Atmosphere,
Journal Year:
2023,
Volume and Issue:
14(1), P. 172 - 172
Published: Jan. 13, 2023
Remote
sensing
is
the
technique
of
acquiring
data
from
earth’s
surface
sensors
installed
on
satellites
or
manned
unmanned
aircrafts.
Its
use
common
in
dozens
sectors
science
and
technology,
agriculture,
atmosphere,
soil,
water,
land
surface,
oceans
coasts,
snow
ice,
natural
disasters,
among
others.
This
article
focuses
an
in-depth
literature
review
some
most
promising
disciplines,
which
are
asbestos–cement
roof
identification,
vegetation
oil
gas
industry,
geology,
with
aim
having
clarity
trends
research
these
issues
at
international
level.
The
relevant
problems
each
sector
have
been
highlighted,
evidencing
need
for
future
area
light
technological
advances
multi-
hyperspectral
availability
satellite
images
more
precise
spatial
resolution.
A
bibliometric
analysis
proposed
discipline
network
related
keywords
discussed.
Finally,
results
suggest
that
policymakers,
urban
planners,
mine,
companies
should
consider
remote
as
primary
tool
when
planning
comprehensive
development
strategies
field
parameter
multitemporal
analysis.
Remote Sensing,
Journal Year:
2025,
Volume and Issue:
17(3), P. 400 - 400
Published: Jan. 24, 2025
Grassland
ecosystems
provide
a
range
of
services
in
semi-arid
and
arid
regions.
However,
they
have
significantly
declined
due
to
overgrazing
desertification.
In
the
current
study,
we
employed
Landsat
time
series
data
(TM,
OLI,
OLI-2)
spanning
from
1990
2024,
combined
with
vegetation
indices
such
as
NDVI
SAVI,
along
NDWI
digital
elevation
models
(DEMs),
analyze
land
cover
dynamics
Ugii
Lake
watershed
area,
Mongolia.
By
integrating
multisource
remote
sensing
into
advanced
XGBoost
(extreme
gradient
boosting)
machine
learning
algorithm,
achieved
high
classification
accuracy,
overall
accuracies
exceeding
94%
Kappa
coefficients
greater
than
0.92.
The
results
revealed
decline
montane
grasslands
(−6.2%)
an
increase
other
grassland
types,
suggesting
ecosystem
redistribution
influenced
by
climatic
anthropogenic
factors.
Cropland
exhibited
resilience,
recovering
significant
1990s
moderate
growth
2024.
Our
findings
highlight
stability
barren
underscore
pressures
ecological
degradation
human
activities.
This
study
provides
up-to-date
statistical
support
decision-making
conservation
sustainable
management
Mongolia
under
changing
conditions.
Ecological Indicators,
Journal Year:
2023,
Volume and Issue:
146, P. 109892 - 109892
Published: Jan. 13, 2023
Accurate
estimation
of
aboveground
biomass
grasslands
is
key
to
sustainable
grassland
utilization.
However,
most
satellites
cannot
provide
high
temporal
and
spatial
resolution
data.
Patterns
dynamics
associated
with
variability
in
climate
conditions
across
spatiotemporal
scales
are
yet
be
adequately
quantified.
A
fusion
model
offers
the
opportunity
combine
advantages
different
remote
sensing
data
achieve
a
frequency
precision
monitoring
vegetation.
We
test
flexible
(FSDAF)
methodology
generate
synthetic
normalized
difference
vegetation
index
(NDVI)
from
Moderate-Resolution
Imaging
Spectroradiometer
(MODIS)
Landsat
sets.
The
tested
for
semi-arid
Xilin
River
Basin,
China.
Based
on
NDVI
field
measured
an
established
watershed.
Exploring
changes
its
relationship
environmental
factors.
results
show
that:
(1)
FSDAF
performs
well
(R2
=
0.75)
has
clear
textural
features.
(2)
Support
Vector
Machine
Aboveground
Biomass
not
only
ensured
accuracy
0.78,
RMSE
15.43
g/m2),
but
also
generated
maps
higher
(30
m)
(8
days).
(3)
this
area
decreases
southeast
northwest,
reaches
peak
at
end
July.
average
order
meadow
>
typical
desert
grassland.
(4)
increased
linearly
increasing
water
content,
organic
carbon
total
nitrogen,
was
sensitive
soil
content.
During
early
growing
rapid
period,
mainly
affected
by
both
air
temperature
precipitation,
while
effects
human
activities
gradually
dominate
middle
late
periods.
This
study
helps
improve
dynamic
biomass,
provides
scientific
basis
protection
management
arid
regions.
Agronomy,
Journal Year:
2023,
Volume and Issue:
13(12), P. 3040 - 3040
Published: Dec. 12, 2023
The
agricultural
sector
is
currently
confronting
multifaceted
challenges
such
as
an
increased
food
demand,
slow
adoption
of
sustainable
farming,
a
need
for
climate-resilient
systems,
resource
inequity,
and
the
protection
small-scale
farmers’
practices.
These
issues
are
integral
to
security
environmental
health.
Remote
sensing
technologies
can
assist
precision
agriculture
in
effectively
addressing
these
complex
problems
by
providing
farmers
with
high-resolution
lenses.
use
vegetation
indices
(VIs)
essential
component
remote
sensing,
which
combines
variability
spectral
reflectance
value
(derived
from
data)
growth
stage
crops.
A
wide
array
VIs
be
used
classify
crops
evaluate
their
state
However,
precisely
this
high
number
leads
difficulty
selecting
best
VI
combination
specific
objectives.
Without
thorough
documentation
analysis
appropriate
VIs,
users
might
find
it
difficult
data
or
obtain
results
very
low
accuracy.
Thus,
objective
review
conduct
critical
existing
art
on
effective
discrimination
monitoring
several
important
(wheat,
corn,
sunflower,
soybean,
rape,
potatoes,
forage
crops),
grasslands
meadows.
This
could
highly
useful
all
stakeholders
involved
activities.
current
has
shown
that
appear
suitable
mapping
crops,
meadows
pastures.
Sentinel-1
Sentinel-2
were
most
utilized
sources,
while
some
frequently
EVI,
LAI,
NDVI,
GNDVI,
PSRI,
SAVI.
In
studies,
needed
employed
achieve
good
prediction
yields.
main
using
related
variation
characteristics
during
period
similarities
signatures
various
semi-natural
further
studies
establish
models
satellite
would
prove
have
greater
accuracy
provide
more
relevant
information
efficient
Ecological Indicators,
Journal Year:
2023,
Volume and Issue:
155, P. 110925 - 110925
Published: Sept. 15, 2023
Ecological
degradation
is
a
global
issue
that
seriously
threatens
sustainable
human
development,
and
the
evaluation
of
ecosystem
health
(EH)
primary
condition
for
regional
ecological
restoration
work.
In
this
study,
we
optimized
classic
vigor
organization
resilience
(VOR)
model
analyzed
spatial
distribution
pattern
index
(EHI)
in
Sichuan
Province
2020
2005.
We
determined
land
zone
(ESZ),
(EDZ),
improvement
(EIZ)
Province.
The
results
indicate
(1)
kappa
values
VOR
models
2005
were
33.84
32.74,
respectively,
which
below
100.
(2)
ranges
EHI
0.238–0.892
0.218–0.896,
respectively.
(3)
areas
EDZ
EIZ
41781.84
26032
km2,
accounting
8.58
5.35%
total
area
Province,
(4)
ESZ
accounts
36.46%
This
study
beneficial
eco-environmental
management
departments
to
develop
strategies
based
on
local
ESZ,
EDZ,
EIZ.
International Journal of Applied Earth Observation and Geoinformation,
Journal Year:
2023,
Volume and Issue:
124, P. 103533 - 103533
Published: Oct. 22, 2023
Precision
agriculture
management
with
remote
sensing
big
data
provides
a
promising
solution
to
monitor
crops.
Alfalfa
is
an
important
forage
crop
for
various
livestock
around
the
world.
Unlike
corn
and
soybean,
alfalfa
growth
difficult
describe
using
typical
phenological
curve
since
it
characterized
by
monthly
harvest
rapid
regrowth.
Limited
availability
of
high
spatio-temporal
resolution
from
single
satellite
sensor,
there
have
been
few
studies
reported
on
remotely
monitoring
alfalfa.
Fusion
Landsat/Sentinel-2
optical
Sentinel-1
Synthetic
Aperture
Radar
(SAR)
can
improve
data.
However,
form
unified
SAR
image
multisource
satellites,
are
still
four
issues
during
fusion.
To
overcome
these
challenges,
this
study
proposes
improved
fusion
Landsat-7/8,
Sentinel-2,
The
framework
includes
three
models,
namely
Landsat2Sentinel-2,
SAR2Optical,
Optical2SAR.
results
indicate
that
random
forest
(RF)
algorithm
known
spectral
bands,
Landsat2Sentinel-2
model
improves
Landsat-based
surface
red
edge
reflectance
root
mean
square
error
(RMSE)
reduced
28.22–31.16
%
compared
multiple
linear
regression
models.
In
addition,
RMSE
was
further
22.61–23.58
if
considering
solar
angles
in
RF
algorithm.
Compared
traditional
model,
more
accurately
fused
vegetation
indices
(VIs)
derived
Landsat
Sentinel-2
46.81–51.16
%.
SAR2Optical
VIs
32.18–34.59
benefited
involved
climate
Optical2SAR
generated
optical-based
data,
proving
be
reciprocally
fused.
Moreover,
developed
models
present
accuracy
fusing
at
both
top-of-atmosphere
(TOA)
levels.
On
other
hand,
proposed
used
build
across
different
types.
Overall,
demonstrated
has
great
potential
crops
satellites.
Remote Sensing of Environment,
Journal Year:
2023,
Volume and Issue:
301, P. 113935 - 113935
Published: Dec. 12, 2023
Monitoring
soil
erosion
in
the
Arctic
tundra
is
complicated
by
highly
fragmentated
nature
of
landscape
and
limited
spatial
resolution
even
high-resolution
satellite
data.
The
expansion
shrubs
across
has
led
to
substantial
changes
vegetation
composition
that
alter
spectral
reflectance
directly
affect
indices
such
as
normalized
difference
index
(NDVI),
which
widely
applied
for
environmental
monitoring.
This
change
can
mask
if
datasets
with
too
coarse
resolutions
are
used,
increases
NDVI
driven
shrub
obscure
concurrent
barren
land
cover.
Here
we
created
cover
maps
from
a
multispectral
uncrewed
aerial
vehicle
(UAV)
survey
assessed
imagery
PlanetScope,
Sentinel-2
Landsat-8
several
areas
north-eastern
Iceland.
Additionally,
used
novel
application
Shannon
evenness
(SHEI)
evaluate
levels
pixel
mixing.
Our
results
show
lead
confusion,
processes
emphasize
importance
considering
when
monitoring
fragmented
landscapes.
We
demonstrate
remote
sensing
data
<
3
m
greatly
improves
amount
information
captured
an
Icelandic
environment.
Landsat
(30
m)
inadequate
our
study
area.
found
best
platform
combination
UAV
acquisitions
validation.
potential
improve
capabilities
introducing
use
SHEI
assess
mixing
determine
optimal
resolutions.
approach
combined
comparing
different
time
scales
significantly
advances
comprehension
changes,
including
greening
degradation,
tundra.
Ecological Informatics,
Journal Year:
2024,
Volume and Issue:
82, P. 102768 - 102768
Published: Aug. 10, 2024
Fractional
Vegetation
Cover
(FVC)
serves
as
a
crucial
indicator
in
ecological
sustainability
and
climate
change
monitoring.
While
machine
learning
is
the
primary
method
for
FVC
inversion,
there
are
still
certain
shortcomings
feature
selection,
hyperparameter
tuning,
underlying
surface
heterogeneity,
explainability.
Addressing
these
challenges,
this
study
leveraged
extensive
field
data
from
Qinghai-Tibet
Plateau.
Initially,
selection
algorithm
combining
genetic
algorithms
XGBoost
was
proposed.
This
integrated
with
Optuna
tuning
method,
forming
GA-OP
combination
to
optimize
learning.
Furthermore,
comparative
analyses
of
various
models
inversion
alpine
grassland
were
conducted,
followed
by
an
investigation
into
impact
heterogeneity
on
performance
using
NDVI
Coefficient
Variation
(NDVI-CV).
Lastly,
SHAP
(Shapley
Additive
exPlanations)
employed
both
global
local
interpretations
optimal
model.
The
results
indicated
that:
(1)
exhibited
favorable
terms
computational
cost
accuracy,
demonstrating
significant
potential
tuning.
(2)
Stacking
model
achieved
among
seven
(R2
=
0.867,
RMSE
0.12,
RPD
2.552,
BIAS
−0.0005,
VAR
0.014),
ranking
follows:
>
CatBoost
LightGBM
RFR
KNN
SVR.
(3)
NDVI-CV
enhanced
result
reliability
excluding
highly
heterogeneous
regions
that
tended
be
either
overestimated
or
underestimated.
(4)
revealed
decision-making
processes
perspectives.
allowed
deeper
exploration
causality
between
features
targets.
developed
high-precision
scheme,
successfully
achieving
accurate
proposed
approach
provides
valuable
references
other
parameter
inversions.
