Sustainability,
Journal Year:
2024,
Volume and Issue:
16(23), P. 10453 - 10453
Published: Nov. 28, 2024
Over
the
past
two
decades,
large-scale
ecological
restoration
in
Loess
Plateau
has
significantly
transformed
land
use
and
cover
(LULC)
Wuding
River
Basin
(WRB),
improving
governance
environmental
conditions.
This
study
examines
spatiotemporal
evolution
of
LULC
its
driving
factors
from
2000
to
2020,
employing
methods
such
as
dynamic
degree,
transfer
matrix,
migration
trajectory,
geographical
detector.
Results
show
that
(1)
grassland
dominates
basin’s
(78.16%),
with
decreases
cropland
desert
areas,
expansions
grassland,
forest,
urban
areas.
Water
bodies
minimal
fluctuations.
The
mean
annual
degree
types
(from
highest
lowest)
is
follows:
forest
>
water
grassland.
overall
fluctuated,
initially
decreasing
(0.85%–0.68%),
then
increasing
(0.68–0.89%),
followed
by
another
decline
(0.89–0.30%).
(2)
patterns
follow
a
northwest-to-southeast
gradient,
primary
transitions
secondary
urban,
bodies.
Spatial
mainly
shifts
westward
northward.
(3)
Under
single-factor
influence,
natural
factors,
especially
slope
(7.2–36.4%)
precipitation
(6.1–22.3%),
are
drivers
changes,
population
density
(7.9%)
GDP
(27.5%)
influencing
In
interaction
topography
climate
(40.5–66.1%)
primarily
drive
increases
cropland,
while
human
activities
(24.8–36.7%)
influence
area
expansion.
Desert
reduction
largely
driven
climatic
(40.3%).
between
shows
either
bi-factorial
or
nonlinear
enhancement
effect,
suggesting
their
combined
offers
stronger
explanatory
power
than
any
single
factor
alone.
highlights
significant
changes
WRB,
both
activities,
contributing
enhanced
sustainability.
Agronomy,
Journal Year:
2024,
Volume and Issue:
14(10), P. 2194 - 2194
Published: Sept. 24, 2024
One
of
the
most
challenging
aspects
agricultural
pest
control
is
accurate
detection
insects
in
crops.
Inadequate
measures
for
insect
pests
can
seriously
impact
production
corn
and
soybean
plantations.
In
recent
years,
artificial
intelligence
(AI)
algorithms
have
been
extensively
used
detecting
field.
this
line
research,
paper
introduces
a
method
to
detect
four
key
species
that
are
predominant
Brazilian
agriculture.
Our
model
relies
on
computer
vision
techniques,
including
You
Only
Look
Once
(YOLO)
Detectron2,
adapts
them
lightweight
formats—TensorFlow
Lite
(TFLite)
Open
Neural
Network
Exchange
(ONNX)—for
resource-constrained
devices.
leverages
two
datasets:
comprehensive
one
smaller
sample
comparison
purposes.
With
setup,
authors
aimed
at
using
these
datasets
evaluate
performance
models
subsequently
convert
best-performing
into
TFLite
ONNX
formats,
facilitating
their
deployment
edge
The
results
promising.
Even
worst-case
scenario,
where
with
reduced
dataset
was
compared
YOLOv9-gelan
full
dataset,
precision
reached
87.3%,
accuracy
achieved
95.0%.
Journal of Infrastructure Policy and Development,
Journal Year:
2024,
Volume and Issue:
8(5), P. 5012 - 5012
Published: May 6, 2024
The
urgency
of
implementing
sharia
economics
and
a
green
economy
is
in
the
same
spirit
as
efforts
made
by
international
community
to
promote
sustainable
development.
purpose
this
study
describe
role
Islamic
realizing
sustainable,
economic
approach
used
research
qualitative
through
literature
content
analysis
methods.
results
state
that
concept
economics,
when
implemented
wisely
human
resources
khalifah
on
earth
based
Qur’an
Hadith
following
law,
including
hifdzhu
al-din,
hifzhu
al-nafs,
al-aql,
al-nasl,
al-maal,
will
realize
goal
ideas.
Maqashid
sharia-based
views
have
complex
mindset,
considering
not
only
environmental
aspects
but
also
moral,
financial,
hereditary
aspects.
Sustainability,
Journal Year:
2024,
Volume and Issue:
16(14), P. 6100 - 6100
Published: July 17, 2024
The
rapid
expansion
of
built-up
land,
a
hallmark
accelerated
urbanization,
has
emerged
as
pivotal
factor
contributing
to
regional
climate
change
and
the
degradation
ecosystem
functions.
decline
in
service
value
(ESV)
consequently
garnered
significant
attention
global
sustainable
development
research.
Shandong
Peninsula
urban
agglomeration
is
crucial
for
promoting
construction
Yellow
River
Economic
Belt
China,
with
its
ecological
status
increasingly
gaining
prominence.
This
study
investigated
ESV
response
land
use/cover
(LUCC)
through
elasticity
coefficient
order
analyze
degree
disturbance
caused
by
use
activities
on
functions
agglomeration.
analysis
was
based
examination
LUCC
characteristics
from
1990
2020.
findings
reveal
that
(1)
experienced
continuous
increase
proportion
2020,
alongside
highly
complex
transfer
between
different
types,
characterized
diverse
trajectories.
most
prominent
features
were
noted
be
simultaneous
agricultural
land.
(2)
four
landscape
pattern
indices,
encompassing
Shannon’s
diversity
index,
indicates
urbanization
led
increased
fragmentation
decreased
connectivity.
However,
obvious
spatial
distribution
differences
exist
among
districts
counties.
(3)
revised
using
normalized
difference
vegetation
revealing
slight
decrease
total
observed
number
counties
exhibiting
low
high
ESVs
continuously
increased,
whereas
those
intermediate
levels
generally
remained
unchanged.
(4)
reveals
exerts
substantial
influence
services,
strongest
ability
occurring
2000
2010.
exhibits
heterogeneity
across
both
entire
within
individual
cities.
Notably,
Qingdao
Jinan,
dual
cores
agglomeration,
exhibit
markedly
distinct
characteristics.
These
disparities
are
closely
related
their
foundations
evolution
over
past
30
years.
displays
variation
time
periods
locations.
Consequently,
it
imperative
formulate
dynamic
management
policies
basis
Such
aim
balance
social
economic
while
ensuring
protection,
thereby
advancement
environment
preservation
Research Square (Research Square),
Journal Year:
2023,
Volume and Issue:
unknown
Published: Oct. 9, 2023
Abstract
The
paper
presents
results
of
using
remote
sensing
time
series
and
machine
learning
to
map
assess
land
potential
based
on
time-series
Fraction
Absorbed
Photosynthetically
Active
Radiation
(FAPAR)
composites.
Monthly
aggregated
FAPAR
three
percentiles
(0.05,
0.50
0.95
probability)
at
250
m
spatial
resolution
were
derived
from
the
8–day
GLASS
V6
product
for
2000–2021
used
determine
long–term
trends
in
FAPAR,
as
well
model
absence
human
pressure.
CCa
3
million
training
points
sampled
12,500
locations
across
globe
overlaid
with
68
bio–physical
variables
representing
climate,
terrain,
form,
vegetation
cover,
several
related
pressure
including:
population
count,
cropland
intensity,
nightlights
a
footprint
index.
an
ensemble
that
stacks
base
learners
(Extremely
Randomized
Trees,
Gradient
Descended
Trees
Artificial
Neural
Network)
linear
regressor
meta-learner.
was
then
projected
by
removing
impact
urbanization
intensive
agriculture
covariate
layers.
strict
cross-validation
show
global
distribution
can
be
explained
R
2
0.89,
most
important
covariates
being
growing
season
length,
forest
cover
indicator
annual
precipitation.
From
this
model,
monthly
recent
year
(2021)
produced,
predict
gaps
actual
vs.
FAPAR.
produced
maps
vs
each
spatially
matched
stable
transitional
classes.
assessment
showed
large
negative
(actual
lower
than
potential)
classes
urban,
needle-leave
deciduous
trees,
flooded
shrub
or
herbaceous
while
strong
found
sparse
rainfed
cropland.
On
other
hand,
irrigated
post-flooded
cropland,
tree
mixed
leaf
type,
broad-leave
largely
positive
trends.
framework
allows
managers
degradation
two
aspects:
declining
trend
observed
difference
between
Research Square (Research Square),
Journal Year:
2024,
Volume and Issue:
unknown
Published: Jan. 22, 2024
Abstract
The
paper
presents
results
of
using
remote
sensing
images
and
machine
learning
to
map
assess
land
potential
based
on
time-series
Fraction
Absorbed
Photosynthetically
Active
Radiation
(FAPAR)
composites.
Land
here
refers
the
vegetation
productivity
in
hypothetical
absence
short–term
anthropogenic
influence,
such
as
intensive
agriculture
urbanization.
Knowledge
this
ecological
could
support
assessment
levels
degradation
well
restoration
potentials.
Monthly
aggregated
FAPAR
three
percentiles
(0.05,
0.50
0.95
probability)
at
250
m
spatial
resolution
were
derived
from
8-day
GLASS
V6
product
for
2000-2021
used
determine
long-term
trends
FAPAR,
model
human
pressure.
CCa
3
million
training
points
sampled
12,500
locations
across
globe
overlaid
with
68
bio-physical
variables
representing
climate,
terrain,
landform,
cover,
several
pressure
including:
population
count,
cropland
intensity,
nightlights
a
footprint
index.
an
ensemble
that
stacks
base
learners
(Extremely
Randomized
Trees,
Gradient
Descended
Trees
Artificial
Neural
Network)
linear
regressor
meta-learner.
was
then
projected
by
removing
impact
urbanization
covariate
layers.
strict
cross-validation
show
global
distribution
can
be
explained
R
2
0.89,
most
important
covariates
being
growing
season
length,
forest
cover
indicator
annual
precipitation.
From
model,
monthly
recent
year
(2021)
produced,
predict
gaps
actual
vs.
FAPAR.
produced
maps
vs
each
spatially
matched
stable
transitional
classes.
showed
large
negative
(actual
lower
than
potential)
classes:
urban,
needle-leave
deciduous
trees,
flooded
shrub
or
herbaceous
while
strong
found
sparse
rainfed
cropland.
On
other
hand,
irrigated
post-flooded
cropland,
tree
mixed
leaf
type,
broad-leave
largely
positive
trends.
framework
allows
managers
two
aspects:
declining
trend
observed
difference
between
Scientific Horizons,
Journal Year:
2024,
Volume and Issue:
27(9), P. 110 - 120
Published: July 15, 2024
The
study
was
aimed
at
identifying
the
potential
of
modern
methods
rational
land
use
in
Karasai
district
Almaty
region
Kazakhstan,
taking
into
account
their
intensive
degradation.
research
methodology
represented
by
statistical
observation,
comparison,
analytical-structural
grouping
and
forecasting.
priority
goals
modernisation
agriculture
Republic
technological
aspect
have
been
analysed.
It
established
that
innovative
approaches
to
increase
level
efficiency
agrarian
sector,
improve
state
local
regional
landscape.
concept
improving
degraded
lands,
including
a
system
management
measures
practical
activities,
has
developed.
proved
it
should
be
based
on
synergy
economic
environmental
safety,
with
mandatory
introduction
approaches.
effectiveness
lands
as
an
effective
tool
for
transformation
sector
determined.
proposed
intensify
development
organic
agricultural
production,
which
is
positioned
gentlest
landscapes.
implementation
sustainable
landscape
complexes
context
implies
information
monitoring
technology,
anticipates
diagnosis,
genesis
forecasting
studied
ecosystems.
Such
will
make
possible
develop
programmes
restoration
ecological
functions
natural
landscapes,
integral
part
programmes.
Actualised
situation
ecologisation
predicted
further
destruction
ecosystems
landscapes
case
aggressive
soil
cultivation.
necessity
improved
substantiated,
specificity
biological
technologies
production
outlined,
indication
tangential
risks
challenges
realities
Kazakhstan.
substantiated
application
integrated
ecosystem
approach
synergistic
ensure
region,
conditions
degradation
Sustainability,
Journal Year:
2024,
Volume and Issue:
16(23), P. 10453 - 10453
Published: Nov. 28, 2024
Over
the
past
two
decades,
large-scale
ecological
restoration
in
Loess
Plateau
has
significantly
transformed
land
use
and
cover
(LULC)
Wuding
River
Basin
(WRB),
improving
governance
environmental
conditions.
This
study
examines
spatiotemporal
evolution
of
LULC
its
driving
factors
from
2000
to
2020,
employing
methods
such
as
dynamic
degree,
transfer
matrix,
migration
trajectory,
geographical
detector.
Results
show
that
(1)
grassland
dominates
basin’s
(78.16%),
with
decreases
cropland
desert
areas,
expansions
grassland,
forest,
urban
areas.
Water
bodies
minimal
fluctuations.
The
mean
annual
degree
types
(from
highest
lowest)
is
follows:
forest
>
water
grassland.
overall
fluctuated,
initially
decreasing
(0.85%–0.68%),
then
increasing
(0.68–0.89%),
followed
by
another
decline
(0.89–0.30%).
(2)
patterns
follow
a
northwest-to-southeast
gradient,
primary
transitions
secondary
urban,
bodies.
Spatial
mainly
shifts
westward
northward.
(3)
Under
single-factor
influence,
natural
factors,
especially
slope
(7.2–36.4%)
precipitation
(6.1–22.3%),
are
drivers
changes,
population
density
(7.9%)
GDP
(27.5%)
influencing
In
interaction
topography
climate
(40.5–66.1%)
primarily
drive
increases
cropland,
while
human
activities
(24.8–36.7%)
influence
area
expansion.
Desert
reduction
largely
driven
climatic
(40.3%).
between
shows
either
bi-factorial
or
nonlinear
enhancement
effect,
suggesting
their
combined
offers
stronger
explanatory
power
than
any
single
factor
alone.
highlights
significant
changes
WRB,
both
activities,
contributing
enhanced
sustainability.
