Remote Sensing,
Год журнала:
2024,
Номер
16(19), С. 3627 - 3627
Опубликована: Сен. 28, 2024
The
Leaf
Area
Index
(LAI)
is
a
critical
parameter
that
sheds
light
on
the
composition
and
function
of
forest
ecosystems.
Its
efficient
rapid
measurement
essential
for
simulating
estimating
ecological
activities
such
as
vegetation
productivity,
water
cycle,
carbon
balance.
In
this
study,
we
propose
to
combine
high-resolution
GF-6
2
m
satellite
images
with
LESS
three-dimensional
RTM
employ
different
machine
learning
algorithms,
including
Random
Forest,
BP
Neural
Network,
XGBoost,
achieve
LAI
inversion
stands.
By
reconstructing
real
stand
scenarios
in
model,
simulated
reflectance
data
blue,
green,
red,
near-infrared
bands,
well
data,
fused
some
inputs
train
models.
Subsequently,
used
remaining
measured
validation
prediction
inversion.
Among
three
Forest
gave
highest
performance,
an
R2
0.6164
RMSE
0.4109,
while
Network
performed
inefficiently
(R2
=
0.4022,
0.5407).
Therefore,
ultimately
employed
algorithm
perform
generated
spatial
distribution
maps,
achieving
innovative,
efficient,
reliable
method
Forests,
Год журнала:
2024,
Номер
15(11), С. 2024 - 2024
Опубликована: Ноя. 17, 2024
The
Global
Ecosystem
Dynamics
Investigation
(GEDI)
system
provides
essential
data
for
estimating
forest
canopy
height
on
a
global
scale.
However,
factors
such
as
complex
topography
and
dense
can
significantly
reduce
the
accuracy
of
GEDI
estimations.
We
selected
South
Taihang
region
Henan
Province,
China,
our
study
area
proposed
an
optimization
framework
to
improve
estimation
accuracy.
This
includes
correcting
geolocation
errors
in
footprints,
screening
analyzing
features
that
affect
errors,
combining
two
regression
models
with
feature
selection
methods.
Our
findings
reveal
error
4
6
m
footprints
at
orbital
scale,
along
overestimation
region.
Relative
(RH),
waveform
characteristics,
topographic
features,
cover
influenced
error.
Some
studies
have
suggested
estimates
areas
high
lead
underestimation,
found
increased
higher
terrain
vegetation.
model’s
performance
improved
after
incorporating
parameter
into
model.
Overall,
R2
best-optimized
model
was
from
0.06
0.61,
RMSE
decreased
8.73
2.23
m,
rRMSE
65%
17%,
resulting
improvement
74.45%.
In
general,
this
reveals
affecting
vegetation
cover,
premise
minimizing
errors.
Employing
enhanced
estimates.
also
highlighted
crucial
role
improving
precision
estimation,
providing
effective
approach
monitoring
regions
conditions.
Future
should
further
classification
tree
species
expand
diversity
sample
test
estimated
by
different
structures,
consider
distortion
optical
remote
sensing
images
caused
rugged
terrain,
mine
information
waveforms
so
enhance
applicability
more
diverse
environments.
Remote Sensing,
Год журнала:
2024,
Номер
16(19), С. 3627 - 3627
Опубликована: Сен. 28, 2024
The
Leaf
Area
Index
(LAI)
is
a
critical
parameter
that
sheds
light
on
the
composition
and
function
of
forest
ecosystems.
Its
efficient
rapid
measurement
essential
for
simulating
estimating
ecological
activities
such
as
vegetation
productivity,
water
cycle,
carbon
balance.
In
this
study,
we
propose
to
combine
high-resolution
GF-6
2
m
satellite
images
with
LESS
three-dimensional
RTM
employ
different
machine
learning
algorithms,
including
Random
Forest,
BP
Neural
Network,
XGBoost,
achieve
LAI
inversion
stands.
By
reconstructing
real
stand
scenarios
in
model,
simulated
reflectance
data
blue,
green,
red,
near-infrared
bands,
well
data,
fused
some
inputs
train
models.
Subsequently,
used
remaining
measured
validation
prediction
inversion.
Among
three
Forest
gave
highest
performance,
an
R2
0.6164
RMSE
0.4109,
while
Network
performed
inefficiently
(R2
=
0.4022,
0.5407).
Therefore,
ultimately
employed
algorithm
perform
generated
spatial
distribution
maps,
achieving
innovative,
efficient,
reliable
method
