Abstract.
Strong
winter
wind
storms
can
lead
to
billions
in
forestry
losses,
disrupt
train
services
and
amount
millions
of
Euro
spend
on
vegetation
management
alongside
the
German
railway
system.
Therefore,
understanding
link
between
tree
fall
is
crucial.
Existing
studies
often
emphasize
soil
factors
more
than
meteorology.
Using
a
dataset
from
Deutsche
Bahn
(2017–2021)
meteorological
data
ERA5
reanalysis
RADOLAN
radar,
we
employed
stepwise
model
selection
build
logistic
regression
predicting
risk
falling
line
31
km
grid
cell.
While
daily
maximum
gust
speed
strongest
factor,
also
found
that
duration
strong
speeds,
precipitation,
water
volume,
air
density
precipitation
sum
previous
year
increase
risk.
A
high
factor
decreases
interaction
terms
speeds
as
well
improves
performance.
our
findings
suggest
prolonged
especially
combination
with
wet
conditions
(high
moisture)
density,
Incorporating
parameters
linked
local
climatological
(through
anomalies
or
relation
percentiles)
improved
accuracy.
This
indicates
importance
taking
adaptation
environment
into
account.
Annals of Forest Science,
Год журнала:
2025,
Номер
82(1)
Опубликована: Янв. 13, 2025
Abstract
Key
message
Although
global
changes
are
expected
to
intensify
the
impact
of
wind
as
a
hazard,
recent
studies
have
emphasized
critical
role
plays
in
tree
growth
and
development.
Wind-induced
swaying
generates
strains
that
perceives,
triggering
process
known
thigmomorphogenesis.
This
alters
tree’s
patterns
wood
properties
enhance
its
mechanical
stability.
Thus,
functions
not
only
hazard
but
also
factor,
enabling
acclimate
loads
reduce
risk.
Despite
significant
thigmomorphogenesis
carbon
allocation,
this
remains
largely
overlooked
forest
ecology
management
models.
We
strongly
advocate
for
integration
wind-induced
strain
sensing,
primary
driver
thigmomorphogenesis,
alongside
established
environmental
factors
models,
well
instrumented
stands
aimed
at
studying
effects
on
growth.
crucial
step
is
essential
comprehensive
understanding
dynamics
informed
decision-making
management.
Remote Sensing,
Год журнала:
2025,
Номер
17(10), С. 1777 - 1777
Опубликована: Май 20, 2025
As
the
frequency
of
strong
storms
and
cyclones
increases,
understanding
wind
risk
in
both
existing
newly
established
plantation
forests
is
becoming
increasingly
important.
Recent
advances
quality
availability
remotely
sensed
data
have
significantly
improved
our
capability
to
make
large-scale
predictions.
This
study
models
loss
radiata
pine
(Pinus
D.Don)
plantations
following
a
severe
cyclone
within
Gisborne
Region
New
Zealand
through
leveraging
repeat
regional
LiDAR
acquisitions,
optical
imagery,
various
surfaces
describing
key
climatic,
topographic,
storm-specific
conditions.
A
random
forest
model
was
trained
on
9713
plots
classified
as
windthrow
or
no-windthrow.
Model
validation
using
50
iterations
80/20
train/test
splits
achieved
robust
accuracy
(accuracy
=
0.835;
F1
score
0.841;
AUC
0.913).
In
comparison
most
European
empirical
(AUC
0.51–0.90),
framework
demonstrated
superior
discrimination,
underscoring
its
value
for
regions
prone
cyclones.
Among
14
predictor
variables,
influential
were
mean
windspeed
during
February,
exposition
index,
site
drainage,
stand
age.
predictions
closely
aligned
with
estimated
3705
hectares
cyclone-induced
damage
indicated
that
20.9%
unplanted
areas
region
would
be
at
age
30
if
pine.
The
resulting
surface
serves
valuable
decision-support
tool
managers,
helping
mitigate
guide
adaptive
afforestation
strategies.
Although
developed
Zealand,
approach
findings
broader
relevance
management
cyclone-prone
worldwide,
particularly
where
forestry
widely
practised.
Abstract.
Strong
winter
wind
storms
can
lead
to
billions
in
forestry
losses,
disrupt
train
services
and
amount
millions
of
Euro
spend
on
vegetation
management
alongside
the
German
railway
system.
Therefore,
understanding
link
between
tree
fall
is
crucial.
Existing
studies
often
emphasize
soil
factors
more
than
meteorology.
Using
a
dataset
from
Deutsche
Bahn
(2017–2021)
meteorological
data
ERA5
reanalysis
RADOLAN
radar,
we
employed
stepwise
model
selection
build
logistic
regression
predicting
risk
falling
line
31
km
grid
cell.
While
daily
maximum
gust
speed
strongest
factor,
also
found
that
duration
strong
speeds,
precipitation,
water
volume,
air
density
precipitation
sum
previous
year
increase
risk.
A
high
factor
decreases
interaction
terms
speeds
as
well
improves
performance.
our
findings
suggest
prolonged
especially
combination
with
wet
conditions
(high
moisture)
density,
Incorporating
parameters
linked
local
climatological
(through
anomalies
or
relation
percentiles)
improved
accuracy.
This
indicates
importance
taking
adaptation
environment
into
account.
Summary
Tropical
cyclones
(TCs)
sporadically
cause
extensive
damage
to
forests.
However,
little
is
known
about
how
TCs
affect
forest
dynamics
in
mountainous
terrain,
due
difficulties
modelling
wind
flows
and
quantifying
structural
changes.
Typhoon
Mangkhut
(2018)
was
the
strongest
TC
strike
Hong
Kong
over
40
yr,
with
gusts
>
250
km
h
−1
.
Remarkably,
event
captured
by
a
dense
anemometer
network
repeated
LiDAR
surveys
across
natural
forests
plantations.
We
mapped
long‐term
mean
extreme
speeds
using
CFD
models
analysed
corresponding
changes
canopy
height,
which
uncovered
TC‐forest
at
unprecedented
scales
(>
400
000
pixels,
1108
2
).
Forest
height
more
strongly
limited
exposure
than
background
topography,
limitation
attributable
dynamic
equilibrium
between
growth
disproportionate
taller
Counterintuitively,
wind‐sheltered
also
suffered
heavy
damage.
As
result,
canopies
of
were
rugged,
contrasted
flat‐topped
wind‐exposed
sites.
Plantations
susceptible
compared
rainforests
similar
stature
(canopy
change
−0.86
m
vs
−0.39
m).
Our
findings
highlight
as
important,
often
overlooked
factor
that
fundamentally
shapes
structure
dynamics.
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 9, 2024
Abstract
In
recent
years,
we
have
witnessed
worldwide,
an
increase
in
natural
forest
disturbances,
particularly
windstorms,
which
caused
significant
direct
and
indirect
damages,
often
triggering
largescale
bark
beetle
outbreaks.
this
study,
investigated
the
interaction
between
windstorm-induced
tree
damage
subsequent
outbreaks
northeastern
Italian
Alps
(Province
of
Belluno
Bolzano),
focusing
on
2018
Vaia
windstorm
successive
infestation
started
2021.
Additionally,
aimed
to
determine
whether
potential
correlation
is
influenced
by
structural
characteristics
such
as
height
heterogeneity
(HH),
density,
mean
using
LiDAR
data,
or
meteorological
factors
(mean
temperature
cumulative
precipitation)
through
in-situ
spatialized
information.
Our
research
findings,
based
a
methodology
centered
spatial
interactions,
indicate
link
event
occurred
three
years
before.
results
suggest
that
variables
are,
most
cases,
significantly
similar
across
all
areas
affected
beetle.
This
similarity
observed
both
forests
impacted
other
Picea
abies
not
windstorm,
indicating
these
may
be
trigger
for
outbreak.
findings
do
show
clear
consistently
difference
conditions.
variability
can
attributed
specific
are
predominantly
mountainous
regions
characterized
distinct
temperatures
precipitation
compared
rest
provinces.
When
analyzing
combined
influence
study
areas,
our
none
were
ultimately
predictors
infestations
windstorm.
suggests
that,
climate
change
increases
frequency
severity
adaptable
management
framework
enhance
resilience
sustainability
needed,
helping
better
withstand
recover
from
future
disturbances.
