Remote Sensing,
Journal Year:
2022,
Volume and Issue:
14(15), P. 3681 - 3681
Published: Aug. 1, 2022
Remote
sensing
(RS)
for
vegetation
monitoring
can
involve
mixed
pixels
with
contributions
from
and
background
surfaces,
causing
biases
in
signals
their
interpretations,
especially
low-density
forests.
In
a
case
study
the
semi-arid
Yatir
forest
Israel,
we
observed
mismatch
between
satellite
(Landsat
8
surface
product)
tower-based
(Skye
sensor)
multispectral
data
contrasting
seasonal
cycles
near-infrared
(NIR)
reflectance.
We
tested
hypothesis
that
this
was
due
to
different
fractional
of
various
components
unique
Employing
an
unmanned
aerial
vehicle
(UAV),
obtained
high-resolution
images
over
selected
plots
estimated
fraction,
reflectance,
cycle
three
main
(canopy,
shade,
sunlit
soil).
determined
Landsat
were
dominated
by
soil
(70%),
while
canopy
(95%).
then
developed
procedure
resolve
(i.e.,
tree
foliage)
normalized
difference
index
(NDVI)
data.
The
retrieved
corrected
canopy-only
resolved
original
indicated
spatial
variations
NDVI
differences
stand
density,
spatially
uniform,
providing
confidence
local
flux
tower
measurements.
Ecosystems,
Journal Year:
2021,
Volume and Issue:
25(1), P. 30 - 43
Published: May 6, 2021
Abstract
Drought
will
increasingly
threaten
forest
ecosystems
worldwide.
Understanding
how
competition
influences
tree
growth
response
to
drought
is
essential
for
management
aiming
at
climate
change
adaptation.
However,
published
results
from
individual
case
studies
are
heterogeneous
and
sometimes
contradictory.
We
reviewed
166
cases
the
peer-reviewed
literature
assess
influence
of
stand-level
on
drought.
monitored
five
indicators
response:
mean
sensitivity
(inter-annual
ring
width
variability);
association
between
inter-annual
variability
water
availability;
resistance;
recovery;
resilience
Vote
counting
did
not
indicate
a
consistent
effect
sensitivity.
Conversely,
higher
resources
strengthened
availability
rates.
Meta-analysis
showed
that
reduced
resistance
(
p
<
0.001)
improved
recovery
0.05),
but
consistently
affect
resilience.
Species,
site
stand
characteristics,
intensity
were
insignificant
or
poor
predictors
large
among
investigated
cases.
Our
review
meta-analysis
show
does
in
unidirectional
universal
way.
Although
density
reduction
(thinning)
can
alleviate
declines
during
drought,
effects
after
stress
uncertain.
The
suggests
local-scale
processes
play
crucial
role
determining
such
responses
should
be
explicitly
evaluated
integrated
into
specific
strategies
adaptation
forests
change.
Earth-Science Reviews,
Journal Year:
2022,
Volume and Issue:
230, P. 104055 - 104055
Published: May 12, 2022
As
CO2
concentration
in
the
atmosphere
rises,
there
is
a
need
for
improved
physical
understanding
of
its
impact
on
global
plant
transpiration.
This
knowledge
gap
poses
major
hurdle
robustly
projecting
changes
hydrologic
cycle.
For
this
reason,
here
we
review
different
processes
by
which
atmospheric
affects
transpiration,
several
uncertainties
related
to
complex
physiological
and
radiative
involved,
gaps
be
filled
order
improve
predictions
Although
high
degree
certainty
that
rising
will
exact
nature
remains
unclear
due
interactions
between
climate,
key
aspects
morphology
physiology.
The
interplay
these
factors
has
substantial
consequences
not
only
future
climate
vegetation,
but
also
water
availability
needed
sustaining
productivity
terrestrial
ecosystems.
Future
transpiration
response
enhanced
are
expected
driven
availability,
evaporative
demand,
processes,
emergent
disturbances
increasing
temperatures,
modification
physiology
coverage.
Considering
universal
sensitivity
natural
agricultural
systems
argue
reliable
projections
an
issue
highest
priority,
can
achieved
integrating
monitoring
modeling
efforts
representation
effects
next
generation
earth
system
models.
Geocarto International,
Journal Year:
2021,
Volume and Issue:
37(15), P. 4361 - 4389
Published: Jan. 21, 2021
Groundwater
scarcity
is
one
of
the
most
concerning
issues
in
arid
and
semi-arid
regions.
In
this
study,
we
develop
validate
a
novel
artificial
intelligence
that
coupling
five
ensemble
benchmark
algorithms
e.g.,
neural
network
(ANN),
reduced-error
pruning
trees
(REPTree),
radial
basis
function
(RBF),
M5P
random
forest
(RF)
with
particle
swarm
optimization
(PSO)
for
delineating
GWP
zones.
Further,
nine
parameters
used
modelling
to
test
train
proposed
PSO-based
models.
Additionally,
study
proposes
receiver
operating
characteristic
(ROC)
based
sensitivity
analysis
modelling.
Multicollinearity
test,
information
gain
ratio,
correlation
attribute
evaluation
methods
choose
important
model.
The
result
shows
drainage
density,
elevation,
land
use/land
cover
have
higher
influence
on
using
methods.
Results
showed
hybrid
PSO-RF
model
performed
better
than
other
Water Resources Research,
Journal Year:
2020,
Volume and Issue:
56(8)
Published: Aug. 1, 2020
Abstract
The
declining
mountain
snowpack
is
expected
to
melt
earlier
and
more
slowly
with
climate
warming.
Previous
work
indicates
that
lower
snowmelt
rates
are
associated
decreased
runoff.
However,
could
increase
runoff
via
vegetation
water
use
in
early
spring.
relative
importance
of
these
factors
regard
linked
site‐specific
conditions
such
as
plant
available
storage
(PAWS)
energy
availability.
To
disentangle
the
effects
rate
timing
on
production,
we
conducted
a
hydrologic
modeling
experiment
at
sites
Colorado
(NR1)
California
(P301)
controlled
for
multicollinearity.
We
tested
sensitivity
season
potential
(
R
),
changes
subsurface
(Δ
S
other
budget
components
sm
r
)
t
using
multiple
linear
regression
global
analysis
(GSA).
Regression
results
confirmed
was
governed
by
competing
influence
.
At
both
sites,
Δ
sensitive
than
while
P301
NR1,
reflecting
limitation
NR1.
GSA
analyses
mirrored
regressions
,
confirming
important
NR1
P301.
This
suggests
increases
from
may
counteract
losses
due
slower
this
process
mediated
PAWS
These
suggest
will
be
susceptible
future
greater
energy.
