Land,
Journal Year:
2024,
Volume and Issue:
13(12), P. 2048 - 2048
Published: Nov. 29, 2024
Studying
the
response
of
runoff
to
climate
change
and
land
use/cover
has
guiding
significance
for
watershed
planning,
water
resource
ecological
environment
protection.
Especially
in
Yellow
River
Basin,
which
a
variable
fragile
ecology,
such
research
is
more
important.
This
article
takes
Huangfuchuan
Basin
(HFCRB)
middle
reaches
as
area,
analyzes
impact
scenarios
on
by
constructing
SWAT
model.
Using
CMIP6
GCMs
obtain
future
data
CA–Markov
model
predict
use
data,
two
are
coupled
estimate
process
HFCRB,
uncertainty
estimated
decomposed
quantified.
The
results
were
follows:
①
good
adaptability
HFCRB.
During
calibrated
period
validation
period,
R2
≥
0.84,
NSE
0.8,
|PBIAS|
≤
17.5%,
all
meet
evaluation
criteria.
②
There
negative
correlation
between
temperature
runoff,
positive
precipitation
runoff.
Runoff
sensitive
rise
increase.
③
types
order
cultivated
>
grassland
forest
land.
④
variation
range
under
combined
effects
LUCC
that
single
or
scenarios.
increase
SSP126,
SSP245,
SSP585
10.57%,
25.55%,
31.28%,
respectively.
Precipitation
main
factor
affecting
changes
Model
source
prediction.
Forests,
Journal Year:
2024,
Volume and Issue:
15(6), P. 1039 - 1039
Published: June 16, 2024
Revealing
the
relationship
between
land
use
changes
and
soil
erosion
provides
a
reference
for
formulating
future
strategies.
This
study
simulated
historical
based
on
RULSE
GeoSOS-FLUS
models
used
random
forest
model
to
explain
relative
importance
of
natural
anthropogenic
factors
erosion.
The
main
conclusions
are
as
follows:
(1)
From
1990
2020,
significant
in
occurred
Kunming,
with
continuous
reduction
woodland,
grassland,
cropland,
being
converted
into
construction
land,
which
grew
by
195.18%
compared
1990.
(2)
During
this
period,
modulus
decreased
from
133.85
t/(km²·a)
130.32
loss
74,485.46
t/a,
mainly
due
conversion
cropland
ecological
lands
(woodland,
grassland).
(3)
expansion
will
continue,
it
is
expected
that
2050,
decrease
3.77
t/(km²·a),
4.27
3.27
under
development,
rapid
protection
scenarios,
respectively.
However,
scenario,
increased
0.26
2020.
(4)
spatial
pattern
influenced
both
factors,
human
activities
intensify
future,
influence
further
increase.
Traditionally,
thought
increase
loss.
Our
may
offer
new
perspective
provide
planning
management
Kunming.
Ecological Informatics,
Journal Year:
2024,
Volume and Issue:
82, P. 102763 - 102763
Published: Aug. 11, 2024
Urban
open
spaces
offer
both
environmental
and
social
benefits.
However,
comprehensive
studies
that
integrate
quantitative
qualitative
evaluations
of
the
factors
driving
change
in
these
their
long-term
predictions
are
lacking.
Most
existing
concentrate
on
land-use
development
rather
than
conducting
empirical
research
specific
to
urban
Shanghai.
This
study
addresses
this
gap
by
employing
a
geographic
detector
(geodetector)
analyze
influence
various
open-space
changes.
These
were
then
used
as
weight
values
multicriteria
CA-Markov
model
simulate
predict
Shanghai's
2050.
The
advantage
analyzing
forces
lies
ability
capture
multifactor
synergy
influencing
spaces,
aligning
with
aim
quantitatively
evaluate
interaction
between
natural,
climatic,
socioeconomic
factors.
Additionally,
semi-structured
interviews
conducted
10
policymakers
planners
assess
reliability
predictions.
results
indicate
primary
drivers
spaces.
Specifically,
normalized
difference
vegetation
index
(NDVI)
population
density
(PD)
emerged
most
influential
variables.
For
prediction
outcomes,
unconstrained
scenario
predicts
decrease
area
from
5610.94
km2
2020
5124.36
planning
intervention
anticipates
minimal
changes
total
almost
no
floating
economic
rapid
decline
Experts
evaluated
three
scenarios
confirmed
accuracy
models.
methods
findings
can
support
zoning
for
systems
other
cities
regions.
Remote Sensing,
Journal Year:
2025,
Volume and Issue:
17(3), P. 492 - 492
Published: Jan. 31, 2025
In
the
past
two
decades,
Islamabad
has
experienced
significant
urbanization.
As
a
result
of
inadequate
urban
planning
and
spatial
distribution,
it
significantly
influenced
land
use–land
cover
(LULC)
changes
green
areas.
To
assess
these
changes,
there
is
an
increasing
need
for
reliable
appropriate
information
about
Landsat
imagery
categorized
into
four
thematic
classes
using
supervised
classification
method
called
support
vector
machine
(SVM):
built-up,
bareland,
vegetation,
water.
The
results
change
detection
post-classification
show
that
city
region
increased
from
6.37%
(58.09
km2)
in
2000
to
28.18%
(256.49
2020,
while
vegetation
decreased
46.97%
(428.28
34.77%
(316.53
bareland
45.45%
(414.37
35.87%
(326.49
km2).
Utilizing
modeler
(LCM),
forecasts
future
conditions
2025,
2030,
2035
are
predicted.
artificial
neural
network
(ANN)
model
embedded
IDRISI
software
18.0v
based
on
well-defined
backpropagation
(BP)
algorithm
was
used
simulate
sprawl
considering
historical
pattern
2015–2020.
Selected
landscape
morphological
measures
were
quantify
analyze
structure
patterns.
According
data,
area
grew
at
pace
4.84%
between
2015
2020
will
grow
rate
1.47%
2035.
This
growth
metropolitan
encroach
further
bareland.
If
existing
patterns
persist
over
next
ten
years,
drop
mean
Euclidian
Nearest
Neighbor
Distance
(ENN)
patches
anticipated
(from
104.57
m
101.46
2020–2035),
indicating
accelerated
transformation
landscape.
Future
prediction
modeling
revealed
would
be
huge
increase
49%
areas
until
year
compared
2000.
rapidly
urbanizing
areas,
urgent
enhance
use
laws
policies
ensure
sustainability
ecosystem,
development,
preservation
natural
resources.
IOP Conference Series Earth and Environmental Science,
Journal Year:
2025,
Volume and Issue:
1443(1), P. 012037 - 012037
Published: Jan. 1, 2025
Abstract
Understanding
the
maximum
percentage
of
urban
area
within
an
administrative
region,
such
as
Semarang
City,
necessitates
examination
spatial
planning
schemes,
development
regulations,
and
local
government
policies.
Concurrently,
cellular
automata
Markov
chain
approaches
can
be
used
to
predict
how
cities
will
grow
in
future
accurately.
This
study
aims
define
growth
boundary
City
by
integrating
with
predictive
modeling
techniques.
The
Cellular
automata-Markov
(CA-MC)
method
predicts
developments
based
on
current
land
use
patterns.
seeks
delineate
areas
suitable
for
using
data
analysis
while
preserving
critical
ecological
agricultural
zones.
findings
this
research
contribute
formulating
informed
policies
aimed
at
achieving
balanced
expansion
environmental
conservation
Semarang,
thus
fostering
resilient
inclusive
landscapes
city.
