Forestry Studies / Metsanduslikud Uurimused,
Год журнала:
2024,
Номер
80(1), С. 1 - 19
Опубликована: Дек. 1, 2024
Abstract
The
study
analysed
2019–2022
summertime
canopy
height
predictions
(
H
ICESat
)
given
in
ICESat-2
ATLAS
dataset
ATL08
for
hemiboreal
forests
growing
on
an
area
of
40,000
km
2
Estonia
around
25.6°
E,
58.8°
N.
In
total
12,711
20×20
m
pixel
observations
were
used
from
3,065
forest
stands
with
homogenous
structure.
Regression
modelling
was
to
explain
variability
ground
surface
elevation
estimates,
and
relationships
basal
weighted
mean
tree
the
inventory
database
FI
95th
percentile
vertical
distribution
airborne
laser
scanning
pulse
return
ALS
).
other
explanatory
variables
observation
geographic
location,
track
beam
energy
indicators,
cover,
evergreen
coniferous
dominance
indicator,
deep
peat
soil
indicator.
linear
model
between
Estonian
digital
terrain
had
a
determination
coefficient
R
=99.97%
residual
standard
error
δ=0.51
when
location
included.
can
be
predicted
=85%
δ=2.7
m.
A
comparison
means
indicated
that,
average,
about
0.3
greater
than
.
All
predictive
(except
location)
significant
models,
best
models
fitted
=95%
δ=1.6
m,
however,
there
no
notable
increase
if
more
predictors
added
models.
practical
applications
using
data
inventories,
inclusion
weak
increases
number
observations,
but
indicator
has
included
Remote Sensing of Environment,
Год журнала:
2024,
Номер
305, С. 114097 - 114097
Опубликована: Март 7, 2024
The
northern
forest-tundra
ecotone
is
one
of
the
fastest
warming
regions
globe.
Models
vegetation
change
generally
predict
a
northward
advance
boreal
forests
and
corresponding
retreat
tundra.
Previous
satellite
remote
sensing
analyses
in
this
region
have
focused
on
mapping
greenness
tree
cover
derived
from
optical
multi-spectral
sensors.
Changes
structure
relating
to
height
biomass
are
less
frequently
investigated
due
limited
availability
lidar
data
over
space
time
comparison
with
platforms.
As
such,
there
an
opportunity
combine
products
for
continuous
at
high-latitudes,
emphasis
transition.
In
study,
we
used
Ice,
Cloud
land
Elevation
Satellite
(ICESat-2)
classify
canopy
presence/absence,
across
120
million
hectares
Canadian
30
m
spatial
resolution.
Spatially
predictors
Landsat
archive
(2012−2021)
ASTER
(Advanced
Spaceborne
Thermal
Emission
Reflection
Radiometer)
Digital
Model
were
extrapolate
98th
percentile
ICESat-2
Land
Vegetation
Height
(ATL08)
product
using
Random
Forests
models
developed
R
(version
4.2.2).
accuracy
was
assessed
Land,
Ice
Sensor
(LVIS),
large-footprint
airborne
system.
overall
presence
classification
89%,
detected
88%
accuracy.
showed
R2
0.54
RMSE
2.09
m.
Finally,
these
methods
map
limit
3
forest
Canada
compared
our
model
outputs
MODIS
Continuous
Fields
datasets.
This
work
demonstrates
challenges
potential
horizontal
vertical
within
sparse,
high
latitude
both
data.
GIScience & Remote Sensing,
Год журнала:
2024,
Номер
61(1)
Опубликована: Авг. 27, 2024
Forest
canopy
height
(FCH)
is
one
of
the
most
important
variables
for
carbon
stock
estimation.
While
many
studies
have
focused
on
extracting
FCH
from
spaceborne
LiDAR
in
regions
with
spatially
continuous
and
large
patch
sizes
forested
lands,
limited
research
has
addressed
challenges
extraction
plain
sparse
fragmented
forest
distributions.
In
this
study,
we
proposed
innovative
processing
approaches
to
extract
ICESat-2
photons
GEDI
footprints
Anhui
Province,
China.
Specifically,
a
sectional
photon
denoising
method
data
geolocation
error
correction
data.
Airborne
were
used
validate
extracted
products
across
typical
regions.
The
results
demonstrated
effectiveness
methods
improving
accuracy.
Evaluation
indicated
that
directly
ATL08
L2A
had
Pearson's
correlation
coefficients
(r)
0.6
0.93,
respectively.
After
methods,
2019
exhibited
r
0.82
relative
root
mean
square
(rRMSE)
31.11%
based
3,217
segments,
showed
0.96
rRMSE
18.35%
4,862
footprints.
Further
application
these
years
2020,
2021,
2022
their
promise
addressing
vegetation
coverage
Communications Earth & Environment,
Год журнала:
2025,
Номер
6(1)
Опубликована: Янв. 30, 2025
Abstract
Climate
warming
has
improved
conditions
for
boreal
forest
growth,
yet
the
region’s
fate
as
a
carbon
sink
of
aboveground
biomass
remains
uncertain.
Forest
height
is
powerful
predictor
biomass,
and
access
to
spatially
detailed
height-age
relationships
could
improve
understanding
dynamics
in
this
ecosystem.
The
capacity
land
grow
trees,
defined
forestry
site
index,
was
estimated
by
analyzing
recent
measurements
canopy
against
chronosequence
stand
age
derived
from
historical
satellite
record.
Forest-height
estimates
were
then
subtracted
predicted
index
estimate
growth
potential
across
region.
Russia,
which
comprised
73%
change
domain,
had
strong
departures
model
expectation
2.4–4.8
±
3.8
m
75th
90th
percentiles.
Combining
observations
revealed
large
young
if
allowed
recover
disturbance.
Earth Surface Processes and Landforms,
Год журнала:
2025,
Номер
50(2)
Опубликована: Фев. 1, 2025
Abstract
Global
digital
elevation
models
(GDEMs)
are
critical
in
the
measurement
and
analysis
of
Earth's
surface,
should
be
evaluated
prior
to
use.
However,
existing
GDEM
evaluations
mainly
use
global
statistical
metrics
evaluate
vertical
(VE)
differences
with
reference
data,
ignoring
relationship
between
a
centre
pixel
its
neighbouring
pixels,
which
is
defined
as
GDEM's
structure
(NS).
Along
track
ATL03
points
allow
evaluation
along
NS
(ATNS).
This
study
comprehensively
accesses
VE
ATNS
accuracy
1
arc‐second
GDEMs,
including
Copernicus,
NASA,
AW3D30
ASTER
DEM,
using
for
first
time
ICESat‐2
throughout
Tibetan
Plateau,
where
rugged
terrains
various
features
make
it
difficult
maintain
NS's
accuracy.
introduces
continuous
discrete
metrics,
then
evaluates
their
effectiveness
by
analysing
relationships
errors
terrain
derivatives.
Finally,
better‐performing
metric
used
across
parameters,
landforms
land
covers.
The
proposed
framework
achieved
DEM
from
pixel‐by‐pixel
local
assessment.
Evaluation
results
demonstrate
that
GDEMs
linearly
correlated
RMSE
errors.
Overall,
ranked
Copernicus<
<
NASA<
ASTER.
conducted
Andes
Alps
reveal
regional
variations
these
rankings.
endeavours
introduce
new
large‐scale
evaluations,
conclusions
beneficial
selection
further
applications.
Remote Sensing,
Год журнала:
2023,
Номер
15(20), С. 4969 - 4969
Опубликована: Окт. 15, 2023
Two
space-borne
light
detection
and
ranging
(LiDAR)
missions,
Global
Ecosystem
Dynamics
Investigation
(GEDI)
Ice,
Cloud,
land
Elevation
Satellite-2
(ICESat-2),
have
demonstrated
high
capabilities
in
extracting
terrain
canopy
heights
forest
environments.
However,
there
been
limited
studies
evaluating
their
performance
for
height
retrievals
short-stature
vegetation.
This
study
utilizes
airborne
LiDAR
data
to
validate
compare
the
accuracies
of
vegetation
using
latest
versions
ICESat-2
(Version
5)
GEDI
2).
Furthermore,
this
also
analyzes
influence
various
factors,
such
as
type,
slope,
height,
cover,
on
retrievals.
The
results
indicate
that
(bias
=
−0.05
m,
RMSE
0.67
m)
outperforms
0.39
1.40
extraction,
with
similar
observed
from
both
missions.
Additionally,
findings
reveal
significant
differences
retrieval
between
under
different
acquisition
scenarios.
Error
analysis
demonstrate
slope
plays
a
pivotal
role
influencing
accuracy
extraction
particularly
data,
where
decreases
significantly
increasing
slope.
has
most
substantial
impact
estimation
heights.
Overall,
these
confirm
strong
potential
areas,
provide
valuable
insights
future
applications
vegetation-dominated
ecosystems.
Abstract
The
Qinghai-Tibet
Plateau
(QTP)
holds
significance
for
investigating
Earth’s
surface
processes.
However,
due
to
rugged
terrain,
forest
canopy,
and
snow
accumulation,
open-access
Digital
Elevation
Models
(DEMs)
exhibit
considerable
noise,
resulting
in
low
accuracy
pronounced
data
inconsistency.
Furthermore,
the
glacier
regions
within
QTP
undergo
substantial
changes,
necessitating
updates.
This
study
employs
a
fusion
of
DEMs
high-accuracy
photons
from
Ice,
Cloud,
land
Satellite-2
(ICESat-2).
Additionally,
cover
canopy
heights
are
considered,
an
ensemble
learning
model
is
presented
harness
complementary
information
multi-sensor
elevation
observations.
innovative
approach
results
creation
HQTP30,
most
accurate
representation
2021
terrain.
Comparative
analysis
with
high-resolution
imagery,
UAV-derived
DEMs,
control
points,
ICESat-2
highlights
advantages
HQTP30.
Notably,
non-glacier
regions,
HQTP30
achieved
Mean
Absolute
Error
(MAE)
0.71
m,
while
it
reduced
MAE
by
4.35
m
compared
state-of-the-art
Copernicus
DEM
(COPDEM),
demonstrating
its
versatile
applicability.
PLoS ONE,
Год журнала:
2024,
Номер
19(10), С. e0309025 - e0309025
Опубликована: Окт. 7, 2024
The
accuracy
of
digital
elevation
models
(DEMs)
in
forested
areas
plays
a
crucial
role
canopy
height
monitoring
and
ecological
sensitivity
analysis.
Despite
extensive
research
on
DEMs
recent
years,
significant
errors
still
exist
due
to
factors
such
as
occlusion,
terrain
complexity,
limited
penetration,
posing
challenges
for
subsequent
analyses
based
DEMs.
Therefore,
CNN-LightGBM
hybrid
model
is
proposed
this
paper,
with
four
different
types
forests
(tropical
rainforest,
coniferous
forest,
mixed
broad-leaved
forest)
selected
study
sites
validate
the
performance
correcting
COP30DEM
forest
area
In
choice
was
made
use
Densenet
architecture
CNN
LightGBM
primary
model.
This
LightGBM’s
leaf-growth
strategy
histogram
linking
methods,
which
are
effective
reducing
data’s
memory
footprint
utilising
more
data
without
sacrificing
speed.
uses
values
from
ICESat-2
ground
truth,
covering
several
parameters
including
COP30DEM,
height,
coverage,
slope,
roughness
relief
amplitude.
To
superiority
correction
compared
other
models,
test
model,
CNN-SVR
SVR
conducted
within
same
sample
space.
prevent
issues
overfitting
or
underfitting
during
training,
although
common
meta-heuristic
optimisation
algorithms
can
alleviate
these
problems
certain
extent,
they
have
some
shortcomings.
overcome
shortcomings,
paper
cites
an
improved
SSA
search
algorithm
that
incorporates
ingestion
FA
increase
diversity
solutions
global
capability,
Firefly
Algorithm-based
Sparrow
Search
Optimization
Algorithm
(FA-SSA
algorithm)
introduced.
By
comparing
multiple
validating
airborne
LiDAR
reference
dataset,
results
show
R
2
(R-Square)
improves
by
than
0.05
performs
better
experiments.
FA-SSA-CNN-LightGBM
has
highest
accuracy,
RMSE
1.09
meters,
reduction
30%
when
models.
Compared
(such
FABDEM
GEDI),
its
50%,
significantly
commonly
used
areas,
indicating
feasibility
method
importance
advancing
topographic
mapping.
Frontiers in Plant Science,
Год журнала:
2025,
Номер
16
Опубликована: Март 20, 2025
ICESat-2
and
GEDI
offer
unique
capabilities
for
terrain
canopy
height
retrievals;
however,
their
performance
measurement
precision
are
significantly
affected
by
conditions.
Furthermore,
differences
in
data
scales
complicate
direct
comparisons
of
capabilities.
This
study
evaluates
the
accuracy
retrievals
from
LiDAR
complex
environments.
Jinghong
City
Pu’er
Southwest
China
were
selected
as
areas,
with
high-precision
airborne
serving
a
reference.
Ground
elevation
retrieval
accuracies
compared
before
after
scale
unification
to
30
m
×
under
varying
slope
Results
indicate
that
shows
significant
advantage
retrieval,
RMSE
values
4.75
4.21
unification,
respectively.
In
comparison,
achieved
4.94
4.96
m.
Both
systems
maintain
high
flat
regions,
but
declines
increasing
slope.
For
outperforms
ICESat-2.
Before
an
R²
0.73
5.15
m,
0.67
5.32
contrast,
showed
lower
performance,
0.65
7.42
0.53
8.29
unification.
maintains
higher
across
all
levels.
Post-scale
both
show
ground
being
superior.
achieves
better
accuracy.
These
findings
highlight
synergistic
strengths
ICESat-2’s
photon-counting
GEDI’s
full-waveform
techniques,
demonstrating
advancements
satellite
laser
altimetry
retrieval.