Temporal and Spatial Analyses of Forest Burnt Area in the Middle Volga Region Based on the Satellite Imagery and Climatic Factors
Опубликована: Янв. 18, 2024
Wildfires
are
important
natural
drivers
of
forest
stands
dynamics,
strongly
influencing
on
their
regeneration
and
ecosystem
services.
This
paper
presents
a
comprehensive
analysis
spatiotemporal
burnt
area
(BA)
patterns
over
the
period
2000–2022
in
Middle
Volga
region
Russian
Federation
base
remote
sensing
time
series,
considering
impact
cli-matic
factors
fires.
The
temporal
trends
were
assessed
with
Mann-Kendall
nonpara-metric
statistical
test
Theil-Sen’s
slope
estimator
using
LandTrendr
algorithm
Google
Earth
Platform
(GEE).
accuracy
assessment
indicated
high
overall
(>
84%)
F-score
value
82%)
for
detection
as
evaluated
against
581
sites
ref-erence
data.
results
revealed
that
fire
occurrences
mainly
irregular
highest
frequency
7.3
22-year
period.
total
BA
was
about
280
thousand
ha,
which
equals
to
1.7%
land
surface
or
4.0%
forested
under
study
region.
coniferous
most
fire-prone
ecosystems
accounting
59.0
%
BA;
deciduous
accounts
25.1%;
insignificant
registered
young
forests
shrub
lands.
On
seasonal
scale,
temperature
generally
has
greater
than
precipitation
wind
speed.
Язык: Английский
Detecting Trends in Post-Fire Forest Recovery in Middle Volga from 2000 to 2023
Forests,
Год журнала:
2024,
Номер
15(11), С. 1919 - 1919
Опубликована: Окт. 31, 2024
Increased
wildfire
activity
is
the
most
significant
natural
disturbance
affecting
forest
ecosystems
as
it
has
a
strong
impact
on
their
recovery.
This
study
aimed
to
investigate
how
burn
severity
(BS)
levels
and
climate
factors,
including
land
surface
temperature
(LST)
precipitation
variability
(Pr),
affect
recovery
in
Middle
Volga
region
of
Russian
Federation.
It
provides
comprehensive
analysis
post-fire
using
Landsat
time-series
data
from
2000
2023.
The
utilized
LandTrendr
algorithm
Google
Earth
Engine
(GEE)
cloud
computing
platform
examine
Normalized
Burn
Ratio
(NBR)
spectral
metrics
quantify
at
low,
moderate,
high
levels.
To
evaluate
spatio-temporal
trends
recovery,
Mann–Kendall
statistical
test
Theil–Sen’s
slope
estimator
were
utilized.
results
suggest
that
significantly
influenced
by
degree
BS
affected
areas.
higher
class
BS,
faster
more
extensive
reforestation
area
occurs.
About
91%
(40,446
ha)
first
5-year
after
belonged
classes
moderate
severity.
A
regression
model
indicated
plays
critical
role
compared
accounting
for
approximately
65%
variance
outcomes.
Язык: Английский
Spatiotemporal Evolution Analysis of Surface Deformation on the Beihei Highway Based on Multi-Source Remote Sensing Data
Remote Sensing,
Год журнала:
2024,
Номер
16(21), С. 4091 - 4091
Опубликована: Ноя. 1, 2024
Under
the
interference
of
climate
warming
and
human
engineering
activities,
degradation
permafrost
causes
frequent
occurrence
geological
disasters
such
as
uneven
foundation
settlement
landslides,
which
brings
great
challenges
to
construction
operational
safety
road
projects.
In
this
paper,
spatial
temporal
evolution
surface
deformations
along
Beihei
Highway
was
investigated
by
combining
SBAS-InSAR
technique
frost
number
model
after
considering
vegetation
factor
with
multi-source
remote
sensing
observation
data.
After
comprehensively
factors
change,
degradation,
anthropogenic
disturbance,
landslide
processes
were
analyzed
in
conjunction
site
surveys
ground
The
results
show
that
average
deformation
rate
is
approximately
−16
mm/a
over
22
km
section
study
area.
on
pavement
related
topography,
subsidence
more
pronounced
areas
high
topographic
relief
a
sunny
aspect.
Permafrost
roads
area
showed
an
insignificant
trend,
at
landslides
large
deformation,
significant
trend.
Meteorological
monitoring
data
indicate
annual
minimum
mean
temperature
increasing
rapidly
1.266
°C/10a
during
last
40
years.
associated
precipitation
freeze–thaw
cycles.
There
are
interactions
between
important
influences
settlement.
Focusing
process
zone
can
help
deeply
understand
mechanism
change
impact
hazards
zone.
Язык: Английский