Fire
severity,
or
how
an
environment
is
affected
by
fire,
can
be
estimated
over
large
areas
using
remotely
sensed
fire
severity
indices,
such
as
the
Relative
Burnt
Ratio
(RBR).
RBR
predictions
are
generally
based
on
data
collected
a
single
date
immediately
before
aggregated
time
to
scalar
value.
However,
accurate
temporal
and
spatial
prediction
of
remains
challenging.
To
improve
predictability
RBR,
we
build
new
predictive
models
series
spanning
several
months
fuel
proxies,
derived
from
optical
remote
sensing
meteorological
data.
The
approach
applied
fires
French
Mediterranean
area
during
summers
2016-2021
period.
Lagged
Generalized
Additive
Model
(LGAM)
Functional
Linear
(FLM)
used
estimate
influence
explanatory
variables
up
prior
while
(GAM),
which
relies
immediate
pre-fire
predictors
at
date,
benchmark.
Training
carried
out
fire–land-cover
scale
with
training
dataset
composed
independent
those
in
test
datasets.
FLM
achieves
best
accuracy
(R=0.68,
RMSE=0.057)
compared
LGAM
(R=0.60,
RMSE=0.063)
benchmark
(R=0.52,
RMSE=0.069)
also
less
sensible
overfitting.
selected
correctly
predicts
even
highest
values
when
Normalized
Difference
Vegetation
Index
decreases
faster
than
average
fire-weather
Duff
Moisture
Code
increases
65
days
fire.
17%
decrease
RMSE
GAM
shows
that
knowledge
dynamics
two
provides
valuable
information
for
ranking
according
severity.
Science,
Год журнала:
2025,
Номер
387(6729), С. 91 - 97
Опубликована: Янв. 2, 2025
Canada
has
experienced
more-intense
and
longer
fire
seasons
with
more-frequent
uncontrollable
wildfires
over
the
past
decades.
However,
effect
of
these
changes
remains
unknown.
This
study
identifies
driving
forces
burn
severity
estimates
its
spatiotemporal
variations
in
Canadian
forests.
Our
results
show
that
fuel
aridity
was
most
influential
driver
severity,
summer
months
were
more
prone
to
severe
burning,
northern
areas
influenced
by
changing
climate.
About
6%
(0.54
14.64%)
modeled
significant
increases
number
days
conducive
high-severity
burning
during
1981
2020,
which
found
2001
2020
spring
autumn.
The
extraordinary
2023
season
demonstrated
similar
spatial
patterns
but
more-widespread
escalations
severity.
Communications Earth & Environment,
Год журнала:
2024,
Номер
5(1)
Опубликована: Ноя. 20, 2024
Drivers
of
forest
wildfire
severity
include
fuels,
topography
and
weather.
However,
because
only
fuels
can
be
actively
managed,
quantifying
their
effects
on
has
become
an
urgent
research
priority.
Here
we
employed
GEDI
spaceborne
lidar
to
consistently
assess
how
pre-fire
fuel
structure
affected
across
42
California
wildfires
between
2019–2021.
Using
a
spatial-hierarchical
modeling
framework,
found
positive
concave-down
relationship
GEDI-derived
severity,
marked
by
increasing
with
greater
loads
until
decline
in
the
tallest
most
voluminous
canopies.
Critically,
indicators
canopy
volumes
(like
biomass
height)
became
decoupled
from
patterns
extreme
topographic
weather
conditions
(slopes
>20°;
winds
>
9.3
m/s).
On
other
hand,
vertical
continuity
metrics
like
layering
ladder
more
predicted
–
especially
where
sparse
understories
were
uniformly
associated
lower
levels.
These
results
confirm
that
estimates
overcome
limitations
optical
imagery
airborne
for
interactive
drivers
severity.
Furthermore,
these
findings
have
direct
implications
designing
treatment
interventions
target
versus
entire
canopies
delineating
risk
conditions.
Wildfire
is
such
as
rather
than
total
range
conditions,
according
analysis
data
fires
Forests,
Год журнала:
2025,
Номер
16(4), С. 640 - 640
Опубликована: Апрель 7, 2025
Satellite
remote
sensing
has
been
widely
recognized
as
an
effective
tool
for
estimating
fire
severity.
Existing
indies
predominantly
rely
on
broad-spectrum
multispectral
data,
limiting
the
ability
to
elucidate
intricate
relationship
between
severity
and
spectral
response.
To
address
this
challenge,
optimal
bands
indices
assessment
were
explored
using
ZY-1
hyperspectral
which
captured
pre-
post-fire
conditions
of
a
forest
site
in
Yuxi
City,
Yunnan
Province,
China.
Separability
contrast
threshold
segmentation
methods
applied
perform
sensitivity
analysis
original
constructed
derived
from
surface
reflectance
image
combination,
respectively.
The
findings
indicate
following:
(1)
exhibited
superior
separability
classification
capabilities
compared
difference
image,
with
highest
accuracy
78.99%
achieved
at
800
nm
central
wavelength.
(2)
normalized
index
category
combination
outperformed
vegetation
other
83.39%
2048
1106
(3)
Unburned
areas
strong
separability,
facilitating
segmentation,
but
burned
showed
poor
severities,
particularly
low
moderate–high
severity,
remains
primary
limitation
assessment.
In
conclusion,
study
advances
understanding
response
by
leveraging
narrow-band
advantages.
It
aims
enhance
satellite-based
estimation,
offering
valuable
technical
guidance
theoretical
insights
assessing
impacts
recovery.
Land,
Год журнала:
2025,
Номер
14(4), С. 793 - 793
Опубликована: Апрель 7, 2025
Extreme
weather
events
are
increasing
the
frequency
and
intensity
of
forest
fires,
generating
serious
environmental
socio-economic
impacts.
These
fires
cause
soil
loss
through
erosion,
organic
matter
depletion,
increased
surface
runoff
release
greenhouse
gases,
intensifying
climate
change.
They
also
affect
biodiversity,
terrestrial
aquatic
ecosystems,
quality.
The
assessment
by
remote
sensing,
such
as
use
Normalised
Difference
Vegetation
Index
(NDVI),
allows
rapid
analysis
damaged
areas,
monitoring
vegetation
changes
design
restoration
strategies.
On
other
hand,
models
RUSLE
key
tools
for
calculating
erosion
planning
conservation
measures.
A
study
impacts
on
soils
in
south
Salamanca,
where
one
worst
province
took
place
2022,
has
been
carried
out
using
NDVI
models,
respectively.
confirms
that
significantly
properties,
increase
hinder
recovery,
highlighting
need
effective
It
was
observed
intensifies
after
(the
maximum
rate
before
is
1551.85
t/ha/year,
while
it
4899.42
t/ha/year)
especially
areas
with
steeper
slopes,
which
increases
vulnerability,
according
to
model.
showed
a
decrease
recovery
most
affected
(with
value
0.3085
event
0.4677
before),
indicating
slow
regeneration
process.
generation
detailed
cartographies
essential
identify
critical
prioritise
actions.
Furthermore,
highlights
importance
implementing
measures,
designing
sustainable
agricultural
strategies
developing
policies
focused
mitigation
land
degradation
fire-affected
ecosystems.
