Assessing Land Cover Changes Using the LUCAS Database and Sentinel Imagery: A Comparative Analysis of Accuracy Metrics
Applied Sciences,
Год журнала:
2024,
Номер
15(1), С. 240 - 240
Опубликована: Дек. 30, 2024
Classification
of
remote
sensing
images
using
machine
learning
models
requires
a
large
amount
training
data.
Collecting
this
data
is
both
labor-intensive
and
time-consuming.
In
study,
the
effectiveness
pre-existing
reference
on
land
cover
gathered
as
part
Land
Use–Land
Cover
Area
Frame
Survey
(LUCAS)
database
Copernicus
program
was
analyzed.
The
classification
carried
out
in
Google
Earth
Engine
(GEE)
Sentinel-2
that
were
specially
prepared
to
account
for
phenological
development
plants.
performed
SVM,
RF,
CART
algorithms
GEE,
with
an
in-depth
accuracy
analysis
conducted
custom
tool.
Attention
given
reliability
different
metrics,
particular
focus
widely
used
(ML)
metric
“accuracy”,
which
should
not
be
compared
commonly
“overall
accuracy”,
due
potential
significant
artificial
inflation
accuracy.
LUCAS
2018
at
Level-1
detail
estimated
86%.
Using
updated
dataset,
best
result
achieved
RF
method,
83%.
An
overestimation
approximately
10%
observed
when
reporting
average
ACC
ML
instead
overall
OA
metric.
Язык: Английский
Diagnóstico de la acidez del suelo en la zona cafetera de Colombia
Revista Cenicafé,
Год журнала:
2024,
Номер
75(2), С. e75204 - e75204
Опубликована: Янв. 1, 2024
La
acidez
del
suelo
afecta
el
crecimiento
café,
Coffea
arabica
L.,
en
todas
las
etapas
cultivo.
información
regional
de
la
puede
ayudar
a
identificar
áreas
con
limitaciones
para
plantas
y
planear
acciones
correctivas.
Este
estudio
tuvo
como
objetivo
caracterizar
mapear
zona
cafetera
Colombia.
Se
consolidó
una
base
datos
resultados
344.652
análisis
suelos,
correspondientes
460
municipios
22
departamentos