Frontiers in Plant Science,
Journal Year:
2023,
Volume and Issue:
14
Published: July 17, 2023
Wetland
vegetation
biomass
is
an
essential
indicator
of
wetland
health,
and
its
estimation
has
become
active
area
research.
Zizania
latifolia
(
Z.
)
the
dominant
species
emergent
in
Honghu
Wetland,
monitoring
aboveground
(AGB)
can
provide
a
scientific
basis
for
protection
restoration
this
other
wetlands
along
Yangtze
River.
This
study
aimed
to
develop
method
AGB
using
high-resolution
RGB
imagery
acquired
from
unoccupied
aerial
vehicle
(UAV).
The
spatial
distribution
was
first
extracted
through
object-based
classification
field
survey
data
UAV
imagery.
Linear,
quadratic,
exponential
back
propagation
neural
network
(BPNN)
models
were
constructed
based
on
17
indices
calculated
images
invert
AGB.
results
showed
that:
(1)
visible
significantly
correlated
with
.
absolute
value
correlation
coefficient
between
CIVE
0.87,
followed
by
ExG
(0.866)
COM2
(0.837).
(2)
Among
linear,
models,
quadric
model
had
highest
inversion
accuracy,
validation
R
2
0.37,
RMSE
MAE
853.76
g/m
671.28
,
respectively.
(3)
BPNN
eight
factors
best
effect,
0.68,
732.88
583.18
Compared
quadratic
CIVE,
achieved
better
results,
reduction
120.88
88.10
MAE.
indicates
that
UAV-based
provides
effective
accurate
technique
species,
making
it
possible
efficiently
dynamically
monitor
cost-effectively.
International Journal of Applied Earth Observation and Geoinformation,
Journal Year:
2022,
Volume and Issue:
112, P. 102890 - 102890
Published: June 30, 2022
Mangroves
are
highly
productive
wetland
ecosystems,
located
at
the
interlocking
area
of
tropical
and
subtropical
coastal
zones.
Accurately
mapping
distribution,
quality
quantity
species
crucial
for
mangrove
management,
protection,
restoration.
This
study
proposed
a
approach
by
combining
recursive
feature
elimination
(RFE)
with
deep
learning
(DL)
algorithms,
further
assess
effectiveness
selection
DL
(DeeplabV3+
PSPNet)
algorithm
to
improve
classification
accuracy
under
high
dimensional
UAV
image
datasets.
We
constructed
an
ensemble
models
(SEL)
stacking
five
base
(Random
Forest,
XGBoost,
LightGBM,
CatBoost,
AdaBoost),
evaluate
ability
between
SEL
RFE-DL
algorithms.
Comparison
classifications
was
differences
models.
Results
indicated
that:
(1)
RFE
could
using
optimal
features
achieved
94.8%
overall
(OA),
which
0.2%-8.5%
higher
than
only
original
multispectral
bands;
(2)
produced
better
performance
1.6%-12.7%
accuracy.
Mcnemar's
test
showed
were
significant
three
algorithms;
(3)
had
strong
stable
classifying
species.
The
OA
six
scenarios
from
75.5%
92.2%,
highest
0.8%-4.2%
models;
(4)
XGBoost
importance,
while
AdaBoost
lowest
importance
in
SEL-based
classifications.
Remote Sensing,
Journal Year:
2022,
Volume and Issue:
14(16), P. 3885 - 3885
Published: Aug. 11, 2022
Timely
and
accurate
information
on
the
spatial
distribution
of
urban
trees
is
critical
for
sustainable
development,
management
planning.
Compared
with
satellite-based
remote
sensing,
Unmanned
Aerial
Vehicle
(UAV)
sensing
has
a
higher
temporal
resolution,
which
provides
new
method
identification
trees.
In
this
study,
we
aim
to
establish
an
efficient
practical
tree
by
combining
object-oriented
approach
random
forest
algorithm
using
UAV
multispectral
images.
Firstly,
image
was
segmented
multi-scale
segmentation
based
scale
determined
Estimation
Scale
Parameter
2
(ESP2)
tool
visual
discrimination.
Secondly,
spectral
features,
index
texture
features
geometric
were
combined
form
schemes
S1–S8,
S9,
consisting
selected
recursive
feature
elimination
(RFE)
method.
Finally,
classification
performed
nine
(RF),
support
vector
machine
(SVM)
k-nearest
neighbor
(KNN)
classifiers,
respectively.
The
results
show
that
RF
classifier
performs
better
than
SVM
KNN,
achieves
highest
accuracy
in
overall
(OA)
91.89%
Kappa
coefficient
(Kappa)
0.91.
This
study
reveals
have
negative
impact
classification,
other
three
types
positive
impact.
importance
ranking
map
shows
are
most
important
type
followed
features.
Most
species
high
accuracy,
but
Camphor
Cinnamomum
Japonicum
much
lower
species,
suggesting
cannot
accurately
distinguish
these
two
so
it
necessary
add
such
as
height
future
improve
accuracy.
illustrates
combination
images
powerful
classification.
Drones,
Journal Year:
2023,
Volume and Issue:
7(1), P. 61 - 61
Published: Jan. 15, 2023
When
employing
remote
sensing
images,
it
is
challenging
to
classify
vegetation
species
and
ground
objects
due
the
abundance
of
wetland
high
fragmentation
objects.
Remote
images
are
classified
primarily
according
their
spatial
resolution,
which
significantly
impacts
classification
accuracy
However,
there
still
some
areas
for
improvement
in
study
effects
resolution
resampling
on
results.
The
area
this
paper
was
core
zone
Huixian
Karst
National
Wetland
Park
Guilin,
Guangxi,
China.
aerial
(Am)
with
different
resolutions
were
obtained
by
utilizing
UAV
platform,
resampled
(An)
pixel
aggregation
method.
In
order
evaluate
impact
accuracy,
Am
An
utilized
based
geographic
object-based
image
analysis
(GEOBIA)
method
addition
various
machine
learning
classifiers.
results
showed
that:
(1)
multi-scale
both
optimal
scale
parameter
(SP)
processing
time
decreased
as
diminished
multi-resolution
segmentation
process.
At
same
SP
greater
than
that
Am.
(2)
case
An,
appropriate
feature
variables
different,
spectral
texture
features
more
significant
those
(3)
classifiers
exhibited
similar
trends
ranging
from
1.2
5.9
cm,
where
overall
increased
then
accordance
decrease
resolution.
Moreover,
higher
An.
(4)
at
scales,
differed
between
IEEE Access,
Journal Year:
2023,
Volume and Issue:
11, P. 117582 - 117621
Published: Jan. 1, 2023
Unmanned
Aerial
Vehicles
(UAVs)
play
an
important
role
in
many
applications,
including
health,
transport,
telecommunications
and
safe
rescue
operations.
Their
adoption
can
improve
the
speed
precision
of
applications
when
compared
to
traditional
solutions
based
on
handwork.
The
use
UAVs
brings
scientific
technological
challenges.
In
this
context,
Machine
Learning
(ML)
techniques
provide
several
problems
concerning
civil
military
applications.
An
increasing
number
papers
ML
context
have
been
published
academic
journals.
work,
we
present
a
literature
review
UAVs,
outlining
most
recurrent
areas
commonly
used
UAV
results
reveal
that
environment,
communication
security
are
among
main
research
topics.
Remote Sensing,
Journal Year:
2022,
Volume and Issue:
14(16), P. 3937 - 3937
Published: Aug. 13, 2022
The
recent
developments
of
new
deep
learning
architectures
create
opportunities
to
accurately
classify
high-resolution
unoccupied
aerial
system
(UAS)
images
natural
coastal
systems
and
mandate
continuous
evaluation
algorithm
performance.
We
evaluated
the
performance
U-Net
DeepLabv3
convolutional
network
two
traditional
machine
techniques
(support
vector
(SVM)
random
forest
(RF))
applied
seventeen
land
cover
types
in
west
Florida
using
UAS
multispectral
imagery
canopy
height
models
(CHM).
Twelve
combinations
spectral
bands
CHMs
were
used.
Our
results
showed
that
(83.80–85.27%
overall
accuracy)
DeepLabV3
(75.20–83.50%
outperformed
SVM
(60.50–71.10%
RF
(57.40–71.0%)
algorithms.
addition
CHM
slightly
increased
accuracy
as
a
whole
models,
while
notably
improved
results.
Similarly,
outside
three
bands,
namely,
near-infrared
red
edge,
classifiers
but
had
minimal
impact
on
classification
difference
accuracies
produced
by
UAS-based
lidar
SfM
point
clouds,
supplementary
geometrical
information,
process
was
across
all
techniques.
highlight
advantage
networks
highly
diverse
landscapes.
also
found
low-cost,
three-visible-band
produces
comparable
do
not
risk
significant
reduction
when
adopting
models.
Plant Methods,
Journal Year:
2023,
Volume and Issue:
19(1)
Published: Jan. 23, 2023
Abstract
Background
Karst
vegetation
is
of
great
significance
for
ecological
restoration
in
karst
areas.
Vegetation
Indices
(VIs)
are
mainly
related
to
plant
yield
which
helpful
understand
the
status
Recently,
surveys
have
gradually
shifted
from
field
remote
sensing-based
methods.
Coupled
with
machine
learning
methods,
Unmanned
Aerial
Vehicle
(UAV)
multispectral
sensing
data
can
effectively
improve
detection
accuracy
and
extract
important
spectrum
features.
Results
In
this
study,
UAV
image
at
flight
altitudes
100
m,
200
400
m
were
collected
be
applied
a
area.
The
resulting
ground
resolutions
5.29,
10.58,
21.16
cm/pixel,
respectively.
Four
models,
including
Random
Forest
(RF),
Support
Vector
Machine
(SVM),
Gradient
Boosting
(GBM),
Deep
Learning
(DL),
compared
test
performance
coverage
detection.
5
spectral
values
(Red,
Green,
Blue,
NIR,
Red
edge)
16
VIs
selected
perform
variable
importance
analysis
on
best
models.
results
show
that
model
each
altitude
has
highest
detecting
its
training
(over
90%),
GBM
constructed
based
all
yields
covering
data,
an
overall
95.66%.
variables
significantly
correlated
not
Modified
Soil
Adjusted
Index
(MSAVI)
Anthocyanin
Content
(MACI),
Finally,
was
used
invert
complete
images
different
altitudes.
Conclusions
general,
GBM_all
imaging
feasible
accurately
detect
coverage.
prediction
models
had
certain
similarity
distribution
index
importance.
Combined
method
visual
interpretation,
green
predicted
by
good
agreement
truth,
other
land
types
hay,
rock,
soil
well
predicted.
This
study
provided
methodological
reference
eastern
China.
Remote Sensing,
Journal Year:
2022,
Volume and Issue:
14(4), P. 850 - 850
Published: Feb. 11, 2022
The
rapid
development
of
remote
sensing
technology
provides
wealthy
data
for
earth
observation.
Land-cover
mapping
indirectly
achieves
biodiversity
estimation
at
a
coarse
scale.
Therefore,
accurate
land-cover
is
the
precondition
estimation.
However,
environment
wetlands
complex,
and
vegetation
mixed
patchy,
so
recognition
based
on
full
challenges.
This
paper
constructs
systematic
framework
multisource
image
processing.
Firstly,
hyperspectral
(HSI)
multispectral
(MSI)
are
fused
by
CNN-based
method
to
obtain
with
high
spatial-spectral
resolution.
Secondly,
considering
sequentiality
spatial
distribution
spectral
response,
vision
transformer
(SSViT)
designed
extract
sequential
relationships
from
images.
After
that,
an
external
attention
module
utilized
feature
integration,
then
pixel-wise
prediction
achieved
mapping.
Finally,
benthos
sites
analyzed
consistently
reveal
rule
benthos.
Experiments
ZiYuan1-02D
Yellow
River
estuary
wetland
conducted
demonstrate
effectiveness
proposed
compared
several
related
methods.
Land,
Journal Year:
2022,
Volume and Issue:
11(11), P. 2039 - 2039
Published: Nov. 14, 2022
The
advancement
of
deep
learning
(DL)
technology
and
Unmanned
Aerial
Vehicles
(UAV)
remote
sensing
has
made
it
feasible
to
monitor
coastal
wetlands
efficiently
precisely.
However,
studies
have
rarely
compared
the
performance
DL
with
traditional
machine
(Pixel-Based
(PB)
Object-Based
Image
Analysis
(OBIA)
methods)
in
UAV-based
wetland
monitoring.
We
constructed
a
dataset
based
on
RGB-based
UAV
data
PB,
OBIA,
methods
classification
vegetation
communities
wetlands.
In
addition,
our
knowledge,
OBIA
method
was
used
for
first
time
this
paper
Google
Earth
Engine
(GEE),
ability
GEE
process
confirmed.
results
showed
that
comparison
PB
methods,
achieved
most
promising
results,
which
capable
reflecting
realistic
distribution
vegetation.
Furthermore,
paradigm
shifts
from
terms
feature
engineering,
training
reference
explained
considerable
by
method.
suggested
combination
UAV,
DL,
cloud
computing
platforms
can
facilitate
long-term,
accurate
monitoring
at
local
scale.
Remote Sensing,
Journal Year:
2022,
Volume and Issue:
14(21), P. 5533 - 5533
Published: Nov. 2, 2022
Mangrove-forest
classification
by
using
deep
learning
algorithms
has
attracted
increasing
attention
but
remains
challenging.
The
current
studies
on
the
transfer
of
mangrove
communities
between
different
regions
and
sensors
are
especially
still
unclear.
To
fill
research
gap,
this
study
developed
a
new
deep-learning
algorithm
(encoder–decoder
with
mixed
depth-wise
convolution
cascade
upsampling,
MCCUNet)
modifying
encoder
decoder
sections
DeepLabV3+
presented
three
transfer-learning
strategies,
namely
frozen
(F-TL),
fine-tuned
(Ft-TL),
sensor-and-phase
(SaP-TL),
to
classify
MCCUNet
high-resolution
UAV
multispectral
images.
This
combined
recursive
feature
elimination
principal
component
analysis
(RFE–PCA),
high-dimensional
dataset
map
communities,
evaluated
their
performance.
results
showed
following:
(1)
outperformed
original
for
classifying
achieving
highest
overall
accuracy
(OA),
i.e.,
97.24%,
in
all
scenarios.
(2)
RFE–PCA
dimension
reduction
improved
performance
algorithms.
OA
species
from
was
7.27%
after
adding
dimension-reduced
texture
features
vegetation
indices.
(3)
Ft-TL
strategy
enabled
achieve
better
stability
than
F-TL
strategy.
improvement
F1–score
Spartina
alterniflora
19.56%,
(4)
SaP-TL
produced
classifications
images
phases
sensors.
Aegiceras
corniculatum
19.85%,
(5)
All
strategies
achieved
high
mean
84.37~95.25%.