Predictive modeling of regional carbon storage dynamics in response to land use/land cover changes: An InVEST-based analysis
Ecological Informatics,
Год журнала:
2024,
Номер
82, С. 102701 - 102701
Опубликована: Июнь 21, 2024
Assessment
of
carbon
stock
(CS)
in
various
land
use/land
cover
(LULC)
types
is
essential
for
environmental
policies
focused
on
reducing
CO2
emissions
and
mitigating
climate
change.
This
study
utilized
the
CA-Markov
model
to
simulate
future
LULC
scenarios
InVEST
evaluate
CS
changes
Pakistan
from
2001
2030.
The
employed
two
decades
yearly
composite
data
MODIS,
achieving
high
accuracy
with
a
kappa
value
0.856.
results
indicate
that
an
increase
38.1
×
103
km2
cultivated
could
lead
increment
13.5
Tg
Pakistan's
total
CS.
In
comparison,
forest
area
can
be
reason
raising
above-ground
(AGC)
by
16.8
Tg.
These
findings
enhance
understanding
long-term
Pakistan.
provides
valuable
insights
governments
refine
use
strategies,
adjust
emission
reduction
policies,
design
better
regulations
based
study's
findings.
Key
recommendations
include
promoting
vertical
urban
development
preserve
sequestration
areas,
implementing
strict
agricultural
zoning
laws,
expanding
afforestation
initiatives
like
Billion
Tree
Tsunami
Green
Pakistan,
establishing
national
monitoring
program.
Integrating
sources
will
create
comprehensive
database
inform
policy
decisions
management
practices,
contributing
global
change
mitigation
efforts.
Язык: Английский
Modelling the impact of ecosystem fragmentation on ecosystem services in the degraded Ethiopian highlands
Ecological Informatics,
Год журнала:
2025,
Номер
unknown, С. 103100 - 103100
Опубликована: Март 1, 2025
Язык: Английский
Refining landsat-based annual NDVImax estimation using shape model fitting and phenological metrics
Ecological Informatics,
Год журнала:
2025,
Номер
unknown, С. 103107 - 103107
Опубликована: Март 1, 2025
Язык: Английский
Estimating Spatiotemporal Dynamics of Carbon Storage in Roinia pseudoacacia Plantations in the Caijiachuan Watershed Using Sample Plots and Uncrewed Aerial Vehicle-Borne Laser Scanning Data
Remote Sensing,
Год журнала:
2025,
Номер
17(8), С. 1365 - 1365
Опубликована: Апрель 11, 2025
Forest
ecosystems
play
a
pivotal
role
in
the
global
carbon
cycle
and
climate
change
mitigation.
aboveground
biomass
(AGB),
critical
indicator
of
storage
sequestration
capacity,
has
garnered
significant
attention
ecological
research.
Recently,
uncrewed
aerial
vehicle-borne
laser
scanning
(ULS)
technology
emerged
as
promising
tool
for
rapidly
acquiring
three-dimensional
spatial
information
on
AGB
vegetation
storage.
This
study
evaluates
applicability
accuracy
UAV-LiDAR
estimating
spatiotemporal
dynamics
Robinia
pseudoacacia
(R.
pseudoacacia)
plantations
gully
regions
Loess
Plateau,
China.
At
sample
plot
scale,
optimal
parameters
individual
tree
segmentation
(ITS)
based
canopy
height
model
(CHM)
were
determined,
was
validated.
The
results
showed
root
mean
square
error
(RMSE)
values
13.17
trees
(25.16%)
count,
0.40
m
(3.57%)
average
(AH),
320.88
kg
(16.94%)
AGB.
regression
model,
which
links
with
AH
generated
estimates
that
closely
matched
observed
values.
watershed
ULS
data
used
to
estimate
R.
Caijiachuan
watershed.
analysis
revealed
total
68,992
trees,
2890.34
Mg
density
62.46
ha−1.
Low-density
forest
areas
(<1500
ha−1)
dominated
landscape,
accounting
94.38%
82.62%
area,
92.46%
Analysis
tree-ring
variation
onset
growth
decline
across
different
classes
aged
0–30
years,
higher-density
stands
exhibiting
delayed
compared
lower-density
stands.
Compared
traditional
methods
diameter
at
breast
(DBH),
assessments
demonstrated
superior
scientific
validity.
underscores
feasibility
potential
estimation
complex
terrain,
such
Plateau.
It
highlights
importance
topographic
factors
enhance
accuracy.
findings
provide
valuable
support
management
high-quality
development
present
an
efficient
approach
precise
sink
accounting.
Язык: Английский
Spatiotemporal Dynamics of Forest Carbon Sinks in China’s Qinba Mountains: Insights from Sun-Induced Chlorophyll Fluorescence Remote Sensing
Remote Sensing,
Год журнала:
2025,
Номер
17(8), С. 1418 - 1418
Опубликована: Апрель 16, 2025
Forest
carbon
sinks
are
crucial
in
mitigating
climate
change
as
integral
components
of
the
global
cycle.
Accurately
estimating
forest
using
traditional
remote
sensing
indices,
such
Normalized
Difference
Vegetation
Index(NDVI),
presents
significant
challenges,
particularly
complex
terrains
and
regions
with
variable
climates.
These
limitations
hinder
effective
capture
photosynthetic
dynamics.
To
address
this
gap,
study
leverages
Sun-Induced
Chlorophyll
Fluorescence
(SIF)
sensing,
highlighting
its
superiority
over
indices
capturing
processes
offering
a
more
precise
approach
to
climate-sensitive
mountainous
areas.
Using
SIF
data
from
GOSIF,
alongside
models
for
light-use
efficiency
ecosystem
respiration,
estimates
Qinba
Mountains
China
during
growing
season
(June
September)
2011
2018.
The
results
further
validated
analyzed
terms
age
type.
Key
findings
include:
(1)
average
annual
was
approximately
24.51
TgC;
(2)
Spatially,
higher
values
(average
36.79
gC·m⁻2·month⁻1)
were
concentrated
western
central
areas,
while
southeastern
central-northern
exhibited
lower
7.75
gC·m⁻2·month⁻1);
(3)
Temporally,
minimal
interannual
variation
observed
northwest,
whereas
southeast
showed
fluctuating
trends,
an
initial
decline
followed
by
increase;
(4)
significantly
influenced
age,
type,
altitude.
Our
demonstrate
that
plantation
forests
aged
10
30
years
exhibit
superior
sequestration
capacity
compared
natural
forests,
70
90
also
show
potential.
underscore
influence
characteristics
on
By
examining
these
spatiotemporal
patterns
Mountains,
our
offers
valuable
insights
advancing
China’s
‘dual
carbon’
goals,
emphasizing
importance
strategic
management
change.
Язык: Английский
Evaluation of Land Use and Land Cover Change on Ecosystem Carbon Storage Using Mlpnn-Markov and Invest Model Using Field Data in Kedarnath Wildlife Sanctuary, Temperate Forests, India from 2010 to 2035
Опубликована: Янв. 1, 2025
Язык: Английский
Mapping forest aboveground carbon stock of combined stratified sampling and RFRK model with mean annual temperature and precipitation
Research Square (Research Square),
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 24, 2025
Abstract
Accurately
estimating
forest
aboveground
carbon
stock
(ACS)
is
essential
for
achieving
neutrality.
At
present,
most
non-parametric
models
still
have
errors
in
regions.
Given
the
autocorrelation
inherent
spatial
interpolation,
combining
with
interpolation
offers
significant
potential.
In
this
study,
we
combined
Random
Forest
(RF)
Ordinary
Kriging
and
Co-Kriging
of
mean
annual
temperature,
precipitation,
slope,
elevation
to
establish
Residual
(RFRK)
model.
Meanwhile,
also
developed
Multiple
Linear
Regression
(MLRRK)
model
Finally,
selected
optimal
estimation
mapping
ACS.
The
results
indicate
that:(1)
achieves
an
R²
0.871,
P
90.4%,
RMSE
3.948
t/hm²;
(2)
RFCK
precipitation
(RFCKpre)
outperforms
one
temperature
(RFCKtem),
while
RFOK
exhibits
lowest
accuracy;(3)
RFCKpre
exponential
has
highest
accuracy,
R²of
0.63
RI
(0.23),
9.3and
SSR
(41612).
These
findings
suggest
that
RFRKpre
improved
accuracy
ACS
regional
forests.
Язык: Английский
Mapping forest aboveground carbon stock of combined stratified sampling and RFRK model with mean annual temperature and precipitation
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Май 19, 2025
Accurately
estimating
forest
aboveground
carbon
stock
(ACS)
is
essential
for
achieving
neutrality.
At
present,
most
non-parametric
models
still
have
errors
in
regions.
Given
the
autocorrelation
inherent
spatial
interpolation,
combining
with
interpolation
offers
significant
potential.
In
this
study,
we
combined
random
(RF)
ordinary
kriging
and
co-kriging
of
mean
annual
temperature,
precipitation,
slope,
elevation
to
establish
residual
(RFRK)
model.
Meanwhile,
also
developed
multiple
linear
regression
(MLRRK)
model
Finally,
selected
optimal
estimation
mapping
ACS.
The
results
indicate
that:
(1)
achieves
an
R2
0.871,
P
90.4%,
RMSE
3.948
t/hm2;
(2)
RFCK
precipitation
(RFCKpre)
outperforms
one
temperature
(RFCKtem),
while
RFOK
exhibits
lowest
accuracy;
(3)
RFCKpre
exponential
has
highest
accuracy,
0.63
RI
(0.23),
9.3
SSR
(41,612).
These
findings
suggest
that
RFRKpre
improved
accuracy
ACS
regional
forests.
Язык: Английский