Geo-spatial Information Science,
Год журнала:
2024,
Номер
unknown, С. 1 - 29
Опубликована: Дек. 20, 2024
Canopy
base
height
(CBH)
and
canopy
bulk
density
(CBD)
are
forest
fuel
parameters
that
key
for
modeling
the
behavior
of
crown
wildfires.
In
this
work,
we
map
them
at
a
pan-European
scale
year
2020,
producing
new
dataset
consisting
two
raster
layers
containing
both
variables
an
approximate
resolution
100
m.
Spatial
data
from
Earth
observation
missions
derived
down-stream
products
were
retrieved
processed
using
artificial
intelligence
to
first
estimate
aboveground
biomass
(AGB).
Allometric
models
then
used
spatial
distribution
CBH
values
as
explanatory
CBD
AGB
values.
Ad-hoc
allometric
defined
study.
Data
provided
by
FIRE-RES
project
partners
acquired
through
field
inventories
was
validating
final
independent
804
ground-truth
sample
plots.
The
maps
have,
respectively,
following
accuracy
regarding
specific
metrics
reported
procedures:
(i)
coefficient
correlation
(R)
0.445
0.330
(p-value
<
0.001);
(ii)
root
mean
square
error
(RMSE)
3.9
m
0.099
kg
m−3;
(iii)
absolute
percentage
(MAPE)
61%
76%.
Regarding
CBD,
improved
in
closed
canopies
(canopy
cover
>
80%)
R
=
0.457,
RMSE
0.085,
MAPE
59%.
short,
believe
degree
is
reasonable
resulting
maps,
datasets
support
fire
mitigation
simulations.
Science of Remote Sensing,
Год журнала:
2024,
Номер
9, С. 100134 - 100134
Опубликована: Май 16, 2024
Wildfires
have
been
progressively
shrinking
the
C
sink
capacity
of
forest
accelerating
climate
change
effects
on
biodiversity,
especially
where
megafires
are
recurrent
and
increased
frequency
such
as
in
Mediterranean.
Data
from
The
Global
Ecosystem
Dynamics
Investigation
(GEDI)
mission
can
inform
changes
structure
to
fire
impacts
vegetation.
In
this
study,
we
assessed
performance
GEDI
at
measuring
biomass
structural
wildfires
using
2020/21
summer
seasons
Spain
Portugal.
hybrid-inference
method
was
used
calculate
mean
total
pre-
post-fire
stages,
while
footprint
data
further
explain
severity
classes
derived
optical
data.
Our
results
showed
increasing
impact
stocks
ecological
metrics
by
severity.
More
than
over
stocks,
severe
fires
substantially
altered
trends
plant
area
volume
density.
integration
had
an
accuracy
52%
considering
five
69%
when
three
main
classes:
unburned,
moderate
high.
Structural
be
improve
optical-based
estimates
globally
evaluate
potential
based
time-series
tracks
showcased
but
also
measure
recovery
between
seasons.
extension
is
a
major
support
for
wildfire
mapping
efforts,
integrated
approaches
capture
biodiversity
monitoring
carbon
stocks.
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Ноя. 11, 2024
Accurately
mapping
aboveground
biomass
(AGB)
in
China's
boreal
forests
is
crucial
for
assessing
global
carbon
stock
and
formulating
forest
management
strategies
but
remains
challenging
as
the
environmental
heterogeneity
complicates
AGB
estimation.
Here,
we
investigated
relative
gains
of
integrating
Sentinel-2
data,
well
synthetic
aperture
radar
(SAR)
images
to
map
forests.
We
used
two
machine
learning
algorithms,
random
gradient
boosting
regression
(GBR),
four
dataset
combinations
develop
models,
then
evaluated
by
carrying
on
uncertainty
analysis
comparing
it
with
existing
products.
Results
showed
that
GBR
model
based
data
presented
best
estimation
capability
(R
Trees Forests and People,
Год журнала:
2024,
Номер
17, С. 100614 - 100614
Опубликована: Июнь 24, 2024
Forest
fires
pose
severe
threats
to
ecosystems
and
communities
globally,
especially
in
vulnerable
semi-arid
regions
like
North
Africa.
Understanding
the
key
factors
influencing
forest
fire
dynamics
is
essential
for
effective
management
mitigation.
This
study
aims
comprehensively
analyze
risk
patterns
Djebel
El
Ouahch's
massive
(Algeria),
focusing
on
integrating
bioclimatic,
fuel,
geomorphological,
human
through
advanced
fuzzy
logic
geographic
information
system
(GIS)
techniques.
Climatic
station
data,
satellite
imagery,
GIS
were
employed
map
bioclimatic
parameters,
land
cover,
geomorphological
features.
Fuzzy
systems
applied
integrate
these
factors,
assigning
appropriate
weights
based
their
significance.
The
resulting
prediction
model
was
defuzzified
generate
predictive
maps
indicating
varying
vulnerability
levels
within
area.
Predictive
delineated
areas
of
low
high
risk.
Low-risk
zones
characterized
by
sparse
vegetation,
while
high-risk
featured
densely
vegetated
slopes
near
settlements.
identified
critical
vulnerability,
emphasizing
impact
climate,
terrain,
activities.
Urgent
attention
directed
toward
areas,
necessitating
tailored
prevention
measures
strategic
urban
planning
minimize
human-induced
risks.
results
underscored
complex
interaction
natural
anthropogenic
shaping
susceptibility.
facilitates
evidence-based
policymaking,
enhancing
preparedness,
biodiversity
preservation,
community
safety.
Additionally,
emphasized
need
continuous
research
incorporating
real-time
climate
data
socio-economic
refine
models.
provided
valuable
insights
into
massive,
serving
as
a
foundation
targeted
strategies.
By
bridging
gap
between
theoretical
knowledge
practical
application,
this
contributes
significantly
sustainable
disaster
mitigation
efforts
importance
proactive
safeguarding
communities.
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
Remote Sensing,
Год журнала:
2025,
Номер
17(5), С. 796 - 796
Опубликована: Фев. 25, 2025
Accurately
monitoring
aboveground
biomass
(AGB)
and
tree
mortality
is
crucial
for
understanding
forest
health
carbon
dynamics.
LiDAR
(Light
Detection
Ranging)
has
emerged
as
a
powerful
tool
capturing
structure
across
different
spatial
scales.
However,
the
effectiveness
of
predicting
AGB
depends
on
type
instrument,
platform,
resolution
point
cloud
data.
We
evaluated
three
distinct
LiDAR-based
approaches
in
25.6
ha
North
American
temperate
forest.
Specifically,
we
following:
GEDI-simulated
waveforms
from
airborne
laser
scanning
(ALS),
grid-based
structural
metrics
derived
unmanned
aerial
vehicle
(UAV)-borne
lidar
data,
individual
detection
(ITD)
ALS
Our
results
demonstrate
varying
levels
performance
approaches,
with
ITD
emerging
most
accurate
modeling
median
R2
value
0.52,
followed
by
UAV
(0.38)
GEDI
(0.11).
findings
underscore
strengths
approach
fine-scale
analysis,
while
used
to
analyze
showed
promise
broader-scale
monitoring,
if
more
uncertainty
acceptable.
Moreover,
complementary
scales
each
method
may
offer
valuable
insights
management
conservation
efforts,
particularly
dynamics
informing
strategic
interventions
aimed
at
preserving
mitigating
climate
change
impacts.
