dashed grey) linear relationships.
Spatial pattern changes of high-severity wildfires in temperate Australian forests
PLOS ONE | November 18, 2020 8 / 17
Fig 5. Estimated coefficients for high-severity spatial metrics by bioregions. Each panel displays results for a single
model for all regions (“Victoria”) and for individual bioregions (Acronyms of bioregions are defined in Table 1); Dot
points represent mean estimated coefficient along with the 90th (solid line) and 95th (dashed line) percentile intervals.
Coefficients denote significant changes when interval does not include zero. Spatial metrics were log transformed
(Number of Patches, Mean Patch Area, Variation Patch Area, NLSI) or arcsine transformed (Edge Density).
Spatial pattern changes of high-severity wildfires in temperate Australian forests
PLOS ONE | November 18, 2020 9 / 17
Our findings are in contrast to similar studies conducted in the US where either an increase in
fire severity was not detected [37,84] or the detected increase was due to increasing fire size
[36]. Our results also show a covariation between fire size and the extent of the area burned by
high-severity fire, a pattern that has been documented before in several north American forests
The increasing trends in total and proportion of high-severity burned area at the state level
were consistent across all bioregions, indicating that these changes occurred irrespective of
forest type and climatic region. This is in contrast to the mixed fire-severity trends assessed
across regions in North America [37,88], which have been argued to be related to fire suppres-
sion policies masking climate-change effects [84,88].
Changes in the area of high-severity fire like those described here have been predicted to
occur as a result of climate change since decades ago [8991]. Our results confirm for the first
time that wildfires in south-east Australia are indeed becoming more severe and, given projec-
tions of a hotter, drier climate [59], this pattern seems set to continue in coming decades.
Trends in landscape configuration: Aggregation of high-severity patches
Our results showed changes in the landscape configuration of high-severity patches that were
consistent at the state level and across bioregions. While we did not detect a significant shift in
patch number or mean patch size, we noted an increase in patch size variability, patch shape
complexity (measured as edge density) and patch aggregation (as evidenced by trends in clum-
piness and NLSI). These changes suggest that the areas burned by high-severity fire have
become more aggregated, more irregular in shape, and have a larger area occupied by the larg-
est patch. Similar changes in spatial patterns of high-severity fire have also been reported in
fire-severity research in North America [27,88,92], where increasing patch aggregation was
related to the increased proportion of high-severity area [42].
Implications of increasing high-severity fire for temperate forests in south-
east Australia
Our quantified increases in high-severity burned area can lead to concerns about the resilience
of Victoria’s temperate forests [20,93,94], similar to those expressed for other forest types else-
where [4,92,95]. High-severity fire influences ecosystem dynamics with effects on vegetation
succession [25,96,97], biogeochemical processes [21,26,98], geomorphic processes [99,100],
and habitat availability and biodiversity [23,101,102]. Recent high-severity fires within our
study area have led to increased mortality of fire-tolerant eucalypt trees and to an increase in
the density of young trees vulnerable to subsequent fires [20,63,103]. If increasing trends in
the extent of high-severity fire detected in our study continue, this indicates potential for
large-scale changes in key structural attributes of even the most fire-tolerant forests.
High-severity fire impacts can be modulated by the size, shape, and configuration of high-
severity patches. For instance, patch size and aggregation can influence runoff connectivity
and post-fire sediment yields and affect the distribution of low- and moderate-severity patches
that serve as refuges for fire-sensitive species [104106]. Patch size and spatial configuration
can also affect dispersal and subsequently influence vegetation succession potentially leading
to forest-type conversions [107109]. Delays in tree re-establishment following high-severity
fires has been detected in non-serotinous forests of the United States and Canada due to a
rapid and extensive shrub establishment via persistent soil seedbanks [109,110]. Eucalypt for-
ests in south-eastern Australia, including those affected by the studied wildfires, are dominated
by either resprouter species that survive most fires, or obligate seeder species that rely on a can-
opy seedbank to regenerate after fire [63,111]. Seed dispersal in both resprouters and obligate
Spatial pattern changes of high-severity wildfires in temperate Australian forests
PLOS ONE | November 18, 2020 10 / 17
seeders eucalypt forests is limited to one or two tree heights, with seeds lacking attributes to
facilitate animal or wind dispersal [112]. Resprouters’ seed viability decreases with fire inten-
sity [113] and therefore regeneration in high-severity patches may depend on dispersal from
adjacent moderate-severity or unburned patches (although see [20] indicating prolific regener-
ation from seed of resprouter eucalypts after a single high-severity wildfire). Increases in high-
severity patch size though aggregation as observed in this study could hinder post-fire tree
establishment by increasing distances from seed source and also altering the regeneration abi-
otic environment [114] contributing to feedbacks that result in an increased risk of forest-type
conversion [115,116]. Spatial configuration of high-severity patches can also influence regen-
eration of obligate seeder forests burnt by recurrent fires in quick succession (~20 years;
[103]). In such circumstances, trees regenerating after the first fire would not have yet pro-
duced meaningful quantities of viable seed before a second fire [117], and eucalypt regenera-
tion would rely on seed dispersal from adjacent patches. Lack of tree regeneration after short-
interval fires in obligate seeder forests has been observed in the last decades with aerial sowing
being required to address post-fire recovery in obligate seeder forests [118]. This highlights the
impact that the observed changes in fire regimes have had on the resilience of eucalypt forests
in south-eastern Australia [63,103].
Changes in high-severity fire, its extent and spatial configuration, can alter a range of ecosys-
tem processes that interactively determine post-fire recovery, including the conversion to non-
forest alternative states. Our analysis showed an increase in both the total and proportion of
high-severity burned area in Victoria between 1987 and 2017. Over that period, high-severity
patches have become more aggregated and more irregular in shape. These trends were consis-
tent across bioregions encompassing a diversity of forest types. Shifts in the spatial patterns of
high-severity fire over time may have cascading effects on forest ecology, highlighting the
increased threat posed by changing fire regimes to forests ecosystems.
Supporting information
S1 File.
We acknowledge the support of many staff and students from the School of Ecosystem and
Forest Sciences at the University of Melbourne, including valuable comments and advice to
improve this manuscript.
Author Contributions
Conceptualization: Bang Nguyen Tran, Lauren T. Bennett, Cristina Aponte.
Data curation: Bang Nguyen Tran, Cristina Aponte.
Formal analysis: Bang Nguyen Tran.
Funding acquisition: Bang Nguyen Tran.
Investigation: Bang Nguyen Tran, Lauren T. Bennett, Cristina Aponte.
Methodology: Bang Nguyen Tran, Mihai A. Tanase, Cristina Aponte.
Project administration: Bang Nguyen Tran.
Spatial pattern changes of high-severity wildfires in temperate Australian forests
PLOS ONE | November 18, 2020 11 / 17
Resources: Bang Nguyen Tran, Lauren T. Bennett.
Software: Bang Nguyen Tran, Mihai A. Tanase.
Supervision: Mihai A. Tanase, Lauren T. Bennett, Cristina Aponte.
Validation: Bang Nguyen Tran.
Visualization: Bang Nguyen Tran, Lauren T. Bennett, Cristina Aponte.
Writing – original draft: Bang Nguyen Tran.
Writing – review & editing: Bang Nguyen Tran, Mihai A. Tanase, Lauren T. Bennett, Cristina
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... BS is closely linked to fire intensity and here is defined as the immediate degree of overall environmental change caused by a fire in ecosystems, including biomass loss, vegetation mortality, and biochemical and physical impacts on soil [15]. Moreover, BS determines the post-fire processes, playing a key role in the future ecosystem pathways [15][16][17][18], and its characterization is essential for the refinement of carbon emission models [5,15,19]. ...
... The evolution of BA and BS arouses great interest in the media and in the scientific community, which frequently warned about the increase or worsening of fire activity during recent years [7], often attributing that assumed trend to climate change (see examples in [17,[27][28][29]). In this sense, climate warming is expected to cause increases in fire weather danger in many regions [14], which is a driver of BA in a large proportion of Earth's land surface [13] and influences BS [24,25]. ...
... For instance, it is well documented a shift from low to high severity fire regimes in southwestern US forests, caused by the implementation of fire suppression policies after the European settlement [1], and extensive spatiotemporal studies have revealed a generalized increase in high-severity fires in some parts of Western US between 1984-2015 [21]. In Australia, an increase in the proportion of BA at high severity has been detected between 2013 and 2017 [17], and in Europe, an increased prevalence of extreme wildfire events attributed to climate change and human alterations of landscapes has been reported [29]. ...
Full-text available
It is a widespread assumption that burned area and severity are increasing worldwide due to climate change. This issue has motivated former analysis based on satellite imagery, revealing a decreasing trend in global burned areas. However, few studies have addressed burn severity trends, rarely relating them to climate variables, and none of them at the global scale. Within this context, we characterized the spatiotemporal patterns of burned area and severity by biomes and continents and we analyzed their relationships with climate over 17 years. African flooded and non-flooded grasslands and savannas were the most fire-prone biomes on Earth, whereas taiga and tundra exhibited the highest burn severity. Our temporal analysis updated the evidence of a decreasing trend in the global burned area (−1.50% year−1; p < 0.01) and revealed increases in the fraction of burned area affected by high severity (0.95% year−1; p < 0.05). Likewise, the regions with significant increases in mean burn severity, and burned areas at high severity outnumbered those with significant decreases. Among them, increases in severely burned areas in the temperate broadleaf and mixed forests of South America and tropical moist broadleaf forests of Australia were particularly intense. Although the spatial patterns of burned area and severity are clearly driven by climate, we did not find climate warming to increase burned area and burn severity over time, suggesting other factors as the primary drivers of current shifts in fire regimes at the planetary scale.
... Wildfires are among the most important agents of natural forest disturbances in regions such as Fenno-Scandia (Clear et al. 2014), Central Europe (Neumann et al. 2022), Northern Asia (Feurdean et al. 2020), Australia (Tran et al. 2020), and Pacific North America (Halofsky et al. 2020). The extent, frequency, and severity of these fires are increasing under global climate change (Zheng et al. 2021). ...
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Key message We propose a framework to derive the direct loss of aboveground carbon stocks after the 2020 wildfire in forests of the Chornobyl Exclusion Zone using optical and radar Sentinel satellite data. Carbon stocks were adequately predicted using stand-wise inventory data and local combustion factors where new field observations are impossible. Both the standalone Sentinel-1 backscatter delta (before and after fire) indicator and radar-based change model reliably predicted the associated carbon loss. Context The Chornobyl Exclusion Zone (CEZ) is a mosaic forest landscape undergoing dynamic natural disturbances. Local forests are mostly planted and have low ecosystem resilience against the negative impact of global climate and land use change. Carbon stock fluxes after wildfires in the area have not yet been quantified. However, the assessment of this and other ecosystem service flows is crucial in contaminated (both radioactively and by unexploded ordnance) landscapes of the CEZ. Aims The aim of this study was to estimate carbon stock losses resulting from the catastrophic 2020 fires in the CEZ using satellite data, as field visitations or aerial surveys are impossible due to the ongoing war. Methods The aboveground carbon stock was predicted in a wall-to-wall manner using random forest modelling based on Sentinel data (both optical and synthetic aperture radar or SAR). We modelled the carbon stock loss using the change in Sentinel-1 backscatter before and after the fire events and local combustion factors. Results Random forest models performed well (root-mean-square error (RMSE) of 22.6 MgC·ha ⁻¹ or 37% of the mean) to predict the pre-fire carbon stock. The modelled carbon loss was estimated to be 156.3 Gg C (9.8% of the carbon stock in burned forests or 1.5% at the CEZ level). The standalone SAR backscatter delta showed a higher RMSE than the modelled estimate but better systematic agreement (0.90 vs. 0.73). Scots pine ( Pinus sylvestris L.)-dominated stands contributed the most to carbon stock loss, with 74% of forests burned in 2020. Conclusion The change in SAR backscatter before and after a fire event can be used as a rough proxy indicator of aboveground carbon stock loss for timely carbon map updating. The model using SAR backscatter change and backscatter values prior to wildfire is able to reliably estimate carbon emissions when on-ground monitoring is impossible.
... In turn, in temperate zone of Central Europe the role of fire in the functioning of woodlands has been traditionally marginalized (Tinner et al. 2005;Adámek et al. 2018). However, especially due to a projected increase in the frequency of heat and drought occurring in growing season (Knutti and Sedlacek 2013;Ciais et al. 2005), nowadays, the risk of the extreme events occurrence in temperate forests has clearly increased (Zell and Hanewinkel 2015), which is also highlighted directly to forest fires with reference to different regions of the globe (Schelhaas et al. 2003;Tran et al. 2020;Masinda et al. 2022;Jahdi et al. 2023). This trend has already been recognized based on the analysis of fire frequency in temperate European forests for the period 2009-2018(Fernandez-Anez et al. 2021. ...
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Due to the ongoing climate changes, temperate forests are increasingly exposed to fires. However, until now the functioning of post-fire temperate forest ecosystems with regard to used forest management method has been weakly recognized. Here, we examined three variants of forest restoration after fire (two variants of natural regeneration with no soil preparation—NR, and artificial restoration by planting following soil preparation—AR) regarding their environmental consequences in development of post-fire Scots pine (Pinus sylvestris) ecosystem. The study was conducted using a 15-year timespan in a long-term research site located in the Cierpiszewo area (N Poland) being one of the biggest post-fire grounds in European temperate forests in last decades. We focused on soil and microclimatic variables as well as on growth dynamics of post-fire pines generation. We found that the restoration rates of soil organic matter, carbon and most studied nutritional elements stocks were higher in NR plots than in AR. This could be primarily linked to the higher (p < 0.05) density of pines in naturally regenerated plots, and the subsequent faster organic horizon reconstruction after fire. The difference in tree density also involved regular differences in air and soil temperature among plots: consistently higher in AR than in both NR plots. In turn, lower water uptake by trees in AR implied that soil moisture was constantly the highest in this plot. Our study delivers strong arguments to pay more attention to restore post-fire forest areas with the use of natural regeneration with no soil preparation.
... These effects, which may be subtle and often neglected (Dafni et al., 2012), are related to medium-and long-term post-fire changes in the abundance and composition of the faunal assemblage, in the quantity and quality of resources available to the animals, and also in the structural characteristics of the vegetation (Barlow and Peres, 2006;Andersen, 2021;González et al., 2021). Postfire vegetative cover and the availability of unburnt patches are affected by fire attributes, such as severity and extent of the burned area (Leonard et al., 2014;Tran et al., 2020). These factors, in their turn, alter the abundance and behavior of rodents, ants, and birds, which are the main predators of post-dispersed seeds (e.g., Manson and Stiles, 1998;Monamy and Fox, 2000;Fox et al., 2003;Lassau et al., 2004;. ...
... Forest resilience is threatened by the extreme fire events identified in central Portugal, as in other regions worldwide (e.g. Bowman et al., 2014;Tepley et al., 2017;Steel et al., 2018;Tran et al., 2020). These concerns are related with potential transitions of plant communities to alternate stable states that could be mediated by large and severe wildfires (Knox and Clarke, 2012), but also with the reburning potential of vegetation legacies , availability of fire refugia (Krawchuk et al., 2016) and stand-age distributions (Whitman et al., 2018) associated with homogeneous patterns of fire severity in the landscape. ...
Characterizing the fire regime in regions prone to extreme wildfire behavior is essential for providing comprehensive insights on potential ecosystem response to fire disturbance in the context of global change. We aimed to disentangle the linkage between contemporary damage-related attributes of wildfires as shaped by the environmental controls of fire behavior across mainland Portugal. We selected large wildfires (≥100 ha, n = 292) that occurred during the 2015-2018 period, covering the full spectrum of large fire-size variation. Ward's hierarchical clustering on principal components was used to identify homogeneous wildfire contexts at landscape scale on the basis of fire size, proportion of high fire severity, and fire severity variability, and their bottom-up (pre-fire fuel type fraction, topography) and top-down (fire weather) controls. Piecewise Structural Equation Modeling was used to disentangle the direct and indirect relationships between fire characteristics and fire behavior drivers. Cluster analysis evidenced severe and large wildfires in the central region of Portugal displaying consistent fire severity patterns. Thus, we found a positive relationship between fire size and proportion of high fire severity, which was mediated by distinct fire behavior drivers involving direct and indirect pathways. A high fraction of conifer forest within wildfire perimeters and extreme fire weather were primarily responsible for those interactions. In the context of global change, our results suggest that pre-fire fuel management should be targeted at expanding the fire weather settings in which fire control is feasible and promote less flammable and more resilient forest types.
... In contrast, the temperate biomes have been experiencing a relatively high rate of increase (>1.5 % /yr) in fire danger potential over all vegetation types (broadleaf and mixed forests; grasslands, savannas and shrublands; and conifer forests) in comparison to all other biomes. The temperate forests in Australia have also seen a significant historical increase in the number of large wildfires (>10 km 2 ) (Tran et al., 2020). Thus, this region is experiencing increasing fire weather conditions that are conducive to the development of more frequent wildfire events. ...
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This study 1) identifies the seasons and biomes that exhibit significant (1980-2019) changes in fire danger potential, as quantified by the Canadian Fire Weather Index (FWI); 2) explores what types of fire behavior potentials may be contributing to changes in fire danger potential, as quantified by the United States Energy Release Component (ERC) and the Ignition Component (IC); 3) provides spatiotemporal insight on how fire danger potential and fire behavior potential are responding in relation to changes in seasonal precipitation totals and seasonal mean air temperature across biomes. Time series of these fire potentials, as well as seasonal mean temperature, and seasonal precipitation totals are generated using data from the 0.25° ECMWF spatial resolution Reanalysis 5th Generation (ERA5) and the Climatic Research Unit gridded Time Series (CRU TS). The Mann-Kendall test is then applied to identify significant spatiotemporal trends across each biome. Results indicate that the September-November season (SON) exhibits the greatest rate of increase in fire danger potential, followed by the June-August season (JJA), December, January-February season (DJF), and March-May season (MAM), and this is predominant over the Tropical and Subtropical Moist Broadleaf Forest Biome, as well as all vegetation types of the temperate biomes. Similarly, the temperate biomes experience the greatest rate of increase in fire intensity potential and ignition potential, but prevalent during the DJF and MAM seasons. Furthermore, there is a significant positive correlation between fire danger potential and seasonal mean air temperature during JJA in the Northern Hemisphere for the temperate biomes in North America and Europe, as well as the Tropical and Subtropical biomes in Africa. Our analysis provides quantitative insight as to how fire danger potential and fire behavior potential have been responding to changes in seasonal mean temperature and seasonal precipitation totals across different ecoregions around the world.
... Mos relevant wildfires that took place in Australia from 2007 to 2021[42][43][44][45]. ...
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One of the United Nations (UN) Sustainable Development Goals is climate action (SDG-13), and wildfire is among the catastrophic events that both impact climate change and are aggravated by it. In Australia and other countries, large-scale wildfires have dramatically grown in frequency and size in recent years. These fires threaten the world's forests and urban woods, cause enormous environmental and property damage, and quite often result in fatalities. As a result of their increasing frequency, there is an ongoing debate over how to handle catastrophic wildfires and mitigate their social, economic, and environmental repercussions. Effective prevention, early warning, and response strategies must be well-planned and carefully coordinated to minimise harmful consequences to people and the environment. Rapid advancements in remote sensing technologies such as ground-based, aerial surveillance vehicle-based, and satellite-based systems have been used for efficient wildfire surveillance. This study focuses on the application of space-borne technology for very accurate fire detection under challenging conditions. Due to the significant advances in artificial intelligence (AI) techniques in recent years, numerous studies have previously been conducted to examine how AI might be applied in various situations. As a result of its special physical and operational requirements, spaceflight has emerged as one of the most challenging application fields. This work contains a feasibility study as well as a model and scenario prototype for a satellite AI system. With the intention of swiftly generating alerts and enabling immediate actions, the detection of wildfires has been studied with reference to the Australian events that occurred in December 2019. Convolutional neural networks (CNNs) were developed, trained, and used from the ground up to detect wildfires while also adjusting their complexity to meet onboard implementation requirements for trusted autonomous satellite operations (TASO). The capability of a 1-dimensional convolution neural network (1-DCNN) to classify wildfires is demonstrated in this research and the results are assessed against those reported in the literature. In order to enable autonomous onboard data processing, various hardware accelerators were considered and evaluated for onboard implementation. The trained model was then implemented in the following: Intel Movidius NCS-2 and Nvidia Jetson Nano and Nvidia Jetson TX2. Using the selected onboard hardware , the developed model was then put into practice and analysis was carried out. The results were positive and in favour of using the technology that has been proposed for onboard data processing to enable TASO on future missions. The findings indicate that data processing onboard can be very beneficial in disaster management and climate change mitigation by facilitating the generation of timely alerts for users and by enabling rapid and appropriate responses.
... According to the analysis of the MODIS burned area dataset (MCD64A1), between 2001 and 2018, approximately 63 million hectares of temperate forests were burned (Hislop et al. 2020). Large and severe forest fire occurrences are expected to grow as warmer temperatures, drier conditions, and longer fire seasons increase in frequency (Tran et al. 2020;Masinda et al. 2022). In temperature-fire-prone ecosystems, shifts in forest composition and structure will result from climate changes (Keenan 2015). ...
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We implemented a fire modeling approach to evaluate the effectiveness of silvicultural treatments in reducing potential losses to the Hyrcanian temperate forests of northern Iran, in the Siahkal National Forest (57,110 ha). We compared the effectiveness of selection cutting, low thinning, crown thinning, and clear-cutting treatments implemented during the last ten years (n = 241, 9500-ha) on simulated stand-scale and landscape-scale fire behavior. First, we built a set of fuel models for the different treatment prescriptions. We then modeled 10,000 fires at the 30-m resolution, assuming low, moderate, high, very high, and extreme weather scenarios and human-caused ignition patterns. Finally, we implemented a One-way ANOVA test to analyze stand-level and landscape-scale modeling output differences between treated and untreated conditions. The results showed a significant reduction of stand-level fire hazard, where the average conditional flame length and crown fire probability was reduced by about 12 and 22%, respectively. The conifer plantation patches presented the most significant reduction in the crown fire probability (>35%). On the other hand, we found a minor increase in the overall burn probability and fire size at the landscape scale. Stochastic fire modeling captured the complex interactions among terrain, vegetation, ignition locations, and weather conditions in the study area. Our findings highlight fuel treatment efficacy for moderating potential fire risk and restoring fuel profiles in fire-sensitive temperate forests of northern Iran, where the growing persistent droughts and fuel buildup can lead to extreme fires in the near future.
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The drivers of fire regimes prior to the European occupation of Australia are still contentious, with some advocating regimes dominated by anthropogenic ignitions and others advocating a climate source or mixture of these elements. Here, we examine an 850-year history of fire regimes at Lake Werri Berri in south-east Australia, prior to and following European occupation. Macroscopic charcoal and FTIR spectroscopy were used to infer broad changes of the fire regime in proximity to the lake. We found little change through much of the 850-year period and most interesting, no apparent change following the initial displacement of Indigenous peoples and the introduction of farming and woodcutting to the region by Europeans. From the mid-20th Century onwards, there was an increase in both area burnt and fire severity or intensity, likely the result of increased fuel load and connectivity following an extended period of increased precipitation and heavier recreational land usage, which likely led to an increase in anthropogenic ignitions.
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Wildfire increases the magnitude of runoff in catchments, leading to the degradation of ecosystems, risk to infrastructure, and loss of life. The Labor Day Fires of 2020 provided an opportunity to compare multiple large and severe wildfires with the objective of determining potential changes to hydrologic processes in Oregon Cascades watersheds. Geographic information systems (GIS) were implemented to determine the total percentage burned and percentage of high burn severity class of six watersheds on the west slope of the Oregon Cascade Range. In addition, two control watersheds were included to contrast the influence of climatic effects. Spatial arrangements of burned patches were investigated for correlation to streamflow response by utilizing landscape metrics algorithms, including Largest Patch Index (LPI), mean gyration (GYRATE), Contiguity Index (CONTIG), Patch Cohesion Index (COHESION), and Clumpiness Index (CLUMPY). Results of the first-year post-fire response were consistent with other studies of fire effects in the Pacific Northwest (PNW) and indicated changes to runoff dynamics were difficult to detect with inferential statistics, but the largest changes in runoff coefficients occurred in watersheds having the greatest percentage burned. Correlation analysis indicated relationships between event runoff coefficients and percentage burned during the 2020 fire season. Control watersheds show confounding runoff coefficients, point to the influence of ongoing drought, and complicate conclusions about the role of spatial burn severity patterns. These results could guide future post-fire studies of spatial patterns of burn severity and could assist watershed managers to prioritize at-risk PNW catchments to minimize harm to ecological and societal values.
Conference Paper
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Machine learning and spectral index (SI) thresholding approaches have been tested for fire-severity mapping from local to regional scales in a range of forest types worldwide. While index thresholding can be easily implemented, its operational utility over large areas is limited as the optimum index may vary with forest type and fire regimes. In contrast, machine learning algorithms allow for multivariate fire classifications. This study compared the accuracy of fire-severity classifications from SI thresholding with those from Random Forests (RF). Reference data were from 3730 plots within the boundaries of eight major wildfires across the six temperate forest ‘functional’ groups of Victoria, south-eastern Australia. The reference plots were randomly divided into training and validation datasets (60/40) for each fire-severity class (unburnt, low, moderate, high) and forest functional group. SI fire-severity classifications were conducted using thresholds derived in a previous study based on the same datasets. A RF classification algorithm was trained to derive fire-severity levels based on appropriate spectral indices and their temporal difference. The RF classification outperformed the SI thresholding approach in most cases, increasing overall accuracy by 11% on a forest-group basis, and 16% on an individual wildfire basis. Adding more predictor variables into the RF algorithm did not improve classification accuracy. Greater overall accuracies (by 12% on average) were achieved when in situ data (rather than data from other fires) were used to train the RF algorithm. Our study shows the utility of Random Forest algorithms for streamlining fire-severity mapping across heterogeneous forested landscapes.
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Quantifying landscape characteristics and linking them to ecological processes is one of the central goals of landscape ecology. Landscape metrics are a widely used tool for the analysis of patch‐based, discrete land‐cover classes. Existing software to calculate landscape metrics has several constraints, such as being limited to a single platform, not being open‐source, or involving a complicated integration into large workflows. We present landscapemetrics, an open‐source R package that overcomes many constraints of existing landscape metric software. The package includes an extensive collection of commonly used landscape metrics in a tidy workflow. To facilitate the integration into large workflows, landscapemetrics is based on a well‐established spatial framework in R. This allows pre‐processing of land‐cover maps or further statistical analysis without importing and exporting the data from and to different software environments. Additionally, the package provides many utility functions to visualize, extract, and sample landscape metrics. Lastly, we provide building‐blocks to motivate the development and integration of new metrics in the future. We demonstrate the usage and advantages of landscapemetrics by analysing the influence of different sampling schemes on the estimation of landscape metrics. In so doing, we demonstrate the many advantages of the package, especially its easy integration into large workflows. These new developments should help with the integration of landscape analysis in ecological research, given that ecologists are increasingly using R for the statistical analysis, modelling, and visualization of spatial data. This article is protected by copyright. All rights reserved.
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More than 70 years of fire suppression by federal land management agencies has interrupted fire regimes in much of the western United States. The result of missed fire cycles is a buildup of both surface and canopy fuels in many forest ecosystems, increasing the risk of severe fire. The frequency and size of fires has increased in recent decades, as has the area burned with high severity in some ecosystems. A number of studies have examined controls on high severity fire occurrence, but none have yet determined what controls the extent of high severity fire. We developed statistical models predicting high severity area burned for the western United States and three sub-regions—the Northern Rocky Mountains, Sierra Nevada Mountains, and Southwest. A simple model with maximum temperature the month of fire, annual normalized moisture deficit and location explains area burned in high severity fire in our west-wide model, with the exception of years with especially large areas burned with high severity fire: 1988, 2002. With respect to mitigation or management of high severity fire, understanding what drives extreme fire years is critical. For the sub-regional models, topography, spring temperature and snowpack condition, and vegetation condition class variables improved our prediction of high severity burned area in extreme fire years. Fire year climate is critical to predicting area burned in high severity fire, especially in extreme fire years. These models can be used for scenario analyses and impact assessments to aid management in mitigating negative impacts of high severity fire.
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Spectral indices derived from optical remote sensing data have been widely used for fire 14-severity classification in forests from local to global scales. However, comparative analyses of 15 multiple indices across diverse forest types are few. This represents an information gap for fire 16 management agencies in areas like temperate southeastern Australia, which is characterized by a 17 diversity of natural forests that vary in structure and in the fire-regeneration strategies of the 18 dominant trees. 19 We evaluate 10 spectral indices across eight areas burnt by wildfires in 1998, 2006, 2007, and 2009 in 20 southeastern Australia. These wildfire areas encompass 13 forest types, which represent 86% of the 21 7.9M ha region's forest area. Forest types were aggregated into six forest groups based on their fire-22 regeneration strategies (seeders, resprouters) and structure (tree height and canopy cover). Index 23 performance was evaluated for each forest type and forest group by examining its sensitivity to four 24 fire-severity classes (unburnt, low, moderate, high) using three independent methods (anova, 25 separability, and optimality). For the best-performing indices, we calculated index-specific 26 thresholds (by forest types and groups) to separate between the four severity classes and evaluated 27 the accuracy of fire-severity classification on independent samples. 28 Our results indicated that the best-performing indices of fire severity varied with forest type and 29 group. Overall accuracy for the best-performing indices ranged from 0.50 to 0.78, and kappa values 30 ranged from 0.33 (fair agreement) to 0.77 (substantial agreement) depending on the forest group 31 and index. Fire severity in resprouter open forests and woodlands was most accurately mapped 32 using the delta Normalized Burnt ratio (dNBR). In contrast, dNDVI (delta Normalised difference 33 vegetation index) performed best for open forests with mixed fire responses (resprouters and 34 seeders), and dNDWI (delta Normalised difference water index) was the most accurate for obligate 35 seeder closed forests. Our analysis highlighted low sensitivity of all indices to fire impacts in 36 Rainforest. 37 We conclude that the optimal spectral index for quantifying fire severity varies with forest type, but 38 that there is scope to group forests by structure and fire-regeneration strategy to simplify fire-39 severity classification in heterogeneous forest landscapes. 40
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Purpose Wildfire spatial patterns drive ecological processes including vegetation succession and wildlife community dynamics. Such patterns may be changing due to fire suppression policies and climate change, making characterization of trends in post-fire mosaics important for understanding and managing fire-prone ecosystems. Methods For wildfires in California’s yellow pine and mixed-conifer forests, spatial pattern trends of two components of the post-fire severity matrix were assessed for 1984–2015: (1) unchanged or very low-severity and (2) high-severity, which represent remnant forest and stand-replacing fire, respectively. Trends were evaluated for metrics of total and proportional burned area, shape complexity, aggregation, and core area. Additionally, comparisons were made between management units where fire suppression is commonly practiced and those with a history of managing wildfire for ecological/resource benefits. Results Unchanged or very low-severity area per fire decreased proportionally through time, and became increasingly fragmented. High-severity area and core area increased on average across most of California, with the high-severity component also becoming simpler in shape in the Sierra Nevada. Compared to suppression units, managed wildfire units lack an increase in high-severity area, have less aggregated post-fire mosaics, and more high-severity spatial complexity. Conclusions Documented changes in severity patterns have cascading ecological effects including increased vegetation type conversion risk, habitat availability shifts, and remnant forest fragmentation. These changes likely benefit early-seral-associated species at the expense of mature closed-canopy forest-associated species. Managed wildfire appears to moderate some effects of fire suppression, and may help buy time for ecosystems and managers to respond to a changing climate.
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Wildland fire is a critical process in forests of the western United States (US). Variation in fire behavior, which is heavily influenced by fuel loading, terrain, weather, and vegetation type, leads to heterogeneity in fire severity across landscapes. The relative influence of these factors in driving fire severity, however, is poorly understood. Here, we explore the drivers of high-severity fire for forested ecoregions in the western US over the period 2002–2015. Fire severity was quantified using a satellite-inferred index of severity, the relativized burn ratio. For each ecoregion, we used boosted regression trees to model high-severity fire as a function of live fuel, topography, climate, and fire weather. We found that live fuel, on average, was the most important factor driving high-severity fire among ecoregions (average relative influence = 53.1%) and was the most important factor in 14 of 19 ecoregions. Fire weather was the second most important factor among ecoregions (average relative influence = 22.9%) and was the most important factor in five ecoregions. Climate (13.7%) and topography (10.3%) were less influential. We also predicted the probability of high-severity fire, were a fire to occur, using recent (2016) satellite imagery to characterize live fuel for a subset of ecoregions in which the model skill was deemed acceptable (n=13). These ‘wall-to-wall’ gridded ecoregional maps provide relevant and up-to-date information for scientists and managers who are tasked with managing fuel and wildland fire. Lastly, we provide an example of the predicted likelihood of high-severity fire under moderate and extreme fire weather before and after fuel reduction treatments, thereby demonstrating how our framework and model predictions can potentially serve as a performance metric for land management agencies tasked with reducing hazardous fuel across large landscapes.
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Predicting wildfire under future conditions is complicated by complex interrelated drivers operating across large spatial scales. Annual area burned (AAB) is a useful index of global wildfire activity. Current and antecedent seasonal climatic conditions, and the timing of snowpack melt, have been suggested as important drivers of AAB. As climate warms, seasonal climate and snowpack co-vary in intricate ways, influencing fire at continental and sub-continental scales. We used independent records of seasonal climate and snow cover duration (last date of permanent snowpack, LDPS) and cell-based Structural Equation Models (SEM) to separate direct (climatic) and indirect (snow cover) effects on relative changes in AAB under future climatic scenarios across western and boreal North America. To isolate seasonal climate variables with the greatest effect on AAB, we ran multiple regression models of log-transformed AAB on seasonal climate variables and LDPS. We used the results of multiple regressions to project future AAB using GCM ensemble climate variables and LDPS, and validated model predictions with recent AAB trends. Direct influences of spring and winter temperatures on AAB are larger and more widespread than the indirect effect mediated by changes in LDPS in most areas. Despite significant warming trends and reductions in snow cover duration, projected responses of AAB to early-mid 21st century are heterogeneous across the continent. Changes in AAB range from strongly increasing (one order of magnitude increases in AAB) to moderately decreasing (more than halving of baseline AAB). Annual wildfire area burned in coming decades is likely to be highly geographically heterogeneous, reflecting interacting regional and seasonal climate drivers of fire occurrence and spread.
Wildfires are predicted to increase in both frequency and severity across Mediterranean climate regions worldwide. While many Mediterranean-type ecosystems are considered broadly fire tolerant, there is little understanding of how differences in fire severity affect plant community dynamics, tree mortality and recruitment. In the tall karri (Eucalyptus diversicolor F. Muell) forests of southwest Australia, low to moderate severity wild and prescribed fires are relatively common. Mature karri trees survive these events and recover rapidly due to their thick bark and ability to prolifically resprout from epicormic buds. However, despite a projected increase in the frequency of high severity wildfires, the impact of such extreme fires on tree mortality and understory community composition is not well understood. We used a large and severe wildfire event in southwest Australia to assess how fire severity impacted recruitment and survival of karri seedlings, the mortality of mature karri trees, and the composition of the understory plant community. Mature karri tree mortality was 87% greater at sites burnt at high severity compared to unburnt sites and sites burnt at low severity. Understorey plant community composition of burnt sites was different to unburnt sites, driven largely by significant shifts in dominance. Notably, the usually dominant understorey shrub Trymalium odoratissimum was entirely absent from forests burnt at extreme high severity. These results indicate that karri forests burnt at very high severity undergo changes in stand structure that will persist for many decades, and that the structure and species composition of the understorey may also be altered significantly. This indication of a possible fire severity threshold is consistent with the findings of recent studies in other Mediterranean climates following catastrophic wildfires. This study highlights the need for further research into the effects of severe wildfire on forest ecosystems that are otherwise considered fire tolerant.