This document describes a study analyzing glacier area changes in the Nevados Caullaraju and Pastoruri regions of Peru between 1984-2011 using Landsat imagery and ancillary data. Historical Landsat 5 TM images from the dry season were processed to estimate clean and debris-covered glacier areas over time. Normalized difference snow index (NDSI) thresholding was used to delineate clean glaciers, but debris-covered areas were difficult to identify. The results found a significant decrease in glaciated area of 3.3 km2 per decade, consistent with ground-based data. However, errors in estimating total area were around 30% due to challenges in identifying debris-covered glaciers with optical remote sensing alone.

1. LANDSAT
IMAGERY
Historical
Landsat-­‐5
TM
level
1G
imagery
for
the
period
1984-­‐2011
acquired
during
the
dry
season
(May-­‐September)
(Figure
2).
ANCILLARY
DATA
Ø Precipita7on
data:
Milpo
meteorological
staJon.
Data
downloaded
from
the
SEMANHI
meteorological
web
service.
Ø Vector
(ground-­‐truth)
data
of
glacial
area
in
years
1970
and
2010
obtained
from
the
Peruvian
“Autoridad
Nacional
del
Agua
(ANA)
–
Unidad
de
Glaciología”
and
developed
according
to
the
Global
Land
Ice
Measurements
from
Space
(GLIMS)
project.
IMAGE
PROCESSING
The
main
analysis
focused
on
NDSI
thresholding
to
delimitate
the
glacier
area
(Silverio
&
Jaquet,
2005).
However,
other
levels
of
processing
were
carried
out
to
obtain
addiJonal
bio/geophysical
parameters
to
support
the
inspecJon
of
the
NDSI
images.
RADIOMETRIC
CORRECTION
ü Conversion
of
Digital
Counts
to
TOA
reflectance
ATMOSPHERIC
CORRECTION
ü Dark
Object
SubstracJon
&
RadiaJve
Transfer
Codes
(MODTRAN)
LAND
SURFACE
PARAMETERS
ü Shortwave
albedo
ü Land
Surface
Emissivity
ü Land
Surface
Temperature
ü Normalized
Indices
(NDVI,
NDSI)
GLACIER
AREA
ESTIMATION
AND
TREND
ANALYSIS
“Clean”
glacier
area
was
esJmated
using
a
threshold
on
the
NDSI
based
on
the
histogram
(Figure
3).
A
value
of
0.4
was
used
in
this
work,
But
other
values
(0.5,
0.6,
0.7)
provided
almost
the
same
results.
“Debris-­‐covered”
glacier
area
was
hardly
idenJfied
using
other
thresholds
On
the
NDSI
or
the
combinaJon
between
NDSI
and
NDVI
(Figure
3).
Trend
analysis:
the
rate
of
change
in
glacier
area
was
calculated
using
simple
linear
regression
(least
squares
fit).
Glacier
area
evoluJon
of
Nevados
Caullaraju/Pastoruri
in
the
last
decades
J.
C.
Jiménez-­‐Muñoz1,
J.J.
Pasapera-­‐Gonzales2,
C.
Majar3,
C.
M.
Gevaert4,
J.A.
Sobrino1,
T.
Chávez5
and
M.
A.
Huayaney5
1GCU-­‐IPL,
University
of
Valencia
(Spain);
2CONIDA
(Peru);
3LAB-­‐University
of
Chile
(Chile);
4M.Sc.
Student,
University
of
Valencia
(Spain);
5Universidad
Nacional
Mayor
de
San
Marcos
(Perú)
ABSTRACT
Glacier
retreat
in
tropical
areas,
such
as
the
Peruvian
Cordillera
Blanca
is
a
great
concern
globally
as
an
indicator
of
climate
change
and
regionally
as
the
most
important
freshwater
source
in
the
region.
The
current
study
analyses
the
recent
trend
in
glacial
area
of
Nevados
Caullaraju
and
Pastoruri
located
in
the
South
of
the
Cordillera
Blanca
(Peru)
using
Landsat
5/TM
imagery.
Results
from
remote
sensing
data
indicate
a
significant
decrease
on
the
glaciated
area
at
a
rate
of
3.3
km2/decade,
in
accordance
with
the
decreasing
rate
of
3.9
km2/decade
extracted
from
ground-­‐based
data.
Errors
on
esJmaJon
of
total
glaciated
area
are
however
less
accurate
(near
to
30%)
because
of
the
difficulty
to
idenJfy
debris-­‐covered
glacier
area
from
remote
sensing
data.
INTRODUCTION
Glaciers
in
the
Peruvian
Cordillera
Blanca
encompass
25%
of
the
total
tropical
glacier
area
in
the
world
(Kaser
and
Osmaston,
2011).
Many
studies
have
documented
the
melJng
of
these
glaciers,
causing
glacier
retreat
and
a
loss
of
glacial
area
(Vuille
et
al,
2008).
At
global
scale
glaciers
are
considered
one
of
the
most
important
climate
change
indicators,
whereas
at
regional
scale
glacial
melt
forms
one
of
the
most
important
freshwater
sources
and
is
criJcally
important
for
domesJc,
agricultural
and
industrial
uses
(Casassa
et
al,
2007).
Remote
sensing
has
proven
to
play
a
key
role
in
glacier
monitoring
(Racoviteanu
et
al.,
2008).
The
availability
of
satellite
imagery
allow
for
(semi-­‐)automated
mulJtemporal
analysis
at
low
costs.
The
historical
Landsat
archive
is
especially
important,
as
it
provides
high-­‐quality
data
for
a
long
Jmespan
and
is
available
at
no
cost.
The
present
study
uses
remote
sensing
data
(Landsat5/TM)
supported
by
an
aereal
photography
and
“ground-­‐truth”
vectors
to
idenJfy
trends
in
glacial
extent
from
the
1950s
to
present.
The
study
area
consists
of
the
Nevados
Caullaraju
and
Pastoruri
(and
adjacent
Nevados)
located
in
the
southeast
of
the
Cordillera
Blanca
(Figure
1).
RESULTS
CONCLUSIONS
• Glacier
area
has
been
monitored
using
Landsat5/TM
imagery
acquired
in
the
last
decades
(1980-­‐2010).
• Band
raJos
or
normalized
raJos
(e.g.
NDSI)
are
useful
to
idenJfy
the
“clean”
ice
of
the
glaciers.
• It
is
difficult
to
idenJfy
“debris-­‐covered”
ice
only
from
VNIR
data.
Thermal
remote
sensing
is
a
good
tool
to
support
the
VNIR
data.
• Results
show
a
decrease
in
glacier
area
higher
than
3
km2
per
decade
over
the
Nevados’
group
(from
Caullaraju
to
Pastoruri).
The
decreasing
rate
is
up
to
9
km2
per
decade
in
the
case
of
Nevados
Tuco
and
Pastoruri.
p
Figure
5:
The
NDVIxNDSI≥-­‐0.02
threshold
applied
to
the
2010
image
(leA),
a
comparison
of
the
LST
and
the
2010
vector
(center)
and
a
detail
of
the
Caullaraju
glacier
(right).
‚
Figure
1:
LocaKon
of
the
study
area.
DATA
&
METHODS
REFERENCES
ü G.
Casassa,
A.
Rivera,
W.
Haeberlib,
G.
Jone,
G.
Kaser,
P.
Ribstein,
and
C.
Schneider,
“Current
status
of
Andean
glaciers,”
Global
and
Planetary
Change,
59,
1-­‐9,
2007.
ü G.
Kaser
and
H.
Osmaston,
“Tropical
Glaciers;”
InternaJonal
Hydrology
Series,
Cambridge
University
Press,
228
pp.,
2001.
ü A.
Racoviteanu,
M.
Williams
and
R.
Barry,
"OpJcal
Remote
Sensing
of
Glacier
CharacterisJcs:
A
Review
with
Focus
on
the
Himalaya,"
Sensors,
8,
3355-­‐3383,
2008.
ü W.
Silverio
and
J.
Jaquet,
"Glacial
cover
mapping
(1987-­‐1996)
of
the
Cordillera
Blanca
(Peru),"
Remote
Sensing
of
Environment,
95,
342-­‐350,
2005.
ü M.
Vuille,
G.
Kaser,
and
I.
Juen,
“Glacier
mass
balance
variability
in
the
Cordillera
Blanca,
Peru
and
its
relaJonship
with
climate
and
the
large-­‐scale
circulaJon,”
Global
and
Planetary
Change,
63,
14-­‐28,
2008.
‚
Figure
2:
Landsat
images
used
in
the
Kme
series
31/05/1987
06/06/1995
27/06/1997
16/07/1998
17/06/2005
18/08/2010
‚
Figure
3:
Histogram
of
the
NDSI
image
(leA)
and
the
NDVIxNDSI
image
(right)
corresponding
to
the
Landsat
image
acquired
on
18/08/2010.
• Average
glacial
area
decreases
at
a
rate
near
to
0.4km2y-­‐1
(Figures
4
and
6).
• Difficult
to
esJmate
the
extent
of
debris-­‐covered
glaciers
using
NDVI/NDSI
thresholds.
• CombinaJon
of
VNIR
data
(NDSI,NDVI)
with
thermal
data
(LST,
emissivity)
can
provide
useful
informaJon
to
idenJfy
debris-­‐covered
areas
(Figure
5).
p
Figure
7:
Detail
of
Nevados
Pastoruri
and
Tuco
glacial
areas
over
an
areal
image
from
1957
(leA),
and
the
glaciar
area
evoluKon
from
1957
to
present
(right)
p
Figure
4:
EvoluKon
of
glacial
area
during
Kme
period
#*
#*
Brazil
Peru
Bolivia
Colombia
Ecuador
LimaLima
HuarazHuaraz
65°0'0"W
65°0'0"W
70°0'0"W
70°0'0"W
75°0'0"W
75°0'0"W
80°0'0"W
80°0'0"W
0°0'0"
0°0'0"
5°0'0"S
5°0'0"S
10°0'0"S
10°0'0"S
15°0'0"S
15°0'0"S
±
0 500 1,000250
Km
Caullaraju
Jenhuaracra
Pastoruri
Tuco
Rajutuna
Santon
0
2000
4000
6000
8000
10000
12000
14000
-­‐0.8
-­‐0.6
-­‐0.4
-­‐0.2
0
0.2
0.4
0.6
0.8
1
Frequency
NDSI
Clean
glacier
ice
0
2000
4000
6000
8000
10000
12000
14000
-­‐0.4
-­‐0.3
-­‐0.2
-­‐0.1
0
0.1
Frequency
NDVI
x
NDSI
Debris-­‐covered
glacier
ice
y
=
-­‐0.0989x
+
203.25
2
3
4
5
6
7
8
9
10
1950
1960
1970
1980
1990
2000
2010
2020
Area
(km2)
Year
• The
Nevados
Pastoruri/Tuco
appears
to
be
melJng
at
an
average
rate
of
0.9km2/year
between
1957
and
2010
(Figure
7).
This
rate
doubles
the
average
decreasing
rate
of
the
Nevados’
group.
p
Figure
6:
SpaKal
evoluKon
of
glacial
extent
during
the
study
period
Debris-­‐covered
Glacier
Clean
Glacier
Ice
y
=
-­‐0.3934x
+
802.84
y
=
-­‐0.3332x
+
688.52
10
15
20
25
30
35
1970
1975
1980
1985
1990
1995
2000
2005
2010
Glacial
area
(km2)
Nuevo
NDSI>0.4
Vectores
LST
30°C
-10°C
NDVIxNDSI
0.03
-0.25
77°9'0"W
77°9'0"W
77°10'0"W
77°10'0"W
77°11'0"W
77°11'0"W
77°12'0"W
77°12'0"W
77°13'0"W
77°13'0"W
77°14'0"W
77°14'0"W
77°15'0"W
77°15'0"W
77°16'0"W
77°16'0"W
77°17'0"W
77°17'0"W
77°18'0"W
77°18'0"W
9°54'0"S
9°54'0"S
9°55'0"S
9°55'0"S
9°56'0"S
9°56'0"S
9°57'0"S
9°57'0"S
9°58'0"S
9°58'0"S
9°59'0"S
9°59'0"S
10°0'0"S
10°0'0"S
10°1'0"S
10°1'0"S
10°2'0"S
10°2'0"S
±
Legend
2010
2005
1998
1997
1995
1987