Remote Sensing,
Год журнала:
2024,
Номер
16(19), С. 3627 - 3627
Опубликована: Сен. 28, 2024
The
Leaf
Area
Index
(LAI)
is
a
critical
parameter
that
sheds
light
on
the
composition
and
function
of
forest
ecosystems.
Its
efficient
rapid
measurement
essential
for
simulating
estimating
ecological
activities
such
as
vegetation
productivity,
water
cycle,
carbon
balance.
In
this
study,
we
propose
to
combine
high-resolution
GF-6
2
m
satellite
images
with
LESS
three-dimensional
RTM
employ
different
machine
learning
algorithms,
including
Random
Forest,
BP
Neural
Network,
XGBoost,
achieve
LAI
inversion
stands.
By
reconstructing
real
stand
scenarios
in
model,
simulated
reflectance
data
blue,
green,
red,
near-infrared
bands,
well
data,
fused
some
inputs
train
models.
Subsequently,
used
remaining
measured
validation
prediction
inversion.
Among
three
Forest
gave
highest
performance,
an
R2
0.6164
RMSE
0.4109,
while
Network
performed
inefficiently
(R2
=
0.4022,
0.5407).
Therefore,
ultimately
employed
algorithm
perform
generated
spatial
distribution
maps,
achieving
innovative,
efficient,
reliable
method
Geomatics,
Год журнала:
2025,
Номер
5(1), С. 11 - 11
Опубликована: Фев. 28, 2025
The
leaf
area
index
(LAI)
in
temperate
forests
is
highly
dynamic
throughout
the
season,
and
lacking
such
information
has
limited
our
understanding
of
carbon
water
flux
patterns
these
ecosystems.
This
study
aims
to
explore
potential
using
vegetation
indices
based
on
Sentinel-2
data,
which
includes
three
additional
spectral
bands
red-edge
region
its
multispectral
imager
(MSI)
sensor
compared
previous
satellite-borne
imagery,
effectively
track
seasonal
variations
LAI
within
typical
cold–temperate
deciduous
originating
rugged
terrain
Japan.
We
evaluated
reported
developed
an
specific
data
monitor
spatiotemporal
changes
mountainous
forests,
providing
more
accurate
for
ecological
monitoring.
Results
showed
that
(SRB12,B7)
was
able
at
both
spatial
scales
(R2
=
0.576).
Further
analyses
revealed
nevertheless
performed
relatively
poorly
during
leaf-maturing
season
when
peaks,
suggesting
it
still
suffers
from
a
“saturation”
problem.
For
high-resolution
tracking
temporal
scales,
future
research
needed
incorporate
information.
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Март 24, 2025
Ecological
quality
(EQ)
and
ecosystem
health
(EH)
are
closely
related.
Previous
studies
haven't
addressed
their
spatial
relationships
fully;
therefore,
whether
there
is
consistency
between
the
two
remains
unclear.
In
this
study,
EQ
EH
of
Mekong
River
Basin
(MRB),
located
in
Southeast
Asia,
were
determined
by
applying
Remote
Sensing
Index
(RSEI)
Vigor,
Organization,
Resilience,
Services
(VORS)
models,
a
comparative
analysis
was
conducted.
The
results
showed
that
(RSEI_mean
=
0.56)
(EHI_mean
0.59)
had
high
degrees
consistency.
However,
some
degree
differences
certain
land
use
types,
such
as
grassland
0.46;
EHI_mean
0.57)
cropland
0.41;
0.47),
may
have
been
influenced
selection
service
types
prioritized
VORS
model.
addition,
significant
areas
with
relatively
elevations,
especially
barren
0.61;
0.23),
showing
asymmetry.
correlation
coefficient
increases
significantly
from
0.62
to
0.72
after
excluding
altitude
areas.
These
indicate
relationship
probably
applicable
natural
environments
low
altitudes
less
human
activity.
Sensors,
Год журнала:
2025,
Номер
25(8), С. 2394 - 2394
Опубликована: Апрель 9, 2025
The
Xinjiang
Uygur
Autonomous
Region,
characterized
by
its
complex
and
fragile
ecosystems,
has
faced
ongoing
ecological
degradation
in
recent
years,
challenging
national
security
sustainable
development.
To
promote
the
development
of
regional
landscape
conservation,
this
study
investigates
Fractional
Vegetation
Cover
(FVC)
dynamics
Xinjiang.
Existing
studies
often
lack
data
exhibit
limitations
selection
driving
factors.
mitigate
issues,
utilized
Google
Earth
Engine
(GEE)
cloud-free
MOD13A2.061
to
systematically
generate
comprehensive
FVC
products
for
from
2000
2024.
Additionally,
a
quantitative
analysis
up
15
potential
factors
was
conducted,
providing
an
updated
more
robust
understanding
vegetation
region.
This
integrated
advanced
methodologies,
including
spatiotemporal
statistical
analysis,
optimized
spatial
scaling,
trend
Geographical
Detector
(GeoDetector).
Notably,
we
propose
novel
approach
combining
Theil–Sen
Median
with
Hurst
index
predict
future
trends,
which
some
extent
enhances
persuasiveness
alone.
following
are
key
experimental
results:
(1)
Over
25-year
period,
Xinjiang’s
cover
exhibited
pronounced
north–south
gradient,
significantly
higher
northern
regions
compared
southern
regions.
(2)
A
time
series
revealed
overall
fluctuating
upward
FVC,
accompanied
increasing
volatility
decreasing
stability
over
time.
(3)
Identification
km
as
optimal
scale
through
using
Moran’s
I
coefficient
variation.
(4)
Land
use
type,
soil
type
emerged
critical
factors,
each
contributing
20%
explanatory
power
variations.
(5)
elucidate
heterogeneity
mechanisms,
conducted
subzone-based
analyses
drivers.
