Optimization of landscape ecological risk assessment method and ecological management zoning considering resilience
Journal of Environmental Management,
Год журнала:
2025,
Номер
376, С. 124586 - 124586
Опубликована: Фев. 18, 2025
Язык: Английский
A Prediction–Interaction–Driving Framework for Ecosystem Services Under Climate Change and Human Activities: A Case Study of Zoigê County
Land,
Год журнала:
2025,
Номер
14(3), С. 441 - 441
Опубликована: Фев. 20, 2025
Under
climate
change
and
human
activities,
ecosystem
service
(ES)
research
lacks
systematic
approaches
scientific
depth.
This
study
develops
a
comprehensive
framework
integrating
advanced
models
to
predict
ESs,
analyze
interactions,
identify
key
drivers,
assess
spatial
effects
on
the
Zoigê
Plateau.
The
results
indicate
following:
(1)
From
2000
2020
across
three
2040
scenarios,
water
conservation
(WC)
improves,
while
carbon
storage
(CS)
habitat
quality
(HQ)
decline,
leading
overall
ES
degradation.
Core
areas
face
rising
degradation
risks
from
9%
29%
under
increasing
environmental
stress
(SSP119
SSP585).
(2)
importance
follows
HQ
>
CS
SC
WC,
with
bivariate
interactions
outperforming
single-factor
effects.
Future
scenarios
show
weakened
correlating
higher
ecological
stress,
indicating
stability
risks.
(3)
Land
use
(>40%
explanatory
power)
is
primary
driver,
urban
expansion,
slope,
evapotranspiration,
precipitation
contribute
(6–12%).
(4)
drivers
showed
weak
patterns
but
became
more
stable
future
suggesting
stronger
control.
provides
methodological
paradigm
for
analysis
supports
planning
in
alpine
wetland–grassland
regions.
Язык: Английский
Integrating ecosystem services supply-demand balance into landscape ecological risk and its driving forces assessment in Southwest China
Journal of Cleaner Production,
Год журнала:
2024,
Номер
unknown, С. 143671 - 143671
Опубликована: Сен. 1, 2024
Язык: Английский
Driving force analysis and multi-scenario simulation of landscape ecological risk in the Jianghan Plain, China
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Янв. 9, 2025
As
a
key
food
production
base,
land
use
changes
in
the
Jianghan
Plain
(JHP)
significantly
affect
surface
landscape
structure
and
ecological
risks,
posing
challenges
to
security.
Assessing
risk
of
JHP,
identifying
its
drivers,
predicting
trends
under
different
scenarios
can
provide
strategic
support
for
management
safeguarding
security
JHP.
In
this
study,
(LER)
index
was
constructed
by
integrating
indices
from
2000
2020,
firstly
analyzing
spatiotemporal
characteristics,
subsequently
influencing
factors
using
GeoDetector
model,
finally,
simulating
four
Markov-PLUS
model.
The
results
showed
that:
(1)
Cropland
dominant
use,
most
significant
decreases
increases
occurred
cropland
built-up
land,
respectively.
primary
conversion
interconversion
water
body.
(2)
LER
exhibited
trend
initially
increasing
decreasing,
levels
were
predominantly
medium
higher.
spatial
pattern
high
southeast
low
central
northern
areas.
(3)
patterns
resulted
combined
effect
multiple
mainly
influenced
natural
environment,
which
NDVI
first
factor.
(4)
intensity
higher
economic
development
than
protection
scenarios,
predicted
2030
former
latter
two.
These
findings
are
important
formulating
scientific
reasonable
planning
strategies
balance
growth
preservation
maintain
sustainability
Язык: Английский
Ecological Management Zoning Identification by Coupling Blue-Green and Gray Infrastructure Networks: A Case Study of Guizhou Province, China
Land,
Год журнала:
2025,
Номер
14(1), С. 204 - 204
Опубликована: Янв. 20, 2025
Ecological
management
zoning
is
crucial
for
maintaining
regional
ecological
security
and
realizing
differentiated
urban
governance.
However,
the
existing
methods
are
overly
focused
on
functional
attributes
fail
to
adequately
consider
impacts
of
human
activities,
resulting
in
an
insufficiently
rational
allocation
resources.
Taking
Guizhou
Province
as
example,
using
multi-source
data
spatial
analysis
tools,
this
study
proposed
framework
based
coupling
blue-green
infrastructure
(BGI)
network
gray
(GI)
network.
The
results
indicated
that
(1)
BGI
area
included
179
sources,
with
a
total
54,228.80
km2,
232
corridors.
(2)
There
were
53
sources
GI
network,
totaling
709.19
corridors
first,
second,
third
levels
11,469.31
km,
6703.54
5341.30
respectively.
(3)
606
barrier
points
identified,
mainly
distributed
central
part
area,
disturbance
zone
was
1132.50
which
had
largest
distribution
Qiandongnan,
followed
by
Qiannan.
(4)
At
county
scale,
five
zones
identified
four
indicators,
namely,
source
ratio
corridor
density
ratio,
point.
Then,
we
targeted
optimizations
restorations
each
zone.
This
organically
linked
anthropogenic
identify
zones,
will
provide
new
perspectives
synergies
between
protection
economic
development.
Язык: Английский
Exploring the interactions and driving factors among typical ecological risks based on ecosystem services: A case study in the Sichuan-Yunnan ecological barrier area
Ecological Indicators,
Год журнала:
2024,
Номер
170, С. 113000 - 113000
Опубликована: Дек. 16, 2024
Язык: Английский
Ecological Zoning Study on the Coupling of Land Use Intensity and Landscape Ecological Risk in Western Jilin: A Production–Living–Ecological Space Perspective
Sustainability,
Год журнала:
2024,
Номер
16(24), С. 10992 - 10992
Опубликована: Дек. 14, 2024
Ecological
zoning
is
essential
for
optimizing
regional
ecological
management
and
improving
environmental
protection
efficiency.
While
previous
studies
have
primarily
focused
on
the
independent
analysis
of
land
use
intensity
(LUI)
landscape
risk
(LER),
there
has
been
limited
research
their
coupled
relationship.
This
study,
conducted
in
Western
Jilin
(WJL),
introduces
an
innovative
method
based
Production–Living–Ecological
Space
(PLES)
framework,
which
explores
interactions
between
LUI
LER,
filling
a
gap
existing
research.
The
employs
coupling
coordination
degree
(CCD)
model
Geographic
Information
System
(GIS)
technology
to
construct
LUI-ERI
model,
used
delineate
zones.
results
indicate
that:
(1)
PLES
study
area
predominantly
production
space
(PS),
with
largest
transfer
being
(PES)
2784.23
km2,
most
significant
PS
3112.33
km2.
(2)
Between
2000
2020,
both
LER
exhibited
downward
trends,
opposite
spatial
distribution
characteristics.
“middle”
zone
“highest”
were
dominant
types,
covering
approximately
46%
45%
total
area,
respectively.
(3)
showed
polarized
trend,
overall
upward
trajectory
from
2020.
(4)
WJL
can
be
categorized
into
core
(ECP)
zone,
potential
governance
(EPG)
comprehensive
monitoring
(ECM)
optimization
(EO)
restoration
(ER)
occupying
61.63%
area.
provides
novel
perspective
offers
systematic
scientific
basis
planning.
Язык: Английский