Remote Sensing,
Год журнала:
2024,
Номер
16(18), С. 3485 - 3485
Опубликована: Сен. 20, 2024
The
Remote
Sensing
Ecological
Index
(RSEI)
model
is
widely
used
for
large-scale,
rapid
Environment
Quality
(EEQ)
assessment.
However,
both
the
RSEI
and
its
improved
models
have
limitations
in
explaining
EEQ
with
only
two-dimensional
(2D)
factors,
resulting
inaccurate
evaluation
results.
Incorporating
more
comprehensive,
three-dimensional
(3D)
ecological
information
poses
challenges
maintaining
stability
large-scale
monitoring,
using
traditional
weighting
methods
like
Principal
Component
Analysis
(PCA).
This
study
introduces
an
Improved
(IRSEI)
that
integrates
2D
(normalized
difference
vegetation
factor,
normalized
built-up
soil
heat
wetness,
factor
air
quality)
3D
(comprehensive
factor)
factors
enhanced
monitoring.
employs
a
combined
subjective–objective
approach,
utilizing
principal
components
hierarchical
analysis
under
minimum
entropy
theory.
A
comparative
of
IRSEI
Miyun,
representative
area,
reveals
strong
correlation
consistent
monitoring
trends.
By
incorporating
quality
provides
accurate
detailed
assessment,
better
aligning
ground
truth
observations
from
Google
Earth
satellite
imagery.
Sustainability,
Год журнала:
2025,
Номер
17(2), С. 414 - 414
Опубликована: Янв. 8, 2025
Detecting
spatiotemporal
changes
in
ecological
environment
quality
(EEQ)
is
of
great
importance
for
maintaining
regional
security
and
supporting
sustainable
economic
social
development.
However,
research
on
EEQ
detection
from
a
remote
sensing
perspective
insufficient,
especially
at
the
basin
scale.
Based
two
indices,
namely,
Ecological
Index
(EI)
Remote
Sensing
(RSEI),
we
established
dual
model,
combining
comprehensive
index
(RSECI)
its
differential
change
to
study
evolutionary
characteristics
Lijiang
River
Basin
(LRB)
2000
2020.
The
RSECI
combines
following
five
indicators:
greenness,
wetness,
heat,
dryness,
aerosol
optical
depth.
results
this
show
that
area
good
excellent
LRB
decreased
3676.22
km2
2083.89
2020,
while
poor
fair
increased
80.81
1375.91
From
curve
difference
first
rose,
fell,
then
rose
again.
wetness
greenness
indicators
had
positive
effects
promoting
EEQ,
depth,
dryness
restraining
effects.
stepwise
regression
analysis
showed
that,
among
selected
indicators,
were
key
factors
improving
during
period.
approach
model
proposed
can
be
used
quantitatively
evaluate
facilitate
spatial
temporal
dynamic
EEQ.
IOP Conference Series Earth and Environmental Science,
Год журнала:
2025,
Номер
1438(1), С. 012024 - 012024
Опубликована: Янв. 1, 2025
Abstract
As
urban
areas
expand,
the
growth
of
human
activities
increasingly
degrades
ecological
environment,
causing
a
significant
decline
in
vegetation,
soil
erosion,
loss
biodiversity,
temperature
elevation,
and
other
adverse
effects.
If
left
unchecked,
these
effects
can
have
severe
consequences
for
living
organisms
inhabiting
those
areas.
Evaluating
correlation
between
development
environment
has
become
an
urgent
matter
requiring
attention
from
all
countries,
particularly
establishing
effective
systematic
environmental
quality
measures.
Remote
Sensing
Ecological
Index
(RSEI)
is
one
remote
sensing
method
designed
to
analyze
using
four
parameters:
wetness,
greenness,
dryness,
temperature.
The
aim
this
research
spatiotemporally
assess
RSEI
parameters
Karawang
Regency.
Principal
Component
Analysis
(PCA)
results
range
70%
80%.
findings
indicate
that
high
temperature,
open
built-up
land
are
negative
driving
factors.
year
2021
had
largest
extent
poor
classification
class,
reaching
917.11
Km
2
,
while
2019
smallest
only
21.31
.
Sustainability,
Год журнала:
2025,
Номер
17(4), С. 1673 - 1673
Опубликована: Фев. 17, 2025
High-altitude
mountainous
regions
are
highly
vulnerable
to
climate
and
environmental
shifts,
with
the
current
global
change
exerting
a
profound
influence
on
ecological
landscape
of
Tianshan
Mountains
in
China.
This
study
assesses
security
quality
China
from
2001
2020
by
employing
various
remote
sensing
techniques
such
as
Remote
Sensing
Ecological
Index
(RSEI)
for
evaluation,
Normalized
Difference
Vegetation
(NDVI)
fractional
vegetation
cover
(FVC)
analysis,
CASA
model
estimating
primary
productivity
(NPP),
carbon
source/sink
calculating
net
ecosystem
(NEP)
vegetation.
The
research
also
delves
into
evolutionary
trends
impact
mechanisms
environment
using
land
use
meteorological
data.
findings
reveal
that
RSEI’s
principal
component
(PC1)
exhibits
significant
explanatory
power,
showing
notable
increase
5.90%
2020.
Despite
relatively
stable
changes
RSEI
over
past
two
decades
covering
61.37%
area,
there
is
prevalent
anti-persistence
pattern
at
72.39%.
Notably,
NDVI,
FVC,
NPP
display
upward
characteristics.
While
most
areas
continue
emit
carbon,
marked
NEP,
signifying
an
enhanced
absorption
capacity.
partial
correlation
coefficients
between
temperature,
well
precipitation,
demonstrate
statistically
relationships
(p
<
0.05),
encompassing
6.36%
1.55%
respectively.
Temperature
displays
predominantly
negative
98.71%
significantly
correlated
zones,
while
precipitation
positive
correlation.
An
in-depth
analysis
how
affects
provides
crucial
insights
strategic
interventions
enhance
regional
protection
promote
sustainability.
