Jurnal Sylva Lestari,
Год журнала:
2024,
Номер
12(2), С. 242 - 257
Опубликована: Март 12, 2024
A
management
unit-based
land
cover
change
analysis
was
examined
in
Kahayan
Tengah
Forest
Management
Unit
(FMU)
to
understand
past,
present,
and
future
assist
forest
planning
FMU.
This
study
aims
model
2011
2016,
predict
2021,
simulate
2026
Modeling
prediction
simulation
using
MOLUSCE
from
the
QGIS
plugin.
The
results
revealed
that
agricultural
experienced
significant
increase
total
area
during
2011–2016.
potential
transitions
2016
with
Artificial
Neural
Network
method
showed
a
Kappa
coefficient
of
0.701
good
category,
2021
Cellular
Automata
0.672
category.
By
2026,
will
continue
while
tends
remain
stable
its
area.
managed
simulated
accuracy.
Thus,
this
data
information
can
support
Keywords:
unit,
Tengah,
change,
prediction,
Sustainable Cities and Society,
Год журнала:
2023,
Номер
96, С. 104653 - 104653
Опубликована: Май 15, 2023
Climate
change
and
rapid
urbanisation
exacerbated
multiple
urban
issues
threatening
sustainability.
Numerous
studies
integrated
machine
learning
remote
sensing
to
monitor
develop
mitigation
strategies
for
However,
few
comparatively
analysed
joint
applications
of
This
paper
presents
a
systematic
review
formulates
framework
integrating
in
studies.
The
literature
analysis
reveals:
Most
occurred
Asia,
Europe,
North
America,
driven
by
technical
ethical
factors,
highlighting
responsible
approaches
data-scarce
regions;
Reviewed
prioritised
physical
spatial
aspects
over
socioeconomic
requiring
multi-source
data
comprehensive
analysis;
Conventional
satellite,
aerial
images,
Lidar
are
prevalent
due
affordability,
quality,
accessibility;
Although
supervised
dominates,
unsupervised
methods
algorithm
selection
paradigms
require
exploration;
Integration
offers
accurate
results
thorough
image
processing
analytics,
while
acquisition
decision-making
necessitate
human
supervision.
provides
an
integrative
sensing,
enriching
insights
into
their
potential
analytics.
study
informs
planning
policymaking
promoting
efficient
management
via
enhanced
integration,
bolstering
data-driven
decision-making.
Heliyon,
Год журнала:
2023,
Номер
9(11), С. e21253 - e21253
Опубликована: Окт. 24, 2023
The
identification
of
land
use/land
cover
(LULC)
changes
is
important
for
monitoring,
evaluating,
and
preserving
natural
resources.
In
the
Kurdistan
region,
utilization
remotely
sensed
data
to
assess
effectiveness
machine
learning
algorithms
(MLAs)
LULC
classification
change
detection
analysis
has
been
limited.
This
study
monitors
analyzes
in
area
from
1991
2021
using
a
quantitative
approach
with
multi-temporal
Landsat
imagery.
Five
MLAs
were
applied:
Support
Vector
Machine
(SVM),
Random
Forest
(RF),
Artificial
Neural
Network
(ANN),
K-Nearest
Neighbor
(KNN),
Extreme
Gradient
Boosting
(XGBoost).
results
showed
that
RF
algorithm
produced
most
accurate
maps
three-decade
period,
accompanied
by
high
kappa
coefficient
(0.93-0.97)
compared
SVM
(0.91-0.95),
ANN
(0.91-0.96),
KNN
(0.92-0.96),
XGBoost
(0.92-0.95)
algorithms.
Consequently,
classifier
was
implemented
categorize
all
obtainable
satellite
images.
Socioeconomic
throughout
these
transition
periods
revealed
results.
Rangeland
barren
areas
decreased
11.33
%
(-402.03
km2)
6.68
(-236.8
km2),
respectively.
transmission
increases
13.54
(480.18
3.43
(151.74
0.71
(25.22
occurred
agricultural
land,
forest,
built-up
areas,
outcomes
this
contribute
significantly
monitoring
developing
regions,
guiding
stakeholders
identify
vulnerable
better
use
planning
sustainable
environmental
protection.
Remote Sensing,
Год журнала:
2024,
Номер
16(16), С. 3032 - 3032
Опубликована: Авг. 18, 2024
Rapid
urbanization
and
climate
change
exacerbate
the
urban
heat
island
effect,
increasing
vulnerability
of
residents
to
extreme
heat.
Although
many
studies
have
assessed
vulnerability,
there
is
a
significant
lack
standardized
criteria
references
for
selecting
indicators,
building
models,
validating
those
models.
Many
existing
approaches
do
not
adequately
meet
planning
needs
due
insufficient
spatial
resolution,
temporal
coverage,
accuracy.
To
address
this
gap,
paper
introduces
U-HEAT
framework,
conceptual
model
analyzing
vulnerability.
The
primary
objective
outline
theoretical
foundations
potential
applications
U-HEAT,
emphasizing
its
nature.
This
framework
integrates
machine
learning
(ML)
with
remote
sensing
(RS)
identify
at
both
long-term
detailed
levels.
It
combines
retrospective
forward-looking
mapping
continuous
monitoring
assessment,
providing
essential
data
developing
comprehensive
strategies.
With
active
capacity,
enables
refinement
evaluation
policy
impacts.
presented
in
offers
sustainable
approach,
aiming
enhance
practical
analysis
tools.
highlights
importance
interdisciplinary
research
bolstering
resilience
stresses
need
ecosystems
capable
addressing
complex
challenges
posed
by
increased
study
provides
valuable
insights
researchers,
administrators,
planners
effectively
combat
challenges.
Sustainability,
Год журнала:
2025,
Номер
17(4), С. 1363 - 1363
Опубликована: Фев. 7, 2025
Urban
expansion
reshapes
spatial
patterns
over
time,
leading
to
complex
challenges
such
as
environmental
degradation,
resource
scarcity,
and
socio-economic
inequality.
It
is
critical
anticipate
these
transformations
in
order
devise
proactive
urban
policies
implement
sustainable
planning
practices
that
minimize
negative
impacts
on
ecosystems
human
livelihoods.
This
study
investigates
LULC
changes
the
rapidly
urbanizing
Manisa
metropolitan
area
of
Turkey
using
Sentinel-2
satellite
imagery
advanced
machine
learning
algorithms.
High-accuracy
maps
were
generated
for
2018,
2021,
2024
Random
Forest,
Support
Vector
Machine,
k-Nearest
Neighbors,
Classification
Regression
Trees
Among
these,
Forest
algorithm
demonstrated
superior
accuracy
consistency
distinguishing
land-cover
classes.
Future
scenarios
2027
2030
simulated
Cellular
Automata–Artificial
Neural
Network
model
QGIS
MOLUSCE
plugin.
The
results
indicate
significant
growth,
with
built-up
areas
projected
increase
by
23.67%
between
2030,
accompanied
declines
natural
resources
bare
land
water
bodies.
highlights
implications
regarding
ecological
balance
demonstrates
importance
integrating
simulation
models
forecast
use
changes,
enabling
management.
Overall,
effective
must
be
developed
manage
urbanization
conduct
a
balanced
manner.
Sustainability,
Год журнала:
2022,
Номер
14(24), С. 16373 - 16373
Опубликована: Дек. 7, 2022
The
land
use
and
cover
change
dynamics
is
in
par
with
the
increasing
growth
of
urban
developments
associated
sprawl.
objective
study
to
quantify
such
changes
caused
due
expansion
along
outer
ring
road
using
Remote
Sensing
GIS.
maps
are
created
for
four
segments
namely
Chikkarayapuram,
Nazarathpettai,
Meppur,
Perungalathur
years
2009,
2012,
2016,
respectively.
analyzed
among
seven
classes,
agriculture,
barren
land,
residential
units,
industry,
water
body,
other
vegetation,
marshland
(swamp).
Further,
terms
spatiotemporal
aspects
(area-based
change),
environmental
(green
economical
factors.
segment
corridor
2016
(5.16%,
20.10%,
7.14%,
12.63%),
(14.31%,
30.62%,
13.9%,
22.18%),
(19.67%,
33.1%,
23.22%,
40.27%),
areas
have
increased
from
2009
by
20,
76,530
sq.
m.
agriculture
regions
been
reduced
12,
62,700
Besides,
MOLUSCE
plugin
open-source
GIS
(QGIS),
simulated
year
2022
were
based
on
three
(2009,
2016)
which
then
validated
ground-truth
points
obtained
Google
Earth.
scope
utilization
Earth
Engine
(GEE)
automated
feature
extraction
algorithms
predictive
analysis.