Quantifying Uncertainty in Projections of Desertification in Central Asia Using Bayesian Networks
Remote Sensing,
Год журнала:
2025,
Номер
17(4), С. 665 - 665
Опубликована: Фев. 15, 2025
Desertification
presents
major
environmental
challenges
in
Central
Asia,
driven
by
climatic
and
anthropogenic
factors.
The
present
study
quantifies
desertification
risk
through
an
integrated
approach
using
Bayesian
networks
the
ESAS
model,
offering
a
holistic
perspective
on
dynamics.
Four
key
variables—vegetation
cover,
precipitation,
land-use
intensity,
soil
quality—were
incorporated
into
model
to
evaluate
their
influence
desertification.
A
probabilistic
was
developed
gauge
with
simulations
conducted
at
200
geospatial
points.
Hazard
maps
for
2030–2050
were
produced
under
climate
scenarios
SSP245
SSP585,
incorporating
projected
changes.
All
procedures
assessment,
mapping,
downscaling
performed
Google
Earth
Engine
platform.
findings
suggest
4%
increase
11%
SSP585
2050,
greatest
threats
observed
western
regions
such
as
Kazakhstan,
Uzbekistan,
Turkmenistan.
Sensitivity
analysis
indicated
that
vegetation
quality
exerts
strongest
desertification,
reflected
Vegetation
Quality
Index
(VQI)
ranging
from
1.582
(low
Turkmenistan)
1.692
(very
low
Kazakhstan).
comparison
of
models
revealed
robust
alignment,
evidenced
R2
value
0.82,
Pearson
correlation
coefficient
0.76,
RMSE
0.18.
These
results
highlight
utility
effective
tool
assessment
scenario
analysis,
underscoring
urgency
targeted
land
management
proactive
adaptation.
Although
reclaimed
opportunities
afforestation
sustainable
agriculture,
carefully
considering
potential
trade-offs
biodiversity
ecosystem
services
remains
essential.
Язык: Английский
Redefining the modified Water Benefit-Based Ecological Index to evaluate the impact of cropland expansion on the ecological environment in an arid area
Journal of Cleaner Production,
Год журнала:
2024,
Номер
unknown, С. 143730 - 143730
Опубликована: Сен. 1, 2024
Язык: Английский