Desert
shrubs
are
the
dominant
vegetation
type
in
arid
deserts
and
serve
as
crucial
elements
sand
retention,
biodiversity
maintenance,
carbon
sequestration.
However,
due
to
their
patchy
scattered
distributions
spectral
resemblance
herbaceous
plants,
desert
shrub
mapping
relies
on
high-resolution
imagery,
which
is
less
accessible
for
large-scale
mapping.
Here,
a
set
of
indices
Sentinel-2
(DSMIS)
universal
medium-resolution
imagery
(DSMIL)
developed
distinguish
with
dense
vimen
canopies.
The
index
exploits
canopy
structure
characteristics,
have
sparse
multilayered
high
proportion
desiccated
branches,
resulting
consistently
low
reflectance
red-edge
near-infrared
range.
effectiveness
DSMI
was
examined
Ordos,
China.
In
experiment,
an
optimal
threshold
10.3
obtained
via
DSMIs
Sentinel-2,
achieved
overall
accuracy
91.6%
identified
minimum
coverage
0.23.
comparison,
9.7
by
DSMIL
Landsat-8,
achieving
90.1%
identifying
0.17.
performance
superior
that
commonly
used
random
forest,
this
could
further
improve
classification
complement
machine
learning
methods.
late
stage
nongrowing
season
period
index.
also
performed
well
at
two
test
sites
diverse
species
growing
conditions.
This
study
provides
novel
practical
tool
monitoring
desertification
regions.
It
offers
new
perspective
historical
dynamic
studies
other
land
cover
types
where
only
optical
data
available.
Ecological Indicators,
Journal Year:
2024,
Volume and Issue:
166, P. 112421 - 112421
Published: Aug. 1, 2024
Land
use
and
cover
changes
have
substantially
altered
surface
landscape
patterns,
resulting
in
ecological
risk
(LER)
jeopardizing
the
continued
supply
of
ecosystem
services
(ES).
Although
ES
LER
are
positive
negative
aspects
security
representation,
respectively,
spatiotemporal
mechanism
their
interaction
still
needs
to
be
further
clarified.
This
study
focused
on
Wuling
Mountain
Area
(WMA),
a
critical
functional
zone.
First,
multi-source
data
multi-models
were
used
evaluate
analyze
characteristics.
Subsequently,
geographically
temporally
weighted
regression
model
was
applied
uncover
heterogeneity
impacts
various
ES.
Finally,
zones
delineated
based
LER-ES
quadrant,
driving
force
analysis
conducted,
management
strategies
proposed.
The
main
results
as
follows:
(1)
showed
overall
decreasing
increasing
trends,
respectively.
(2)
Except
for
water
yield,
impact
clear
non-stationarity,
with
significant
impact,
which
most
pronounced
habitat
quality.
(3)
WMA
divided
into
four
zones:
prevention
zone,
conservation
enhancement
reshaping
Elevation,
normalized
difference
vegetation
index,
human
footprint
identified
drivers
zoning.
research
lays
solid
foundation
in-depth
understanding
proposed
zoning
scheme
provides
strong
support
ecologically
sustainable
development
WMA.
Geophysical Research Letters,
Journal Year:
2024,
Volume and Issue:
51(20)
Published: Oct. 13, 2024
Abstract
Climate
change
and
large‐scale
ecological
restoration
programs
have
profoundly
influenced
vegetation
greening
gross
primary
productivity
(GPP)
in
China's
drylands.
However,
the
specific
pathways
through
which
climatic
factors
influence
GPP
remain
poorly
understood.
This
study
examines
spatiotemporal
changes
across
drylands
from
2001
to
2020
investigates
direct
indirect
effects
of
leaf
area
index
(LAI)
on
GPP.
The
results
reveal
that
overall
improvement
cover
has
positively
increased
these
regions.
Although
are
minimal,
they
exert
a
substantial
effect
by
regulating
growth,
highlighting
LAI
is
key
intermediary
mediating
Furthermore,
complex
interactions
vary
significantly
along
aridity
gradient.
emphasizes
necessity
comprehensively
considering
intricate
among
multiple
climate
factors.
Earth system science data,
Journal Year:
2025,
Volume and Issue:
17(3), P. 855 - 880
Published: March 6, 2025
Abstract.
Irrigation
accounts
for
the
major
form
of
human
water
consumption
and
plays
a
pivotal
role
in
enhancing
crop
yields
mitigating
effects
drought.
Accurate
mapping
irrigation
distribution
is
essential
effective
resource
management
assessment
food
security.
However,
resolution
global
irrigated
cropland
map
coarse,
typically
approximately
10
km,
it
lacks
regular
updates.
In
our
study,
we
present
robust
methodology
that
leverages
performance
during
drought
stress
as
an
indicator
productivity
to
identify
cropland.
Within
each
zone
(IMZ),
identified
dry
months
growing
season
from
2017
2019
or
driest
2010
2019.
To
delineate
cropland,
utilized
collected
samples
calculate
normalized
difference
vegetation
index
(NDVI)
thresholds
NDVI
deviation
10-year
average
month.
By
integrating
most
accurate
results
these
two
methods,
generated
Global
Maximum
Extent
dataset
at
100
m
(GMIE-100),
achieving
overall
accuracy
83.6
%
±
0.6
%.
The
GMIE-100
reveals
maximum
extent
encompasses
403.17
9.82
Mha,
accounting
23.4
Concentrated
fertile
plains
regions
adjacent
rivers,
largest
areas
are
found
India,
China,
United
States,
Pakistan,
which
rank
first
fourth,
respectively.
Importantly,
spatial
surpasses
dominant
map,
offering
more
detailed
information
support
estimates
agricultural
use
regional
security
assessments.
Furthermore,
with
help
deep
learning
(DL)
method,
central
pivot
system
(CPIS)
was
using
Pivot-Net,
novel
convolutional
neural
network
built
on
U-net
architecture.
We
there
11.5
0.01
Mha
CPIS,
2.90
0.03
total
Namibia,
Saudi
Arabia,
South
Africa,
Canada,
Zambia,
CPIS
proportion
greater
than
knowledge,
this
inaugural
study
undertake
identification
specific
focus
CPIS.
containing
both
publicly
available
Harvard
Dataverse
https://doi.org/10.7910/DVN/HKBAQQ
(Tian
et
al.,
2023a).
Land Degradation and Development,
Journal Year:
2025,
Volume and Issue:
unknown
Published: March 12, 2025
ABSTRACT
Rapid
global
urbanization
had
significantly
altered
land
use
(LU),
threatening
the
ecology
and
sustainability
of
arid
regions.
Systematic
forward‐looking
analyses
changes
(LUCs)
ecological
risks
in
Asia's
zones,
particularly
urban
agglomeration
on
northern
slope
Tianshan
Mountains
(UANSTM),
remained
limited.
Herein,
LUCs
UANSTM
under
four
scenarios,
including
ecology‐economy
balanced
development
scenario
(EES),
protection
(EPS),
economic
(EDS),
natural
(NDS)
2030,
was
predicted
by
employing
PLUS
model
multi‐objective
programming
(MOP)
model.
Then,
an
evaluation
system
developed
from
dimensions
expansion,
risk,
food
demand,
degradation
to
assess
corresponding
risk
each
case.
The
results
showed
that:
(1)
Under
scenario,
desert
bare
grassland
were
found
be
main
LU
modes
UANSTM,
with
a
significant
increase
cultivated
negligible
change
water
forest;
(2)
area
decreased
NDS
while
areas
grassland,
forest
land,
construction
increased
other
especially
unused
grassland;
(3)
LU‐induced
these
scenarios
similarities,
overall
high
risks.
Among
them,
52.04%
at
relatively
high‐risk
levels,
only
2.97%
low‐risk
levels.
This
study
reveals
diversified
different
thereby
facilitating
individualized
planning
environmental
restoration
UANSTM.