Remote Sensing,
Journal Year:
2024,
Volume and Issue:
16(22), P. 4209 - 4209
Published: Nov. 12, 2024
In
recent
decades,
remote
sensing
of
vegetative
drought
and
phenology
has
gained
considerable
attention
from
researchers,
leading
to
a
significant
increase
in
research
activity
this
area.
While
new
indices
are
being
proposed,
there
is
also
growing
on
how
variations
affect
detection.
This
review
begins
by
exploring
the
crucial
role
satellite
optical
thermal
technologies
monitoring
drought.
It
presents
common
methods
after
revisiting
foundational
concepts.
Then,
examines
land
surface
(LSP)
due
its
strong
connection
with
Subsequently,
we
investigate
detection
techniques
that
consider
phenological
variability
recommend
approaches
improve
drought,
emphasizing
necessity
incorporate
metrics.
Finally,
suggest
potential
future
work
directions.
Unlike
other
papers
uniquely
surveys
comprehensive
advancements
both
detecting
estimating
LSP
through
sensing.
highlights
applications
for
these
practices.
International Journal of Intelligent Computing and Cybernetics,
Journal Year:
2024,
Volume and Issue:
18(1), P. 133 - 152
Published: Nov. 13, 2024
Purpose
Vision
transformers
(ViT)
detectors
excel
in
processing
natural
images.
However,
when
remote
sensing
images
(RSIs),
ViT
methods
generally
exhibit
inferior
accuracy
compared
to
approaches
based
on
convolutional
neural
networks
(CNNs).
Recently,
researchers
have
proposed
various
structural
optimization
strategies
enhance
the
performance
of
detectors,
but
progress
has
been
insignificant.
We
contend
that
frequent
scarcity
RSI
samples
is
primary
cause
this
problem,
and
model
modifications
alone
cannot
solve
it.
Design/methodology/approach
To
address
this,
we
introduce
a
faster
RCNN-based
approach,
termed
QAGA-Net,
which
significantly
enhances
recognition.
Initially,
propose
novel
quantitative
augmentation
learning
(QAL)
strategy
sparse
data
distribution
RSIs.
This
integrated
as
QAL
module,
plug-and-play
component
active
exclusively
during
model’s
training
phase.
Subsequently,
enhanced
feature
pyramid
network
(FPN)
by
introducing
two
efficient
modules:
global
attention
(GA)
module
long-range
dependencies
multi-scale
information
fusion,
an
pooling
(EP)
optimize
capability
understand
both
high
low
frequency
information.
Importantly,
QAGA-Net
compact
size
achieves
balance
between
computational
efficiency
accuracy.
Findings
verified
using
different
models
detector’s
backbone.
Extensive
experiments
NWPU-10
DIOR20
datasets
demonstrate
superior
23
other
or
CNN
literature.
Specifically,
shows
increase
mAP
2.1%
2.6%
challenging
dataset
top-ranked
respectively.
Originality/value
paper
highlights
impact
detection
performance.
fundamentally
data-driven
approach:
module.
Additionally,
introduced
modules
FPN.
More
importantly,
our
potential
collaborate
with
method
does
not
require
any
Engineering Science & Technology Journal,
Journal Year:
2024,
Volume and Issue:
5(4), P. 1214 - 1230
Published: April 10, 2024
This
paper
explores
the
integration
of
AI
in
smart
drilling
technologies,
examining
its
applications,
benefits,
challenges,
and
future
prospects.
By
harnessing
power
AI,
technologies
enable
proactive
decision-making,
automation,
optimization
throughout
lifecycle.
From
well
planning
design
to
real-time
monitoring
control,
AI-driven
systems
improve
operational
performance,
reduce
risks,
maximize
resource
recovery.
Despite
facing
challenges
such
as
data
integration,
technology
adoption,
regulatory
compliance,
potential
benefits
are
substantial.
Enhanced
precision,
improved
safety,
increased
efficiency,
sustainable
practices
among
key
offered
by
these
technologies.
Looking
towards
future,
opportunities
for
further
innovation
advancement
abound,
including
development
advanced
algorithms,
with
IoT
big
analytics,
a
focus
on
environmental
sustainability.
embracing
innovation,
collaboration,
commitment
sustainability,
oil
gas
industry
can
unlock
new
growth
resilience
evolving
landscape
construction.
Smart
hold
promise
reshaping
construction,
paving
way
safer,
more
efficient,
operations
industry.
revolutionizing
industry,
offering
unprecedented
levels
precision
safety
integrating
artificial
intelligence
(AI)
into
processes,
optimize
parameters,
recovery..
sustainability.
Keywords:
drilling,
Artificial
(AI),
Oil
Efficiency,
Safety,
Sustainability.
Sustainability,
Journal Year:
2024,
Volume and Issue:
16(18), P. 8125 - 8125
Published: Sept. 18, 2024
Agricultural
droughts
in
South
Africa,
particularly
the
Amahlathi
Local
Municipality
(ALM),
significantly
impact
socioeconomic
activities,
sustainable
livelihoods,
and
ecosystem
services,
necessitating
urgent
attention
to
improved
resilience
food
security.
The
study
assessed
interdecadal
drought
severity
duration
Amahlathi’s
agricultural
potential
zone
from
1989
2019
using
various
vegetation
indicators.
Landsat
time
series
data
were
used
analyse
land
surface
temperature
(LST),
soil-adjusted
index
(SAVI),
normalized
difference
(NDVI),
standardized
precipitation
(SPI).
utilised
GIS-based
weighted
overlay,
multiple
linear
regression
models,
Pearson’s
correlation
analysis
assess
correlations
between
LST,
NDVI,
SAVI,
SPI
response
extent.
results
reveal
a
consistent
negative
LST
NDVI
ALM,
with
an
increase
(R2
=
0.9889)
temperature.
accuracy
dry
areas
increased
55.8%
2019,
despite
dense
high
average
of
40.12
°C,
impacting
water
availability,
land,
local
ecosystems.
shows
ALM
increasing
since
2019.
SAVI
indicates
slight
improvement
overall
health
0.18
0.25
2009,
but
decrease
0.21
at
12
24
months
that
severely
impacted
cover
2014
notable
recovery
during
wet
periods
1993,
2000,
2003,
2006,
2008,
2013,
possibly
due
temporary
relief.
findings
can
guide
provincial
monitoring
early
warning
programs,
enhancing
resilience,
productivity,
especially
farming
communities.
Remote Sensing,
Journal Year:
2024,
Volume and Issue:
16(23), P. 4437 - 4437
Published: Nov. 27, 2024
Floods
stand
out
as
one
of
the
most
expensive
natural
calamities,
causing
harm
to
both
lives
and
properties
for
millions
people
globally.
The
increasing
frequency
intensity
flooding
underscores
need
accurate
timely
flood
mapping
methodologies
enhance
disaster
preparedness
response.
Earth
observation
data
obtained
through
satellites
offer
comprehensive
recurring
perspectives
areas
that
may
be
prone
flooding.
This
paper
shows
suitability
high-resolution
PlanetScope
imagery
an
efficient
accessible
approach
a
case
study
in
South
Chickamauga
Creek
(SCC),
Chattanooga,
Tennessee,
focusing
on
significant
event
2020.
extent
water
was
delineated
mapped
using
image
classification
density
slicing
Normalized
Difference
Water
Index
(NDWI).
results
indicate
performed
well
narrow
creek
like
SCC,
achieving
overall
accuracy
more
than
90%
Kappa
coefficient
over
0.80.
findings
this
research
contribute
better
understanding
Chattanooga
demonstrate
can
utilized
very
useful
resource
streams
with
widths.
Black Sea Journal of Agriculture,
Journal Year:
2024,
Volume and Issue:
7(4), P. 407 - 417
Published: July 15, 2024
Determining
canopy
cover
(CC)
temporal
variation
is
critical
for
sustainable
management
of
natural
resources
and
environmental
protection
efforts.
Data
analysis
interpretation
methods
remote
sensing
are
important
understanding
these
changes
adapting
to
systems.
In
this
study
used
the
Parcel
Identification
System
(LPIS)
database
physical
blocks
as
field
ground
data.
area,
agricultural
areas
were
determined
from
LPIS
data,
including
classes
A0,
A1,
A3,
A4,
S1,
T0,
T1,
a
total
8424
an
area
14651.9
hectares
evaluated.
CC
estimates
made
using
3-m
spatial
resolution
Planet
Scope
multispectral
satellite
images
July
August
2023,
it
was
that
there
significant
differences
in
parcel-based
distinctions,
especially
parcels
T1
(P<0.05).
According
results,
estimated
A0
(69.27%)
T0
(30.43%)
land
types
could
be
successfully
determine
phenological
period
caused
by
impact
assessment
such
climate
change.
At
same
time,
contributes
rapid
monitoring
production
change
within
determination
management,
support
payments
with
regard,
use
modern
technologies
data
will
contribute
increasing
sustainability.
