Environmental Research Ecology,
Journal Year:
2024,
Volume and Issue:
3(4), P. 045007 - 045007
Published: Dec. 1, 2024
Abstract
The
Arctic
is
warming
at
over
twice
the
rate
of
rest
Earth,
resulting
in
significant
changes
vegetation
seasonality
that
regulates
annual
carbon,
water,
and
energy
fluxes.
However,
a
crucial
knowledge
gap
exists
regarding
intricate
interplay
among
climate,
permafrost,
generates
high
phenology
variability
across
extensive
tundra
landscapes.
This
oversight
has
led
to
discrepancies
phenological
patterns
observed
experiments,
long-term
ecological
observations,
satellite
modeling
studies,
undermining
our
ability
understand
forecast
plant
responses
climate
change
Arctic.
To
address
this
problem,
we
assessed
three
low-Arctic
landscapes
on
Seward
Peninsula,
Alaska,
using
combination
in-situ
phenocam
observations
high-resolution
PlanetScope
CubeSat
data.
We
examined
drivers
diversity
landscape
by
(1)
quantifying
dominant
function
types
(PFTs)
(2)
interrelations
between
fine-scale
features,
such
as
topography,
snowmelt,
vegetation.
Our
findings
reveal
both
spring
fall
varied
significantly
PFTs,
accounting
for
about
25%–44%
34%–59%
landscape-scale
variation
start
[SOS]
[SOF],
respectively.
Deciduous
tall
shrubs
(e.g.
alder
willow)
had
later
SOS
(∼7
d
behind
mean
other
PFTs),
but
completed
leaf
expansion
(within
2
weeks)
considerably
faster
compared
PFTs.
modeled
SOF
Random
Forest,
which
showed
can
be
accurately
captured
suite
variables
related
composition,
topographic
characteristics,
snowmelt
timing
(variance
explained:
53%–68%
59%–82%
SOF).
Notably,
was
determinant
SOS,
factor
often
neglected
most
models.
study
highlights
impact
snow
seasonality,
features
heterogeneity.
Improved
understanding
considerable
intra-site
associated
proximate
controls
offers
critical
insights
representation
process
models
assessments
with
change.
Plants,
Journal Year:
2025,
Volume and Issue:
14(1), P. 143 - 143
Published: Jan. 6, 2025
Studying
climate
change’s
impact
on
vegetation
canopy
growth
and
senescence
is
significant
for
understanding
predicting
dynamics.
However,
there
a
lack
of
adequate
research
changes
across
the
lifecycles
different
types.
Using
GLASS
LAI
(leaf
area
index)
data
(2001–2020),
we
investigated
development
(April–June),
maturity
(July–August),
(September–October)
rates
in
Northeast
China,
focusing
their
responses
to
preseason
climatic
factors.
We
identified
that
early
stages
saw
acceleration,
with
over
71%
areas
experiencing
such
acceleration
April
May.
As
grew,
accelerating
slowed
down,
reached
its
maturation
earlier.
By
analyzing
partial
correlation
between
factors,
it
was
were
most
significantly
affected
by
air
temperature.
A
positive
observed
stages,
which
shifted
negative
during
senescence.
Notably,
transition
timing
varied
among
types,
grasslands
(June)
occurring
earlier
than
forests
(July)
farmlands
(August).
Additionally,
grassland
showed
stronger
response
precipitation
farmlands,
lagged
effect
2.50
months.
Our
findings
improve
holding
importance
ecological
environmental
monitoring,
land-use
planning,
sustainable
development.
Remote Sensing,
Journal Year:
2025,
Volume and Issue:
17(8), P. 1409 - 1409
Published: April 16, 2025
Accurate
diagnostics
of
crop
yields
are
essential
for
climate-resilient
agricultural
planning;
however,
conventional
datasets
often
conflate
environmental
covariates
during
model
training.
Here,
we
present
HHHWheatYield1km,
a
1
km
resolution
winter
wheat
yield
dataset
China’s
Huang-Huai-Hai
Plain
spanning
2000–2019.
By
integrating
climate-independent
multi-source
remote
sensing
metrics
with
Random
Forest
model,
calibrated
against
municipal
statistical
yearbooks,
the
exhibits
strong
agreement
county-level
records
(R
=
0.90,
RMSE
542.47
kg/ha,
MRE
9.09%),
ensuring
independence
from
climatic
influences
robust
driver
analysis.
Using
Geodetector,
reveal
pronounced
spatial
heterogeneity
in
climate–yield
interactions,
highlighting
distinct
regional
disparities:
precipitation
variability
exerts
strongest
constraints
on
Henan
and
Anhui,
whereas
Shandong
Jiangsu
exhibit
weaker
dependencies.
In
Beijing–Tianjin–Hebei,
March
temperature
emerges
as
critical
determinant
variability.
These
findings
underscore
need
tailored
adaptation
strategies,
such
enhancing
water-use
efficiency
inland
provinces
optimizing
agronomic
practices
coastal
regions.
With
its
dual
ability
to
resolve
pixel-scale
dynamics
disentangle
drivers,
HHHWheatYield1km
represents
resource
precision
agriculture
evidence-based
policymaking
face
changing
climate.
Earth Surface Processes and Landforms,
Journal Year:
2024,
Volume and Issue:
49(12), P. 3968 - 3988
Published: Aug. 8, 2024
Abstract
Estuarine
tidal
channels
are
active
geomorphic
units
in
flats.
However,
accurate
information
on
the
spatiotemporal
changes
channel
systems
remains
scarce.
The
width
of
may
vary
from
several
kilometres
to
tens
centimetres.
Monitoring
evolution
is
complicated
because
periodic
scouring,
anthropogenic
activities
and
sea
level
rise.
In
this
study,
we
propose
a
synergetic
classification
method
detect
extract
morphological
estuarine
with
spatial
resolution
up
3
m
by
fusing
PlanetScope
multispectral
data
C‐band
GaoFen‐3
fully
polarised
Synthetic
Aperture
Radar
(SAR)
machine
learning
algorithms.
Considering
Yellow
River
Estuary
as
an
example,
spectral
features,
vegetation
water
index,
polarisation
texture
features
derived
SAR
images
were
selected
input
for
classifiers
according
feature
importance
ranking.
Comparison
maximum
likelihood,
support
vector
classifiers,
random
forest
showed
best
performance,
overall
accuracy
99.6%.
Based
these
results,
total
number
reached
872,
length
348.8
km.
central
axis
over
last
4
years
(2019–2022)
suggest
that
was
mainly
controlled
ocean
dynamics
activities.
This
provides
cost‐effective
alternative
accurately
map
global
coastal
zones
helps
quantitatively
describe
their
evolution,
stability
drivers.
