Lidar
has
been
widely
used
in
Autonomous
driving
and
robot.
To
accelerate
the
research
development
of
technology,
test
validation
is
vital
important.
In
this
work,
a
target
simulator
for
designed
demonstrated.
Mainly
two
functions
are
realized
including
distance
environmental
attenuation
simulation.
For
simulation,
time-of-flight
(ToR)
method
introduced
cascaded
optical
delay
module
to
generate
an
from
2ns
up
4μs
with
step
as
low
40ps,
corresponding
simulated
nearly
zero
more
than
500m.
various
factors
that
may
influence
backscattered
power
analyzed
relationship
between
loss
established.
A
commercial
tested
using
result
shows
good
consistence
obtained
reflector
panel.
Also
main
challenge
further
solution
discussed
optimization
simulator.
The
rapid
urbanization
process
in
recent
decades
has
altered
the
carbon
cycle
and
exacerbated
impact
of
climate
change,
prompting
many
cities
to
develop
tree
planting
green
area
preservation
as
mitigation
adaptation
measures.
While
numerous
studies
have
estimated
stocks
urban
trees
temperate
subtropical
cities,
data
from
tropical
regions,
including
botanic
gardens,
are
scarce.
This
study
aimed
quantify
aboveground
biomass
(AGB
AGC,
respectively)
at
Rio
de
Janeiro
Botanical
Garden
arboretum,
Janeiro,
Brazil.
Our
survey
included
6793
stems
with
a
diameter
breast
height
(DBH)
≥
10
cm.
total
AGB
was
8,047.24
Mg,
representing
4,023.62
Mg
AGC.
density
207.4
Mg.ha-1
(AGC
=
103.7
Mg.ha-1),
which
is
slightly
lower
than
stored
Brazil's
main
forest
complexes:
Atlantic
Amazon
forests,
but
much
higher
worldwide.
results
suggest
that,
addition
their
global
importance
for
plant
conservation,
gardens
could
function
significant
sinks
within
matrix.
Research Square (Research Square),
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 11, 2024
Abstract
Accurately
estimating
above-ground
biomass
(AGB)
is
critical
for
understanding
carbon
storage
and
ecosystem
dynamics,
which
are
essential
sustainable
forest
management
climate
change
mitigation.
This
study
evaluated
the
performance
of
four
machine
learning
models
XGBoost,
Random
Forest
(RF),
Gradient
Boosting
(GBM),
Support
Vector
Machine
(SVM)
in
predicting
AGB
Miombo
Woodlands
using
UAV-derived
spectral
height
data.
A
total
52
model
configurations
were
tested,
incorporating
up
to
five
predictor
variables.
XGBoost
demonstrated
superior
performance,
explaining
99%
variance
(R²
=
0.99),
with
a
low
RMSE
9.82
Mg/ha
an
rRMSE
8.25%.
Although
it
showed
slight
underestimation
bias
(-2.48),
proved
highly
reliable
handling
complex
ecosystems
like
Miombo.
also
performed
well,
91%
0.91),
though
exhibited
higher
error
rates
(RMSE
30.81
Mg/ha).
In
contrast,
GBM
SVM
weaker
R²
values
0.23
0.81,
respectively.
highlights
potential
UAV
data
combined
advanced
models,
particularly
accurate
estimation.
Future
research
should
explore
integrating
technologies
LiDAR
or
satellite
imagery
further
improve
prediction
accuracy
across
diverse
ecosystems.
Lidar
has
been
widely
used
in
Autonomous
driving
and
robot.
To
accelerate
the
research
development
of
technology,
test
validation
is
vital
important.
In
this
work,
a
target
simulator
for
designed
demonstrated.
Mainly
two
functions
are
realized
including
distance
environmental
attenuation
simulation.
For
simulation,
time-of-flight
(ToR)
method
introduced
cascaded
optical
delay
module
to
generate
an
from
2ns
up
4μs
with
step
as
low
40ps,
corresponding
simulated
nearly
zero
more
than
500m.
various
factors
that
may
influence
backscattered
power
analyzed
relationship
between
loss
established.
A
commercial
tested
using
result
shows
good
consistence
obtained
reflector
panel.
Also
main
challenge
further
solution
discussed
optimization
simulator.
