Moving Science Towards Operational Sustainability: The Use of Geospatial Decision Support Systems
Land Degradation and Development,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 12, 2025
The
demands
on
our
landscapes
increasingly
conflict
with
each
other
(e.g.,
the
transition
to
sustainable
agriculture
versus
expansion
of
urban
areas).
We
must
address
increasing
land
degradation
problems
erosion,
loss
soil
fertility
and
biodiversity,
floods,
pollution)
imperative
move
toward
use
sustainability.
Making
decisions
meet
these
diverse
requirements
solve
is
often
highly
complex
challenging.
Similar
issues
are
evident
in
implementation
multiscale
policies
at
global
SDGs),
European
CAP),
national
climate
change
adaptation
plans
EU
Member
States),
regional
Nitrates
Directive),
local
planning)
levels.
There
a
need
bridge
knowledge
gap
between
decision-makers,
who
require
operational
support
implement
actions
sustainability,
research
community.
development
Decision
Support
Systems
(DSS),
particularly
geoSpatial
(S-DSS)
including
Digital
Twin-based
tools,
offers
promising
avenue
challenges.
Over
past
decade,
advancements
S-DSS
were
driven
by
progress
management
sciences,
databases,
modelling,
datacube
technologies,
high-performance
computing
(HPC),
Earth
Observation
(EO),
artificial
intelligence
(AI),
WebGIS
facilities,
Geospatial
Cyberinfrastructure.
Together,
have
significantly
enhanced
connection
resource
science
practical,
management.
In
this
scenario,
current
LDD
special
issue
collects
papers
advanced
approaches
methodologies
land-based
Spatial
(S-DSS),
aiming
whether
can
effectively
handle
complexity
challenges
mentioned
above
facilitate
use.
focus
cover
following
items:
(i)
diversity
approaches,
both
from
methodological
end-user
perspective,
using
multidisciplinary
multifunctional
strategies
view
Systems,
(ii)
promote
uses
various
sectors
such
as
agriculture,
forestry,
planning,
revitalization,
change,
environmental
human
health.
This
aims
test
feasibility
facilitating
scientific
paradigm
shift
working
physical
landscape
overcoming
fragmentation
many
separate
applied
common
objective
planning
More
specifically,
promise
integrate
geospatial
that
helps
scientists
end-users.
issue,
specific
management,
aligns
well
core
Land
Degradation
Development.
It
advance
field
exploring
domains
developing
operational,
scientifically
sound
tools
empower
"management,
policy-making
relating
promotion
ecological
sustainability
counteraction
degradation."
decision
systems
incorporate
information,
making
them
valuable
for
addressing
Their
applications
span
sectors,
protection,
planning.
These
history
marked
successes
failures
Geertman
Stillwell
2009).
To
analyze
across
different
1990s
onward,
we
employed
citation-mapping
analysis
methods
Trujillo
Long
(2018).
Figure
1
shows
most
taken
place
last
it
evident—though
expected—that
there
very
diverse,
unconnected
clusters.
Besides
transport
waste
clusters
"Land
Use
Policies
&
Ecosystem
Services"
"Rural
Development."
two
themes
relevant
issue.
A
deeper
insight
into
within
"soil
science"
domain
shown
2.
be
observed
majority
developments
area
occurred
decade.
Notably,
four
distinct
clusters—land
policy,
sealing,
agriculture—that
again
do
not
interact
other.
appear
evolved
independently,
objectives.
new
generation
makes
possible
link
clusters,
basis
solutions
territories
mono-disciplinary
but
rather
trans-disciplinary
multistakeholder
needs.
Special
Issue
assembles
15
broad
spectrum
topics,
weed
control
all
analyzed
through
perspective
S-DSSs.
Approximately,
60%
refer
actual
applications,
while
half
preliminary
steps,
analyzing
features
DSS
or
S-DSS.
Below,
provide
detailed
contributions.
Approximately
45%
submitted
related
Horizon
2020
project
named
LANDSUPPORT
(www.landsupport.eu),
funded
Commission
(Call
RUR-03-2017),
its
connected
final
international
conference.
develop
web-based,
free,
open-access
GeoSpatial
System
evaluate
land-use
trade-offs,
contribute
policy
Europe.
developed
comprehensive
set
transdisciplinary
based
smart
CyberInfrastructure
(GCI)
geographical
governance
scales,
ensuring
substantial
transferability
physical,
socio-economic,
cultural
settings.
describe
results
tools.
Terribile
et
al.
(2024)
detail
central
concept
multiscale,
multistakeholder,
S-DSS,
demonstrating
reconciling
environment
feasible
appropriate
Manna
showcase
an
tool
used
quantify
EU,
fulfilling
SDG
15.3
requirements.
Baumann
critical
database
architecture
(datacube
technology)
necessary
implementing
systems.
Bonfante
present
estimate
impact
NUTS
levels
municipalities
countries)
additional
crop
adaptability
Italy.
Mileti
produce
biodiversity
Habitat
Directive
level
(Campania
region
Italy)
Bancheri
demonstrate
process-based
(crop
growth)
modelling
implemented
evaluating
agricultural
best
practices
real-time
site-specific
pedoclimatic
information.
Finally,
Stankovics
highlight
importance
stakeholder
engagement
successful
Two
multi-criteria
aquaculture
(Dharani
Shrree
2024)
prediction
Festuca
ovina
restoration
degraded
lands
(Saffariha
2023).
remaining
40%
contributions
development,
Spatiotemporal
evolution
driving
mechanisms
slope
cultivated
(Yu,
Li
2024),
pressure
evaluation
lens
production-living-ecology
framework
Chen
quantifying
integrating
green
China,
Adaptability-Vitality-Resistance
(AVR)
(Niu
variability
organic
carbon
China's
terrestrial
ecosystems
(Li
Optimization
security
patterns
(Wei
Risk
assessment
contaminated
(Mondaca
2024).
Development
reveals
dynamic
evolving
(S-DSS).
compelling
overview
latest
methodologies,
showcasing
sophisticated
designed
key
theme
emerging
collected
necessity
collaboration.
effective
requires
expertise
domains,
science,
computer
social
science.
collaborative
approach
ensures
only
also
practical
needs
stakeholders
policymakers.
Furthermore,
emphasizes
accessible
wider
audience.
Several
freely
available,
web-based
commitment
open
access
sharing.
facilitates
broader
adoption
managers,
researchers,
policymakers,
ultimately
promoting
more
practices.
Looking
ahead,
insights
presented
underscore
potential
revolutionize
By
fostering
collaboration
end-users,
informed
decision-making,
application,
future
lands.
widespread
crucial
steps
facing
world
today.
wish
thanks
Dr.
M.
H.
Sellami
providing
co-citation
mapping
(Figures
2).
Data
available
upon
request
authors.
S1.
Please
note:
publisher
responsible
content
functionality
any
supporting
information
supplied
Any
queries
(other
than
missing
content)
should
directed
corresponding
author
article.
Язык: Английский
A causal prediction method for soil organic carbon storage change estimation, with Shaanxi Province as a case study
Computers and Electronics in Agriculture,
Год журнала:
2025,
Номер
234, С. 110271 - 110271
Опубликована: Март 15, 2025
Язык: Английский
Spatial patterns of soil organic carbon stocks and its controls in Chinese grassland ecosystems
Geoderma,
Год журнала:
2024,
Номер
448, С. 116970 - 116970
Опубликована: Июль 30, 2024
Estimations
of
the
patterns
and
controls
soil
organic
carbon
(SOC)
could
provide
instructive
insights
into
potential
impact
future
global
change
on
(C).
In
this
work,
we
combined
GeoDetector
random
forest
(RF)
to
estimate
SOC
stocks
in
Chinese
grassland
ecosystems
with
uncertainty
assessments,
identified
a
network
cross-correlated
environmental
covariates
for
determining
based
dataset
collected
from
813
sampling
sites
(0–20
cm)
2000
2014.
We
predicted
that
17.50
Pg
C
was
stored
grasslands
depth
20
cm
average
density
4.69
kg
C/m−2(−|−).
The
effectiveness
using
RF
predict
demonstrated
by
an
accuracy
assessment
10-fold
cross-validation,
ratio
performance
deviation
(RPD)
2.89.
southern
China
were
lower
than
those
northern
China.
A
high
found
northeastern
Qinghai–Tibet
Plateau.
Soil
properties
had
strongest
direct
effect
SOC.
Climate
significantly
negatively
associated
indirectly
affected
via
its
properties.
Topography
significant
vegetation,
but
relatively
weak.
This
study
emphasizes
heterogeneity
as
well
relative
significance
climate,
characteristics,
topography,
their
complex
interrelationships
controlling
These
results
may
theoretical
foundation
developing
sustainable
management
systems
calibrating
process
models.
Язык: Английский
Estimating Regional Terrestrial Ecosystem Carbon Sinks on Multi-Model Coupling Approach
Research Square (Research Square),
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 25, 2024
Abstract
The
regional
terrestrial
ecosystems
serve
as
primary
carbon
sinks,
characterized
by
strong
spatial
heterogeneity
and
significant
interannual
fluctuations.
In
Xinjiang,
one
of
China's
five
autonomous
regions,
storage
increased
from
12,967.89
TG
to
14,262.31
TG.
Traditional
sink
assessment
methods
struggle
fully
account
for
the
combined
impacts
human
activities
environmental
factors,
impeding
accurate
depiction
distribution
evolution
stocks.
This
study
proposes
a
ecosystem
density
estimation
method
based
on
an
ARIMA-CatBoost-RNN
coupled
model.
Firstly,
ARIMA
model
forecasts
time
series,
CatBoost
reduces
heterogeneity,
RNN
estimates
values.
Secondly,
is
estimated
using
improved
InVEST
model,
with
accuracy
up
78.4%.
Finally,
Geodetector
quantifies
influence
nine
driving
factors
capacity.
results
reveal
that
soil
stocks
comprise
55%-61%
total
storage,
making
them
main
component
Xinjiang's
ecosystems.
Annual
average
sequestration
39.02
T/km²,
forests
showing
highest
capacity
at
103.33
T/km².
NDVI(Normalized
Difference
Vegetation
Index)
has
most
impact
capacity,
contributing
0.615.
Язык: Английский