Grass and Forage Science,
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 25, 2024
ABSTRACT
Grasslands,
as
a
vital
component
of
arid
and
semi‐arid
terrestrial
ecosystems,
play
pivotal
role
in
carbon
cycling
ecosystem
functioning.
Climate
change
human
activities
significantly
affected
grassland
productivity.
Understanding
the
main
driving
factors
their
contribution
rates
is
great
significance
for
protection
sustainable
development
grasslands.
However,
we
still
lack
comprehensive
understanding
changes
productivity
Xinjiang.
This
study
investigated
spatiotemporal
characteristics
underlying
actual
net
primary
(AcNPP)
Xinjiang
from
2000
to
2022,
utilising
Carnegie‐Ames‐Stanford
Approach
geospatial
detectors.
Employing
nonlinear
Random
Forest
technique,
assessed
dual
impacts
climate
on
Our
findings
revealed
that
exhibited
fluctuating
growth
during
this
period,
with
an
average
annual
AcNPP
rate
0.33
g
C
m
−2
year
−1
.
Comprehensive
evaluation
soil
type,
precipitation,
moisture
content
were
key
determinants
spatial
distribution
AcNPP,
higher
values
mountainous
regions
lower
basins.
The
further
change,
activities,
combined
effects
contributed
recovery
60.97%
grasslands
drivers
degradation,
reaching
67.71%.
Further
analysis
indicated
water
conditions,
particularly
precipitation
content,
forces
Although
grazing
management
strategies,
such
rotational
stocking
deferred
stocking,
facilitated
36.71%
areas
impacted
by
remains
significant
anthropogenic
factor
contributing
degradation.
These
provide
valuable
scientific
insights
effective
conservation
Xinjiang's
ecosystems.
Sustainability,
Год журнала:
2024,
Номер
16(4), С. 1447 - 1447
Опубликована: Фев. 8, 2024
The
environmental
quality
of
a
mining
city
has
direct
impact
on
regional
sustainable
development
and
become
key
indicator
for
assessing
the
effectiveness
national
policies.
However,
against
backdrop
accelerated
urbanization,
increased
demand
resource
development,
promotion
concept
ecological
civilization,
cities
are
faced
with
major
challenge
balancing
economic
protection.
This
study
aims
to
deeply
investigate
spatial
temporal
variations
its
driving
mechanisms
mineral
resource-based
cities.
utilizes
wide
coverage
multitemporal
capabilities
MODIS
optical
thermal
infrared
remote
sensing
data.
It
innovatively
develops
index
(RSEI)
algorithm
PIE-Engine
cloud
platform
quickly
obtain
RSEI,
which
reflects
environment.
evolution
characteristics
in
seven
typical
China
from
2001
2022
were
analyzed.
Combined
vector
mine
surface
data,
variability
impacts
activities
environment
quantitatively
separated
explored.
In
particular,
taken
into
account
by
creating
buffer
zones
zoning
statistics
analyze
response
relationship
between
RSEI
these
factors,
including
distance
area
percentage
area.
addition,
drivers
2019
analyzed
through
Pearson
correlation
coefficients
pixel
10
natural,
economic,
mining.
Regression
modeling
was
performed
using
random
forest
(RF)
model,
ranked
order
importance
factor
assessment.
results
showed
that
(1)
changed
significantly
during
period,
negative
significant.
(2)
areas
low
values
closely
related
(3)
generally
lower
than
average
level
gradually
as
site
increased.
(4)
increase
size
initially
exacerbates
environment,
but
is
weakened
beyond
certain
threshold.
(5)
most
important
affecting
followed
DEM,
GDP,
precipitation.
great
advancing
formulating
strategies.
Forests,
Год журнала:
2025,
Номер
16(3), С. 518 - 518
Опубликована: Март 15, 2025
Gross
primary
productivity
(GPP)
quantifies
the
rate
at
which
plants
convert
atmospheric
carbon
dioxide
into
organic
matter
through
photosynthesis,
playing
a
vital
role
in
terrestrial
cycle.
Machine
learning
(ML)
techniques
excel
handling
spatiotemporally
complex
data,
facilitating
accurate
spatial-scale
inversion
of
forest
GPP
by
integrating
limited
ground
flux
measurements
with
Remote
Sensing
(RS)
observations.
Enhancing
ML
algorithm
performance
for
precise
estimation
is
key
research
focus.
This
study
introduces
Random
Grid
Search
Algorithm
(RGSA)
hyperparameters
tuning
to
improve
Forest
(RF)
and
eXtreme
Gradient
Boosting
(XGB)
models
across
four
major
regions
China.
Model
optimization
progressed
three
stages:
Unoptimized
(UO)
XGB
model
achieved
R2
=
0.77
RMSE
1.42
g
Cm−2
d−1;
Hyperparameter
Optimized
(HO)
using
RGSA
improved
5.19%
(0.81)
reduced
9.15%
(1.29
d−1);
Variable
Combination
(HVCO)
selected
variables
(LAI,
Temp,
NR,
VPD,
NDVI)
further
enhanced
0.83
decreased
1.23
d−1.
The
optimized
estimates
exhibited
high
spatial
consistency
existing
high-quality
products
like
GOSIF
GPP,
GLASS
FLUXCOM
validating
model’s
reliability
effectiveness.
provides
crucial
insights
improving
accuracy
optimizing
methodologies
ecosystems
Ecological Indicators,
Год журнала:
2023,
Номер
154, С. 110902 - 110902
Опубликована: Сен. 4, 2023
As
an
important
component
of
urban
green
space,
residential
space
is
crucial
in
improving
the
carbon
sequestration
performance
ecosystem.
At
present,
there
a
lack
spatial
analysis
on
space.
Taking
main
area
Nanjing
as
example,
study
firstly
adopted
ENVI-met,
normalized
difference
vegetation
index
(NDVI)
and
net
primary
productivity
(NPP)
remote
sensing
estimation
model
to
calculate
then
carried
out
coupling
results
with
characteristics
Results
showed
that
scattered
layout
present
higher
performance;
ratio
trees
shrubs
was
most
critical
factor
affecting
space;
suitable
microclimate
environment
prerequisite
ensure
high
The
revealed
affected
performance,
provided
new
method
at
scales,
which
pointed
direction
for
future
sustainable
development.
Ecological Processes,
Год журнала:
2024,
Номер
13(1)
Опубликована: Май 1, 2024
Abstract
Background
Soil
organic
carbon
(SOC)
is
a
critical
component
of
the
global
cycle,
and
an
accurate
estimate
regional
SOC
stock
(SOCS)
would
significantly
improve
our
understanding
sequestration
cycles.
Zoige
Plateau,
locating
in
northeastern
Qinghai-Tibet
has
largest
alpine
marsh
wetland
worldwide
exhibits
high
sensitivity
to
climate
fluctuations.
Despite
increasing
use
optical
remote
sensing
predicting
SOCS,
obvious
limitations
Plateau
due
highly
cloudy
weather,
knowledge
on
spatial
patterns
SOCS
limited.
Therefore,
current
study,
distributions
within
100
cm
were
predicted
using
XGBoost
model—a
machine
learning
approach,
by
integrating
Sentinel-1,
Sentinel-2
field
observations
Plateau.
Results
The
results
showed
that
content
exhibited
vertical
distribution
cm,
with
highest
topsoil.
tenfold
cross-validation
approach
model
satisfactorily
efficiency
0.59
root
mean
standard
error
95.2
Mg
ha
−1
.
Predicted
distinct
heterogeneity
average
355.7
±
123.1
totaled
0.27
×
10
9
carbon.
Conclusions
High
topsoil
highlights
risks
significant
loss
from
human
activities
Combining
Sentinel-1
model,
which
demonstrates
importance
selecting
modeling
approaches
satellite
images
at
fine
resolution
m.
Furthermore,
study
emphasizes
potential
radar
(Sentinel-1)
developing
mapping,
newly
developed
fine-resolution
mapping
having
important
applications
land
management,
ecological
restoration,
protection
efforts
Deleted Journal,
Год журнала:
2024,
Номер
44(4), С. 625 - 638
Опубликована: Май 20, 2024
Surface
waters
and
effluents
from
wastewater
treatment
plants
contain
micropollutants
derived
a
wide
variety
of
inorganic
organic
substances,
including
medications,
cosmetics,
agrochemicals.
Consequently,
monitoring
the
number
environmental
that
persist
is
current
priority.
This
article
gives
brief
history
these
contaminants
their
effects
on
water,
as
well
literature
review
topic
using
cheap
adsorbents
to
filter
out
most
common
kinds
surface
water.
Although
showed
impressive
adsorption
capacities
in
different
environments,
practical
use
treating
water
will
depend
aspects
like
production
cost,
recyclability,
competitiveness.
