Land,
Journal Year:
2024,
Volume and Issue:
13(11), P. 1814 - 1814
Published: Nov. 1, 2024
Land
use
and
cover
change
(LUCC)
is
a
key
factor
influencing
global
environmental
socioeconomic
systems.
Many
long-term
geospatial
LUCC
datasets
have
been
developed
at
various
scales
during
the
recent
decades
owing
to
availability
of
satellite
data,
statistical
data
computational
techniques.
However,
most
existing
products
cannot
accurately
reflect
spatiotemporal
patterns
regional
scale
in
China.
Based
on
these
products,
normalized
difference
vegetation
index
(NDVI),
we
multiple
procedures
represent
both
spatial
temporal
changes
major
LUC
types
by
applying
machine-learning,
regular
decision-tree
hierarchical
assignment
methods
using
northeastern
China
(NEC)
as
case
study.
In
this
approach,
each
individual
type
was
sequence
under
different
schemes
methods.
The
accuracy
evaluation
sampling
plots
indicated
that
our
approach
can
actual
shares
NEC,
with
an
overall
82%,
Kappa
coefficient
0.77
regression
0.82.
Further
comparisons
also
datasets.
Our
unfolded
mixed-pixel
issue
integrated
strengths
through
fusion
processes.
analysis
based
dataset
forest,
cropland
built-up
land
area
increased
17.11
×
104
km2,
15.19
km2
2.85
respectively,
1980–2020,
while
grassland,
wetland,
shrubland
bare
decreased
26.06
4.24
3.97
0.92
NEC.
reconstructed
all
1980–2020
This
be
further
applied
entirety
China,
worldwide,
provide
accurate
supports
for
studying
consequences
making
effective
policies.
Scientific Data,
Journal Year:
2024,
Volume and Issue:
11(1)
Published: May 22, 2024
Long-term,
daily,
and
gap-free
Normalized
Difference
Vegetation
Index
(NDVI)
is
of
great
significance
for
a
better
Earth
system
observation.
However,
gaps
contamination
are
quite
severe
in
current
daily
NDVI
datasets.
This
study
developed
0.05°
dataset
from
1981-2023
China
by
combining
valid
data
identification
spatiotemporal
sequence
gap-filling
techniques
based
on
the
National
Oceanic
Atmospheric
Administration
dataset.
The
generated
more
than
99.91%
area
showed
an
absolute
percent
bias
(|PB|)
smaller
1%
compared
with
original
data,
overall
R2
root
mean
square
error
(RMSE)
0.79
0.05,
respectively.
PB
RMSE
between
our
MODIS
gap-filled
(MCD19A3CMG)
during
2000
to
2023
7.54%
0.1,
three
monthly
datasets
(i.e.,
GIMMS3g,
MOD13C2,
SPOT/PROBA)
only
-5.79%,
4.82%,
2.66%,
To
best
knowledge,
this
first
long-term
far.
Remote Sensing,
Journal Year:
2025,
Volume and Issue:
17(10), P. 1663 - 1663
Published: May 8, 2025
Maintaining
or
improving
habitat
quality
is
essential
for
conserving
biodiversity
and
ensuring
the
long-term
survival
of
species.
Nevertheless,
increasing
global
warming
intensifying
human
activities
have
led
to
varying
degrees
degradation
loss,
especially
in
semi-arid
regions.
Focusing
on
China’s
West
Songnen
Plain—the
nation’s
largest
saline-alkali
region
confronting
acute
environmental
challenges—this
study
introduced
soil
salinization
level
mean
NDVI
farmland
during
growing
season
as
dynamic
threat
factors
systematically
explored
spatiotemporal
characteristics
semiarid
area
Plain
from
1990
2020.
The
results
showed
following:
(1)
Habitat
exhibited
a
continuous
decline
period,
following
“degradation–recovery”
trajectory
with
deterioration
peaking
2010;
low-
poor-quality
habitats
predominantly
distributed
central
areas
characterized
by
severe
salinization,
interspersed
patches
good-quality
habitat.
(2)
was
mainly
concentrated
natural
land
cover
types,
whereas
improvements
were
observed
locally
bare
land.
However,
slight
opposite
trends
detected
between
values
change
forests,
waters,
As
elevation
continuously
increased,
grade
shifted
towards
better
conditions.
(3)
A
spatial
autocorrelation
analysis
revealed
significant
clustering
quality,
but
extent
hot
spots
cold
gradually
shrank
grassland
saline
management
progressed.
By
incorporating
integrating
multi-source
data,
this
improved
assessment
framework
regions
provided
scientific
support
spatially
stratified
conservation
strategies.
Remote Sensing,
Journal Year:
2024,
Volume and Issue:
16(19), P. 3648 - 3648
Published: Sept. 29, 2024
China’s
arid
regions
are
particularly
vulnerable
to
the
adverse
effects
of
climate
change
and
human
activities,
which
pose
threats
habitat
quality.
Consequently,
evaluations
these
vital
for
devising
ecological
strategies
initiating
regional
remediation
efforts.
However,
environmental
variations
in
areas
can
cause
quality
fluctuations,
complicates
precise
assessments.
This
study
introduces
a
refined
methodology
that
integrates
remote
sensing
data
field
survey
biomass
modify
estimates
obtained
from
InVEST
model
Altai
region
over
three
decades.
A
comparative
analysis
unmodified,
normalized
difference
vegetation
index
(NDVI)-modified
biomass-modified
was
conducted.
The
results
revealed
an
improvement
correlation
between
observations,
with
significant
increase
R2
value
0.129
0.603.
unmodified
exhibits
subtle
mountainous
areas,
slight
decline
plains.
modified
shows
increasing
trend
areas.
finding
contrasts
reductions
mountains
typically
reported
by
other
studies.
approach
accurately
expresses
across
different
types,
declines
forested
improvements
shrubland
grassland
regions.
is
suitable
accommodates
urban
agricultural
ecosystems
affected
offering
empirical
biodiversity
management.
