Spatiotemporal Changes of Vegetation Growth and Its Influencing Factors in the Huojitu Mining Area from 1999 to 2023 Based on kNDVI
Remote Sensing,
Год журнала:
2025,
Номер
17(3), С. 536 - 536
Опубликована: Фев. 5, 2025
Vegetation
indices
are
important
representatives
of
plant
growth.
Climate
change
and
human
activities
seriously
affect
vegetation.
This
study
focuses
on
the
Huojitu
mining
area
in
Shendong
region,
utilizing
kNDVI
index
calculated
via
Google
Earth
Engine
(GEE)
cloud
platform.
The
Mann–Kendall
mutation
test
linear
regression
analysis
were
employed
to
examine
spatiotemporal
changes
vegetation
growth
over
a
25-year
period
from
1999
2023.
Through
correlation
analysis,
geographic
detector
models,
land
use
map
fusion,
combined
with
climate,
topography,
soil,
mining,
data,
this
investigates
influencing
factors
evolution.
key
findings
as
follows:
(1)
is
more
suitable
for
analyzing
compared
NDVI.
(2)
Over
past
25
years,
has
exhibited
an
overall
fluctuating
upward
trend,
annual
rate
0.0041/a.
average
value
0.121.
Specifically,
initially
increased
gradually,
then
rapidly
increased,
subsequently
declined
rapidly.
(3)
significantly
improved,
areas
improved
accounting
89.08%
total
area,
while
degraded
account
11.02%.
(4)
Precipitation
air
temperature
primary
natural
fluctuations
precipitation
being
dominant
factor
(r
=
0.81,
p
<
0.01).
spatial
heterogeneity
influenced
by
use,
soil
nutrients,
activities,
having
greatest
impact
(q
0.43).
Major
contribute
46.45%
improvement
13.43%
degradation.
provide
scientific
basis
ecological
planning
development
area.
Язык: Английский
Nonlinearity of China's Carbon Sink Increasing and Its Nonlinear Relationship With Land Use Patterns
Land Degradation and Development,
Год журнала:
2025,
Номер
unknown
Опубликована: Май 19, 2025
ABSTRACT
China's
terrestrial
carbon
sink,
quantified
by
net
ecosystem
productivity
(NEP),
has
exhibited
significant
yet
spatially
heterogeneous
growth
over
the
past
four
decades,
driven
climate
change,
land
use
transitions,
and
ecological
restoration
policies.
However,
nonlinearity
of
NEP
enhancement
its
coupling
mechanisms
with
dynamic
patterns
remain
poorly
understood.
This
study
integrates
linear
trend
analysis,
ensemble
empirical
mode
decomposition,
boosted
regression
tree
(BRT)
modeling
to
systematically
unravel
nonlinear
characteristics
trends
(1981–2019)
their
landscape‐mediated
drivers
across
ecoregions.
Key
findings
reveal
that:
(1)
While
43.75%
area
showed
a
increase
in
NEP,
only
13.46%
monotonic
(Trend
IN
),
whereas
16.46%
displayed
reversals
DE‐TO‐IN
highlighting
dominant
dynamics.
(2)
Land
pattern
indices
(LUPI)—spanning
fragmentation
(PD),
dominance
(LPI),
connectivity
(CONTAG),
shape
complexity
(AWMPFD),
diversity
(SHDI)—demonstrated
divergent
trajectories:
South
China
Tibetan
Plateau
(TP)
experienced
increasing
(PD
increases)
alongside
declining
(CONTAG
decreases),
while
Northwest
(NWC)
inverse
patterns,
reflecting
region‐specific
anthropogenic
pressures.
(3)
Trend
regions
(e.g.,
NWC
TP)
were
governed
LPI
CONTAG,
where
threshold
exceedance
(slope
>
0)
stabilized
accumulation.
The
reversal
relied
on
PD
AWMPFD,
initial
declines
edge
effects
<
preceded
recovery.
Notably,
responses
LUPI
gradients
U‐shaped
thresholds
=
monotonically
but
shifts
zones,
underscoring
legacy
historical
landscape
configurations.
By
bridging
theory
this
advances
understanding
how
multiscale
regulate
sequestration,
offering
actionable
insights
for
adaptive
management
support
“dual
carbon”
goals.
Язык: Английский
Analysis of Vegetation Changes and Driving Factors on the Qinghai-Tibet Plateau from 2000 to 2022
Forests,
Год журнала:
2024,
Номер
15(12), С. 2188 - 2188
Опубликована: Дек. 12, 2024
This
study
assesses
the
impact
of
climate
change
and
human
activities
on
vegetation
dynamics
(kNDVI)
Qinghai-Tibet
Plateau
(QTP)
between
2000
2022,
considering
both
lag
cumulative
effects.
Given
QTP’s
high
sensitivity
to
activities,
it
is
imperative
understand
their
effects
for
sustainable
development
regional
national
terrestrial
ecosystems.
Using
MOD13Q1
NDVI
activity
data,
we
applied
methods
such
as
Sen-MK,
effect
analysis,
improved
residual
geographical
detector
analysis.
The
outcomes
were
follows.
(1)
kNDVI
QTP
showed
an
overall
fluctuating
growth
trend
2022;
regions
more
significant
than
degraded
regions,
with
primarily
distributed
in
humid
semi-humid
areas
favorable
conditions,
arid
semi-arid
areas;
this
implies
that
conditions
have
a
changes
QTP.
(2)
analysis
revealed
temperature
precipitation
substantial
0
months
1
month
temperature,
2
precipitation,
respectively.
(3)
Improved
based
positively
contributed
66%
QTP,
suggesting
notable
positive
activities.
Geographical
indicated
that,
among
different
factors
affecting
changes,
explanatory
power
2005
2015
ranked
X3
(livestock
density)
>
X1
(population
X2
(per
capita
GDP)
X4
(artificial
afforestation
X5
(land
use
type),
2020,
X2.
density
land
type
has
relatively
increased,
indicating
recent
efforts
ecological
protection
restoration
including
developing
artificial
forest
programs,
considerably
greening.
Язык: Английский
Applicability of Different Assimilation Algorithms in Crop Growth Model Simulation of Evapotranspiration
Agronomy,
Год журнала:
2024,
Номер
14(11), С. 2674 - 2674
Опубликована: Ноя. 14, 2024
Remote
sensing
spatiotemporal
fusion
technology
can
provide
abundant
data
source
information
for
assimilating
crop
growth
model
data,
enhancing
monitoring,
and
providing
theoretical
support
irrigation
management.
This
study
focused
on
the
winter
wheat
planting
area
in
southeastern
part
of
Loess
Plateau,
a
typical
semi-arid
region,
specifically
Linfen
Basin.
The
SEBAL
ESTARFM
were
used
to
obtain
8
d,
30
m
evapotranspiration
(ET)
period
wheat.
Then,
based
‘localization’
CERES-Wheat
model,
fused
results
incorporated
into
assimilation
process
further
determine
optimal
method.
indicate
that
(1)
ET
accurately
capture
spatial
details
(R
>
0.9,
p
<
0.01).
(2)
calibrated
characteristic
curve
effectively
reflects
variation
throughout
while
being
consistent
with
trend
magnitude
variation.
(3)
correlation
between
Ensemble
Kalman
filter
(EnKF)
(R2
=
0.7119,
0.01)
was
significantly
higher
than
Four-Dimensional
Variational
(4DVar)
0.5142,
particle
(PF)
0.5596,
guidance
improve
yield
water
use
efficiency
which
will
help
promote
sustainable
agricultural
development.
Язык: Английский