Forests,
Год журнала:
2024,
Номер
15(7), С. 1207 - 1207
Опубликована: Июль 12, 2024
Elucidating
the
response
mechanism
of
vegetation
change
trends
is
great
value
for
environmental
resource
management,
especially
in
coal
mining
areas
where
climate
fluctuations
and
human
activities
are
intense.
Taking
Shendong
area
as
an
example,
based
on
Google
Earth
Engine
cloud
platform,
this
study
used
kernel
Normalized
Vegetation
Index
(kNDVI)
to
spatiotemporal
characteristics
cover
during
1994–2022.
Then,
it
carried
out
attribution
analysis
through
partial
derivative
method
explore
driving
behind
greening.
The
results
showed
that
(1)
growth
rate
from
1994
2022
was
0.0052/a.
with
upward
trend
kNDVI
accounted
94.11%
total
area.
greening
effect
obvious,
would
continue
rise.
(2)
Under
scenario
regional
warming
humidifying,
responds
slightly
differently
different
climatic
factors,
positively
correlated
temperature
precipitation
85.20%
average
contribution
precipitation,
temperature,
were
0.00094/a,
0.00066/a,
0.0036/a,
respectively.
relative
rates
69.23%
30.77%,
Thus,
main
factor
changing
area,
secondary
factor.
(3)
dynamic
land
use
presents
increase
forest
under
ecological
restoration
project.
can
provide
a
scientific
basis
future
construction
help
realization
green
sustainable
development
goals.
Agriculture,
Год журнала:
2024,
Номер
14(6), С. 794 - 794
Опубликована: Май 22, 2024
The
accurate
prediction
of
crop
yields
is
crucial
for
enhancing
agricultural
efficiency
and
ensuring
food
security.
This
study
assesses
the
performance
CNN-LSTM-Attention
model
in
predicting
maize,
rice,
soybeans
Northeast
China
compares
its
effectiveness
with
traditional
models
such
as
RF,
XGBoost,
CNN.
Utilizing
multi-source
data
from
2014
to
2020,
which
include
vegetation
indices,
environmental
variables,
photosynthetically
active
parameters,
our
research
examines
model’s
capacity
capture
essential
spatial
temporal
variations.
integrates
Convolutional
Neural
Networks,
Long
Short-Term
Memory,
an
attention
mechanism
effectively
process
complex
datasets
manage
non-linear
relationships
within
data.
Notably,
explores
potential
using
kNDVI
multiple
crops,
highlighting
effectiveness.
Our
findings
demonstrate
that
advanced
deep-learning
significantly
enhance
yield
accuracy
over
methods.
We
advocate
incorporation
sophisticated
technologies
practices,
can
substantially
improve
production
strategies.
Forests,
Год журнала:
2024,
Номер
15(9), С. 1573 - 1573
Опубликована: Сен. 7, 2024
The
growth
of
vegetation
directly
maintains
the
ecological
security
coal
mining
areas.
It
is
great
significance
to
monitor
dynamic
changes
in
areas
and
study
driving
factors
spatial
division.
This
focuses
on
Yima
area
Henan
Province.
Utilizing
MODIS
multi-dimensional
explanatory
variable
data,
Theil–Sen
Median
+
Mann–Kendall
trend
analysis,
variation
index,
Hurst
optimal-parameter-based
geographical
detector
model
(OPGD)
are
employed
analyze
spatiotemporal
future
trends
EVI
(enhanced
index)
from
2000
2020.
further
investigates
underlying
that
contribute
vegetation.
results
indicate
following:
(1)
During
period
studied,
was
primarily
characterized
by
a
moderate-to-low
cover.
exhibited
significant
variation,
with
notable
pattern
“western
improvement
eastern
degradation”.
indicated
experienced
greatly
outnumbered
underwent
degradation.
Moreover,
there
an
inclination
towards
deterioration
future.
(2)
Based
optimal
parameter
geographic
detector,
it
found
2
km
scale
for
analysis
change
this
area.
combination
determined
employing
five
data
discretization
methods
selecting
interval
classification
range
5–10.
approach
effectively
addresses
subjective
bias
scales
discretization,
leading
enhanced
accuracy
identification
its
factors.
(3)
heterogeneity
influenced
various
factors,
such
as
topography,
socio-economic
conditions,
climate,
etc.
Among
these
population
density
mean
annual
temperature
were
primary
forces
area,
Q
>
0.29
elevation
being
strongest
factor
(Q
=
0.326).
interaction
between
night
light
most
powerful
explanation
0.541),
average
value
other
0.478,
which
cofactor
among
interactions.
interactions
any
two
their
impact
vegetation’s
changes,
each
had
suitable
affecting
vegetative
within
region.
research
provides
scientific
support
conserving
restoring
system.
Frontiers in Environmental Science,
Год журнала:
2024,
Номер
12
Опубликована: Авг. 6, 2024
Objectives:
The
game
between
socio-economic
development
and
ecological
has
always
been
the
core
issue
in
coal
areas,
but
internal
mechanism
of
tradeoff
cooperative
dynamic
change
ecosystem
services
mining
areas
under
long-term
mineral
resources
is
still
lacking
in-depth
research.
Methods:
Therefore,
taking
Shendong
area
as
an
example,
this
study
used
InVEST
model
to
evaluate
changes
four
major
service
functions
from
1990
2020,
namely,
water
yield
(WY),
net
primary
productivity
(NPP),
soil
conservation
(SC)
habitat
quality
(HQ).
Meanwhile,
correlation
analysis
was
explore
trade-off
synergistic
relationship
among
these
services.
On
basis,
coupling
effect
further
discussed
by
using
constraint
line
method.
Finally,
key
drivers
trade-offs/synergies
region
are
explored
geodetectors
explanations
each
influence
factor
for
RMS
errors
obtained.
Results:
results
show
that
1)
retention
decrease
first
then
increase,
increase
slowly,
mainly
southeast
area.
2)
In
terms
relationship,
all
showed
hump-like
is,
there
obvious
threshold
effect.
3)
area,
dominant
services,
occurs
quality.
4)
driving
tradeoff/synergy,
land
use
type,
temperature,
rainfall
main
factors
cause
spatial
differentiation
synergy
intensity
Conclusions:
provide
a
scientific
basis
improvement
environment
sustainable
utilization
exploitation.
Remote Sensing,
Год журнала:
2025,
Номер
17(2), С. 299 - 299
Опубликована: Янв. 16, 2025
Spatiotemporal
vegetation
changes
serve
as
a
key
indicator
of
regional
ecological
environmental
quality
and
provide
crucial
guidance
for
developing
strategies
protection
sustainable
development.
Currently,
change
studies
in
the
Yangtze
River
Basin
primarily
rely
on
Normalized
Difference
Vegetation
Index
(NDVI).
However,
NDVI
is
susceptible
to
atmospheric
soil
conditions
exhibits
saturation
phenomena
areas
with
high
coverage.
In
contrast,
kernel
(kNDVI)
demonstrates
significant
advantages
suppressing
background
noise
improving
thresholds
through
nonlinear
transformation,
thereby
enhancing
sensitivity
changes.
To
elucidate
spatiotemporal
characteristics
driving
mechanisms
Basin,
this
study
constructed
temporal
kNDVI
using
MOD09GA
data
from
2000
2022.
Considering
sectional
heterogeneity,
rather
than
analyzing
entire
region
whole
previous
studies,
research
examined
evolution
by
sections
four
statistical
metrics.
Subsequently,
Partial
Least
Squares
Path
Modeling
(PLSPM)
was
innovatively
introduced
quantitatively
analyze
influence
topographic,
climatic,
pedological,
socioeconomic
factors.
Compared
traditional
correlation
analysis
geographical
detector
method,
PLSPM,
theoretically
driven
can
simultaneously
process
path
relationships
among
multiple
latent
variables,
effectively
revealing
intensity
pathways
factors’
influences,
while
providing
more
credible
interpretable
explanations
variation
mechanisms.
Results
indicate
that
overall
exhibited
an
upward
trend,
midstream
demonstrating
most
improvement
minimal
interannual
fluctuations,
upstream
displaying
east-increasing
west-stable
spatial
pattern,
downstream
coexisting
degradation
characteristics,
these
trends
expected
persist.
Driving
mechanism
reveals
predominantly
influenced
climatic
factor,
dominated
terrain,
displayed
terrain–soil
coupling
effects.
Based
findings,
it
recommended
focus
adaptation
management
climate
change,
need
coordinate
relationship
between
topography
human
activities,
should
concentrate
controlling
negative
impacts
urban
expansion
vegetation.
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.
Land,
Год журнала:
2024,
Номер
13(9), С. 1337 - 1337
Опубликована: Авг. 23, 2024
The
Hubao–Egyu
Urban
Agglomeration
(HBEY)
was
a
crucial
ecological
barrier
in
northern
China.
To
accurately
assess
the
impact
of
climate
change
on
vegetation
growth,
it
is
essential
to
consider
effects
time
lag
and
accumulation.
In
this
study,
we
used
newly
proposed
kernel
Normalized
Difference
Vegetation
Index
(kNDVI)
as
metric
for
condition,
employed
partial
correlation
analysis
ascertain
accumulation
period
response
by
considering
different
scenarios
(No/Lag/Acc/LagAcc)
various
combinations.
Moreover,
further
modified
traditional
residual
model.
results
are
follows:
(1)
From
2000
2022,
HBEY
experienced
extensive
persistent
greening,
with
kNDVI
slope
0.0163/decade.
Precipitation
identified
dominant
climatic
factor
influencing
dynamics.
(2)
HBEY,
effect
temperature
most
distinct,
particularly
affecting
cropland
grassland.
precipitation
pronounced
(3)
Incorporating
into
models
increases
explanatory
power
impacts
dynamics
6.95%
compared
models.
Our
findings
hold
implications
regional
regulation
research.
Remote Sensing,
Год журнала:
2024,
Номер
16(17), С. 3204 - 3204
Опубликована: Авг. 29, 2024
This
study
aims
to
provide
a
comprehensive
analysis
of
the
impacts
high-intensity
coal
mining
on
vegetation
in
Liangbei
Town,
typical
deep
area
central
China.
Using
Landsat
remote
sensing
data
from
2000
2023,
processed
by
Google
Earth
Engine
(GEE)
platform,
calculates
Normalized
Difference
Vegetation
Index
(NDVI).
Temporal
and
spatial
distribution
patterns
were
assessed
using
LandTrendr
algorithm,
Sen’s
slope
estimation,
Mann–Kendall
test,
coefficient
variation,
Hurst
index.
growth
dynamics
further
analyzed
through
transfer
matrix
intensity
frameworks.
Driving
factors
influencing
trends
evaluated
local
climate
surface
deformation
variables
SAR
imagery.
Dimension:
From
annual
NDVI
Township
showed
an
upward
trend
with
rate
0.0894
(10a)−1,
peaking
at
0.51
2020.
Spatial
The
displayed
pattern
being
lower
center
higher
around
edges,
values
concentrated
between
0.4
0.51,
covering
50.34%
total
area.
Trend
Change:
Between
83.28%
experienced
significant
improvement
NDVI,
shifting
primarily
slight
improvement,
encompassing
10.98
km².
shift
exhibited
marked
tendency.
Factors:
Deep
is
eastern
part,
imagery
indicating
maximum
subsidence
0.26
m.
As
increases,
significantly
decreases.
findings
suggest
that
future,
91.13%
will
display
antipersistent
change
trend.
offers
critical
insights
into
interaction
activities
cover
can
serve
as
reference
for
environmental
evolution
management
similar
areas.
Frontiers in Environmental Science,
Год журнала:
2024,
Номер
12
Опубликована: Июль 10, 2024
The
climate
change
and
extension
of
human
activities
are
shedding
more
stresses
on
ecosystems.
Ecological
zoning
could
help
manage
the
ecosystem
deal
with
environmental
problems
effectively.
Geology
topography
affect
ecology
primarily
vital
perspectives
ecological
zoning.
It
is
worth
preliminarily
understanding
spatial-temporal
patterns
ecological-environmental
attributes
within
various
geological-topographical
zones
(GTEZs).
objective
this
study
was
to
delineate
GTEZs
present
a
analysis
soil
land
surface
parameters
GTEZs.
Firstly,
Landsat
imageries,
high
resolution
satellite
imagery
products,
digital
elevation
model,
regional
geological
map,
black
thickness,
bulk
density,
meteorological
data,
ground
survey
were
collected
conducted.
Secondly,
in
Hailun
District
delineated
according
topographical
background.
Thirdly,
composition,
monthly
temperature
(LST),
enhanced
vegetation
index
(EVI),
net
primary
productivity
(NPP)
derived
from
imageries.
Finally,
different
revealed
analyzed.
Results
show
that
sand
alluvial
plain
zone
silt-clay
undulating
mainly
possess
thick
fine-medium
granule
higher
covered
by
crops
grass,
flourish
most
August
highest
EVI
NPP.
While
sand-conglomerate
hill
zone,
sandstone
granite
relatively
thin
medium-coarse
lower
forest,
June
July,
has
yearly
total
With
thinner
thickness
NPP,
tend
have
vulnerability
disturbance
contribution
carbon
neutrality
target.