Biogeosciences,
Journal Year:
2024,
Volume and Issue:
21(2), P. 473 - 511
Published: Jan. 25, 2024
Abstract.
Vegetation
productivity
is
a
critical
indicator
of
global
ecosystem
health
and
impacted
by
human
activities
climate
change.
A
wide
range
optical
sensing
platforms,
from
ground-based
to
airborne
satellite,
provide
spatially
continuous
information
on
terrestrial
vegetation
status
functioning.
As
Earth
observation
(EO)
data
are
usually
routinely
acquired,
can
be
monitored
repeatedly
over
time,
reflecting
seasonal
patterns
trends
in
metrics.
Such
metrics
include
gross
primary
productivity,
net
biomass,
or
yield.
To
summarize
current
knowledge,
this
paper
we
systematically
reviewed
time
series
(TS)
literature
for
assessing
state-of-the-art
monitoring
approaches
different
ecosystems
based
remote
(RS)
data.
the
integration
solar-induced
fluorescence
(SIF)
processing
chains
has
emerged
as
promising
source,
also
relatively
recent
sensor
modality.
We
define
three
methodological
categories
derive
remotely
sensed
TS
indices
quantitative
traits:
(i)
trend
analysis
anomaly
detection,
(ii)
land
surface
phenology,
(iii)
assimilation
TS-derived
into
statistical
process-based
dynamic
models
(DVMs).
Although
majority
used
streams
originate
acquired
satellite
aircraft
unoccupied
aerial
vehicles
have
found
their
way
studies.
facilitate
processing,
list
common
toolboxes
inferring
further
discuss
validation
strategies
RS
derived
metrics:
(1)
using
situ
measured
data,
such
yield;
(2)
networks
distinct
sensors,
including
spectroradiometers,
flux
towers,
phenological
cameras;
(3)
inter-comparison
Finally,
address
challenges
propose
conceptual
framework
derivation,
fully
integrated
DVMs
radiative
transfer
here
labelled
“Digital
Twin”.
This
novel
meets
requirements
multiple
enables
both
an
improved
understanding
temporal
dynamics
response
environmental
drivers
enhances
accuracy
monitoring.
New Phytologist,
Journal Year:
2024,
Volume and Issue:
243(1), P. 111 - 131
Published: May 6, 2024
Summary
Leaf
traits
are
essential
for
understanding
many
physiological
and
ecological
processes.
Partial
least
squares
regression
(PLSR)
models
with
leaf
spectroscopy
widely
applied
trait
estimation,
but
their
transferability
across
space,
time,
plant
functional
types
(PFTs)
remains
unclear.
We
compiled
a
novel
dataset
of
paired
spectra,
47
393
records
>
700
species
eight
PFTs
at
101
globally
distributed
locations
multiple
seasons.
Using
this
dataset,
we
conducted
an
unprecedented
comprehensive
analysis
to
assess
the
PLSR
in
estimating
traits.
While
demonstrate
commendable
performance
predicting
chlorophyll
content,
carotenoid,
water,
mass
per
area
prediction
within
training
data
efficacy
diminishes
when
extrapolating
new
contexts.
Specifically,
locations,
seasons,
beyond
leads
reduced
R
2
(0.12–0.49,
0.15–0.42,
0.25–0.56)
increased
NRMSE
(3.58–18.24%,
6.27–11.55%,
7.0–33.12%)
compared
nonspatial
random
cross‐validation.
The
results
underscore
importance
incorporating
greater
spectral
diversity
model
boost
its
transferability.
These
findings
highlight
potential
errors
large
spatial
domains,
diverse
PFTs,
time
due
biased
validation
schemes,
provide
guidance
future
field
sampling
strategies
remote
sensing
applications.
Environmental Data Science,
Journal Year:
2025,
Volume and Issue:
4
Published: Jan. 1, 2025
Abstract
Forests
play
a
crucial
role
in
the
Earth’s
system
processes
and
provide
suite
of
social
economic
ecosystem
services,
but
are
significantly
impacted
by
human
activities,
leading
to
pronounced
disruption
equilibrium
within
ecosystems.
Advancing
forest
monitoring
worldwide
offers
advantages
mitigating
impacts
enhancing
our
comprehension
composition,
alongside
effects
climate
change.
While
statistical
modeling
has
traditionally
found
applications
biology,
recent
strides
machine
learning
computer
vision
have
reached
important
milestones
using
remote
sensing
data,
such
as
tree
species
identification,
crown
segmentation,
biomass
assessments.
For
this,
significance
open-access
data
remains
essential
data-driven
algorithms
methodologies.
Here,
we
comprehensive
extensive
overview
86
datasets
across
spatial
scales,
encompassing
inventories,
ground-based,
aerial-based,
satellite-based
recordings,
country
or
world
maps.
These
grouped
OpenForest,
dynamic
catalog
open
contributions
that
strives
reference
all
available
datasets.
Moreover,
context
these
datasets,
aim
inspire
research
applied
biology
establishing
connections
between
contemporary
topics,
perspectives,
challenges
inherent
both
domains.
We
hope
encourage
collaborations
among
scientists,
fostering
sharing
exploration
diverse
through
application
methods
for
large-scale
monitoring.
OpenForest
is
at
following
url:
https://github.com/RolnickLab/OpenForest
.
Remote Sensing of Environment,
Journal Year:
2024,
Volume and Issue:
302, P. 113958 - 113958
Published: Jan. 5, 2024
Plant
functional
diversity
(FD)
is
a
component
of
biodiversity
linking
plant
traits
to
ecosystem
processes
(e.g.,
photosynthesis)
and
services
gross
primary
production).
Development
remote
sensing
capabilities
monitor
forest
FD
across
various
spatio-temporal
scales
critical,
especially
in
view
increasing
global
climate
anthropogenic
pressures.
Here,
we
focus
on
investigating
the
capability
unoccupied
aerial
systems
(UAS),
acquiring
imaging
spectroscopy
data
high
spatial
(pixel
size
≤0.1
m)
spectral
(band-width
<
5
nm
between
400
1000
nm)
resolutions,
map
two
trait-based
metrics,
namely,
richness
divergence,
open
sclerophyll
forests
at
plot-scale
(<0.2
km2).
An
emerging
scalable
kernel-based
trait
probability
density
(TPD)
approach
was
implemented
compute
spatially
explicit
metrics
different
areal
extents
pixel
sizes
through
resampled
products.
Narrow-band
indices
were
utilized
as
proxies
selected
traits,
including
photoprotective
zeaxanthin-to-antheraxanthin
transformation
ratio
(VAZ),
foliar
pigments
chlorophylls
anthocyanins
(Cab
Cant).
The
combination
high-resolution
imagery
TPDs
presents
suitable
alternative
traditional
need
for
taxonomic
information
alleviates
pixel-based
mixing
issues
known
affect
metrics.
A
moving
kernel
(6
×
6
applied
UAS
data,
allowed
capture
fine
medium-scale
drivers
within-crown
complex
branching
variance,
topography,
sun
aspect,
speciation.
For
same
size,
computed
from
coarsened
pseudo-airborne
products
2
found
be
57–68%
that
derived
Functional
divergence
did
not
portray
substantial
differences
even
though
this
metric
further
emphasized
complexity
surveyed
open-forest
sites.
have
potential
become
an
efficient
tool
monitoring
linked
with
key
sites,
validation
support
large-scale
but
less
detailed
airborne
satellite
Finally,
study
highlights
sensitivity
variations
scale,
resolution,
TPD
parametrization
suggesting
more
research
needed
standardize
protocols
quantification
temporal
scales.
Nature,
Journal Year:
2025,
Volume and Issue:
unknown
Published: March 5, 2025
Tropical
forest
canopies
are
the
biosphere's
most
concentrated
atmospheric
interface
for
carbon,
water
and
energy1,2.
However,
in
Earth
System
Models,
diverse
heterogeneous
tropical
biome
is
represented
as
a
largely
uniform
ecosystem
with
either
singular
or
small
number
of
fixed
canopy
ecophysiological
properties3.
This
situation
arises,
part,
from
lack
understanding
about
how
why
functional
properties
vary
geographically4.
Here,
by
combining
field-collected
data
more
than
1,800
vegetation
plots
tree
traits
satellite
remote-sensing,
terrain,
climate
soil
data,
we
predict
variation
across
13
morphological,
structural
chemical
trees,
use
this
to
compute
map
diversity
forests.
Our
findings
reveal
that
Americas,
Africa
Asia
tend
occupy
different
portions
total
trait
space
available
American
forests
predicted
have
40%
greater
richness
African
Asian
Meanwhile,
highest
divergence-32%
7%
higher
forests,
respectively.
An
uncertainty
analysis
highlights
priority
regions
further
collection,
which
would
refine
improve
these
maps.
predictions
represent
ground-based
remotely
enabled
global
space.
