Remote Sensing,
Journal Year:
2023,
Volume and Issue:
15(15), P. 3762 - 3762
Published: July 28, 2023
Land
use
simulations
are
critical
in
predicting
the
impact
of
land
change
(LUC)
on
Earth.
Various
assumptions
and
policies
influence
structure
a
key
factor
decisions
made
by
policymakers.
Meanwhile,
spatial
autocorrelation
effect
between
types
has
rarely
been
considered
existing
simulation
models,
accuracy
needs
to
be
further
improved.
Thus,
this
study,
driving
mechanisms
LUC
analyzed.
The
quantity
demand
distribution
predicted
under
natural
development
(ND),
economic
(ED),
ecological
protection
(EP),
sustainability
(SD)
scenarios
Zhengzhou
based
coupled
Multi-Objective
Programming
(MOP)
model
Patch-generating
Use
Simulation
(PLUS)
considering
Spatial
Autocorrelation
(PLUS-SA).
We
conclude
following.
(1)
type
was
mainly
cultivated
land,
83.85%
for
urban
expansion
from
2000
2020.
reduction
forest
2010
2020
less
than
that
due
implementation
policy
which
farmland
is
transformed
back
into
forests.
(2)
PLUS-SA
better
traditional
PLUS
Future
(FLUS)
its
Kappa
coefficient,
overall
accuracy,
FOM
were
0.91,
0.95,
0.29,
respectively.
(3)
Natural
factors
(temperature,
precipitation,
DEM)
contributed
significantly
increase
forest,
grass,
construction
greatly
affected
socioeconomic
(population,
GDP,
proximity
town).
(4)
will
more
line
with
current
requirements
sustainable
SD
scenario,
benefits
0.75
×
104
billion
CNY
1.71
CNY,
respectively,
2035
compared
those
we
proposed
had
higher
Compared
FLUS
our
research
framework
can
provide
basis
decision-makers
formulate
achieve
high-quality
development.
Ecological Indicators,
Journal Year:
2023,
Volume and Issue:
147, P. 110009 - 110009
Published: Feb. 14, 2023
Land
use
is
a
crucial
factor
affecting
ecosystem
service
value
(ESV),
and
forecasting
future
land
changes
ESV
response
can
guide
urban
planning
sustainable
development
decisions.
However,
the
traditional
Cellular
Automata
(CA)
model
supposes
that
each
cell
has
only
one
type
at
time
step,
neglects
mixed
structure
proportional
distribution
of
units,
does
not
take
into
account
its
quantitative
continuous
dynamic
change,
lacks
exploration
quantity
spatial
pattern
optimization.
This
study
employed
novel
mixed-cell
cellular
automata
(MCCA)
approach,
coupled
with
system
dynamics
(SD)
to
predict
spatiotemporal
under
natural
increase
scenario
(NIS),
economic
(EDS)
ecological
protection
(EPS)
in
Xi’an,
China,
2030.
The
equivalent
coefficient
method
was
utilized
investigate
heterogeneity
sensitivity
ESV.
results
demonstrated
SD-MCCA
exhibited
remarkable
prediction
accuracy
robustness.
main
2000–2015
were
due
expansion,
conversion
arable
construction
land,
between
grassland
land.
total
increased
from
19554.36×106
CNY
2000
19618.39×106
EPS
2030,
contribution
climate
regulation
hydrological
highest.
Spatial
revealed
certain
regularity,
high
region
chiefly
concentrated
woodland
favorable
conditions.
variations
NIS
improved
ESV,
while
had
negative
transformations
EDS.
research
provides
new
way
identify
relationship
utilization
scenarios
which
great
significance
for
management
resources
formulation
compensation
standards.
Remote Sensing,
Journal Year:
2022,
Volume and Issue:
14(19), P. 4751 - 4751
Published: Sept. 23, 2022
Land
use
and
land
cover
(LULC)
contribute
to
both
carbon
storage
emissions.
Therefore,
regulating
the
LULC
is
an
important
means
of
achieving
neutrality
under
global
environmental
change.
Here,
West
Liaohe
River
Basin,
a
semiarid
watershed,
was
taken
as
case
study.
Based
on
assessment
emissions
induced
by
from
2000–2020,
we
set
up
three
different
coupled
shared
socioeconomic
pathway
(SSP)
representative
concentration
(RCP)
scenarios
(SSP119,
SSP245,
SSP585),
2030–2060,
optimize
LULC.
Then,
patterns
each
scenario
were
simulated
using
patch-generating
simulation
(PLUS)
model,
corresponding
changes
in
compared
analyzed.
It
found
that,
since
2000,
with
expansion
forest,
cropland,
construction
land,
well
degradation
grassland,
have
significantly
increased,
but
increase
lower
than
that
The
simulations
revealed
when
LULC,
mainly
including
protection
ecological
such
forest
grassland
western
southern
edges
basin,
control
management
cropland
northeast
central
parts
there
will
be
significant
reduction
2030–2060.
This
indicates
zone-based
measures
rational
regulation
can
achievement
study
area.
Supported
results
this
study,
direct
decision-making
basis
for
policy
promote
regional
sustainable
development
undertaken
basin.
also
provides
reference
low-carbon
other
regions.
