Forests,
Год журнала:
2024,
Номер
15(11), С. 2024 - 2024
Опубликована: Ноя. 17, 2024
The
Global
Ecosystem
Dynamics
Investigation
(GEDI)
system
provides
essential
data
for
estimating
forest
canopy
height
on
a
global
scale.
However,
factors
such
as
complex
topography
and
dense
can
significantly
reduce
the
accuracy
of
GEDI
estimations.
We
selected
South
Taihang
region
Henan
Province,
China,
our
study
area
proposed
an
optimization
framework
to
improve
estimation
accuracy.
This
includes
correcting
geolocation
errors
in
footprints,
screening
analyzing
features
that
affect
errors,
combining
two
regression
models
with
feature
selection
methods.
Our
findings
reveal
error
4
6
m
footprints
at
orbital
scale,
along
overestimation
region.
Relative
(RH),
waveform
characteristics,
topographic
features,
cover
influenced
error.
Some
studies
have
suggested
estimates
areas
high
lead
underestimation,
found
increased
higher
terrain
vegetation.
model’s
performance
improved
after
incorporating
parameter
into
model.
Overall,
R2
best-optimized
model
was
from
0.06
0.61,
RMSE
decreased
8.73
2.23
m,
rRMSE
65%
17%,
resulting
improvement
74.45%.
In
general,
this
reveals
affecting
vegetation
cover,
premise
minimizing
errors.
Employing
enhanced
estimates.
also
highlighted
crucial
role
improving
precision
estimation,
providing
effective
approach
monitoring
regions
conditions.
Future
should
further
classification
tree
species
expand
diversity
sample
test
estimated
by
different
structures,
consider
distortion
optical
remote
sensing
images
caused
rugged
terrain,
mine
information
waveforms
so
enhance
applicability
more
diverse
environments.
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.
