Highlights in Science Engineering and Technology,
Journal Year:
2024,
Volume and Issue:
108, P. 6 - 13
Published: Aug. 13, 2024
Rapid
urbanization
has
led
to
an
increase
in
the
urban
heat
island
(UHI)
effect.
The
UHI
effect
leads
localized
high
temperatures,
reduced
air
quality,
increased
risk
of
stress
and
other
diseases
among
residents.
At
same
time,
it
also
reduces
socialization
Remote
sensing
technology,
with
its
advantages
quantification,
automation
real-time,
can
be
used
analyze
further
assess
livability
cities.
In
this
paper,
based
on
remote
image
processing
methods,
indexes
land
surface
temperature
(LST),
normalized
vegetation
index
(NDVI),
difference
build-up
(NDBI),
intensity
(UHII)
were
selected
scope
influence
livability.
important
is
human
comfort,
while
affects
comfort
by
influencing
humidity.
This
paper
concludes
that
mainly
reflected
significant
decrease
NDVI
value
NDBI
value.
Meanwhile,
there
a
linear
regression
relationship
between
addition,
increases
energy
consumption
decreases
environmental
destroying
proposed
green
city
programs
such
as
roofs
cool
sidewalks
improve
spatial
structure.
However,
still
have
limitations
lower
efficiency
higher
cost.
Therefore,
future,
will
mitigated
at
source
directly
reducing
solar
radiation.
Remote Sensing,
Journal Year:
2024,
Volume and Issue:
16(7), P. 1286 - 1286
Published: April 5, 2024
Rapid
urbanisation
in
the
global
south
has
often
introduced
substantial
and
rapid
uncontrolled
Land
Use
Cover
(LULC)
changes,
considerably
affecting
Surface
Temperature
(LST)
patterns.
Understanding
relationship
between
LULC
changes
LST
is
essential
to
mitigate
such
effects,
considering
urban
heat
island
(UHI).
This
study
aims
elucidate
spatiotemporal
variations
alterations
of
areas
compared
changes.
The
focused
on
a
peripheral
area
Phnom
Penh
(Cambodia)
undergoing
development.
Using
Landsat
images
from
2000
2021,
analysis
employed
an
exploratory
time-series
LST.
revealed
noticeable
variability
(20
69
°C),
which
was
predominantly
influenced
by
seasonal
also
provided
insights
into
how
varies
within
different
at
exact
spatial
locations.
These
did
not
manifest
uniformly
but
displayed
site-specific
responses
accounts
for
changing
land
surfaces’
complex
physical
energy
interaction
over
time.
methodology
offers
replicable
model
other
similarly
structured,
rapidly
urbanised
regions
utilising
novel
semi-automatic
processing
images,
potentially
inspiring
future
research
various
planning
monitoring
contexts.
Environment International,
Journal Year:
2023,
Volume and Issue:
183, P. 108385 - 108385
Published: Dec. 12, 2023
The
impacts
of
the
availability
and
spatial
configuration
urban
green
spaces
(UGS)
on
their
cooling
effects
can
vary
with
background
climate
conditions.
However,
large-scale
studies
that
assess
potential
heterogeneous
relationships
UGS
thermal
environment
are
still
lacking.
In
this
study,
we
investigated
land
surface
temperature
(LST)
taking
306
cities
in
China
as
a
case
study
covering
multi-biome-scale.
We
first
calculated
surrounding
for
built-up
pixels
each
city
using
distance-weighted
approach,
its
was
quantified
through
Gini
coefficient.
Then,
employed
various
regression
models
to
explore
how
coefficient
LST
varies
across
different
quantiles
between
day-
nighttime.
results
revealed
negatively
associated
both
daytime
nighttime
LST,
while
showed
positive
impact
solely
indicating
an
adequate
equally
distributed
contributes
lower
environmental
temperatures
during
daytime.
Furthermore,
decreased
increased
quantiles.
Whereas
only
quantile
levels,
effect
remaining
almost
insignificant
night.
Our
findings
provide
new
insights
into
environment,
offering
significant
implications
infrastructure
planning
aiming
at
lowering
heat
island.
Theoretical and Applied Climatology,
Journal Year:
2024,
Volume and Issue:
155(5), P. 3841 - 3859
Published: Feb. 5, 2024
Abstract
Urban
air
temperature
is
a
crucial
variable
for
many
urban
issues.
However,
the
availability
of
often
limited
due
to
deficiency
meteorological
stations,
especially
in
areas
with
heterogeneous
land
cover.
Many
studies
have
developed
different
methods
estimate
temperature.
variables
and
local
climate
zone
(LCZ)
been
less
used
this
topic.
Our
study
new
method
canopy
layer
during
clear
sky
days
by
integrating
surface
(LST)
from
MODIS,
based
on
reanalysis
data,
LCZ
data
Szeged,
Hungary.
Random
forest
algorithms
were
developing
estimation
model.
We
focused
four
seasons
distinguished
between
daytime
nighttime
situations.
The
cross-validation
results
showed
that
our
can
effectively
temperature,
average
root
mean
square
error
(RMSE)
0.5
℃
(
R
2
=
0.99)
0.9
0.95),
respectively.
test
dataset
2018
2019
indicated
optimal
model
selected
had
best
performance
summer,
time-synchronous
RMSE
2.1
0.6,
daytime)
2.2
0.86,
nighttime)
seasonal
1.5
0.34,
1.2
0.74,
nighttime).
In
addition,
we
found
was
more
important
at
night,
while
contributed
daytime,
which
revealed
temporal
mechanisms
effect
these
two
estimation.
provides
novel
reliable
tool
explore
thermal
environment
researchers.
