International Multidisciplinary Scientific GeoConference SGEM ...,
Journal Year:
2024,
Volume and Issue:
24, P. 131 - 138
Published: Nov. 15, 2024
Agricultural
productivity
and
environmental
changes
can
be
greatly
affected
by
agricultural
other
land
use.
Mapping
of
vegetation
cover
is
a
fundamental
way
managing
the
natural
resources
on
earth
surface.
To
determine
or
study
crop
productivities
any
geographical
location,
use
one
crucial
clues
for
reliable
information.
We
aimed
to
investigate
effects
urbanization
lands
in
Sao
Paulo
city.
A
30-year
multi-temporal
satellite
imagery
dataset
from
four
distinct
years
were
mapped:
1992
(Landsat
TM),
2002
ETM+),
2012
2022
(Sentinel)
collected
analyzed
using
geospatial
tools.
Identified
waterbody,
settlement,
land,
wetland,
forest.
Change
detection
analysis
was
performed
Erdas
imagine
software
future
prediction
achieved
applying
Idrisi
selva
15
software.
The
result
indicated
between
settlement
wetland
increased
areas
while
forest
waterbody
decreased.
These
observed
spatial
pattern
LULC
could
attributed
encroachment
converted
uses
such
as
urban
agriculture.
overall
depicted
evolution
matrix
map
demonstrated
that,
because
speculation
practices,
has
primarily
Application
technologies
(remote
sensing
GIS)
proved
effective
monitoring
providing
vital
information
policy
making
City�s
food
(in)security
sustainable
development.
Environmental Challenges,
Journal Year:
2024,
Volume and Issue:
16, P. 101002 - 101002
Published: Aug. 1, 2024
The
urban
climate
has
undergone
significant
changes
due
to
the
rapid
population
growth,
leading
a
decline
in
vegetation
cover
and
an
increase
land
surface
temperature
(LST).
This
study
aims
assess
influence
of
use
(LULC)
on
LST
four
major
areas
southwestern
Ethiopia,
namely
Jimma,
Bonga,
Metu
Nekemte,
during
period
from
2002
2024.
To
investigate
impact
LULC
dynamics
LST,
30m
spatial
resolution
images
Landsat
were
utilized,
including
Thematic
Mapper
(TM)
for
year
Operational
Land
Imager
(OLI)
Thermal
Infrared
(TIRS)
years
2014
Over
past
22
years,
mean
increased
by
2.81°C,
2.94°C,
3.37°C,
3.96°C
Metu,
respectively.
can
be
attributed
various
factors,
but
one
primary
reasons
is
linked
urbanization
decrease
forest
cover.
Changes
triggered
significantly
influences
cities.
results
highlight
increment
impervious
as
key
factors
contributing
upward
trend
LST.
indicate
that
centers
with
less
experience
higher
compared
their
surroundings.
this
necessity
effective
planning
through
greenery
parks
mitigate
increasing
trends
which
improve
thermal
comfort
levels.
Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: March 8, 2025
The
frequent
droughts
and
floods,
closely
associated
with
the
Asian
summer
monsoon
(ASM),
has
profoundly
affected
ecological
environment
economy
in
East
Asia.
While
changes
ASM
are
related
to
precipitation
patterns,
specific
mechanism
still
requires
further
investigation.
This
study
utilized
stalagmite
records
from
Feilong
Cave
southwest
China
reconstruct
evolution
of
since
Medieval
Warm
Period
(MWP).
results
indicated
that
strengthened
during
MWP
weakened
Little
Ice
Age
(LIA),
intensity
primarily
driven
by
solar
activity
variations
tropical
ocean-atmosphere
circulation.
Different
phase
combinations
Atlantic
Multidecadal
Oscillation,
Pacific
Decadal
Oscillation
also
influenced
on
ASM.
During
MWP,
warming
northern
hemisphere
landmasses,
intensified,
enhancing
long-range
transport
moisture
(Indian
monsoon),
leading
northward
shifts
rain
belt
eastern
region
increased
China.
Conversely,
LIA,
cooling
landmasses
led
a
weakening
reduced
transport,
resulting
southward
southern
Additionally,
abnormal
shift
Western
Subtropical
High
prolonged
retention
China,
causing
an
increase
monsoonal
rainfall
Comparison
Chinese
terrestrial
proxy
reveals
antiphase
relationship
between
parts
counterparts
showed
"wet
north-dry
south"
pattern,
while
south-dry
north"
pattern
emerged.
Furthermore,
suggest
human
activities
exacerbated
deterioration
karst
Middle
Ages.
Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: March 12, 2025
Urban
infrastructure,
particularly
in
ageing
cities,
faces
significant
challenges
maintaining
building
aesthetics
and
structural
integrity.
Traditional
methods
for
detecting
diseases
on
exteriors,
such
as
manual
inspections,
are
often
inefficient,
costly,
prone
to
errors,
leading
incomplete
assessments
delayed
maintenance
actions.
This
study
explores
the
application
of
advanced
deep
learning
techniques
accurately
detect
exterior
surfaces
buildings
urban
environments,
aiming
enhance
detection
efficiency
accuracy
while
providing
a
real-time
monitoring
solution
that
can
be
widely
implemented
infrastructure
health
management.
The
research
model
improves
feature
extraction
by
integrating
DenseNet
blocks
Swin-Transformer
prediction
heads,
trained
validated
using
dataset
289
high-resolution
images
collected
from
diverse
environments
China.
Data
augmentation
improved
model's
robustness
against
varying
conditions.
proposed
achieved
high
rate
84.42%,
recall
77.83%,
an
F1
score
0.81,
with
speed
55
frames
per
second.
These
metrics
demonstrate
effectiveness
identifying
complex
damage
patterns,
minute
cracks,
even
within
noisy
significantly
outperforming
traditional
methods.
highlights
potential
transform
strategies
offering
practical
ultimately
enhancing
contributing
practices
timely
interventions.
Land,
Journal Year:
2025,
Volume and Issue:
14(4), P. 762 - 762
Published: April 3, 2025
Yunnan
Province,
which
is
located
in
the
mountainous
plateau
region
of
China,
faces
numerous
challenges,
including
population
decline
rural
areas.
Achieving
coordinated
development
between
transportation
and
systems
crucial
for
fostering
sustainable
growth.
In
this
study,
we
developed
a
pressure
state
response
(PPSR)
model
comprehensive
transport
superiority
(TS)
that
considers
influence
aviation.
We
quantified
system
horizontal
across
Yunnan’s
districts
counties
period
2013
to
2021,
examining
their
temporal
spatial
heterogeneity.
Using
autocorrelation
model,
also
explored
trade-offs
synergy
TS
PPSR.
The
main
findings
are
as
follows.
(1)
From
polarization
pattern
PPSR
Province
gradually
weakened,
there
were
different
degrees
contraction
overall.
(2)
significantly
increased,
with
aviation
conditions
having
notably
positive
impact,
further
strengthening
Kunming’s
position
regional
core.
(3)
Yunnan,
relationship
significant,
collaborative
emerging
counties,
reflecting
distinct
characteristics
degree
polarization.
This
study
provides
valuable
insights
integrating
urban
areas
offers
new
perspective
revitalization.
Remote Sensing,
Journal Year:
2025,
Volume and Issue:
17(8), P. 1433 - 1433
Published: April 17, 2025
Zhejiang
Province,
a
pivotal
economically
developed
region
within
China’s
Yangtze
River
Delta,
requires
systematic
investigation
of
spatiotemporal
vegetation
dynamics
and
their
drivers
to
formulate
targeted
ecological
protection
policies
optimize
restoration
strategies.
Utilizing
the
Google
Earth
Engine
(GEE)
platform,
this
study
applied
Kernel
Normalized
Difference
Vegetation
Index
(kNDVI)
assess
responses
climate
variability
human
activities
in
Province
from
2000
2022.
Analytical
methods
included
simple
linear
regression,
Theil
Sen
trend
analysis
(Sen),
Mann
Kendall
test
(MK),
Hurst
index,
partial
correlation
analysis,
analysis.
The
results
show:
(1)
kNDVI
exhibited
significant
upward
(0.001/year),
covering
61.5%
province.
index
revealed
that
69.1%
changes
anti-sustainability
characteristics,
with
future
degradation
areas
(56.4%)
projected
exceed
improvement
(28.1%).
(2)
Human
(57.11%)
contributed
more
than
change
(42.89%).
(3)
Against
backdrop
change,
demonstrated
positive
temperature
(coefficient:
0.44)
but
negative
precipitation
−0.056),
confirming
as
dominant
climatic
driver.
Overall,
2022
were
jointly
driven
by
activities.
Geography and sustainability,
Journal Year:
2024,
Volume and Issue:
5(3), P. 357 - 369
Published: Sept. 1, 2024
There
are
urgent
calls
for
new
approaches
to
map
the
global
urban
conditions
of
complexity,
diffuseness,
diversity,
and
connectivity.
However,
existing
methods
mostly
focus
on
mapping
urbanized
areas
as
bio
physical
entities.
Here,
based
continuum
urbanity
framework,
we
developed
an
approach
cross-scale
from
town
city
megaregion
with
different
spatial
resolutions
using
Google
Earth
Engine.
This
was
multi-source
remote
sensing
data,
Points
Interest
–
Open
Street
Map
(POIs-OSM)
big
random
forest
regression
model.
is
scale-independent
revealed
significant
variations
in
urbanity,
underscoring
differences
urbanization
patterns
across
megaregions
between
rural
areas.
Urbanity
observed
transcending
traditional
boundaries,
diffusing
into
settlements
within
non-urban
locales.
The
finding
communities
far
challenges
gradient
theory
urban-rural
development
distribution.
By
livelihoods,
lifestyles,
connectivity
simultaneously,
maps
present
a
more
comprehensive
characterization
than
that
by
land
cover
or
population
density
alone.
It
helps
enhance
understanding
beyond
biophysical
form.
can
provide
multifaceted
urbanization,
thereby
insights
regional
sustainability.
Forests,
Journal Year:
2024,
Volume and Issue:
15(6), P. 898 - 898
Published: May 22, 2024
Studying
the
spatio-temporal
changes
and
driving
mechanisms
of
vegetation’s
net
primary
productivity
(NPP)
is
critical
for
achieving
green
low-carbon
development,
as
well
carbon
peaking
neutrality
goals.
This
article
employs
various
analytical
approaches,
including
Carnegie–Ames–Stanford
approach
(CASA)
model,
Theil–Sen
median
estimator,
coefficient
variation,
Hurst
index,
land-use
land-cover
change
(LUCC)
transition
matrix,
to
conduct
a
thorough
study
NPP
variations
in
Shandong
Hilly
Plain
(SDHP)
region.
Furthermore,
geographic
detector
method
was
used
investigate
synergistic
effects
meteorological
human
activities
on
this
Between
2000
2020,
vegetation
SDHP
exhibited
an
average
increase
rate
0.537
g
C·m−2·a−1.
However,
fluctuation
mean
annual
NPP,
ranging
from
203
230
C·m−2·a−1,
underscores
uneven
growth
pattern.
Significant
regional
disparities
are
evident
gradually
ascending
southeast
northwest
coastal
areas
inland
regions.
The
index
entire
area
stands
at
0.556,
indicating
overall
sustained
trend
time
series
NPP.
can
be
explained
by
climate
variables
(mean
temperature,
precipitation)
(LUCC,
night
light
index);
these,
LUCC
(q
=
0.684)
has
highest
explanatory
power
impact
major
influencing
factor.
deepens
understanding
factors
patterns
dynamic
response
At
same
time,
it
provides
valuable
scientific
insights
improving
ecosystem
quality
promoting