Land Degradation and Development,
Год журнала:
2024,
Номер
35(17), С. 5295 - 5307
Опубликована: Окт. 10, 2024
ABSTRACT
Overgrazing
affects
the
grass‐livestock
balance
and
endangers
grassland
ecological
security.
Despite
extensive
studies
conducted
on
identifying
quantifying
grazing
intensity,
there
is
still
room
for
improvement
in
research
gridding
particularly
areas
with
limited
data
Qinghai–Tibet
Plateau.
Therefore,
we
proposed
a
intensity
spatialization
method
using
geographically
weighted
random
forest
(GWRF)
to
gain
further
insights
into
spatial
heterogeneity
of
alpine
intensity.
This
incorporates
multiple
remote
sensing
related
human
activities
natural
factors,
as
well
annual
livestock
statistics
at
township
level
over
several
years,
while
adequately
considering
autocorrelation
Additionally,
employed
Lindeman
Merenda
Gold
(LMG),
geographical
detector
model,
structural
equation
model
(SEM)
assess
contribution
influence
path
driving
factors
We
also
utilize
partial
correlation
analysis
dual‐phase
mapping
examine
impact
distribution
The
results
demonstrate
that
GWRF‐based
accurately
predicts
by
demonstrating
its
consistency
township‐scale
(
R
2
=
0.92
p
<
0.01),
RMSE
1.07).
provides
valuable
technical
support
pastoral
availability.
evaluate
trends
observe
an
increase
Gar
Purang
counties.
Furthermore,
population
density,
normalized
difference
vegetation
index
(NDVI),
temperature
are
identified
three
influential
affecting
areas.
other
indirectly
influencing
density
NDVI
levels,
their
interactions
amplify
overall
influence.
technique
has
demonstrated
significant
45.92%
0.01)
study
area,
emphasizing
substantial
Our
novel
framework
spatially
analyzing
unraveling
intricated
mechanisms
behind
spatiotemporal
changes,
Remote Sensing,
Год журнала:
2025,
Номер
17(3), С. 488 - 488
Опубликована: Янв. 30, 2025
Net
primary
productivity
(NPP)
is
a
core
ecological
indicator
within
terrestrial
ecosystems,
representing
the
potential
of
vegetation
growth
to
offset
anthropogenic
carbon
emissions.
Thus,
assessing
NPP
in
given
region
crucial
for
promoting
regional
restoration
and
sustainable
development.
This
study
utilized
CASA
model
GEE
calculate
annual
average
Shandong
Province
(2001–2020).
Through
trend
analysis,
Moran’s
Index,
PLS−SEM,
spatiotemporal
evolution
driving
factors
were
explored.
The
results
show
that:
(1)
From
2001
2020,
showed
an
overall
increasing
trend,
rising
from
254.96
322.49
g
C·m⁻2/year.
shift
was
accompanied
by
gradual
eastward
movement
centroid,
indicating
significant
spatial
changes
productivity.
(2)
Regionally,
47.9%
experienced
improvement,
27.6%
saw
slight
20.1%
exhibited
degradation,
highlighting
notable
heterogeneity.
(3)
Driver
analysis
that
climatic
positively
influenced
across
all
four
periods
(2005,
2010,
2015,
2020),
with
strongest
impact
2015
(coefficient
=
0.643).
Topographic
such
as
elevation
slope
also
had
positive
effects,
peaking
at
0.304
2015.
In
contrast,
human
activities,
especially
GDP
nighttime
light
intensity,
negatively
impacted
NPP,
negative
effect
2010
−0.567).
These
findings
provide
valuable
scientific
evidence
ecosystem
management
offer
key
insights
development
strategies
national
level.
Remote Sensing,
Год журнала:
2024,
Номер
16(4), С. 682 - 682
Опубликована: Фев. 14, 2024
As
a
region
susceptible
to
the
impacts
of
climate
change,
evaluating
temporal
and
spatial
variations
in
ecological
environment
quality
(EEQ)
potential
influencing
factors
is
crucial
for
ensuring
security
Tibetan
Plateau.
This
study
utilized
Google
Earth
Engine
(GEE)
platform
construct
Remote
Sensing-based
Ecological
Index
(RSEI)
examined
dynamics
Plateau’s
EEQ
from
2000
2022.
The
findings
revealed
that
RSEI
Plateau
predominantly
exhibited
slight
degradation
trend
2022,
with
multi-year
average
0.404.
Utilizing
SHAP
(Shapley
Additive
Explanation)
interpret
XGBoost
(eXtreme
Gradient
Boosting),
identified
natural
as
primary
influencers
on
Plateau,
temperature,
soil
moisture,
precipitation
variables
exhibiting
higher
values,
indicating
their
substantial
contributions.
interaction
between
temperature
showed
positive
effect
RSEI,
value
increasing
rising
precipitation.
methodology
results
this
could
provide
insights
comprehensive
understanding
monitoring
dynamic
evolution
amidst
context
change.
Land Degradation and Development,
Год журнала:
2024,
Номер
35(11), С. 3606 - 3626
Опубликована: Май 3, 2024
Abstract
Intensified
human
activities
have
been
seriously
threatening
the
structure
and
ecological
processes
of
ecosystems,
resulting
in
habitat
degradation.
Therefore,
coordinating
coupling
between
quality
(HQ)
is
crucial
for
high‐quality
sustainable
regional
development
well‐being.
This
study
evaluated
HQ
Pearl
River
Delta
(PRD)
urban
agglomeration
China
from
2000
to
2020
using
footprint
index
(HFI)
integrated
valuation
ecosystem
services
tradeoffs
model.
Then,
we
employed
bivariate
spatial
autocorrelation
a
coordination
degree
(CCD)
model
explore
synergistic
relationship
HQ.
The
results
show
that
changes
were
predominantly
driven
by
activities.
gradual
outward
expansion
resulted
significant
Slight
improvement
restoration
outskirts
cannot
offset
losses
caused
urbanization.
During
period,
high‐HQ
low‐HFI
clusters
decreased
1.02%,
while
low‐HQ
high‐HFI
increased
4.67%,
two
main
clustering
types
PRD.
Despite
CCD
HFI
after
2010,
continuous
characteristics
significantly
lagged
type
lagged.
showed
an
inverted
U‐shaped
with
CCD.
peaks
during
2000–2020
corresponded
decreasing
0.711
0.566.
indicates
risk
decoupling
gradually
increased.
Furthermore,
levels
different
exhibited
varying
over
time.
These
reveal
spatiotemporal
dislocation
urbanization
induced
nonstationarity
Urbanization
exacerbates
imbalance
biodiversity
conservation.
suggest
reasonably
delimiting
boundaries,
controlling
scale
sprawl,
strengthening
protection
areas
undergoing
rapid
In
addition,
advocate
division
barrier
zones,
buffer
built‐up
areas,
each
tailored
management
measures.
Our
findings
can
provide
important
reference
agglomerations.
