Study on spatiotemporal changes of wetlands based on PLS-SEM and PLUS model: The case of the Sanjiang Plain
Jinhao Shi,
No information about this author
Peng Zhang,
No information about this author
Yang Liu
No information about this author
et al.
Ecological Indicators,
Journal Year:
2024,
Volume and Issue:
169, P. 112812 - 112812
Published: Nov. 9, 2024
Language: Английский
The Impact of Climate Change and Human Activities on the Spatial and Temporal Variations of Vegetation NPP in the Hilly-Plain Region of Shandong Province, China
Forests,
Journal Year:
2024,
Volume and Issue:
15(6), P. 898 - 898
Published: May 22, 2024
Studying
the
spatio-temporal
changes
and
driving
mechanisms
of
vegetation’s
net
primary
productivity
(NPP)
is
critical
for
achieving
green
low-carbon
development,
as
well
carbon
peaking
neutrality
goals.
This
article
employs
various
analytical
approaches,
including
Carnegie–Ames–Stanford
approach
(CASA)
model,
Theil–Sen
median
estimator,
coefficient
variation,
Hurst
index,
land-use
land-cover
change
(LUCC)
transition
matrix,
to
conduct
a
thorough
study
NPP
variations
in
Shandong
Hilly
Plain
(SDHP)
region.
Furthermore,
geographic
detector
method
was
used
investigate
synergistic
effects
meteorological
human
activities
on
this
Between
2000
2020,
vegetation
SDHP
exhibited
an
average
increase
rate
0.537
g
C·m−2·a−1.
However,
fluctuation
mean
annual
NPP,
ranging
from
203
230
C·m−2·a−1,
underscores
uneven
growth
pattern.
Significant
regional
disparities
are
evident
gradually
ascending
southeast
northwest
coastal
areas
inland
regions.
The
index
entire
area
stands
at
0.556,
indicating
overall
sustained
trend
time
series
NPP.
can
be
explained
by
climate
variables
(mean
temperature,
precipitation)
(LUCC,
night
light
index);
these,
LUCC
(q
=
0.684)
has
highest
explanatory
power
impact
major
influencing
factor.
deepens
understanding
factors
patterns
dynamic
response
At
same
time,
it
provides
valuable
scientific
insights
improving
ecosystem
quality
promoting
Language: Английский
Spatial Heterogeneity and the Increasing Trend of Vegetation and Their Driving Mechanisms in the Mountainous Area of Haihe River Basin
Remote Sensing,
Journal Year:
2024,
Volume and Issue:
16(3), P. 587 - 587
Published: Feb. 4, 2024
In
addition
to
serving
as
North
China’s
water
supply
and
ecological
barrier,
the
mountainous
area
of
Haihe
River
basin
(MHRB)
is
a
crucial
location
for
application
engineering.
Vegetation
an
important
component
in
conservation
eco-hydrological
progress
MHRB.
A
better
understanding
regional
vegetation
growth
can
be
achieved
by
thorough
investigation
indicators.
this
research,
leaf
index
(LAI)
gross
primary
productivity
(GPP)
were
chosen
The
characteristics
driving
forces
spatiotemporal
variations
LAI
GPP
MHRB
explored
through
Sen’s
slope,
Mann–Kendall
test,
optimal
parameter-based
geographical
detector
model,
correlation
analysis.
From
2001
2018,
annual
increased
significantly
on
scale.
areas
with
accounted
more
than
81%
Land
use
was
most
influential
element
spatial
heterogeneity
GPP,
humidity
one
among
climate
Non-linear
enhancement
or
bivariate
discovered
between
any
two
factors,
strongest
interaction
from
land
index.
lowest
cover
found
dry
regions
precipitation
below
407
mm
under
0.41;
while
both
forests
large
undulating
mountains,
higher
observed.
About
87%
unaltered
use.
increase
2018
promoted
reduced
vapor
pressure
deficit.
sensitivity
change
stronger
that
LAI.
These
findings
serve
theoretical
guide
engineering
preservation
Language: Английский
Spatial Downscaling of Nighttime Land Surface Temperature Based on Geographically Neural Network Weighted Regression Kriging
Jihan Wang,
No information about this author
Nan Zhang,
No information about this author
Laifu Zhang
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et al.
Remote Sensing,
Journal Year:
2024,
Volume and Issue:
16(14), P. 2542 - 2542
Published: July 10, 2024
Land
surface
temperature
(LST)
has
a
wide
application
in
Earth
Science-related
fields,
and
spatial
downscaling
is
an
important
method
to
retrieve
high-resolution
LST
data.
However,
existing
methods
have
difficulties
simultaneously
constructing
expressing
non-stationarity,
autocorrelation,
complex
non-linearity
during
the
process,
which
limits
performance
of
models.
Moreover,
there
lack
research
on
nighttime
land
(NLST)
reconstruction
based
downscaling,
does
not
meet
data
needs
for
urban-scale
urban
heat
island
(UHI)
studies.
Therefore,
this
study
combined
Geographically
Neural
Network
Weighted
Regression
(GNNWR)
with
Area-to-Point
Kriging
interpolation
(ATPK)
propose
(GNNWRK)
model
NLST
downscaling.
To
verify
model’s
generality
robustness,
selected
four
areas
different
landform
climate
type
experiments.
The
GNNWRK
was
compared
benchmark
methods,
including
TsHARP,
Random
Forest,
Regression,
GNNWR.
results
show
that
these
higher
accuracy
maximum
Pearson’s
Correlation
Coefficient
(Pcc)
0.930
minimum
Root
Mean
Square
Error
(RMSE)
0.886
K.
validation
MODIS
ground-measured
also
indicates
can
obtain
more
accurate,
richer
detailed
texture.
This
enhances
potential
studying
effects
islands
at
finer
scale.
Language: Английский
Disentangling the Spatiotemporal Dynamics, Drivers, and Recovery of NPP in Co-Seismic Landslides: A Case Study of the 2017 Jiuzhaigou Earthquake, China
Yuying Duan,
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Xiangjun Pei,
No information about this author
Jing Luo
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et al.
Forests,
Journal Year:
2024,
Volume and Issue:
15(8), P. 1381 - 1381
Published: Aug. 7, 2024
The
2017
Jiuzhaigou
earthquake,
registering
a
magnitude
of
7.0,
triggered
series
devastating
geohazards,
including
landslides,
collapses,
and
mudslides
within
the
World
Natural
Heritage
Site.
These
destructive
events
obliterated
extensive
tracts
vegetation,
severely
compromising
carbon
storage
in
terrestrial
ecosystems.
Net
Primary
Productivity
(NPP)
reflects
capacity
vegetation
to
absorb
dioxide.
Accurately
assessing
changes
NPP
is
crucial
for
unveiling
recovery
ecosystem
after
earthquake.
To
this
end,
we
designed
study
using
Moderate
Resolution
Imaging
Spectroradiometer
(MODIS)
datasets.
findings
are
as
follows.
co-seismic
landslide
areas
remained
stable
between
525
575
g
C/m2
before
earthquake
decreased
533
This
decline
continued,
reaching
483
due
extreme
rainfall
2018,
2019,
2020.
Recovery
commenced
2021,
by
2022,
had
rebounded
544
C/m2.
rate
revealed
that,
five
years
only
18.88%
exhibited
an
exceeding
pre-earthquake
state.
However,
17.14%
these
less
than
10%,
indicating
that
has
barely
begun
most
areas.
factor
detector
temperature,
precipitation,
elevation
significantly
influenced
recovery.
Meanwhile,
interaction
highlighted
lithology,
slope,
aspect
also
played
roles
when
interacting
with
other
factors.
Therefore,
not
determined
single
factor,
but
rather
interactions
among
various
resilience
demonstrated
current
primarily
stems
from
restoration
grassland
Overall,
while
potential
optimistic,
it
will
require
considerable
amount
time
return
Language: Английский
Impact of climate and human activity on NDVI of various vegetation types in the Three-River Source Region, China
Qing Lu,
No information about this author
Haili Kang,
No information about this author
Fuqing Zhang
No information about this author
et al.
Journal of Arid Land,
Journal Year:
2024,
Volume and Issue:
16(8), P. 1080 - 1097
Published: Aug. 1, 2024
Language: Английский
Spatiotemporal pattern of post-earthquake vegetation recovery in a mountainous catchment in southwestern China
Jiaorong Lv,
No information about this author
Xiubin He,
No information about this author
Yuhai Bao
No information about this author
et al.
Natural Hazards,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Sept. 20, 2024
Language: Английский
Responses of vegetation dynamics to complex environmental changes in the Runoff Producing area of the World’s Sixth Longest River: Evolution, Identification, and prediction
Qingsong Wu,
No information about this author
Xing Yuan
No information about this author
Journal for Nature Conservation,
Journal Year:
2024,
Volume and Issue:
unknown, P. 126776 - 126776
Published: Dec. 1, 2024
Language: Английский
Assessing the Impacts of Urbanization and Climate Change on NPP Under Different Habitat Quality Conditions over the Last Two Decades in the Tibetan Plateau, China
Tian Xia,
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Liusheng Han,
No information about this author
Yunmin Chen
No information about this author
et al.
Land,
Journal Year:
2024,
Volume and Issue:
13(12), P. 2139 - 2139
Published: Dec. 9, 2024
The
processes
of
urbanization
and
climate
change
have
exerted
a
marked
influence
on
net
primary
productivity
(NPP).
However,
the
underlying
mechanisms
that
drive
these
effects
remain
intricate
insufficiently
understood.
both
an
adverse
effect
habitat
quality
(HQ)
biodiversity
loss.
HQ
has
direct
health
stability
ecosystems,
which
regulate
level
NPP.
A
higher
is
associated
with
stronger
Now,
quantification
assessment
impacts
NPP
are
still
challenging
because
various
driving
factors
influencing
production
terrestrial
vegetation.
Therefore,
new
perspective
was
adopted
to
study
in
Qinghai–Tibet
Plateau
China
during
2000–2020.
spatiotemporal
analysis
method
employed
investigate
impact
night
light
index
different
regions
(the
divided
into
five
levels,
each
area
type
corresponding
specific
level).
Then,
coupled
coordination
model
(CCD)
used
analyze
coupling
relationship
between
HQ.
Finally,
relative
contribution
studied
using
scenario
simulation.
results
showed
(1)
whole
Tibetan
increased
very
little,
average
growth
rate
0.42
g
C
m⁻2
per
year.
(2)
It
surprising
find
urban
areas
did
not
decline
significantly
as
result
urbanization.
there
notable
areas.
(3)
mean
found
be
17%,
while
other
69%
14%,
respectively.
These
findings
provide
valuable
insights
interactions
human
development
environmental
factors,
enhancing
our
comprehension
their
role
Plateau’s
carbon
cycle.
Language: Английский