Ecological Informatics,
Journal Year:
2024,
Volume and Issue:
80, P. 102493 - 102493
Published: Jan. 22, 2024
In
the
context
of
global
change,
it
is
vital
to
comprehensively
understand
spatial
pattern
and
driving
mechanism
vegetation
growth
maintain
stability
watershed
ecosystems.
Previous
research
has
focused
mainly
on
identifying
main
drivers
growth,
while
direct
indirect
effects
climate,
terrain,
human
activity
have
rarely
been
explored.
This
study
used
Minjiang
River
Basin
(MRB),
an
important
ecological
barrier
largest
in
southeastern
China,
as
example.
The
kernel
normalized
difference
index
(kNDVI)
was
calculated
Google
Earth
Engine
(GEE)
platform
examine
evolution
characteristics
growth.
optimal
parameter-based
geographical
detector
(OPGD)
partial
least
squares
structural
equation
modeling
(PLS-SEM)
were
analyze
how
influenced
kNDVI.
(1)
From
2001
2020,
MRB
predominantly
rated
excellent
or
good,
88.93%
area
showed
increasing
trend
(2)
OPGD
revealed
that
primary
influencing
distribution
kNDVI
included
population
density,
nighttime
light,
elevation
temperature,
which
explained
>40%
variation
interaction
all
paired
enhanced
explanatory
power
kNDVI,
among
strongest
between
density
elevation,
second
temperature.
(3)
PLS-SEM
had
a
negative
effect
terrain
climate
positive
Overall,
total
0.594,
0.233
−
0.495,
respectively,
indicating
outweighed
MRB.
These
findings
not
only
provide
scientific
evidence
for
conservation
management
but
also
offer
useful
reference
other
regions
exploring
complex
causes
patterns
Frontiers in Environmental Science,
Journal Year:
2025,
Volume and Issue:
12
Published: Jan. 13, 2025
The
Qilian
Mountains
and
Huangshui
River
Basin
(HRB)
represent
significant
ecological
functional
areas
carbon
reservoirs
within
China.
estimation
prediction
of
vegetation
net
primary
productivity
(NPP)
in
this
area
is
beneficial
for
the
management
China’s
terrestrial
ecosystems.
Nevertheless,
existing
methods
NPP
at
local
scale
are
characterised
by
considerable
uncertainty
error,
have
not
accounted
influence
multi-factor
interactions.
Accordingly,
study
initially
sought
to
quantify
data
HRB
from
2000
2019
through
implementation
an
improved
Carnegie-Ames-Stanford
Approach
(CASA)
model.
Subsequently,
it
endeavoured
elucidate
spatiotemporal
evolution
patterns
influencing
factors
over
years.
ConvGRU
model
was
employed
investigate
prospective
trajectory
HRB.
findings
revealed
a
notable
upward
annual
variation
between
2019.
majority
regions
demonstrated
increase
NPP,
although
few
exhibited
decline.
Furthermore,
correlation
PRE,
TEMP,
SR,
NDVI
exhibits
regional
disparities.
spatial
characteristics
future
also
demonstrate
overall
increasing
trend.
Additionally,
distribution
characteristics,
with
evident
trends
hot
spot
contraction
or
cold
expansion.
This
provides
pivotal
theoretical
support
assessment
sequestration
status
analogous
regions.
Journal of Environmental Management,
Journal Year:
2025,
Volume and Issue:
377, P. 124600 - 124600
Published: Feb. 22, 2025
Forest
fires
are
increasing
in
frequency
and
intensity
worldwide
due
to
the
anthropogenic
climate
change,
threatening
people's
lives
causing
huge
economic
environmental
damages.
Recent
forest
fire
events
suggest
that
also
an
urgent
issue
European
Alps,
but
studies
assessing
hazard
under
future
scenarios
still
rare.
Thus,
this
study
aims
analyse
impacts
of
change
on
probability
across
Alps
surrounding
areas.
In
specific,
we
(1)
explain
current
based
a
set
parameters,
(2)
map
conditions
area
using
geographically
weighted
regression.
Our
results
mainly
depends
lightning
strikes,
annual
mean
temperature,
precipitation
seasonality.
Overall,
our
indicate
increase
hazard,
which
is
already
significant
SSP126
(+15.5%),
while
highest
increases
occur
SSP370
(30.6%)
SSP585
(35.4%).
However,
less
pronounced
fire-prone
regions
southwestern
France,
will
greatly
Northern
Eastern
regions.
findings
emphasize
need
address
these
climate-related
challenges
by
decision-making
management
through
fire-smart
management.
Nevertheless,
further
efforts
needed
overcome
limitations
related
data
availability
uncertainties
scenarios.
Remote Sensing,
Journal Year:
2025,
Volume and Issue:
17(6), P. 958 - 958
Published: March 8, 2025
Vegetation
dynamics
significantly
influence
watershed
ecohydrological
processes.
Physically
based
hydrological
models
often
have
general
plant
development
descriptions
but
lack
vegetation
data
for
simulations.
Solar-induced
chlorophyll
fluorescence
(SIF)
and
the
Normalized
Difference
Index
(NDVI)
are
widely
used
in
monitoring
research.
Accurately
predicting
long-term
SIF
NDVI
can
support
of
anomalies
trends.
This
study
proposed
a
SWAT-ML
framework,
combining
Soil
Water
Assessment
Tool
(SWAT)
machine
learning
(ML),
Jinsha
River
Basin
(JRB).
The
lag
effects
that
responds
to
using
hydrometeorological
elements
were
considered
while
SWAT-ML.
Based
on
SWAT-ML,
series
from
1982
2014
reconstructed.
Finally,
spatial
temporal
characteristics
JRB
analyzed.
results
showed
following:
(1)
framework
simulate
processes
with
satisfactory
(NS
>
0.68,
R2
0.79
SWAT;
NS
0.77,
MSE
<
0.004
ML);
(2)
index’s
mean
value
increases
(the
Z
value,
significance
indicator
Mann–Kendall
method,
is
1.29
0.11
NDVI),
whereas
maximum
decreases
(Z
=
−0.20
−0.42
NDVI);
(3)
greenness
−2.93
−0.97
value)
middle
reaches.
However,
intensity
vegetation’s
physiological
activity
value=
3.24
2.68
value).
Moreover,
increase
lower
reaches
3.24,
2.68,
1.84
SIFmax,
SIFave,
NDVImax,
NDVIave,
respectively).
In
reaches,
connection
between
factors
stronger
than
NDVI.
research
developed
new
provide
reference
complex
simulation.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,
Journal Year:
2024,
Volume and Issue:
17, P. 16119 - 16138
Published: Jan. 1, 2024
s-The
Yellow
River
Basin
(YRB)
is
a
major
ecological
functional
area
in
China,
and
its
safety
development
change
have
extremely
significant
impacts
on
the
natural
environment
human
society.
However,
existing
studies
YRB
lack
spatiotemporal
characteristics
analysis
prediction
of
with
vegetation
as
core.
Therefore,
this
study
proposes
to
construct
an
index
(ESI)
based
comprehensive
multi-dimensional
evaluation
system
"vigor-pressure-state-response,"using
normalized
difference
index,
carbon
sink
indicator
parameters,
temperature,
precipitation,
digital
elevation
model,
population
density,
per
capita
gross
domestic
product
from
2000
2020.
The
ESI
were
then
analyzed
for
YRB,
long-term
short-term
memory
network
model
was
constructed
predict
trend
over
next
10
years.
According
results,
2020,
showed
fluctuating
upward
trend,
annual
average
changed
abruptly
2015
due
drastic
changes
hazardous
areas.
most
areas
stability
weak
some
areas,
overall
spatial
distribution
positive
agglomeration
characteristics.
Further,
response
landscape
complexity
different
reaches
varied.
Most
middle
positively
correlated
complexity,
while
upper
lower
not
significantly
or
negatively
correlated.
Notably,
years,
YRB's
growth
will
slow
down,
degradation
increasing,
decreasing,
currently
showing
improving.
