Watershed Ecology and the Environment,
Journal Year:
2023,
Volume and Issue:
5, P. 161 - 172
Published: Jan. 1, 2023
The
livestock
resources
of
Bangladesh
are
under
tremendous
strain
due
to
several
natural
and
anthropogenic
causes.
Especially
in
the
Northwestern
region
Bangladesh,
these
more
vulnerable
deterioration
resulting
from
human
actions,
a
lack
environmental
rangeland
legislation,
climate
change,
drought,
poor
management,
inadequate
disaster
mitigation
plans.
GIS
based
multicriteria
decision
analysis
(MCDA)
remote
sensing
techniques
have
been
used
this
research
locate
ideal
land
for
sheep,
goats,
buffalo
cow
production.
In
study,
suitability
production
has
considered
eight
thematic
layers:
slope,
use
&
cover
(LULC),
soil
types,
rainfall,
water
accessibility,
road
distance,
relative
humidity,
average
temperature.
Besides,
had
geospatial
tools
combining
geographical
layers,
when
analytical
hierarchy
process,
MCDA
approach
helped
measure
weight
each
criterion.
final
map
that
is
perfect
raising
cattle
divided
into
four
categories,
such
as
low,
medium,
high
excellent.
Each
groups
portions
fall
following
percentages:
11.14%,
26.07%,
35.27%,
27.53%.
This
also
depicts
western
part
study
region,
which
includes
Thakurgaon,
Panchagar,
Dinajpur,
Naogaon,
Joypurhat
Bogra
low
index
while
eastern
Kurigram,
Nilphamari,
Pabna,
Lalmonirhat,
Gaibandha,
Rangpur
Sirajganj
contributes
an
excellent
zone.
outcome
will
be
useful
identify
best
places
Bangladesh.
Finally,
may
additionally
assist
government
officials
creating
strategies
population
area.
Global Ecology and Conservation,
Journal Year:
2024,
Volume and Issue:
53, P. e03010 - e03010
Published: May 27, 2024
Ecological
stability
(ES)
is
recognized
as
a
crucial
factor
for
sustainable
development
at
global
and
regional
scales.
However,
the
importance
of
this
was
not
considered
significant.
Hence,
main
aim
study
to
introduce
new
approach
that
focuses
on
detecting
ES
over
Maharloo
watershed
in
Iran.
To
achieve
goal,
we
extracted
land
use
cover
(LULC)
data
from
Google
Earth
Engine
(GEE)
platform
by
applying
random
forest
(RF)
machine
learning
method,
which
obtained
Kappa
statistics
0.85,
0.86,
0.87
years
2002,
2013,
2023,
respectively.
We
identified
both
stable
unstable
regions
based
LULC
changes
employed
them
using
forecast
ES.
The
most
important
predictors
ecological
were
elevation,
soil
organic
carbon
index,
precipitation,
salinity.
results
research
revealed
certain
areas
within
have
experienced
instability
recent
years,
with
gardens
showing
highest
percentage
(60.65%)
among
all
land-use
categories.
performance
validation
our
model
suggest
are
reliable
(AUC
=
0.86).
This
offers
detailed
maps
trends,
offering
valuable
insights
decision
makers
support
landscape
conservation
restoration
efforts.
Overall,
findings
contribute
more
comprehensive
understanding
dynamics
provide
efforts
other
regions.
