Desertification in northern China from 2000 to 2020: The spatial–temporal processes and driving mechanisms
Ecological Informatics,
Год журнала:
2024,
Номер
82, С. 102769 - 102769
Опубликована: Авг. 11, 2024
Desertification
is
one
of
the
most
significant
environmental
and
social
challenges
globally.
Monitoring
desertification
dynamics
quantitatively
identifying
contributions
its
driving
factors
are
crucial
for
land
restoration
sustainable
development.
This
study
develops
a
standardized
methodological
framework
that
combines
with
mechanisms
at
pixel
level,
applied
to
northern
China
from
2000
2020.
Using
multisource
data
employing
Time
Series
Segmentation
Residual
Trend
analysis
(TSS-RESTREND)
method
alongside
geographical
detector,
we
assessed
reversion,
expansion,
abrupt
change
processes,
along
impacts
interactions
natural
human
were
assessed.
Over
past
two
decades,
proportion
desertified
decreased
by
5.60%.
Notably,
32.88%
area
experienced
while
only
5.86%
underwent
expansion.
Abrupt
changes
in
both
reversed
expanding
areas
observed,
primarily
central
western
regions,
these
concentrated
periods
2009–2011
2014–2016.
The
various
different
sub-regions
exhibited
spatial
heterogeneity.
Increased
precipitation,
temperature,
evapotranspiration
contributed
reversion
area,
wind
speed
influenced
eastern
area.
Additionally,
population
density
afforestation
activities
also
promoted
reversion.
In
contrast,
precipitation
increased
temperature
expansion
areas,
respectively,
exacerbating
this
process.
Overall,
between
enhanced.
Future
control
ecological
engineering
planning
should
focus
on
coupling
effects
relevant
vegetation
changes.
Язык: Английский
Mapping forest-agroforest frontiers in the Peruvian Amazon with deep learning and PlanetScope satellite data
Ecological Informatics,
Год журнала:
2025,
Номер
unknown, С. 103034 - 103034
Опубликована: Янв. 1, 2025
Язык: Английский
Canopy density affects nutrient limitation and soil quality index in a secondary forest, in China
Plant and Soil,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 24, 2025
Язык: Английский
Tree Species Detection and Enhancing Semantic Segmentation Using Machine Learning Models with Integrated Multispectral Channels from PlanetScope and Digital Aerial Photogrammetry in Young Boreal Forest
Remote Sensing,
Год журнала:
2025,
Номер
17(11), С. 1811 - 1811
Опубликована: Май 22, 2025
The
precise
identification
and
classification
of
tree
species
in
young
forests
during
their
early
development
stages
are
vital
for
forest
management
silvicultural
efforts
that
support
growth
renewal.
However,
achieving
accurate
geolocation
through
field-based
surveys
is
often
a
labor-intensive
complicated
task.
Remote
sensing
technologies
combined
with
machine
learning
techniques
present
an
encouraging
solution,
offering
more
efficient
alternative
to
conventional
methods.
This
study
aimed
detect
classify
using
remote
imagery
techniques.
mainly
involved
two
different
objectives:
first,
detection
the
latest
version
You
Only
Look
Once
(YOLOv12),
second,
semantic
segmentation
(classification)
random
forest,
Categorical
Boosting
(CatBoost),
Convolutional
Neural
Network
(CNN).
To
best
our
knowledge,
this
marks
first
exploration
utilizing
YOLOv12
identification,
along
integrates
digital
aerial
photogrammetry
Planet
achieve
forests.
used
datasets:
RGB
from
unmanned
vehicle
(UAV)
ortho
photography
RGB-NIR
PlanetScope.
For
YOLOv12-based
detection,
only
was
used,
while
performed
three
sets
data:
(1)
Ortho
(3
bands),
(2)
+
canopy
height
model
(CHM)
(8
(3)
CHM
12
vegetation
indices
(20
bands).
With
models
applied
these
datasets,
nine
were
trained
tested
57
images
(1024
×
1024
pixels)
corresponding
mask
tiles.
achieved
79%
overall
accuracy,
Scots
pine
performing
(precision:
97%,
recall:
92%,
mAP50:
mAP75:
80%)
Norway
spruce
showing
slightly
lower
accuracy
94%,
82%,
90%,
71%).
segmentation,
CatBoost
20
bands
outperformed
other
models,
85%
80%
Kappa,
81%
MCC,
CHM,
EVI,
NIRPlanet,
GreenPlanet,
NDGI,
GNDVI,
NDVI
being
most
influential
variables.
These
results
indicate
simple
boosting
like
can
outperform
complex
CNNs
Язык: Английский
Random forest model that incorporates solar-induced chlorophyll fluorescence data can accurately track crop yield variations under drought conditions
Ecological Informatics,
Год журнала:
2024,
Номер
unknown, С. 102972 - 102972
Опубликована: Дек. 1, 2024
Язык: Английский
Forecasting basal area increment in forest ecosystems using deep learning: A multi-species analysis in the Himalayas
Ecological Informatics,
Год журнала:
2024,
Номер
unknown, С. 102951 - 102951
Опубликована: Дек. 1, 2024
Язык: Английский
Modelling height to crown base using non-parametric methods for mixed forests in China
Ecological Informatics,
Год журнала:
2024,
Номер
unknown, С. 102957 - 102957
Опубликована: Дек. 1, 2024
Язык: Английский
Analysis of the Effect of Land Cover Changes on the Increase in Land Surface Temperature in PT Amman Mineral Mining Area
IOP Conference Series Earth and Environmental Science,
Год журнала:
2024,
Номер
1422(1), С. 012019 - 012019
Опубликована: Дек. 1, 2024
Abstract
Global
warming
is
the
process
of
increasing
average
temperature
atmosphere,
one
which
caused
by
human
activities
in
use
space
influences
high
level
land
conversion.
The
aim
this
research
to
determine
effect
changes
cover
on
surface
temperatures
area
around
AMNT
mining
using
analytical
methodsNormalized
Difference
Vegetation
Index
(NDVI),
Land
Surface
Temperature
(LST)
and
Regression
statistical
analysis.
Research
resultshows
last
10
years
from
2013
2023.
An
increase
at
study
location,
based
results
analysis
shows
an
1
°C
where
minimum
reached
22
while
maximum
30
influence
simple
linear
regression
showed
85.33%
was
included
very
influential
category.
Язык: Английский