wwwelseviercomlocaterse

Remote Sensing of Environm

Glacial cover mapping (1987ndash1996) of the Cordillera Blanca (Peru)

using satellite imagery

Walter Silverioa Jean-Michel JaquetbTaRemote Sensing and GIS Unit Earth Sciences Section University of Geneva 13 rue des Maraıchers CH-1205 Geneva Switzerland

bEarth Observation Section UNEPDEWA-Europe GRID Geneva 11 Chemin des Anemones CH-1219 Geneva Switzerland

Received 3 May 2004 received in revised form 16 December 2004 accepted 24 December 2004

Abstract

Multitemporal glacier area mapping is a key element in accurately determining fresh water reserves as well as providing an indicator of

climate change

In Peru the first glacier inventory was based on visual interpretation of aerial photos requiring several years of effort Landsat Thematic

Mapper satellite imagery on the other hand provides an increasingly employed alternative for the monitoring of changes in glacier area and

in other glaciological parameters

By means of Normalized Difference Snow Index (NDSI) computations on TM images an estimate of the glacierized area in Cordillera

Blanca (Peru) was carried out for 1987 (643F63 km2) and 1996 (600F61 km2) Compared to an estimate of 721 km2 in 1970 it can be

concluded that the glacier area has retreated in this massif by more than 15 in 25 years

D 2005 Elsevier Inc All rights reserved

Keywords Remote sensing Landsat Snow index Andes Climate change Glaciology

1 Introduction

In 1970 the 721 km2 of glaciers (HIDRANDINA 1988)

in the Peruvian Cordillera Blanca amounted to 35 of the

total national glacierized area According to Kaser and

Osmaston (2002) this total was equal to 26 of the global

area of tropical glaciers (eg Andes Africa and Irian Jaya

Indonesia)

The Cordillera Blancarsquos glaciers represent the largest

freshwater breservoirQ for the region The seasonal melting

of these glaciers compensates for the lack of water caused

by the climate variability all the more so because for 6

months of the year precipitation is extremely sparse The

role of glaciers on the local resources is therefore quite

significant If these breservoirsQ were to disappear local

agriculture farming activities and cities would lose their

0034-4257$ - see front matter D 2005 Elsevier Inc All rights reserved

doi101016jrse200412012

T Corresponding author

E-mail address Jean-MichelJaquetgridunepch (J-M Jaquet)

main water supply which enables them to withstand the

long dry seasons (Francou amp Wagnon 1998)

Like temperate glaciers in the Alps tropical glaciers are

sensitive to climate change (Hastenrath 1992) The

variations of temperature as well as the amount of

precipitation and solar energy received are the key factors

that will influence the glacierrsquos mass balance (Francou amp

Wagnon 1998 Pouyaud et al 1997) Valley and mountain

glaciers as well as ice caps ice fields and associated outlet

glaciers respond to changes in regional climate on the scale

of decades even less with Icelandic glaciers (Sigurdsson amp

Jonsson 1995) thus they can serve as indicators of

regional climate change (Hall 2002) Following a global

climate trend tropical glaciers have retreated substantially

during the 1980s and 1990s many of them are close to

vanishing entirely in some parts of the tropical high

mountains (Kaser et al 2003) Globally mountain glaciers

generally have been retreating since the later part of the

19th century (end of the bLittle Ice AgeQ Hall et al

1995a)

ent 95 (2005) 342ndash350

W Silverio J-M Jaquet Remote Sensing of Environment 95 (2005) 342ndash350 343

Multitemporal satellite image analysis is an important

tool for monitoring land cover (LULCC 2003) as well as

variations in the total area of glaciers the position of their

front and their general facies (Hall 2002 Hall et al 1987

1988 1992 1995a Williams et al 1991) Satellite imagery

serves both as an excellent base map for regional studies of

alpine glaciation and as a means of placing local field

studies within their regional context (Klein amp Isacks 1998)

To be efficient multitemporal satellite image analysis must

be carried out within a Geographic Information System

(Paul 2003 Silverio 2001)

We present and discuss here the approach we have taken

towards the multitemporal mapping of the Cordillera Blanca

glaciers based on 1987 and 1996 Landsat Thematic Mapper

satellite images

2 Cordillera Blanca location and general aspects

The Cordillera Blanca (CB) or Yurak Janka (in Quechua

language) is located between the 08830Vndash10810V S latitude

and 77800Vndash78800V W longitude in the Peruvian State of

Ancash 400 km north of the capital city of Lima The

mountain range is approximately 180 km long by 30 km

wide (Fig 1) In 1975 3400 km2 of the Cordillera Blancarsquos

Fig 1 Location of the study site left within the national territory of Peru center

and the Huascaran National Park (georeferenced to the Universal Transverse Mer

area became the Huascaran National Park (PNH) which

was later recognized as a bBiosphere ReserveQ under the

UNESCOrsquos Man and Biosphere Convention (PNH 1990)

The Cordillera Blanca includes more than 200 mountains

higher than 5000 m of which 27 are higher than 6000 m

such as Huascaran Sur (6768 m) the highest summit in

Peru The Cordillera Blanca also harbours numerous lakes

and glacial valleys (Silverio 2001) According to HIDRAN-

DINA (1988) 711 glaciers were inventoried in 1970 with a

total area of 721 km2 (excluding the Nevados Rosco and

Pelagatos) bMost of the glaciers 91 of the total are

classified as mountain glaciers they are generally short and

have extremely steep slopes The rest are classified as valley

glaciers except for one ice cap Four are similar to rock

glaciersQ (Morales Arnao 1998 p 156) In the same year

out of the 251 lakes that were identified 176 flew into the

Pacific Ocean and 75 into the Atlantic Ocean Thirty-nine of

the former and 13 of the latter had a volume exceeding

1106 m3 (Electroperu 1974)

The climate in the Cordillera Blanca is characterized by

relatively large daily and small seasonal temperature

variations as well as by a distinct succession between dry

(MayndashSeptember) and wet seasons (OctoberndashApril) (Kaser

et al 1990 1996 Kaser amp Osmaston 2002) During the

wet season it rains daily in valleys and snow falls at higher

area encompassed by two Landsat TM images right The Cordillera Blanca

cator zone 18 south)

W Silverio J-M Jaquet Remote Sensing of Environment 95 (2005) 342ndash350344

elevations with a peak in the amount of precipitation

between the months of January and March During the dry

season it rarely rains in valleys (Jaeger 1979) In the

mountains short periods of bbad weatherQ can occur (1 to 2

days) along with light snowfalls but it does not take long

for snow to melt

The tropical temperatures and intense insolation prevent

any major accumulation of snow cover outside the glaciers

lasting more than a few days as a maximum Usually snow

outside the glaciers melts within hours of the same day that

the snow falls or the day after (Kaser et al 2003)

According to Francou and Wagnon (1998) and Kaser et

al (1990) the glacier regime of the Cordillera Blanca is

characterized by exclusive accumulation during the wet

season and an active ablation during the entire year

Table 1

Satellite images

Type Date Resolution

(m)

pathrow

Solar

elevation

angle (8)

Format Georeference

Landsat

5 TM

31 May

1987

2850 Mean

4323

LinesColumns

866 4378 NLAPS

867 4267 NLAPS

Landsat

5 TM

11 August

1996

30 Mean

425

UTM

866 43 GeoTIF

867 42 GeoTIF

3 History

In 1927 the Peruvian engineer Jorge Broggi carried out

the first glaciological studies in Peru Since 1932 several

Austrian and German scientists have been taking part in

many expeditions led by P Borchers H Kinzi and E

Schneider in the Cordilleras Blanca and Huayhuash Their

fieldwork and studies produced the first topographic maps at

the scales 1200000 1100000 and 150000 based on

photogrammetric methods (Morales Arnao 1998)

Following the Laguna Palcacoha (Cojup valley) overflow

on 13 December 1941 which destroyed one third of the city

of Huaraz (Morales Arnao 1998 Silverio 1999) and killed

more than 5000 people the bInstituto Geologico del PeruQcreated the bComision de Control de Lagunas de la

Cordillera BlancaQ This entity undertook an inventory of

lakes and glaciers in the Cordillera Blanca In addition

engineering projects were initiated to prevent or mitigate

flood disasters caused by glacierndashlake outbursts (Morales

Arnao 1998) Between 1945 and 1972 the unit of

glaciology of the bCorporacion Peruana del SantaQ carriedout a number of glaciological and bathymetric studies

leading to a better knowledge and understanding of lakes in

the Cordillera Blanca

The first Peruvian glacier inventory was begun in 1978 at

the Geological Mining and Metallurgical Institute

(INGEMMET official Peruvian institution) and was com-

pleted in 1988 in the offices of the bEmpresa Regional

Electronorte MedioQ (HIDRANDINA SA) [HIDRAN-

DINA 1988] According to this document the Cordillera

Blanca together with Nevados Rosco and Pelagatos

mountain ranges contained 722 glaciers covering an area

of 723 km2 and having an average ice thickness of 3125 m

with a total estimated volume of 23 km3 For convenience

reason we have included in this inventory the Nevados

Pelagatos and Rosco (respectively 30 km NndashNWand 50 km

N of our study area) in the Cordillera Blanca because they

form units with very small glacierized areas (Hidrandina

1988)

According to the 1962 aerial photographs archived at

HIDRANDINA (1988) the Nevados Rosco contained 4

glaciers for a total area of 053 km2 and the Nevados

Pelagatos range had 7 glaciers for a total area of 156 km2

The Cordillera Blanca had 711 glaciers for a total area of

721 km2

The first glacier inventory of the Cordillera Blanca was

based on analysis of 1962 and 1970 aerial photographs

Information sources for the entire mountain range were

heterogeneous Obviously the Peruvian socio-economical

context as well as the size of the Cordillera Blanca did not

allow the acquisition of uniform information because a

stereoscopic vertical aerial photographic survey of such a

large area requires a substantial investment However it is

essential to acknowledge the effort made by Peru to

complete that first inventory

We would like to point out that in this first inventory

rock glaciers were not distinguished from ice glaciers These

elements were considered as part of the same entity

However the total area of rocky inclusions within the

glaciers was later subtracted Owing to the analysis method

(mirror stereoscopy) the areal extent of all glaciers is not

accurate (A Ames Glaciological Study Department of

HIDRANDINA personal communication)

4 Satellite and map data

The US Geological Surveyrsquos Eros Data Center provided

two Landsat 5 Thematic Mapper (TM) images (north 0866

and south 0867) taken on 31 May 1987 covering a wide

area of the Ancash Department The picture element (pixel)

resolution of these products is 285 m and their quality is

excellent

A second Landsat TM image mosaic (north 0866 and

south 0867) acquired on 11 August 1996 was provided by

UNEPDEWAGRID-Sioux Falls (USA) Its pixel resolu-

tion is 30 m and it is of good quality Ninety-nine percent

of the Cordillera Blanca area was cloud free and the

spectral contrast in the TM5 band between clouds and

glaciers makes differentiation possible (ERDAS 1999)

(Table 1)

Table 2

Spatial differentiation between glacier and debris-covered glacier entities

for years 1987 and 1996

Years NDSI criterion of limits

Glaciers Debris-covered glaciers

1987 NDSIz052 NDSIz028

1996 NDSIz040 NDSIz031

W Silverio J-M Jaquet Remote Sensing of Environment 95 (2005) 342ndash350 345

5 Methodology

The complex topography of the Cordillera Blanca

produces a strong shadow effect (high relief) on the images

and hence on the spectral signatures of land-cover classes

(Silverio amp Jaquet 2003) This effect could ideally be

corrected through a topographic normalisation using a digital

elevation model (DEM) (Dymond amp Shepherd 1999) which

must at least have a spatial resolution 4 times that of the

image needing correction (Sandmeier 1995) The DEM

interpolated by Silverio (2001) using INGEMMETrsquos contour

lines (50-m resolution) does not meet this requirement For

the 1987 image we have nevertheless tried a normalization

using the cosine correction available from the ERDAS

Software (1999) Results were of poor quality with especially

over- and under-correction artifacts (Silverio amp Jaquet

2003) We therefore abandoned topographic normalization

and instead used Landsat TM band ratios (indices) that

enable relief attenuation

51 Image pretreatment

For the 31 May 1987 image geometric correction was

achieved using 91 ground-control points with a first-degree

polynomial transformation and nearest neighbour resam-

pling (285 m pixel) The root-mean-square (RMS) error

was 24 pixels which is acceptable considering the uneven

topography and relative inaccuracy of the 1100000-scale

map used for geo-correction The resulting image from this

transformation has 7730 lines by 5381 columns from which

a window showing the working area was extracted

For the 11 August 1996 Landsat TM images the original

mosaic was in GeoTIF format and georeferenced in the

Universal Transverse Mercator (UTM) coordinate system A

geometric transformation with geodetic ground-control

points (GCP) was however necessary For this purpose a

sub-image was extracted bounded by the following UTM

coordinates (zone 18 south)

Xmin Ymin 168 000 8 850 000

Xmax Ymax 280 000 9 060 000

The geometric correction was completed using 23 GCPs

with a first-degree polynomial transformation and a nearest

neighbour resampling (30-m pixel) The RMS error turned

out to be of 23 pixels which is comparable to the 1987

image The resulting image from this transformation has

7000 lines by 3723 columns from which a window showing

the working area was extracted

The registration error was estimated by overlaying both

images to be around 1 pixel (30 m)

52 Index calculation

Indices or spectral-band ratios are known for their ability

to eliminate or at least to minimise illumination differences

due to topography (shading of surface caused by solar

illumination angle and slope orientation) (Colby 1991)

These ratios should be calculated from visible and near

infra-red channels with low correlation and ideally after

elimination of additive noise (Bonn amp Rochon 1993) Since

haze was not visible on the images this last treatment was

not deemed necessary (Silverio amp Jaquet 2003)

In order to have an optimal representation of the

Cordillera Blanca high-altitude land-cover themes between

1987 and 1996 ranging from pure ice to rock outcrops we

used the Normalized Difference Snow Index (NDSI) it can

be determined using digital numbers (DN) of two TM bands

from the following equation (Hall et al 1995b)

NDSI frac14 TM2 TM5frac12 = TM2thorn TM5frac12

According to Dozier (1989) NDSI allows a spectral

discrimination between snow soil rock and cloud cover to

be made Sidjak and Wheate (1999) showed that this index

is efficient for snow mapping in rough topography NDSI

provides a sharp image of the boundary between the glacier

terminus and the surrounding moraine it also permits a

fairly accurate intercomparison of the bare-ice part of the

glacier tongue positions in different years (Hall et al 2001)

We have found that NDSI values are similar for snow

areas exposed to the sun and for those in the shadows

(NDSIN052 in 1987 and N040 in 1996 see Table 2)

Moreover the outside limits of glaciers are never located in

topographic shadows For these reasons NDSI can be

considered as a robust means of delineating glacial

boundaries

53 Spatial segmentation

The NDSI the values of which range from 1 to +1 was

used to characterise and separate the spectral-classification

themes of glaciers and debris-covered glaciers (LGGE

2003 NSIDC 2003 USGS 2002) (Table 2)

Glacier based on visual interpretation and on the image

gray-level histograms (Fig 2) the NDSI images were

segmented using the criteria given in Table 2 (Caloz amp

Collet 2001) We have tested several threshold values and

chosen those reported in Table 2 because they gave the best

match with the glacier limits seen on the colour composite

image The margins of glaciers were obtained by raster

vector conversion leaving rock outcrops inside the margins

(Silverio amp Jaquet 2003)

Fig 2 NDSI Histogram (from DN) for Landsat TM images (1987) Glacier ice is above the 052 threshold

W Silverio J-M Jaquet Remote Sensing of Environment 95 (2005) 342ndash350346

Debris-covered glaciers were also obtained by a NDSI

image thresholding (Table 2) In this case because the

histograms were unimodal threshold values were estimated

by visual inspection only For the discrimination of glaciers

and debris-covered glaciers from surrounding land-cover

elements we applied the methodology presented in Silverio

and Jaquet (2003) Here again rastervector conversion was

used only to delineate the outer margins

6 Results

In 1970 the total glacierized area of the Cordillera

Blanca (without distinction between ice and debris-covered

glaciers) was estimated to be 721 km2 (Hidrandina 1988)

In 1987 we measured 625 km2 of glaciers (snow and ice

cover) and 18 km2 of debris-covered glaciers For 1996 we

67

825

400

450

500

550

600

650

700

750

800

850

1930 1940 1950 1960 19

Years

Gla

cie

r exte

nt

(km

2)

Fig 3 Evolution of glacial cover in the Cordillera Blanca between 1930 and 20

Source for 1970 Hidrandina (1988) for 1987 and 1996 our calculations with er

measured 582 km2 of glaciers (snow and ice cover) and 18

km2 of debris-covered glaciers In total the Cordillera

Blanca had a glacierized area of 643 km2 in 1987 and of 600

km2 in 1996 (Fig 3)

According to Silverio (2001) and Silverio and Jaquet

(2003) in 1987 the rock inclusions amounted to only

02 of the glacier area In the Cordillera Blanca at the

end of July north walls of certain peaks usually lose their

snow cover indicating that for 1996 rock inclusions

within glacier margin could have been more frequent

However because of their very steep slopes their area

should not exceed 03 of glacier cover for the image

recording date

Between 1970 and 1987 the glacierized area was

reduced by 78 km2 a mean retreat of 46 km2 year1

Between 1987 and 1996 recession was about 43 km2 a

retreat of 48 km2 year1 Between 1970 and 1996 the total

600620

0 643

600

721

70 1980 1990 2000

Georges

This study

00 (according to Georges 2003) and between 1970 and 1996 (this study)

ror bar

W Silverio J-M Jaquet Remote Sensing of Environment 95 (2005) 342ndash350 347

retreat was about 121 km2 The retreat trend is therefore N4

km2 year1 Considering the relatively small areal extent of

the Cordillera Blanca glaciers these figures are high It is

expected that if present trends continue the majority of

small glaciers (with an area between 01 and 05 km2) will

disappear within 20 or 30 years (Francou amp Wagnon 1998)

Indeed according to Hidrandina (1988) in 1970 within the

711 glaciers indexed in the Cordillera Blanca only 194

(27) of them had an area larger than 1 km2

7 Discussion

The graphs in Fig 3 clearly illustrate the general retreat

of the Cordillera Blanca glaciers If the value for 1970 is

rated at 100 and the glacierized area for 1987 and 1996

are at 89 and 83 respectively this means that the

Cordillera Blanca glacier cover has been slowly shrinking

since the 1970s This decrease amounts to 15 during a

period of 25 years (Silverio amp Jaquet 2002)

Within the total glacier cover of 1987 and 1996 no

subclass distinction was considered (ice snow of different

texture see Hall et al 1987 1988 Williams et al 1991)

because the key objective of this study was to estimate the

total glacierized area of the Cordillera Blanca Moreover for

this period we did not have access to glaciological data on

the six glaciers monitored in the mountain range which

prevented verification of our results

Regarding georeference satellite images were registered

with topographic maps at a 1100000 scale (about F100-m

accuracy) The study results must therefore be considered

at a similar scale in spite of the higher pixel resolution of

the images

The monitoring accuracy of the position of glacial tongue

front using traditional cartographic methods (theodolite)

and photogrammetry is respectivelyF5 and F10 m (Sturm

et al 1991) For satellite imagery this accuracy is limited

by the sensor resolution 79 m for Landsat MSS and 30 m

for Landsat TM (Hall et al 1992 1995a Williams et al

1997) According to Hall et al (2003) for multi-temporal

measures of the glacier front position using satellite images

each position has an uncertainty that can be calculated by

the following formula

Uncertainty frac14hpixel resolution image 1987eth THORN2

thorn pixel resolution image 1996eth THORN2i1=2

thorn registration error

In our case

Uncertainty frac14 28 5eth THORN2 thorn 30eth THORN2h i1=2

thorn 30c70 m

According to Ames (personal communication) and Kaser

et al (2003) the cartographic representation of glaciers for

the year 1970 is somewhat inaccurate Not knowing the

error for this date and the method used being different from

ours we cannot compare their level of accuracy However

the regional topographic complexity (difficulty in determin-

ing the margins of some glaciers because of relief

shadowing) is a potentially large source of variability

For the 1987 analysis satellite images were taken at the

end of May which significantly reduces the presence of

snowpack outside the margins of glaciers For this reason

the changes in glacierized area shown in Fig 3 are

representative Because the limits between snow and ice

and non-snow and ice are well defined by NDSI they can

be considered reliable at F1 pixel for the image date This

value is equal to the image resolution and gives an

approximation of F285 m around the limit We then

calculate a total area of 643F63 km2 that is to say a

variation of F10 That number is similar to the difference

between 1970 and 1987 (78 km2 Fig 3)

For 1996 the satellite images were acquired in August

so the determination of glacier margins can be expected to

give optimum results Snow fallen the year before had time

to melt leaving only bare ice in the ablation area of the

glaciers This ice has a much lower reflectance (Hall et al

1992) This can be explained by the lowest NDSI value

(040) Ice and non-ice is then clearly delineated using

NDSI However as in previous case the glacier limit

estimate is reliable at F1 pixel Taking into account this

F30 m approximation leads to a total surface of 600F61

km2 or a possible error of F10 higher than the difference

between 1987 and 1996 (43 km2 Fig 3)

According to Hall et al (2001) if measurements are

considered during a relatively short period (a decade or so)

errors are often larger than recession of the glacier however

if the study period is longer uncertainty will become smaller

than recession

Despite these inaccuracies we consider our estimates to

be acceptable Indeed according to Silverio and Jaquet

(2002) glacier retreat is clearly visible in the Pastoruri

Glacier sector (Fig 4) Moreover Georges (2003) gives for

the year 1990 and counting the Cordillera Blanca an

estimated glacier area of 620 km2 this number decreases by

the end of the 20th century to b600 km2 The same author

quotes figures of 800ndash850 km2 for 1930 and 660ndash680 km2

for 1970 Although we do not know the cartographic

accuracy of these estimates our results fall in the range

given by this author (Fig 3)

The validity of our estimates is also confirmed by ground

observation from Peruvian glaciologists According to

experts quoted by Rizo (1999) the average frontal retreat

of the Pastoruri Glacier is about 17 ma1 Ames (1988)

estimates that between 1980 and 1987 the Pastoruri Glacier

experienced a mean annual retreat of 168 ma1 Analyzing

and interpreting satellite imagery from 1987 and 1996

confirms that conclusion Indeed retreat of the terminus of

the Pastoruri Glacier during that time span is approximately

155F70 m which represents a annual retreat of 172 ma1

(Fig 5 left) In the absence of information regarding other

W Silverio J-M Jaquet Remote Sensing of Environment 95 (2005) 342ndash350 347

retreat was about 121 km2 The retreat trend is therefore N4

km2 year1 Considering the relatively small areal extent of

the Cordillera Blanca glaciers these figures are high It is

expected that if present trends continue the majority of

small glaciers (with an area between 01 and 05 km2) will

disappear within 20 or 30 years (Francou amp Wagnon 1998)

Indeed according to Hidrandina (1988) in 1970 within the

711 glaciers indexed in the Cordillera Blanca only 194

(27) of them had an area larger than 1 km2

7 Discussion

The graphs in Fig 3 clearly illustrate the general retreat

of the Cordillera Blanca glaciers If the value for 1970 is

rated at 100 and the glacierized area for 1987 and 1996

are at 89 and 83 respectively this means that the

Cordillera Blanca glacier cover has been slowly shrinking

since the 1970s This decrease amounts to 15 during a

period of 25 years (Silverio amp Jaquet 2002)

Within the total glacier cover of 1987 and 1996 no

subclass distinction was considered (ice snow of different

texture see Hall et al 1987 1988 Williams et al 1991)

because the key objective of this study was to estimate the

total glacierized area of the Cordillera Blanca Moreover for

this period we did not have access to glaciological data on

the six glaciers monitored in the mountain range which

prevented verification of our results

Regarding georeference satellite images were registered

with topographic maps at a 1100000 scale (about F100-m

accuracy) The study results must therefore be considered

at a similar scale in spite of the higher pixel resolution of

the images

The monitoring accuracy of the position of glacial tongue

front using traditional cartographic methods (theodolite)

and photogrammetry is respectivelyF5 and F10 m (Sturm

et al 1991) For satellite imagery this accuracy is limited

by the sensor resolution 79 m for Landsat MSS and 30 m

for Landsat TM (Hall et al 1992 1995a Williams et al

1997) According to Hall et al (2003) for multi-temporal

measures of the glacier front position using satellite images

each position has an uncertainty that can be calculated by

the following formula

Uncertainty frac14hpixel resolution image 1987eth THORN2

thorn pixel resolution image 1996eth THORN2i1=2

thorn registration error

In our case

Uncertainty frac14 28 5eth THORN2 thorn 30eth THORN2h i1=2

thorn 30c70 m

According to Ames (personal communication) and Kaser

et al (2003) the cartographic representation of glaciers for

the year 1970 is somewhat inaccurate Not knowing the

error for this date and the method used being different from

ours we cannot compare their level of accuracy However

the regional topographic complexity (difficulty in determin-

ing the margins of some glaciers because of relief

shadowing) is a potentially large source of variability

For the 1987 analysis satellite images were taken at the

end of May which significantly reduces the presence of

snowpack outside the margins of glaciers For this reason

the changes in glacierized area shown in Fig 3 are

representative Because the limits between snow and ice

and non-snow and ice are well defined by NDSI they can

be considered reliable at F1 pixel for the image date This

value is equal to the image resolution and gives an

approximation of F285 m around the limit We then

calculate a total area of 643F63 km2 that is to say a

variation of F10 That number is similar to the difference

between 1970 and 1987 (78 km2 Fig 3)

For 1996 the satellite images were acquired in August

so the determination of glacier margins can be expected to

give optimum results Snow fallen the year before had time

to melt leaving only bare ice in the ablation area of the

glaciers This ice has a much lower reflectance (Hall et al

1992) This can be explained by the lowest NDSI value

(040) Ice and non-ice is then clearly delineated using

NDSI However as in previous case the glacier limit

estimate is reliable at F1 pixel Taking into account this

F30 m approximation leads to a total surface of 600F61

km2 or a possible error of F10 higher than the difference

between 1987 and 1996 (43 km2 Fig 3)

According to Hall et al (2001) if measurements are

considered during a relatively short period (a decade or so)

errors are often larger than recession of the glacier however

if the study period is longer uncertainty will become smaller

than recession

Despite these inaccuracies we consider our estimates to

be acceptable Indeed according to Silverio and Jaquet

(2002) glacier retreat is clearly visible in the Pastoruri

Glacier sector (Fig 4) Moreover Georges (2003) gives for

the year 1990 and counting the Cordillera Blanca an

estimated glacier area of 620 km2 this number decreases by

the end of the 20th century to b600 km2 The same author

quotes figures of 800ndash850 km2 for 1930 and 660ndash680 km2

for 1970 Although we do not know the cartographic

accuracy of these estimates our results fall in the range

given by this author (Fig 3)

The validity of our estimates is also confirmed by ground

observation from Peruvian glaciologists According to

experts quoted by Rizo (1999) the average frontal retreat

of the Pastoruri Glacier is about 17 ma1 Ames (1988)

estimates that between 1980 and 1987 the Pastoruri Glacier

experienced a mean annual retreat of 168 ma1 Analyzing

and interpreting satellite imagery from 1987 and 1996

confirms that conclusion Indeed retreat of the terminus of

the Pastoruri Glacier during that time span is approximately

155F70 m which represents a annual retreat of 172 ma1

(Fig 5 left) In the absence of information regarding other

Fig 4 Pastoruri Glacier between 1987 (left) and 1996 (right) Yellow outline 1987 situation black 1996 For the purple frame see Fig 5 (For interpretation

of the references to colour in this figure legend the reader is referred to the web version of this article)

W Silverio J-M Jaquet Remote Sensing of Environment 95 (2005) 342ndash350348

representative glaciers we are unable to determine mean

annual retreat We can however mention another example

retreat of the terminus of the Uruashraju Glacier has been

approximately of 260F70 m between 1987 and 1996

representing an average retreat of 29 ma1 (Fig 5 right)

These examples (Figs 4 and 5) show that satellite

imagery can be used to map the changes in position of

glacier termini and margins in the Cordillera Blanca

inasmuch as topography is very rugged and access for

surface observations is cumbersome and perilous The

generally steep accumulation areas of the glaciers are often

Fig 5 Pastoruri (left) and Uruashraju (right) Glaci

threatened by ice avalanches and therefore usually inacces-

sible (Kaser et al 1990) Because of geological and tectonic

circumstances glaciers in the Cordillera Blanca are also

very steep and consequently rather shallow and crevassed

which increases the difficulties of in situ measurements

(Kaser et al 2003)

In spite of occasional difficulties such as too much cloud

cover satellite remote-sensing techniques are a powerful tool

for monitoring and mapping glacierized areas such as the

Cordillera Blanca Besides they provide a synoptic view of

the glaciers and geomorphic evolution of the many glacial

ers termini changes between 1987 and 1996

W Silverio J-M Jaquet Remote Sensing of Environment 95 (2005) 342ndash350 349

lakes in the area Indeed according to Lliboutry et al (1977)

glacier retreat in the Cordillera Blanca has created dangerous

water bodies at the terminus of numerous glaciers Accord-

ing to Silverio (1999) natural hazards in that region often

take place in remote areas and consequently are a challenge

to detect and to control Under such conditions remote

sensing could allow catastrophic events such as jfkulhlaupsfrom glacial lake to be forecast after confirmation from

direct field observations by local glaciologists (Kieffer et al

2000) to avoid bfalse alarmsQ such as occurred in 2003 with

respect to Laguna Palcacocha

8 Conclusions

Under current climatic conditions in the tropical Andes

30-m pixel resolution satellite imagery can be used for

mapping of glacier recession during a period of two

decades and for a single decade with generally less

accuracy In this respect Landsat TM imagery offers an

optimal combination between scene area (185185 km)

spatial (pixel) resolution and cost It is crucial however to

check results and trends by visual analysis of the images

(see Figs 4 and 5) and whenever by obtaining confirma-

tion from field observations (see Williams et al 1997)

According to our estimates the Cordillera Blanca had a

glacierized area of 643F63 km2 in 1987 compared to

600F61 km2 in 1996 this 43 km2 difference for a 9-year

period means a decrease rate of 48 km2 year1 Compared

to the 721 km2 estimated for 1970 the glacier recession

amounts to 15 in 25 years

Acknowledgments

We would like to offer our gratitude to Mark A Ernste

UNEPGRID-Sioux Falls (DEWA) USGS EROS Data

Center SD Dakota (USA) for providing the 1996 Landsat

5 TM images and to Pascal Peduzzi UNEPGRIDDEWA

(Geneva) for his much appreciated help Our thanks also go

to Stephane Kluser GRID-Geneva for his help in the

translation of this paper from the original French into

English We greatly appreciate the many useful comments

and corrections proposed by three reviewers

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Bonn F amp Rochon G (1993) Precis de Teledetection Volume 1

Principes et Methodes Sainte-Foy7 Presses de lrsquoUniversite de Quebec

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Caloz R amp Collet C (2001) Precis de Teledetection volume 3

Traitements numeriques drsquoimages de teledetection Sainte-Foy7 Presses

de lrsquoUniversite de Quebec et AUPELF 386 pp

Colby J D (1991) Topographic normalization in rugged terrain Photo-

grammetric Engineering and Remote Sensing 57(5) 531ndash537

Dozier J (1989) Spectral signature of Alpine snow cover from Landsat

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Dymond J R amp Shepherd J D (1999) Correction of the topographic

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ERDAS (1999) Field Guide (Fifth edition) Atlanta GA7 Erdas Inc

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Georges C (2003) The 20th century glacier fluctuations in the tropical

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Hall D (2002) Monitoring Glacier Changes from Space httpsdcdgsfc

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Hall D K Bayr K Schfner W Bindschadler R A amp Chien Y L

(2003) Consideration of the errors inherent in mapping historical

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Remote Sensing of Environment 86 566ndash577

Hall D K Benson C S amp Field W O (1995a) Changes of Glacier Bay

Alaska using ground and satellite measurements Physical Geography

16(1) 27ndash41

Hall D K Riggs G A amp Salomonson V V (1995b) Development of

methods for mapping global snow cover using Moderate Resolution

Imaging Spectroradiometer (MODIS) data Remote Sensing of Environ-

ment 54 127ndash140

Hall D K Chang A T C amp Siddalingaiah H (1988) Reflectances of

glaciers as calculated using Landsat-5 Thematic Mapper Data Remote

Sensing of Environment 25 311ndash321

Hall D K Ormsby J p Bindschadler R A amp Siddalingaiah H (1987)

Characterization of snow and ice reflectance zones on glacier using

Landsat Thematic Mapper data Annals of Glaciology 9 104ndash108

Hall D K Williams Jr R S amp Bayr K (1992) Glacier recession in

Iceland and Austria EOS (Transactions American Geophysical Union)

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Hastenrath S (1992) Greenhouse indicators in Kenya Nature 355(6360)

503ndash504

HIDRANDINA S A Unit of Glaciology and Hydrology Huaraz (1988)

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Jaeger N (1979) Les Andes du Perou Au cKur de la Cordillere Blanche

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Kaser G Ames A amp Zamora M (1990) Glacier fluctuations and climate

in the Cordillera Blanca Peru Annals of Glaciology 14 136ndash140

Kaser G Georges C amp Ames A (1996) Modern glacier fluctuations in

the Huascaran-Chopicalqui massif of the Cordillera Blanca Peru

Zeitschrift fur Gletscherkunde und Glazialgeologie 32 91ndash99

Kaser G Juen I Georges C Gomez J amp Tamayo W (2003) The

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history from hydrological data in the Cordillera Blanca Peru Journal of

Hydrology 282 130ndash144

Kaser G amp Osmaston H (2002) Tropical Glaciers Cambridge

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Kieffer H H Kargel J S et al (2000) New eyes in the sky measure

glaciers and ice sheets EOS (Transactions American Geophysical

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Klein A amp Isacks B (1998) Alpine glacial geomorphological studies in

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LGGE (Laboratoire de Glaciologie et de Geophysique de lrsquoEnvironne-

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Lliboutry L Morales B Pautre A amp Schneider B (1977) Glacio-

logical problems set by the control of dangerous lakes in Cordillera

Blanca Peru I Historical failures of morainic dams their causes and

prevention Journal of Glaciology 18(78ndash80) 239ndash254

LULCC (2003) Land use and land cover change a core project at the

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Morales Arnao B (1998) Glaciers of Peru with sections on the Cordillera

Blanca on Landsat Imagery and Quelcaya ice cap by Hastenrath S

In R S Williams Jr amp J G Ferrigno (Eds) Satellite image atlas of

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NSIDC (National Snow and Ice Data Center) (2003) All About Glaciers

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Paul F (2003) The new Swiss glacier inventory (2000) Application of

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PNH (Parque Nacional Huascaran) (1990) Plan Maestro Generalidades y

diagnostico Parque Nacional Huascaran Huaraz Peru internal docu-

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Pouyaud B Francou B Chevallier P amp Ribstein P (1997) Contrib-

ucion del programa bNieves y Glaciares TropicalesQ (NGT) al

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wwwunescoorguyphilibrosensopouyaudhtml

Rizo J (1999) El Pastoruri se deshiela El Comercio (Lima) Wednesday

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Sandmeier S (1995) A physically-based radiometric correction model

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University of Zurich7 Remote Sensing Laboratories Department of

Geography 42 pp

Sidjak R W amp Wheate R D (1999) Glacier mapping of the Illecillewaet

Icefield British Columbia Canada using Landsat TM and digital

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273ndash284

Sigurdsson O amp Jonsson T (1995) Relation of glacier variations to

climate changes in Iceland Annals of Glaciology 21 263ndash270

Silverio W (1999) Essai drsquoevaluation des instabilites de pente par un

systeme drsquoinformation geographique et leur interpretation dans la region

de Huascaran (departement drsquoAncash Perou) Validation Memoir Post

Graduate Certificate in Analysis and Management of Geological Risks

Earth Science Section University of Geneva 65 pp

Silverio W (2001) Elaboration drsquoun SIG pour la gestion drsquoune zone

protegee de haute montagne application au Parc national Huascaran

Perou Validation memoir post graduate certificate in Geomatics

University of Geneva 112 pp (httpwwwunigechsciencesterre

geologieframeworkhtm)

Silverio W amp Jaquet J -M (2002) Land cover changes in Cordillera

Blanca (Peru) glacial retreat avalanches and mining development In

bAtlas of Global Change Q UNEP GRID-Sioux Falls (USA) http

wwwgridunepchproserremotesenscordillera_blancaphp

Silverio W amp Jaquet J -M (2003) Cartographie provisoire de la

couverture du sol du Parc national Huascaran (Perou) a lrsquoaide des

images TM de Landasat Teledetection 3(1) 69ndash83

Sturm M Hall D K Benson C S amp Field W O (1991) Non-climatic

control of glacier-terminus fluctuations in the Wrangell and Chugach

Mountains Alaska USA Journal of Glaciology 37(127) 348ndash356

USGS (2002) Glossary of Selected Glacier and Related Terminology

httpvulcanwrusgsgovGlossaryGlaciersframeworkhtml

Williams Jr R S Hall D K amp Benson C S (1991) Analysis of

glacier facies using satellite techniques Journal of Glaciology 37(125)

120ndash128

Williams Jr R S Hall D K Sigurdsson O amp Chien J Y L (1997)

Comparison of satellite-derived with ground-based measurements of the

fluctuations of the margins of Vatnajfkull Iceland 1973ndash92 Annals ofGlaciology 24 72ndash80