Ecological Informatics,
Год журнала:
2023,
Номер
78, С. 102333 - 102333
Опубликована: Окт. 11, 2023
Sustainable
natural
resources
management
relies
on
effective
and
timely
assessment
of
conservation
land
practices.
Using
satellite
imagery
for
Earth
observation
has
become
essential
monitoring
cover/land
use
(LCLU)
changes
identifying
critical
areas
conserving
biodiversity.
Remote
Sensing
(RS)
datasets
are
often
quite
large
require
tremendous
computing
power
to
process.
The
emergence
cloud-based
techniques
presents
a
powerful
avenue
overcome
limitations
by
allowing
machine-learning
algorithms
process
analyze
RS
the
cloud.
Our
study
aimed
classify
LCLU
Talassemtane
National
Park
(TNP)
using
Deep
Neural
Network
(DNN)
model
incorporating
five
spectral
indices
differentiate
six
classes
Sentinel-2
imagery.
Optimization
DNN
was
conducted
comparative
analysis
three
optimization
algorithms:
Random
Search,
Hyperband,
Bayesian
optimization.
Results
indicated
that
improved
classification
between
with
similar
reflectance.
Hyperband
method
had
best
performance,
improving
accuracy
12.5%
achieving
an
overall
94.5%
kappa
coefficient
93.4%.
dropout
regularization
prevented
overfitting
mitigated
over-activation
hidden
nodes.
initial
results
show
machine
learning
(ML)
applications
can
be
tools
management.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,
Год журнала:
2024,
Номер
17, С. 9858 - 9875
Опубликована: Янв. 1, 2024
Rapid
urbanization
and
industrialization
in
Lahore
Faisalabad
have
intensified
air
pollution
issues,
influencing
nitrogen
dioxide
(NO2)
concentrations,
land
surface
temperature
(LST),
vegetation.
The
study
aims
to
comprehensively
assess
changes
NO2,
LST,
vegetation
induced
by
industrialization,
focusing
on
seasonal
variations
from
2019-2022.
evaluates
NO2
concentrations
health
using
indices
Normalized
Difference
Vegetation
Index
(NDVI),
Enhanced
(EVI),
Atmospherically
Resistant
(ARVI),
LST
variations.
analysis
reveals
a
notable
increase
during
both
summer
winter,
with
approximately
0.021
(×103
mol/m2)
0.03
rises
observed
Lahore.
In
comparison,
experienced
more
modest
increases
of
around
0.0034
0.007
the
respective
seasons.
Simultaneously,
decline
cities,
indicating
substantial
deterioration.
Moreover,
upward
trend
occurred,
experiencing
an
1.59
℃
0.92°C
winter.
also
showed
1.64
0.54
corresponding
Pearson
correlation
highlights
robust
negative
between
indices,
underlining
impact
declining
quality.
A
positive
indicates
interconnected
nature
rising
temperatures
pollution.
findings
emphasize
need
for
environmental
regulations
Faisalabad.
Addressing
levels
is
critical
policymakers
urban
planners.
These
insights
contribute
Sustainable
Development
Goal
(SDG-11),
fostering
strategies
sustainable
cities
communities
combat
pressing
challenges
these
areas.
