As
we
travel
across
Earth's
varied
topographies,
changes
in
land
cover
display
how
nature
and
human
activities
interact
change
over
time.
The
main
objective
is
to
analyze
water
body
around
Siruvani
Dam,
India,
between
2022
2024
using
Landsat
imagery
a
random
forest
classifier
trained
with
the
hydrological
Normalized
Difference
Water
Index
(NDWI)
data.
results
derived
from
NDWI-based
machine
learning
model
achieved
an
average
accuracy
of
$97.045
\%$
for
classified
maps.
findings
both
maps
hold
significant
implications
safeguarding
resources,
assisting
sustainable
management
decision-making
Dam
other
regions
world.
Forests,
Journal Year:
2024,
Volume and Issue:
15(10), P. 1681 - 1681
Published: Sept. 24, 2024
While
numerous
studies
have
employed
deep
learning
and
high-resolution
remote
sensing
to
predict
future
land
use
cover
(LULC)
changes,
no
study
has
integrated
these
predictive
tools
with
the
current
urban
planning
context
find
a
potential
issues
for
sustainability.
This
addresses
this
gap
by
examining
of
Busan
Metropolitan
City
(BMC)
analyzing
paradoxical
objectives
within
city’s
2040
Master
Plan
subordinate
2030
Parks
Greenbelts.
Although
plans
advocate
increased
green
areas
enhance
sustainability
social
wellbeing,
they
simultaneously
support
policies
that
may
lead
reduction
in
due
development.
Using
CA-ANN
model
MOLUSCE
plugin,
learning-based
LULC
change
analysis,
we
forecast
further
expansion
continued
shrinkage
natural
areas.
During
1980–2010,
underwent
high-speed
expansion,
wherein
urbanized
almost
doubled
agricultural
lands
areas,
including
forests
grassland,
reduced
considerably.
Forecasts
years
2010–2040
show
at
expense
agriculture
forest
grasslands.
Given
master
plans,
highlight
critical
tension
between
growth
Despite
push
more
spaces,
replacement
landscapes
artificial
parks
threaten
long-term
In
view
apparently
conflicting
goals,
framework
BMC
would
take
up
increasingly
stronger
conservation
adaptive
practices
consider
environmental
preservation
on
par
economic
development
light
trajectory
urbanization.
As
we
travel
across
Earth's
varied
topographies,
changes
in
land
cover
display
how
nature
and
human
activities
interact
change
over
time.
The
main
objective
is
to
analyze
water
body
around
Siruvani
Dam,
India,
between
2022
2024
using
Landsat
imagery
a
random
forest
classifier
trained
with
the
hydrological
Normalized
Difference
Water
Index
(NDWI)
data.
results
derived
from
NDWI-based
machine
learning
model
achieved
an
average
accuracy
of
$97.045
\%$
for
classified
maps.
findings
both
maps
hold
significant
implications
safeguarding
resources,
assisting
sustainable
management
decision-making
Dam
other
regions
world.
