Random Forest Classifier Algorithm of Geographic Resources Analysis Support System Geographic Information System for Satellite Image Processing: Case Study of Bight of Sofala, Mozambique
Coasts,
Journal Year:
2024,
Volume and Issue:
4(1), P. 127 - 149
Published: Feb. 26, 2024
Mapping
coastal
regions
is
important
for
environmental
assessment
and
monitoring
spatio-temporal
changes.
Although
traditional
cartographic
methods
using
a
geographic
information
system
(GIS)
are
applicable
in
image
classification,
machine
learning
(ML)
present
more
advantageous
solutions
pattern-finding
tasks
such
as
the
automated
detection
of
landscape
patches
heterogeneous
landscapes.
This
study
aimed
to
discriminate
patterns
along
eastern
coasts
Mozambique
ML
modules
Geographic
Resources
Analysis
Support
System
(GRASS)
GIS.
The
random
forest
(RF)
algorithm
module
‘r.learn.train’
was
used
map
landscapes
shoreline
Bight
Sofala,
remote
sensing
(RS)
data
at
multiple
temporal
scales.
dataset
included
Landsat
8-9
OLI/TIRS
imagery
collected
dry
period
during
2015,
2018,
2023,
which
enabled
evaluation
dynamics.
supervised
classification
RS
rasters
supported
by
Scikit-Learn
package
Python
embedded
GRASS
Sofala
characterized
diverse
marine
ecosystems
dominated
swamp
wetlands
mangrove
forests
located
mixed
saline–fresh
waters
coast
Mozambique.
paper
demonstrates
advantages
areas.
integration
Earth
Observation
data,
processed
decision
tree
classifier
land
cover
characteristics
recent
changes
ecosystem
Mozambique,
East
Africa.
Language: Английский
Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh
Water,
Journal Year:
2024,
Volume and Issue:
16(8), P. 1141 - 1141
Published: April 17, 2024
Mapping
spatial
data
is
essential
for
the
monitoring
of
flooded
areas,
prognosis
hazards
and
prevention
flood
risks.
The
Ganges
River
Delta,
Bangladesh,
world’s
largest
river
delta
prone
to
floods
that
impact
social–natural
systems
through
losses
lives
damage
infrastructure
landscapes.
Millions
people
living
in
this
region
are
vulnerable
repetitive
due
exposure,
high
susceptibility
low
resilience.
Cumulative
effects
monsoon
climate,
rainfall,
tropical
cyclones
hydrogeologic
setting
Delta
increase
probability
floods.
While
engineering
methods
mitigation
include
practical
solutions
(technical
construction
dams,
bridges
hydraulic
drains),
regulation
traffic
land
planning
support
systems,
geoinformation
rely
on
modelling
remote
sensing
(RS)
evaluate
dynamics
hazards.
Geoinformation
indispensable
mapping
catchments
areas
visualization
affected
regions
real-time
monitoring,
addition
implementing
developing
emergency
plans
vulnerability
assessment
warning
supported
by
RS
data.
In
regard,
study
used
monitor
southern
segment
Delta.
Multispectral
Landsat
8-9
OLI/TIRS
satellite
images
were
evaluated
(March)
post-flood
(November)
periods
analysis
extent
landscape
changes.
Deep
Learning
(DL)
algorithms
GRASS
GIS
modules
qualitative
quantitative
as
advanced
image
processing.
results
constitute
a
series
maps
based
classified
Language: Английский
Evaluating the Potential of Landsat 8/9 and Sentinel 2 Data and Different Spectral and Spatial Indices for Segment Extraction in Large Watersheds for OBIA Approach in Remote Sensing: A Case Study of the Sebou Watershed
Remote Sensing Applications Society and Environment,
Journal Year:
2025,
Volume and Issue:
38, P. 101575 - 101575
Published: April 1, 2025
Language: Английский
Land Cover Analysis in the Yangtze River Basin for Detection of Wetland Agriculture and Urban Dynamics in Wuhan Area (China)
SSRN Electronic Journal,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 1, 2025
Language: Английский
Machine Learning Algorithms of Remote Sensing Data Processing for Mapping Changes in Land Cover Types over Central Apennines, Italy
Journal of Imaging,
Journal Year:
2025,
Volume and Issue:
11(5), P. 153 - 153
Published: May 12, 2025
This
work
presents
the
use
of
remote
sensing
data
for
land
cover
mapping
with
a
case
Central
Apennines,
Italy.
The
include
8
Landsat
8-9
Operational
Land
Imager/Thermal
Infrared
Sensor
(OLI/TIRS)
satellite
images
in
six-year
period
(2018-2024).
operational
workflow
included
image
processing
which
were
classified
into
raster
maps
automatically
detected
10
classes
types
over
tested
study.
approach
was
implemented
by
using
set
modules
Geographic
Resources
Analysis
Support
System
(GRASS)
Information
(GIS).
To
classify
(RS)
data,
two
approaches
carried
out.
first
is
unsupervised
classification
based
on
MaxLike
and
clustering
extracted
Digital
Numbers
(DN)
landscape
feature
spectral
reflectance
signals,
second
supervised
performed
several
methods
Machine
Learning
(ML),
technically
realised
GRASS
GIS
scripting
software.
latter
four
ML
algorithms
embedded
from
Python's
Scikit-Learn
library.
These
classifiers
have
been
to
detect
subtle
changes
as
derived
showing
different
vegetation
conditions
spring
autumn
periods
central
northern
Language: Английский
Land Cover Analysis in the Yangtze River Basin for Detection of Wetland Agriculture and Urban Dynamics in Wuhan Area (China)
Transylvanian Review of Systematical and Ecological Research,
Journal Year:
2025,
Volume and Issue:
27(1), P. 1 - 16
Published: April 1, 2025
Abstract
This
study
presents
environmental
analysis
of
the
Yangtze
River
Basin,
Wuhan
region
central
China,
performed
using
machine
learning
(ML)
methods
Remote
Sensing
(RS)
data
classification.
The
workflow
is
Geographic
Resources
Analysis
Support
System
(GRASS)
Information
(GIS)
scripting
software
for
processing
Landsat
images
by
two
approaches:
unsupervised
clustering
and
supervised
ML
algorithms.
Six
were
taken
biennially
in
autumn
from
2013
to
2023
detect
wetland
changes
area.
article
demonstrates
application
GIS
landscape
dynamics
riverine
lacustrine
areas
around
River.
Language: Английский