U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2015–5035

Alteration, Slope-Classified Alteration, and Potential Lahar 

Inundation Maps of Volcanoes for the Advanced Spaceborne 

Thermal Emission and Reflection Radiometer (ASTER) 

Volcanoes Archive
FRONT COVER

Google Earth perspective view of hydrothermally altered rocks and alluvium (red and green pixels) on the slopes of the 

volcano Nevado Cayembe, situated east of the town of Cayembe, Ecuador. 
Alteration, Slope-Classified Alteration, 

and Potential Lahar Inundation Maps of 

Volcanoes for the Advanced Spaceborne 

Thermal Emission and Reflection 

Radiometer (ASTER) Volcanoes Archive

By John C. Mars, Bernard Hubbard, David Pieri, and Justin Linick

Scientific Investigations Report 2015–5035

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2015

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Suggested citation:

Mars, J.C., Hubbard, B., Pieri, D., and Linick, J.L., 2015, Alteration, slope-classified alteration, and potential lahar 

inundation maps of volcanoes for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 

Volcanoes Archive: U.S. Geological Survey Scientific Investigations Report 2015–5035, 19 p., http://www.dx.doi.

org/10.3133/sir20155035.

ISSN 2328-0328 (online)
 iii

Figures

1. Comparison of two images of Mount Shasta, California, showing pixels having minerals 

associated with hydrothermal alteration..................................................................................3

2. Plot showing reflectance of alteration minerals at wavelengths measured by ASTER............5

3. Comparison of two maps of Pico de Orizaba, Mexico, showing minerals associated with 

hydrothermal alteration ...............................................................................................................7

4. Plots of ASTER image spectra of the lower southeastern slopes of Mount Shasta, and a 

sample spectrum from Pico de Orizaba compared to a composite spectrum and USGS 

library spectra ...............................................................................................................................9

5. Vertical profiles showing modeled volumes of selected altered rock masses on Mount 

Shasta, Mount Rainier, and Mount Adams.............................................................................11

6. An ASTER hydrothermal alteration mineral map of the Iztaccíhuatl volcano showing argillic 

and phyllic altered rocks and jarosite-rich rocks .................................................................13

7. A shaded relief map at 30-m resolution of the Iztaccíhuatl volcano in Mexico compiled from 

the Global Digital Elevation Model...........................................................................................14

8. A false color composite image of the Iztaccíhuatl volcano, 15-m resolution, compiled from 

ASTER data ..................................................................................................................................15

9. Example of lahar inundation map, showing Iztaccíhuatl volcano in Mexico and potential 

inundation along the Río Alseseca valley near the city of Puebla.....................................16

Contents

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................1

Mineral Composition and Spectral Characteristics of Volcanic Hydrothermal Deposits .................2

Data and Methods .........................................................................................................................................4

Methodology for Selection and Prioritization of Volcanoes..........................................................4

Calibration of ASTER Data...................................................................................................................4

Georegistration of ASTER Reflectance Data ...................................................................................4

GDEM Data—Description and Uses .................................................................................................6

Hydrothermal Alteration Mapping Algorithms.................................................................................6

Validation of Hydrothermal Alteration Mapping..............................................................................8

Slope-Classified Hydrothermal Alteration........................................................................................8

Lahar Type Used for Modeling of Potential Inundation Areas....................................................10

LAHARZ Model, Methods, and Uncertainties................................................................................10

Products and Delivery.................................................................................................................................12

Results ...........................................................................................................................................................12

Conclusions...................................................................................................................................................17

Acknowledgments.......................................................................................................................................17

References....................................................................................................................................................17
iv 

Conversion Factors

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft)

kilometer (km) 0.6214 mile (mi)

meter (m) 1.094 yard (yd)

Area

square meter (m2) 0.0002471 acre

square kilometer (km2) 247.1 acre

square meter (m2) 10.76 square foot (ft2)

Volume

cubic meter (m3) 35.31 cubic foot (ft3)

cubic meter (m3) 1.308 cubic yard (yd3)

cubic kilometer (km3) 0.2399 cubic mile (mi3)

Acronyms and Abbreviations Used

ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer

AVA ASTER Volcano Archive

AVIRIS Airborne Visible Infrared Imaging Spectrometer

DEM Digital elevation model

ERSDAC Earth Remote Sensing Data Analysis Center

GDEM Global digital elevation model

GIS Geographic information system

GPV Global Volcanism Program database

IDL Interactive data language

LPDAAC Land Process Distributed Active Archive Center

MODIS Moderate Resolution Imaging Spectroradiometer

NASA National Aeronautics and Space Administration

SWIR Short-wave near infrared

TIR Thermal infrared

USGS U.S. Geological Survey

VNIR Visible near infrared
Alteration, Slope-Classified Alteration, and Potential 

Lahar Inundation Maps of Volcanoes for the Advanced 

Spaceborne Thermal Emission and Reflection Radiometer 

(ASTER) Volcanoes Archive

By John C. Mars1, Bernard Hubbard1, David Pieri2, and Justin Linick2

1

U.S. Geological Survey. 

2

National Aeronautics and Space Administration Jet Propulsion 

Laboratory/California Institute of Technology. 

Abstract

This study identifies areas prone to lahars from hydrothermally altered volcanic edifices on a global scale, using visible 

and near infrared (VNIR) and short wavelength infrared (SWIR) 

reflectance data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and digital elevation 

data from the ASTER Global Digital Elevation Model (GDEM) 

dataset. This is the first study to create a global database of 

hydrothermally altered volcanoes showing quantitatively compiled alteration maps and potentially affected drainages, as well 

as drainage-specific maps illustrating modeled lahars and their 

potential inundation zones. We (1) identified and prioritized 

720 volcanoes based on population density surrounding the 

volcanoes using the Smithsonian Institution Global Volcanism 

Program database (GVP) and LandScanTM digital population 

dataset; (2) validated ASTER hydrothermal alteration mapping 

techniques using Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) and ASTER data for Mount Shasta, California, and Pico de Orizaba (Citlaltépetl), Mexico; (3) mapped and 

slope-classified hydrothermal alteration using ASTER VNIRSWIR reflectance data on 100 of the most densely populated 

volcanoes; (4) delineated drainages using ASTER GDEM data 

that show potential flow paths of possible lahars for the 100 

mapped volcanoes; (5) produced potential alteration-related 

lahar inundation maps using the LAHARZ GIS code for Iztaccíhuatl, Mexico, and Mount Hood and Mount Shasta in the 

United States that illustrate areas likely to be affected based on 

DEM-derived volume estimates of hydrothermally altered rocks 

and the ~2x uncertainty factor inherent within a statisticallybased lahar model; and (6) saved all image and vector data 

for 3D and 2D display in Google EarthTM, ArcGIS® and other 

graphics display programs. In addition, these data are available 

from the ASTER Volcano Archive (AVA) for distribution (available at http://ava.jpl.nasa.gov/recent_alteration_zones.php). 

Using the GVP and the LandScanTM digital population 

dataset, 350 of the most densely populated stratovolcanoes were 

assessed for study. Of the 350 volcanoes, 250 volcanoes were 

not mapped due to excessive snow, ice, and (or) vegetation. 

Results from mapping the remaining 100 stratovolcanoes show 

that 87 contain slopes with hydrothermal alteration, and 49 have 

hydrothermally altered rocks on steep slopes situated above 

areas with populations >100 people per km2. Of these, 17 stratovolcanoes exhibit laterally extensive hydrothermal alteration 

on slopes >35° and cover an area >0.25 km2, which may pose a 

significant possibility of generating debris flows. 

This study was undertaken during 2012–2013 in cooperation with the National Aeronautics and Space Administration 

(NASA). Since completion of this study, a new lahar modeling 

program (LAHAR_pz) has been released, which may produce 

slightly different modeling results from the LAHARZ model 

used in this study. The maps and data from this study should not 

be used in place of existing volcano hazard maps published by 

local authorities. For volcanoes without hazard maps and (or) 

published lahar-related hazard studies, this work will provide 

a starting point from which more accurate hazard maps can be 

produced. This is the first dataset to provide digital maps of 

altered volcanoes and adjacent watersheds that can be used for 

assessing volcanic hazards, hydrothermal alteration, and other 

volcanic processes in future studies.

Introduction

This study was undertaken during 2012–2013 in cooperation with the National Aeronautics and Space Administration 

(NASA). In this study we identify hydrothermally altered 

volcanic edifices and vulnerable areas downstream by (1) 

mapping hydrothermal alteration on volcanoes using data from 

the Advanced Spaceborne Thermal Emission and Reflection 

Radiometer (ASTER), (2) highlighting watersheds that are 

prone to lahars using ASTER Global Digital Elevation Model 

(GDEM) data, and (3) modeling potential lahar inundation 

areas using LAHARZ, an ArcGIS® flow-fill modeling program (Iverson and others, 1998; Schilling, 1998). Many of the 

altered and potentially hazardous areas are difficult to study 

on the ground because of the inaccessibility of the terrain. 

Remote sensing mitigates this problem and helps to identify 

areas of study that might be otherwise missed by conventional 

field-based mapping missions. 
2 Lahar Inundation Maps of Volcanoes for the ASTER Volcanoes Archive

Lahar is an Indonesian term that describes a fast-moving, 

hot or cold mixture of water and rock debris that flows down 

volcanic slopes or river valleys (Vallance, 2000). Lahars can 

kill large numbers of people, sometimes without precursors 

such as seismicity, edifice deformation, or volcanism (Opfergelt and others, 2006; Scott and others, 2005; Vallance, 2000). 

For example, in 1995, excessive rain from Hurricane Mitch 

triggered a lahar on the flank Casita volcano, Nicaragua, that 

killed approximately 2,500 people in the towns of El Porvenir 

and Rolando Rodriguez (Scott and others, 2005). 

Depending on the sediment-water mixture, lahars can 

be further classified as debris flows, transitional flows, or 

hyperconcentrated flows (Vallance, 2000). Debris flows are 

sediment-water mixtures containing between 50 and 75 percent sediment by volume relative to water, and most typically 

exhibit non-Newtonian flow properties. Hyperconcentrated 

flows are sediment-water mixtures containing between 20 

and 60 percent sediment by volume relative to water (Beverage and Culbertson, 1964; Pierson and Costa, 1987; Vallance, 2000). Transitional flows between hyperconcentrated 

and debris flows occur within the 50–60 percent gradational 

boundary (Vallance, 2000). 

Lahars have a variety of triggering mechanisms and 

modes of emplacement (Vallance, 2000). The large volumes of 

water that generate lahars on volcanoes may be derived from 

storms that saturate the ground, from accumulation of water 

in crater lakes, and (or) from melting of glaciers by volcanic 

eruptions (Vallance, 2000, 2005). Volcanoes are prone to landslides, due to their inherently weak rock structure. Some volcanic edifices are further weakened by hydrothermal systems 

that alter the volcanic rock to clay-rich minerals. The interaction of excessive water and weak or unconsolidated rock 

debris can produce landslides that become lahars that scour 

slopes and river valleys (Vallance, 2000, 2005). For example, 

some avalanche lahars are triggered by an avalanche of unaltered clastic rock that temporarily blocks drainages; lahars 

are generated later when the dams break and flood the valley 

below. Other avalanche lahars may be formed from the mixing 

of pyroclastic deposits on steep slopes with glacial melt water 

or water from large rain storms (Vallance, 2000, 2005). 

This study focuses on mapping hydrothermally altered 

rocks on steep slopes that can generate clay-rich, edifice- or 

flank-collapse-induced lahars (rather than clay-poor lahars 

emplaced by other mechanisms; Vallance, 2000, 2005). 

Hydrothermal alteration occurs when heated groundwater 

interacts with volcanic rocks and produces argillic and phyllic minerals such as alunite, kaolinite, smectite, and sericite, which has been shown to weaken volcanic edifices and 

increase the potential for triggering lahars (Crandell, 1971; 

Scott and others, 1995). For example, although rain was the 

primary trigger of the Casita volcano lahar, the volcanic sector 

at the origin of the collapse consisted primarily of hydrothermally altered, smectite-rich, volcanic rock (Devoli and others, 

2009; Opfergelt and others, 2006; Scott and others, 2005, 

Vallance and others, 2004). Many hydrothermally altered 

volcanoes, such as Mount Rainier, have produced larger 

clay-rich, edifice-collapse lahars (Scott and others, 1995; Cameron and Pringle, 1986). For example, the towns of Enumclaw 

and Buckley, Washington, are built on top of hydrothermally 

altered deposits up to 70 feet thick from the ancient Osceola 

mudflow lahar that originated from Mount Rainier about 5,600 

years ago (Crandall and Waldron, 1956; Vallance and Scott, 

1997). The Osceola mudflow occurred when a large hydrothermally altered section of the volcano collapsed. The lahar 

deposit extends more than 70 kilometers from Mount Rainier to 

Puget Sound near Tacoma, Washington, an area with a population of approximately 200,000 (Bhaduri and others, 2007; Scott 

and others, 1995). 

Hydrothermally altered rocks have been successfully 

mapped on stratovolcanoes using Visible Near Infrared (VNIR) 

and Short-Wave Infrared (SWIR) data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Hyperion 

(fig. 1; Crowley and Zimbelman, 1997; Crowley and others, 

2003). In addition, Crowley and others (2003) have compiled 

slope-classified alteration maps from Digital Elevation Maps 

(DEM), AVIRIS, and Hyperion data that illustrate hazardous 

source areas based on volumes of altered rocks and degrees of 

slope on which these rocks are exposed (Crowley and others, 

2003). These maps illustrate where laterally extensive hydrothermally altered rocks are located on steeply sloping volcanic 

edifices, suggesting a high potential for lahars and highlighting 

the direction and drainage pathways of potential flows. 

ASTER measures reflected radiation in 3 bands in the 

0.52 to 0.86-μm wavelength region (VNIR); 6 bands in the 

1.6 to 2.43-μm wavelength region (SWIR); and 5 bands of 

emitted radiation in the 8.125 to 11.65-μm wavelength region 

(TIR, Thermal Infrared) with 15-m, 30-m, and 90-m resolution, 

respectively (Fujisada, 1995). ASTER also has a backwardlooking VNIR telescope with 15-m resolution. Thus, stereoscopic VNIR images can be acquired at 15-m resolution and 

have been used to produce an ASTER Global Digital Elevation 

Map (ASTER GDEM). ASTER has a swath width of 60 km 

and can be pointed off nadir up to +24° for the VNIR and +8.5°

for the SWIR and TIR (Fujisada, 1995).

Mineral Composition and Spectral 

Characteristics of Volcanic 

Hydrothermal Deposits 

Previous studies have identified argillic and phyllic hydrothermal alteration that result in clay-dominated lithofacies 

associated with volcanic edifices (Crowley and Zimbelman, 

1997; John and others, 2008; Zimbelman, 1996; Zimbelman 

and others, 2005). Both types of alteration are interpreted to 

result from prolonged circulation of hydrothermal fluids, which 

results in mineral replacements and fracture filling that alters 

the host rock into clays (John and others, 2008; Zimbelman, 

1996; Zimbelman and others, 2005). Argillic hydrothermal 

alteration on volcanic edifices primarily consists of alunite-, 
Mineral Composition and Spectral Characteristics of Volcanic Hydrothermal Deposits 3

Argillic-altered rocks (alunite,

kaolinite)

Fe-oxide

Kaolinite

Alunite ± kaolinite ± gypsum

Red banks oxide

Hydrous silica

Amphibole-rich flows

ASTER mineral map:

AVIRIS mineral map:

Phyllic-altered rocks (muscovite,

smectite)

A

B

Jarosite-rich rocks

Jarosite

0 1

1 2

MILES

0 KILOMETERS

0 1

1 2

MILES

0 KILOMETERS

Figure 1. Comparison of two images of Mount Shasta, California, showing pixels having minerals associated with hydrothermal 

alteration, made using spectral data from AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) and ASTER (Advanced Spaceborne 

Thermal Emission and Reflection Radiometer). A, Alteration mineral map of Mount Shasta, California, derived from AVIRIS data. Argillic 

minerals include kaolinite and alunite (blue and yellow pixels). B, Alteration mineral map of Mount Shasta, California, derived from 

ASTER data.
4 Lahar Inundation Maps of Volcanoes for the ASTER Volcanoes Archive

kaolinite-, and smectite-rich volcanic rocks (John and others, 2008). Phyllic hydrothermal alteration consists of sericite 

(fine-grained muscovite), chlorite, and quartz-rich porphyrystyle breccias, veins, and stockworks (Zimbelman, 1996). 

The phyllic alteration mineral assemblage typically represents 

higher temperatures and pressures and is therefore not often 

exposed at the surface of a volcano, but may be present on 

deeply eroded volcanic edifices (John and others, 2010; Mars, 

2014). The mineral jarosite is a common weathering product 

of pyrite-rich, acid-generating, hydrothermally altered rocks 

on volcanic slopes and is another indicator of hydrothermal 

alteration in volcanic settings (Hubbard, 2001; Zimbelman 

and others, 2005). Thus, hydrothermal alteration maps for this 

study include argillic hydrothermally altered rocks that contain 

alunite, kaolinite, and smectite, phyllic hydrothermally altered 

rocks that contain sericite (illite and/or muscovite), and the 

mineral jarosite. 

SWIR spectral absorption features used to map argillic and phyllic hydrothermally altered rocks are caused by 

Al-O-H and O-H molecular vibrations (fig. 2; Hunt, 1977; 

Hunt and Ashley, 1979). Sericite and smectite have a distinct 

Al-O-H spectral absorption feature at 2.2 μm. Alunite exhibits 

a strong Al-O-H spectral absorption feature at 2.165 μm and 

a weaker Al-O-H spectral absorption feature at 2.2 μm and 

kaolinite exhibits a weak Al-O-H spectral absorption feature 

at 2.165 μm and a stronger Al-O-H spectral absorption feature 

at 2.2 μm (fig. 2; Hunt, 1977; Hunt and Ashley, 1979; Rowan 

and others, 2003). Jarosite has a distinctive O-H spectral 

absorption feature at 2.26 μm (fig. 2; Hunt and Ashley, 1979; 

Rowan and others, 2006). ASTER SWIR bands 4, 5, 6, 7 and 

8 are centered at 1.65, 2.165, 2.2, 2.26 and 2.33 μm, respectively (fig. 2; Fujisada, 1995). ASTER bands 4 through 7 

have sufficient spectral resolution to define Al-O-H spectral 

absorption features of alunite, kaolinite, smectite, and sericite, 

and ASTER bands 6, 7, and 8 have sufficient spectral resolution to distinguish the jarosite O-H spectral absorption feature 

(fig. 2; Mars and Rowan, 2006; Rowan and others, 2003, 

2006). Thus, ASTER SWIR data have has sufficient spectral 

resolution to map SWIR spectral absorption features exhibited by argillic and phyllic mineral groups and the mineral 

jarosite.

Data and Methods

Methodology for Selection and Prioritization of 

Volcanoes

To select and prioritize volcanoes with the most densely 

populated areas, the Smithsonian Institution Global Volcanism 

Program (GVP) dataset, and a global information system 

(GIS) global population data set (LandscanTM) were used to 

determine volcano location and population density and distribution within a 50-km radius for each volcano (Bhaduri and 

others, 2007; GVP database is available at http://www.volcano.

si.edu/search_volcano.cfm). The 50-km radius was selected 

on the basis that most lahars extend less than 50 km from their 

origin, but there are some lahars that have traveled over 50 km 

(Manville, 2004; Siebe and others, 1996; Vallance and Scott, 

1997; Vallance, 2005). The LandscanTM population density 

dataset consists of the number of people estimated to inhabit 

each 1-km-by-1-km resolution raster cell. LandscanTM population data were compiled from remote sensing data that includes 

lights at night, topographic slopes, roads, and crop patterns, 

coupled with census and statistical information from land cover 

and land use analysis (Bhaduri and others, 2007). 

A list of stratovolcanoes from the GVP database was used 

to identify the type and location of volcanoes worldwide 

(http://www.volcano.si.edu/search_volcano.cfm). Stratovolcanoes were selected from the database because they are more 

susceptible to lahars and avalanches than other types of volcanoes due to their large size, frequent eruptive activity, volcanic 

rock composition, and typically steep slopes. The population 

data for each stratovolcano was then sorted from the highest 

population to lowest population using a digital worksheet. In 

the first year of the study, 98 of the most densely populated 

volcanoes that had sufficient bare rock exposure were selected 

from this list (http://ava.jpl.nasa.gov/recent_alteration_zones.

php). Evaluation of volcanoes for sufficient rock exposure was 

done by visual analysis of Google Earth and ASTER images. 

Mount Adams, Washington, and Mount Jefferson, Oregon, 

were included due to their relatively accessible locations in the 

United States, despite having few nearby population centers. 

Calibration of ASTER Data

ASTER Level 1B radiance data were converted to reflectance data for mapping SWIR spectral absorption features and 

producing alteration maps (Mars and Rowan, 2010). The 30-m 

spatial resolution SWIR data were resampled to 15-m spatial 

resolution and combined with the VNIR data. The radiance data 

were corrected for crosstalk and radiance coefficient anomalies (Mars and Rowan, 2010). ASTER does not have a spectral 

band in the 0.9 to 1.20-μm region to use for determining water 

vapor values for atmospheric removal calculations and, thus, 

Moderate Resolution Imaging Spectrometer (MODIS) MOD_05 

precipitable water vapor data were used in conjunction with 

atmospheric removal software to convert ASTER Level 1B 

radiance data to reflectance data (ImSpec LLC, 2004; Mars and 

Rowan, 2010). MODIS flies aboard the Terra satellite platform 

with ASTER, and both instruments simultaneously acquire multispectral image data, such that the 1-km MOD_05 precipitable 

water vapor and ASTER Level 1B data are both acquired at the 

same time. 

Georegistration of ASTER Reflectance Data

 Individual ASTER scenes used in this study were recorded 

at four different viewing angles ranging from nadir to 8.2° off 
Data and Methods 5

Muscovite

Smectite

Jarosite

Kaolinite

Alunite

1 2 3 4

0.5 1.0

Wavelength, in micrometers

1.5 2.0 2.5

5 6 7 8 9

ASTER bands

10% Reflectance

Reflectance (offset for clarity)

Al-O-H

O-H

Al-O-H

Al-O-H

Figure 2. Plot showing reflectance 

spectra of lab samples and ASTER

(Advanced Spaceborne Thermal Emission 

and Reflection Radiometer) convolved 

spectral pairs of muscovite (sericite) 

smectite, jarosite, alunite and kaolinite. 

This project maps spectral absorption 

features illustrated by notable reductions 

in reflectance in ASTER bands 5, 6, 7, and 

8, which are related to the vibration of 

molecular bonds between aluminum and 

hydroxide (Al-O-H) and between hydrogen 

and oxygen (O-H) in certain minerals. Lab 

sample and ASTER convolved spectral pairs 

of muscovite (sericite) smectite, jarosite, 

alunite and kaolinite. 
6 Lahar Inundation Maps of Volcanoes for the ASTER Volcanoes Archive

nadir, which causes misregistration of up to 600 m in high 

relief terrain due to parallax displacement. Therefore, each 

ASTER 9-band reflectance dataset was geometrically registered to an orthorectified Landsat Thematic Mapper (TM) 

mosaic (Tucker, and others, 2004). The Landsat TM mosaic 

data have a spatial resolution of 28 m and horizontal registration accuracy of ±50 m. Although this registration procedure 

corrected for the off-nadir viewing offset, the ASTER images 

were not corrected for terrain displacement. Using a either 

a first rotation or second order polynomial warp registration 

algorithm and at least 9 ground control points for each scene, 

we were able to limit root mean square errors to less than 60 m. 

ASTER scenes that could not be accurately georegistered using Landsat TM mosaic data were georegistered to 

an orthorectified image from the AST14OTH ASTER dataset 

downloaded from the Land Process Distributed Active Archive 

Center (LPDAAC, https://lpdaac.usgs.gov/). Use of a triangular 

registration algorithm and at least 900 ground control points for 

each scene typically limited errors to less than 60 m. All georegistration was done using ENVI® image processing software 

(Excelis, 2008). 

GDEM Data—Description and Uses

ASTER GDEM data are used to compile slope maps, 

to classify hydrothermal alteration data by slope, to generate 

shaded relief maps, to compile stream flow lines, and for GIS 

computer flow modeling of potential lahar inundation zones 

(Hubbard and others, 2007). ASTER data from band 3 and 

the backward looking stereo band 3b are used to compile the 

global digital elevation model database which is available at no 

charge through the Earth Remote Sensing Data Analysis Center (ERSDAC) and LPDAAC Web sites (http://asterweb.jpl.

nasa.gov/gdem.asp). Land coverage of the ASTER GDEM is 

continuous between 83°N and 83°S and is composed of 22,600 

1°-by-1° tiles. The ASTER GDEM product is in GeoTIFF format with geographic latitude/longitude coordinates and a 1 arcsecond (30 m) grid of elevation postings. Estimated accuracies 

for this global product are ±20 meters at 95 percent confidence 

for vertical accuracy and ±30 meters at 95 percent confidence 

for horizontal registration accuracy (ASTER GDEM Validation 

Team, 2009). 

Hydrothermal Alteration Mapping Algorithms

 For this study, 100 hydrothermal alteration maps of volcanoes were compiled using Interactive Data Language (IDL®) 

logical operators. Logical operator algorithms were used to 

map rocks displaying argillic, phyllic and jarosite absorption 

features using ASTER VNIR 15-m resolution and resampled 

SWIR 30-m reflectance data, and ENVI® processing software 

based on the following three logical operators (eqn.1, argillic; 

eqn. 2, phyllic; eqn. 3, jarosite) modified from previous ASTER 

mineral mapping studies (Excelis, 2008; John and others, 2010; 

Mars and Rowan, 2006):

Equation 1, for argillic minerals

((float(b3)/b2) le 1.35) and (b4 gt 1600) and 

((float(b4)/b6) gt 1.35) and ((float(b5)/b6)le 1.089) 

and ((float(b7)/b6) gt 1.0) 

Equation 2, for phyllic minerals 

((float(b3)/b2) le 1.35) and (b4 gt 1600) and 

((float(b4)/b6) gt 1.35) and ((float(b5)/b6)gt 1.089) 

and ((float(b7)/b6) gt 1.0) 

Equation 3, for jarosite

((float(b3)/b2) le 1.35) and (b4 gt 1600) and 

((float(b4)/b6) gt 1.35) and ((float(b6)/b7) ge 

1.026) and ((float(b8)/b7) ge 1.029) 

where 

b is ASTER band number; 

ge is greater than or equal to; 

gt is greater than; 

le is less than or equal to; 

float is the calculation to be done using floatin

point numbers 

These logical operator algorithms use a series of band 

ratios and user-defined thresholds to map spectral absorption features and to mask low-reflectance and noisy pixels. 

Threshold numbers for band ratios and band threshold 

values were determined by compiling ASTER argillic and 

phyllic mineral maps for well-known calibration-validation 

sites in Cuprite, Nevada, and Mountain Pass, California 

(Mars and Rowan, 2010). Hyperspectral and field mineral 

maps from previous studies at Mount Shasta, California, 

and Pico De Orizaba, Veracruz-Puebla, Mexico, were also 

used to modify band thresholds (figs. 1 and 3; CarrascoNúñez and others, 2006; Crowley and others, 2003; Hubbard, 2001).

Because ASTER VNIR and SWIR bands measure 

reflective radiation, only surficial spectral information is 

collected. Altered rocks covered in snow, ice, clouds or 

vegetation cannot be measured. In addition, low reflectance, 

spectrally noisy areas such as water, damp soils, and shadowed areas can produce erroneous results and were omitted. The logical operator algorithms mask green vegetation 

by utilizing the chlorophyll absorption feature at 0.65 μm 

using an ASTER 3/2 band ratio (eqns. 1–3). Water, snow, 

and noisy, low reflectance pixels were eliminated in logical 

operators by using a threshold of ASTER band 4 scaled 

reflectance values centered at 1.65 μm (eqns. 1–3).

The argillic logical operator (eqn. 1) maps the 2.165- 

and 2.2-μm Al-O-H absorption features exhibited by 

argillic-altered rocks using 4/6, 5/6, and 7/6 ASTER band 

ratios (fig. 2). The phyllic logical operator (eqn. 2) maps the 

2.2-μm Al-O-H absorption feature illustrated by phyllicaltered rocks using the same 4/6, 5/6, and 7/6 ASTER 

band ratios that are used to map argillic altered rocks. The 

5/6 band ratio threshold for the phyllic algorithm, however, is higher than the 5/6 band ratio value for the argillic 
Data and Methods 7

Kaolinite + natroalunite + smectite

Kaolinite

Natroalunite + other sulfates

Opaline silica

Hematite (coarse & specular)

Goethite + hematite

Jarosite + other ferric iron

N

Argillic-altered rocks (alunite,

kaolinite)

ASTER mineral map:

AVIRIS mineral map:

Phyllic-altered rocks (muscovite,

smectite)

Jarosite-rich rocks

Jarosite

A

B

0 1

1 2

MILES

0 KILOMETERS

0 1

1 2

MILES

0 KILOMETERS

Figure 3. Comparison of two maps of Pico de Orizaba, Mexico, showing minerals associated with hydrothermal alteration, made using 

spectral data from AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) and ASTER (Advanced Spaceborne Thermal Emission and 

Reflection Radiometer). A, Alteration mineral map from AVIRIS data. Argillic minerals include kaolinite and natroalunite (yellow, blue, 

and orange). Background image is a composite of AVIRIS visible Landsat-equivalent wavelength channels, stretched to simulate true 

color. B, Alteration mineral map from ASTER data, illustrating argillic-altered rocks (red), phyllic-altered rocks (green) and jarosite-rich 

rocks (yellow). Background image is ASTER band 3.
8 Lahar Inundation Maps of Volcanoes for the ASTER Volcanoes Archive

algorithm (eqns. 1 and 2). The jarosite logical operator (eqn. 

3) maps the 2.26-μm O-H absorption feature using 6/7 and 8/7 

ASTER band ratios (fig. 2). 

Although the ASTER VNIR reflectance data set has a 

spatial resolution of 15 m, the mapped hydrothermal alteration units have 30-m spatial resolution because the Al-O-H and 

O-H spectral absorption features are mapped using the SWIR 

30-m resolution data. The SWIR data are resampled from 30-m 

spatial resolution to 15 m and combined with the VNIR data so 

that the VNIR and SWIR spectral absorption features can be 

mapped using a single algorithm. The resulting logical operator 

mineral bit maps consist of raster data containing pixels with 

values of 0 or 1, which were then converted to vector data for 

import into other datasets and maps. 

Although in other studies smectite has been classified 

as intermediate argillic alteration, the phyllic logical operator 

maps smectite as phyllic alteration in this study because they 

have similar 2.2 μm spectral absorption features and there is not 

enough spectral variation in ASTER VNIR-SWIR data using 

the current spectral regional mapping techniques to distinguish 

smectite from sericite (fig. 2; John and others, 2008, 2010). 

Thus, in this study, the phyllic unit may also include the intermediate argillically altered mineral smectite. 

Validation of Hydrothermal Alteration Mapping

To validate alteration mapping on volcanoes, AVIRIS mineral maps and field data of volcanic edifices for Mount Shasta, 

California, and Pico de Orizaba, Veracruz-Puebla, Mexico, 

were compared to ASTER-mapped argillic, phyllic and jarosite 

maps of the same areas (figs. 1 and 3). The ASTER and AVIRIS 

mineral maps for Mount Shasta and Pico De Orizaba show good 

agreement in mapping almost 100 percent of the argillic altered 

rocks around the summit areas (figs. 1 and 3; Crowley, and others, 2003; Hubbard, 2001). 

Mount Shasta shows more argillic alteration on the 

southeastern parts of the lower slopes in the ASTER mineral 

map, which is because of greater snow cover in the AVIRIS 

image and lower threshold values used to map altered rock in 

the ASTER data (fig. 1). The lower threshold values result in 

mapping more weakly hydrothermally altered rocks than were 

mapped using AVIRIS data (eqs. 1-3). Although the argillicaltered rocks on the southeastern lower slopes of Mount Shasta 

are not mapped in the AVIRIS image, the ASTER-mapped 

altered rock units in this area exhibit prominent 2.165- and 

2.2-μm spectral absorption features, which are diagnostic of 

hydrothermally altered rocks (figs. 2 and 4A). Jarosite was also 

mapped in small areas on Mount Shasta. An average image 

spectrum of jarosite-mapped areas shows a prominent 2.26-μm 

spectral absorption feature (figs. 2 and 4A). 

Phyllic alteration that was mapped on Mount Shasta, using 

ASTER data, was not mapped using AVIRIS data (fig. 1). In 

some areas, scarce phyllic rocks are interspersed with argillic rocks and may indicate areas where mineral mixing, data 

noise, and insufficient data calibration are interfering with the 

discrimination of argillic from phyllic units. This incoherent 

pattern of phyllic-altered rocks is caused by the low spectral 

resolution of ASTER SWIR data in comparison to AVIRIS 

data, in which the discrimination between argillic and phyllic 

units is dependent on a single 5/6 ASTER ratio. Even though 

the ASTER algorithm may have incorrectly mapped some of 

the argillic-altered rocks as phyllic-altered rocks, the areas 

mapped as phyllic-altered rocks match AVIRIS-mapped 

areas of hydrothermally altered rocks, which is the primary 

focus of the study. 

At Pico de Orizaba, there is significantly less jarosite 

in the ASTER mineral map than in the AVIRIS mineral map 

(fig. 3). In addition, several areas are mapped in the AVIRIS 

data as goethite- and hematite-rich rocks whereas in the 

ASTER image those areas are mapped as primarily argillic rocks (fig. 3). Sample spectral data collected from Pico 

de Orizaba indicate that the lack of jarosite in the ASTER 

mineral map is mostly due to mixing of the jarosite, alunite, 

goethite and hematite (fig. 4B; Hubbard, 2001). The ASTER 

mapping algorithm only maps kaolinite-alunite because the 

logical operator algorithm maps the argillic SWIR absorption component of the hydrothermally altered rocks instead 

of the VNIR spectral absorption features of goethite and 

hematite, which are situated at 0.9 μm. The Pico de Orizaba 

AVIRIS mineral map was generated using spectral unmixing 

methods that considered spectral absorption features across 

a wider range of wavelengths, including the VNIR spectral 

region for which ferric iron minerals tend to dominate over 

any clay or sulfate mineral present (Hubbard, 2001). The 

lower proportion of jarosite illustrated on the ASTER mineral map compared to the AVIRIS mineral map is also due 

to spectral mixing of alunite and jarosite, which has subdued 

the Al-O-H and O-H spectral absorption features (fig. 4; 

Hubbard, 2001). 

Slope-Classified Hydrothermal Alteration

Alteration units consisting of argillic, phyllic and 

jarosite-bearing rocks are classified by surface slope in order 

to determine areas that have the greatest potential risk of 

edifice collapse. Areas considered at greatest risk for generating rock avalanches and lahars consist of hydrothermally 

altered bedrock on slopes greater than their depositional 

angle of repose (Watters and others, 2000). At Mount Shasta, 

the angle of repose that separates bedrock cliffs from talus 

slopes is approximately 35° (Crowley and others, 2003). In 

this study, three slope-classified units were selected based 

on the depositional angles of repose derived from ASTER 

GDEM data. Thus, surface slopes >35° were interpreted to 

consist primarily of exposed hydrothermally altered bedrock, 

surfaces with slopes of 35–30° represent a transition zone 

from mostly bedrock to talus-covered slopes, and surfaces 

with slopes <30° are interpreted to consist of mostly talus. 

Slope maps were compiled from ASTER GDEM data 

and slope masks were generated using ENVI® imaging 
Data and Methods 9

1 2 3 4 56 7 8 9

ASTER bands

Al-O-H

Averaged ASTER image spectra

Sample spectra

0.5 1.0

Wavelength, in micrometers

1.5 2.0 2.5

0.4 0.9

0.93 2.21

2.26

1.4 1.9

Alunite 

Jarosite 

Kaolinite 

50% Jarosite +

 50% Alunite

TR-005B

2.4 2.9

Wavelength, in micrometers

10% reflectance

Argillic-altered

rocks

Phyllic-altered

rocks

Jarosite-rich

rocks

Reflectance (offset for clarity) Reflectance (offset for clarity)

O-H

Al-O-H

A

B

10% reflectance

Figure 4. A, A plot of ASTER (Advanced 

Spaceborne Thermal Emission and 

Reflection Radiometer) image reflectance 

spectra of the lower southeastern slopes 

of Mount Shasta, California. The spectra 

are spectral averages (n=number of 

pixels mapped) for argillic-altered rocks 

(n=17,184), phyllic-altered rocks (n=1,612) 

and jarosite-rich rocks (n=8). B, Sample 

spectra from the U.S. Geological Survey 

spectral library of alunite, kaolinite, and 

jarosite, a spectrum from sample TR005B 

from Pico de Orizaba, and composite 

spectrum from U.S. Geological Survey 

spectral library of mixed alunite and 

jarosite (from Hubbard, 2001).
10 Lahar Inundation Maps of Volcanoes for the ASTER Volcanoes Archive

processing software (Excelis, 2008). Masks for slopes steeper 

than 35°, 30° to 35°, and less than 30° were applied to hydrothermal alteration maps to produce slope classified datasets. 

Lahar Type Used for Modeling of Potential 

Inundation Areas

Edifice-collapse-induced lahars are debris flows that 

originate as avalanching debris that contains enough water to 

liquefy, often beginning as a clay-rich slurry (Vallance, 2000). 

These flows are typically large volume, such as the Osceola 

mudflow from Mount Rainier 5,600 years ago, which flowed 

into the Puget Sound with an estimated volume of 4 billion m3

(Vallance and Scott, 1997). In general, edifice-collapse-induced 

lahars are more common at glaciated and snow-covered volcanoes, where there are usually ample amounts of meltwater 

from snow and ice to water-saturated clay-rich hydrothermally 

altered rock and sediment. Lahars may also originate as a liquefaction slide, like at Mount Meager, British Columbia, when 

several smaller rock slides of hydrothermally altered rocks 

transformed directly into debris flow slurries through liquefaction, including a 49million-m3

 flank failure that occurred on 

August 6, 2010 (Guthrie and others, 2012). The flank collapse 

volume was derived by computing the difference between 

DEMs generated both before and after the collapse event 

(Guthrie and others, 2012). Also, the initial failure mass had 

completely disintegrated and become fluidized at the source 

by mixing with pore water contained in the saturated source 

bedrock areas (Guthrie and others, 2012).

Because this study focuses on mapping hydrothermally 

altered rocks and their relationship to lahars, we model lahar 

inundation areas for Mount Shasta in northern California, 

Mount Hood in northern Oregon, and Iztaccíhuatl volcano, 

Mexico, that could result from clay-rich, edifice- or flankcollapse-induced lahars. To estimate lahar inundation areas, 

we assume that mapped hydrothermally altered bedrock areas 

are water-saturated enough to liquefy directly into lahars. Also, 

we assume that the resulting clay-rich lahars will not transform into more dilute flow types, and will not substantially 

volumetrically increase in bulk downstream, which is typical 

of these types of flows (Vallance and Scott, 1997). Our lahar 

inundation maps do not apply to avalanche lahar processes 

related to clay-poor lahars, typically triggered by eruptions, or 

rain-triggered lahars mobilizing pyroclastic deposits on steep 

slopes (Vallance, 2000). 

LAHARZ Model, Methods, and Uncertainties

Lahars were modeled using the “LAHARZ” GIS code 

(Iverson and others, 1998; Schilling, 1998). Since completion of this study in 2013, a new lahar modeling program 

(LAHAR_pz) has been released, which may produce slightly 

different modeling results from the LAHARZ model used in 

this study (Schilling, 2014). 

LAHARZ routes user-input lahar source volumes downstream using hydrologic grids and hydrographic networks 

derived from ASTER GDEM data. The model is based on a 

log-linear scaling relationship between lahar volume and the 

cross-sectional and planimetric areas of inundation, using a 

statistical database of debris flows of various sizes and extents 

studied throughout the world (Iverson and others, 1998). 

LAHARZ also provides fully automated extraction of hydrographic networks, which are especially customized for the 

unique topography and geomorphology of most volcanoes, as 

well as potential inundation zones that can be rendered into 

various raster or vector formats for GIS. 

The geometry and resulting volumes of material derived 

from volcano sector collapses is complex and often influenced 

by a range of geotechnical factors such as rock strength, cohesion, and internal angle of friction, as well as structural considerations like the magnitude of mass loading, preexisting faults 

and fractures, and over-steepened slopes from glacial erosion 

(Watters and others, 2000). Internal stressors such as pore 

fluid pressure and magmatic intrusions such as cryptodomes 

and (or) accompanying eruptions of overlying lava flows and 

pyroclastic deposits need to be considered as well (Voight and 

Elsworth, 1997; van Wyck de Vries and Francis, 1997; van 

Wyck de Vries and others, 2000; Reid and others, 2000, 2006). 

Wedge collapse models such as SCOOPS have been used 

to determine volumes of flank collapse materials using the 

parameters described above (Reid and others, 2000; Reid and 

others 2006). Because we lack such detailed information about 

the internal structure, stratigraphy, and hydrology for most of 

the volcanoes around the world, we use a simple geometric 

model in which the vertical detachment zone is at a 90° angle 

from a horizontal basal decollement zone (fig. 5; Crowley and 

others, 2003). The volumes of altered rocks were compiled 

using areas of hydrothermally altered rocks on slopes >35°

and extrapolated to the basal decollement level elevations 

approximated by the lowermost extent of exposed hydrothermally altered rocks (fig. 5; Crowley and others, 2003). Thus, 

the geometry of the hydrothermally altered rock volume is a 

wedge bounded by a hydrothermally altered surface, a base 

level floor, side walls projecting toward the volcano central 

axis, and a vertical interior wall (fig. 5; Crowley and others, 

2003). The advantage of this simpler geometric and structural 

model is that the calculated volume reflects the maximum volume available based on the surficial, exposed, hydrothermally 

altered rocks, and is easily and rapidly calculated using the 

standard terrain analysis functions within ArcGIS. In addition, 

modeled areas of inundation from lahars for Iztaccíhuatl are 

consistent with areas impacted by ancient lahars (for example, 

Siebe and others, 1996).

Because we use the maximum angle between the vertically approximated detachment zone and horizontally 

approximated decollement zone, the volumes we derive can 

be considered to be larger than those produced by a typical 

arcuate-wedge slope failure. However, our volume estimates 

include only those altered rocks underlying ASTER-mapped 

altered areas. Altered rocks concealed by snow, ice, glaciers, 

vegetation and even thin veneers of fresh ash and tephra 

deposits cannot be mapped and, thus, are not incorporated into 

our volume estimates. For example, at the pervasively altered 
Data and Methods 11

238 m

238 m

101 m

148 m

131 m

172 m

302 m

315 m

A

C D

B

Ash Creek watershed

Mount Shasta

Base level = 4,020 m

Volume = 1,788,150 m3 Base level = 3,934 m

Volume = 1,237,287 m3

Base level = 3,640 m

Volume = 14,380,755 m3

Base level = 3,411 m

Volume = 53,120,632 m3

Sunset Amphitheater

Mount Rainier

Avalanche Glacier scarp

Mount Adams

Mud Creek watershed

Mount Shasta

3,850

3,900

3,950

4,000

4,050

4,100

4,150

4,200

4,250

4,300

4,350

3,850

3,800

3,900

3,950

4,000

4,050

4,100

4,150

4,200

4,250

4,300

3,500

0 50 100 150 200 250 300 350 400 450 500 0 50 100 150 200 250 300 350 400 450 500

0 50 100

Distance, in meters

Elevation, in metersElevation, in meters

Elevation, in metersElevation, in meters

Distance, in meters

Distance, in meters Distance, in meters

0 50 100 150 200 250 300 350 400 450 500 150 200 250 300 350 400 450 500

3,550

3,600

3,650

3,700

3,750 3,500

3,450

3,550

3,600

3,300

3,250

3,350

3,400

3,650

3,700

3,750

3,800

3,850

3,900

3,950

4,000

Figure 5. Vertical profiles showing modeled volumes of selected altered rock masses on Mount Shasta, Mount Rainier, and Mount 

Adams. The model uses AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) spectral data for Mount Shasta and Mount Rainier 

profiles and uses ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) spectral data for the Mount Adams 

profile in conjunction with digital elevation data from the U.S. Geological Survey. An irregular triangular network with dense mesh 

spacing was used to define the surface topography of each rock mass. Altered rock vertical extents and base level elevations were 

determined from topographic profiles extracted across each of the rock masses. Volumes between the topographic surface and each 

base level elevation were calculated using ArcINFO®. Profile and cross section of modeled volume (shaded) of: A, Ash Creek watershed 

on Mount Shasta’s eastern flank; B, Mud Creek watershed on the southeast flank of Mount Shasta; C, Sunset Amphitheater on Mount 

Rainier’s northwest flank; D, Avalanche Glacier scarp area on the southern flank of Mount Adams (from Crowley, 2003).

Avalanche Glacier scarp area of Mount Adams, Crowley and 

others (2003) estimated a volume of altered rock ~0.5 km3, 

using the simple 90° failure geometry described above. Using 

airborne geophysical data, Finn and others (2007) calculated 

a volume of ~1.8 km3 for this same electro-magnetic lowresistivity and magnetic low-susceptibility area. The difference between the volume calculated by Finn and others 

(2007) using subsurface geophysics is more than three times 

that calculated by Crowley and others (2003) using combined 

remote sensing and DEM mapping as described above; though 

both estimates are less than the 3.3 km3 maximum volume 

originally estimated by Vallance (1999) for the entire summit area (note that Vallance, 1999 also estimated a minimum 

volume of 0.9 km3). Thus, in some cases, LAHARZ input 

volumes are minimum estimates, and an actual lahar generated 

from these areas could be larger than what we model because 

of the inability of ASTER to detect altered rocks in covered 

areas.

Two inundation areas were simulated using (1) the 

original ArcGIS-derived volume of altered rock derived using 

the ASTER GDEM topographic surface and (2) a 2x volume 

encompassing the farthest possible inundation extent, as 
12 Lahar Inundation Maps of Volcanoes for the ASTER Volcanoes Archive

suggested by statistical uncertainties inherent in the LAHARZ 

model (for example, Iverson and others, 1998; Robinson and 

Clynne, 2012). Iverson and others (1998) report a standard 

error of around 30 percent in their equations for calculating 

cross sectional and planimetric areas of inundation, and note 

that such errors compounded yield approximately 150 percent 

error in predicting areas of inundation from specified volumes 

(Robinson and Clynne, 2012). In our case, we rounded the 

uncertainty factor up to 2x to ensure that the full volumetric 

range of events are covered when trying to map the maximum 

possible inundation limit from the lahar types we specify in 

the lahar types section.

Given these unknown factors, our LAHARZ-generated 

inundation maps should not be used as forecasts or predictions, but as a means of considering the potential effects of 

hydrothermal alteration on lahar genesis and emplacement. 

Products and Delivery

To effectively display hydrothermal alteration and related 

potential lahar inundation areas, each volcano has a series of 

maps that show (1) composition and distribution of hydrothermally altered rocks, (2) distribution of slope-classified 

hydrothermally altered rocks, with watersheds and populated 

areas that may be affected, and (3) a false-color composite 

image indicating bare rock and soil, snow and ice, vegetation, 

and cloud cover, which shows what parts of the volcano were 

mapped and not mapped (figs. 6, 7, and 8, respectively). All 

of these maps can be used to qualitatively determine collapseprone areas on the volcanic edifice and the watersheds that 

may be affected. 

Lahar flow models were completed for Mount Shasta 

in northern California, Mount Hood in northern Oregon, and 

Iztaccíhuatl Volcano in central Mexico. These volcanoes 

exhibit hydrothermal alteration on slopes >35° over an area 

>0.25 km2 and have nearby populations >100 people per km2

that may be affected. Lahar flows based on total DEM-derived 

altered rock volume and a 2x uncertainty factor are shown for 

each volcano on a shaded relief map depicting population, 

slope classified alteration units, and stream flow lines (fig. 9). 

Hydrothermal alteration and lahar inundation units are 

available for download as shape files and in kml format. 

In addition, all raster maps are provided in geotiff and kml 

format. All data can be loaded into popular image and GIS 

software packages such as ArcGIS®, Adobe Photoshop®, and 

Google EarthTM. Data products can be accessed from the 

ASTER Volcano Archive Web site (http://ava.jpl.nasa.gov/

recent_ alteration_zones.php). 

Results

In this study we (1) identified and prioritized 720 volcanoes based on population density using the GVP online 

database and LandScanTM digital population dataset; (2) 

validated ASTER hydrothermal alteration mapping techniques using AVIRIS and ASTER data for Mount Shasta and 

Pico de Orizaba; (3) mapped and slope-classified hydrothermal alteration using ASTER VNIR-SWIR reflectance data 

on 100 of the most densely populated volcanoes; (4) delineated drainages using ASTER GDEM data that show flow 

paths of potential lahars for the 100 mapped volcanoes; (5) 

produced potential lahar inundation maps for Iztaccíhuatl in 

Mexico and for Mount Hood and Mount Shasta in the United 

States that illustrate areas likely to be inundated based on 

our DEM-derived volume estimates and the 2x LAHARZ 

model uncertainty factor; and (6) saved all image and vector data formats for 3D and 2D display in Google EarthTM, 

ArcGIS® and other graphics display programs. In addition, all 

data have been uploaded and are available from the ASTER 

Volcano Archive Web site (http://ava.jpl.nasa.gov/recent_ 

alteration_zones.php). 

A major concern was that because of snow, ice, clouds, 

and (or) vegetation cover, there would not be a sufficient 

number of volcanoes available to map for this study. The 

GVP catalog of volcanoes (http://www.volcano.si.edu/) was 

used as the database for identifying and prioritizing volcanoes for hydrothermal alteration mapping. The database 

currently has approximately 1,500 volcanoes listed (but the 

total may change from year to year). In the database there 

are a total of 720 stratovolcanoes listed that have significant 

populations within a 50-km radius of their volcanic centers. 

Stratovolcanoes were selected due to their typically larger 

size and steeper slopes as compared to other volcano types. 

Of the 720 stratovolcanoes identified and prioritized based 

on population density in the GVP, 350 of the most densely 

populated stratovolcanoes were assessed for bare earth 

exposure using ASTER and Google EarthTM imagery. Of 

those 350 volcanoes, 250 volcanoes were not mapped due to 

excessive snow, ice, and (or) vegetation cover. Results from 

mapping hydrothermally altered rocks on the remaining 100 

stratovolcanoes show that 87 exhibit hydrothermally altered 

edifices. There are a total of 49 stratovolcanoes that show 

hydrothermally altered rocks on slopes >35° situated above 

areas with populations >100 people per km2. Of the 49 stratovolcanoes, 17 exhibit laterally extensive, hydrothermally 

altered rocks that cover an area >0.25 km2 on slopes >35° 

that may pose a significant threat of generating lahars. 

Most of the densely populated stratovolcanoes are in 

forested tropical areas such as Indonesia. Most of the less 

densely populated stratovolcanoes in the GVP are in areas 

with less vegetation cover, suggesting that, of the remaining 370 stratovolcanoes not assessed nor mapped, there may 

be an additional 100–200 stratovolcanoes in the Smithsonian database that may be sufficiently exposed to allow for 

mapping hydrothermal alteration. This estimate is only for 

volcanic edifices classified as stratovolcanoes in the GVP 

database. 
Results 13

ARGILLIC HYDROTHERMAL ALTERATION

ASTER HYDROTHERMAL ALTERATION MAP  (ASTER BAND 3 BACKGROUND IMAGE)

PHYLLIC HYDROTHERMAL ALTERATION JAROSITE 

N

Scale1 km

Figure 6, Mars and others

Figure 6. An ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) hydrothermal alteration mineral map of the Iztaccíhuatl volcano showing argillic (red) and phyllic (green) altered rocks and jarosite-rich rocks (yellow). The background is a georegistered ASTER band 3 image. Alteration was mapped at 30 m resolution. Dark, shadowed areas and areas covered by vegetation, snow, or ice were not mapped. Illustration is shown as it is downloaded from the ASTER Volcano Archive Web page (http://ava.jpl.nasa.gov/recent_alteration_zones.php).
14 Lahar Inundation Maps of Volcanoes for the ASTER Volcanoes Archive POPULATION CLASSIFICATION UNITS HYDROTHERMAL ALTERATION ON SLOPES > 35 SLOPE CLASSIFIED HYDROTHERMAL ALTERATION SHADED RELIEF MAP WITH STREAMLINES AND POPULATION HYDROTHERMAL ALTERATION ON SLOPES 30 - 35 HYDROTHERMAL ALTERATION ON SLOPES < 30 STREAMLINES N 0 - 100 100 - 415 415 - 993 993 - 1861 1861 - 3067 3067 - 4649 4649 - 6656 6656 - 9304 9304 - 12864 12864 - 17613 17613 - 23975 23975 - 32707 32707 - 44899 44899 - 61472 61472 - 111570 o o o o Scale 2 km

Figure 7, Mars and others

Figure 7. A shaded relief map at 30-m resolution of the Iztaccíhuatl volcano in Mexico compiled from the Global Digital Elevation Model (GDEM). Units include hydrothermally altered rocks on slopes <30° (blue), hydrothermally altered rocks on slopes ranging from 30° to 35° (pink), and hydrothermally altered rocks on slopes >35° (green), streamlines (yellow), and population density data (shades of purple). Altered rocks on slopes >35° have a higher likelihood of collapsing and producing a lahar. Illustration is shown as it is downloaded from the ASTER Volcano Archive Web page (http://ava.jpl.nasa.gov/recent_alteration_zones.php). 
Results 15

ASTER FALSE COLOR COMPOSITE  (RED = 6, GREEN = 3, BLUE = 1)

N

Scale1 km

Snow and ice

Bare rock and sediment

Vegetation

Shadows

Figure 8, Mars and others

Figure 8. A false color composite image of the Iztaccíhuatl volcano, 15-m resolution, compiled from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data. ASTER bands 6, 3, and 1 are color bands red, green, and blue, respectively. Hydrothermally altered rocks were mapped in white, pink and reddish pink areas (bare rock and sediment). Hydrothermally altered rocks were not mapped in green areas (vegetation), cyan areas (typically snow or ice), and dark areas (shadows or water). Illustration is shown as it is downloaded from the ASTER Volcano Archive Web page (http://ava.jpl.nasa.gov/recent_alteration_zones.php). 
16 Lahar Inundation Maps of Volcanoes for the ASTER Volcanoes Archive

Lahar map with source from Iztaccihuatl Volcano, Mexico 

Figure 9, Mars and others

Figure 9. Example of a lahar inundation map, showing Iztaccíhuatl volcano in Mexico and potential inundation along the Río Alseseca valley near the city of Puebla. The lahar units are superimposed on a shaded relief map compiled from 30-m resolution Global Digital Elevation Model (GDEM) data from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer). The red unit shows areas of inundation from the ASTER GDEM-calculated volume (~ 6 x 108 m3) of altered rocks on slopes >35°, using the method illustrated in figure 5 and discussed in the text. The orange unit shows areas of inundation from twice that volume (the uncertainty factor based on the standard error of the statistically-derived LAHARZ equations, ~91.2 x 109 m3) of altered rocks on slopes >35°. Additional units include hydrothermally altered rocks on slopes <30° (blue), hydrothermally altered rocks on slopes ranging from 30° to 35° (pink), and hydrothermally altered rocks on slopes >35° (green), stream lines (yellow), and population density data (shades of purple). Illustration is shown as it is downloaded from the ASTER Volcano Archive Web page (http://ava.jpl.nasa.gov/recent_alteration_zones.php). 
References 17

Conclusions

Hydrothermal alteration mineral maps consisting of argillic and phyllic hydrothermal alteration mineral groups and the 

mineral jarosite were compiled for 100 volcanoes with nearby 

populations. ASTER SWIR data were used to compile the 

hydrothermal alteration maps because the data have sufficient 

spectral resolution to distinguish SWIR AL-O-H and O-H spectral absorption features for argillic and phyllic mineral groups, 

and jarosite (fig. 1). When ASTER-derived mineral maps were 

compared to AVIRIS-derived maps, which have a greater spectral range, the overall patterns of hydrothermally altered rocks 

were very similar (figs. 1 and 3). 

ASTER-mapped hydrothermally altered rock units were 

classified based on degree of surface slope to characterize 

potential lahar hazards. Three slope-classified units were 

selected based on the depositional angles of repose derived 

from ASTER GDEM data. Surfaces >35° were interpreted to 

consist primarily of exposed hydrothermally altered bedrock, 

while surfaces with slopes of 35–30° represent a transition zone 

from mostly bedrock to talus-covered surfaces, and surfaces 

with slopes <30° were interpreted to consist of mostly talus and 

were considered to be less prone to collapse. 

This is the first study to compile a global database of 

hydrothermally altered volcanoes showing hydrothermal alteration maps that depict potentially affected drainages, populated 

areas, and potential inundation maps illustrating modeled lahar 

inundation zones. Of the 100 volcanoes mapped, 17 exhibit 

laterally extensive, altered summits and potentially pose a significant threat of generating lahars that could impact populated 

areas. Some of the volcanoes such as Iztaccíhuatl in Mexico 

have documented, ancient lahars that covered areas that now 

have large population centers (Siebe and others, 1996). In addition, the ancient Iztaccíhuatl lahars are similar in size to the 

Iztaccíhuatl lahar model compiled for this study (fig. 9; Siebe 

and others, 1996). 

For each mapped volcano, geotiff and kml files are provided for (1) a map illustrating composition and distribution 

of hydrothermally altered rocks, (2) a map of slope-classified 

hydrothermally altered rocks, with watersheds and populated 

areas that may be affected, and (3) a false color composite 

image indicating bare rock and soil, snow and ice, vegetation, 

and cloud cover. In addition, hydrothermal alteration and lahar 

inundation units are available for download as ArcGIS® shape 

files and in kml format for Mount Shasta in northern California, 

Mount Hood in northern Oregon, and Iztaccíhuatl Volcano in 

central Mexico. All data can be loaded into popular image and 

GIS software packages such as ArcGIS®, Adobe Photoshop®, 

and Google EarthTM. Data products can be accessed from the 

ASTER Volcano Archive Web site (http://ava.jpl.nasa.gov/

recent_alteration_zones.php). 

Although these data provide useful information showing 

hydrothermally altered rocks on steep volcanic slopes and the 

populated areas downstream that may be affected by potential 

lahar inundation, they should not be used in place of existing 

volcano hazard maps published by local authorities. These 

datasets can also be used to (1) provide additional safety and 

hazard information related to hydrothermally altered source 

rocks, (2) delineate stream drainages that may have been 

inundated in the past by lahars, and allow for field studies 

of related lahar terrace deposits, and (3) provide additional 

constraints about noneruptive factors (snow, ice, slope, and 

hydrothermal alteration) that could be considered in constructing or revising official volcano hazard maps that include zones 

likely to be inundated by lahars. For international volcanoes 

without hazard maps and (or) published lahar-related hazard 

studies, this work will provide a starting point from which 

more accurate hazard maps can be produced. 

Acknowledgments 

Funding for this study was provided by the NASA 

Applied Sciences Disasters Program, and was carried out, in 

part, under contract to NASA at the Jet Propulsion Laboratory 

of the California Institute of Technology. We wish to thank 

Jim Vallance, Greg Vaughn, and Mike Abrams for reviewing 

the manuscript. 

References

ASTER GDEM Validation Team, 2009, ASTER global DEM 

validation summary report: The Land Processes Distributed 

Active Archive Center, 28 p., https://lpdaac.usgs.gov/sites/

default/files/public/aster/docs/ASTER_GDEM_Validation_Summary_Report.pdf.

Beverage, J. P., and Culbertson, J. K., 1964, Hyperconcentrations of suspended sediment: Journal of the Hydraulics 

Division, v. 90, no. HY6, p. 117–126.

Bhaduri, B., Bright, E., Coleman, P., Urban, M., 2007, 

LandScan USA; A high resolution geospatial and temporal 

modeling approach for population distribution and dynamics: GeoJournal, v. 69, p. 103–117.

Cameron, K.A., and Pringle, P.T., 1986, Post-glacial lahars of 

the Sandy River basin, Mount Hood, Oregon: Northwest 

Science, v. 60, no. 4, p. 225–237.

Carrasco-Núñez, G., Díaz-Castellón, R., Siebert, L., Hubbard, B., 

Sheridan, M.F., Rodríguez, S.R., 2006, Multiple edifice-collapse 

events in the Eastern Mexican Volcanic Belt; the role of sloping 

substrate and implications for hazard assessment: Journal of 

Volcanology and Geothermal Research, v. 158, p. 151–176.

Crandell, D.R., 1971, Postglacial lahars from Mount Rainier 

volcano, Washington: U.S. Geological Survey Professional 

Paper 677, 75 p.
18 Lahar Inundation Maps of Volcanoes for the ASTER Volcanoes Archive

Crandell, D. R., and Waldron, H. H., 1956, A recent volcanic 

mudflow of exceptional dimensions from Mount Rainier, 

Washington: American Journal of Science, v. 256, p. 

384–397.

Crowley, J. K., and Zimbelman, D. R., 1997, Mapping hydrothermally altered rocks on Mount Rainier, Washington, with 

Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) 

data: Geology, v. 25, p. 559–562.

Crowley, J.K., Hubbard, B.E., and Mars, J.C., 2003, Analysis 

of potential debris flow source areas on Mount Shasta, California, by using airborne and satellite remote sensing data: 

Remote Sensing of the Environment, v. 87, p. 345–358.

Devoli, G., Cepeda, J., and Kerle, N., 2009, The 1998 Casita 

volcano flank failure revisited—New insights into geological setting and failure mechanisms: Engineering Geology, v. 

105, p. 65–83.

Excelis, 2008, The Environment for Visualizing Images 

(ENVI®) software: Boulder, Colorado, Excelis Visual 

Information Solutions, http://www.exelisvis.com/ProductsServices/ENVIProducts/ENVI.aspx.

Finn, C.A., Deszcz-Pan, M., Anderson, E.D., and John, D.A., 

2007, Three-dimensional geophysical mapping of rock 

alteration and water content at Mount Adams, Washington; 

Implications for lahar hazards: Journal of Geophysical 

Research, v. 112, B10204, doi:10.1029/2006JB004783, 21 

p.

Fujisada, H., 1995, Design and performance of ASTER instrument, in Fujisada, H., and Sweeting, M.N., eds., Advanced 

and next-generation satellites: Proceedings of International 

Society Optical Engineering, v. 2583, p. 16–25.

Guthrie, R.H., Friele, P., Allstadt, K., Roberts, N., Evans, S.G., 

Delaney, K.B., Roche, D., Clague, J.J., and, Jakob, M., 

2006, The 6 August 2010 Mount Meager rock slide-debris 

flow, Coast Mountains, British Columbia; Characteristics, 

dynamics, and implications for hazard and risk assessment: 

Natural Hazards and Earth System Science, v. 12, L15305, 

p. 1277–1294, doi:10.5194/nhess-12-1277-2012.

Hubbard, B.E., 2001, Volcanic hazard mapping using aircraft, 

satellite, and digital topographic data; Pico de Orizaba (Citlaltépetl), Mexico: Buffalo, N.Y., State University of New 

York at Buffalo, Ph.D. dissertation, 354 p.

Hubbard, B. E., Sheridan, M. F., Carrasco-Núñez, G., DíazCastellón, R., Rodríguez, S. R., 2007, Comparative lahar 

hazard mapping at Volcan Citlaltepetl, Mexico using SRTM, 

ASTER and DTED-1 digital topographic data: Journal of 

Volcanology and Geothermal Research, v. 160, p. 99–124.

Hunt, G.R., 1977, Spectral signatures of particulate minerals in the visible and near infrared, Geophysics, v. 42, p. 

501–513.

Hunt, G.R., and Ashley, R.P., 1979, Spectra of altered rocks 

in the visible and near infrared: Economic Geology and the 

Bulletin of the Society of Economic Geologists, v. 74, p. 

1613–1629.

ImSpec LLC., 2004, ACORN 5.0 tutorial manual: Pasadena, 

Calif., ImSpec, LLC, 143 p., http://www.imspec.com.

Iverson, R.M., Schilling, S.P., and Vallance, J.W., 1998, 

Objective delineation of lahar-inundation hazard zones: 

Geological Society of America Bulletin, v. 110, p. 972–984, 

doi: 10.1130/0016-7606(1998). 

John, D.A., Sisson, T.W., Breit, G.N., Rey, R.O., and Vallance, 

J.W., 2008, Characteristics, extent and origin of hydrothermal alteration at Mount Rainier Volcano, Cascades Arc, 

USA: Implications for debris-flow hazards and mineral 

deposits: Journal of Volcanology and Geothermal Research, 

v. 175, p. 289–314.

John, D.A., Ayuso, R.A., Barton, M.D., Blakely, R.J., Bodnar, R.J., Dilles, J.H., Gray, F., Graybeal, F.T., Mars, J.C., 

McPhee, D.K., Seal, R.R., Taylor, R.D., and Vikre, P.G., 

2010, Porphyry copper deposit model, chapter B of Mineral 

deposit models for resource assessment: U.S. Geological 

Survey Scientific Investigations Report 2010–5070–B, 

169 p., accessed January 15, 2011, at http://pubs.usgs.gov/

sir/2010/5070/b/.

Manville, V., 2004, Paleohydraulic analysis of the 1953 Tangiwai lahar; New Zealand’s worst volcanic disaster: Acta 

Vulcanologica, v. 16, p. 137–152.

Mars, J.C., and Rowan, L.C., 2006, Regional mapping of phyllic- and argillic-altered rocks in the Zagros magmatic arc, 

Iran, using Advanced Spaceborne Thermal Emission and 

Reflection Radiometer (ASTER) data and logical operator 

algorithms: Geosphere, v. 2, p. 161–186, 2 plates. 

Mars, J.C., and Rowan, L.C., 2010, Spectral assessment of 

new ASTER SWIR surface reflectance data products for 

spectroscopic mapping of rocks and minerals: Remote Sensing of Environment, v 114, p. 2011–2025.

Mars, J.C., 2014, Regional mapping of hydrothermally altered 

igneous rocks along the Urumieh-Dokhtar, Chagai, and 

Alborz Belts of western Asia using Advanced Spaceborne 

Thermal Emission and Reflection Radiometer (ASTER) 

data and Interactive Data Language (IDL) logical operators—A tool for porphyry copper exploration and assessment: U.S. Geological Survey Scientific Investigations 

Report 2010–5090–O, 36 p., 10 plates, and spatial data, 

available at http://dx.doi.org/10.3133/sir20105090O.

Opfergelt, S., Delmelle, P., Boivin, P., Delvaux, B., 2006, The 

1998 debris avalanche at Casita volcano, Nicaragua; Investigation of the role of hydrothermal smectite in promoting slope instability: Geophysical Research Letters, v. 33, 

L15305, p. 59–79, doi:10.1029/2006GL026661.
References 19

Pierson, T.C., and Costa, J.E., 1987, A rheologic classification 

of subaerial sediment-water flows: Reviews in Engineering 

Geology, v. 7, p. 1-12. 

Reid, M.E., Christian, S.B., and Brien, D.L., 2000, Gravitational stability of three-dimensional stratovolcano edifices: 

Journal of Geophysical Research, v. 105, no. B3, p. 6043–

6056.

Reid, M.E., Brien, D.L., and Waythomas, C.F., 2006, Preliminary Slope-Stability Analysis of Augustine Volcano, chap. 

14 in Power, J.A., Coombs, M.L. and Freymueller, J.T., 

eds., The 2006 Eruption of Augustine Volcano, Alaska: U.S. 

Geological Survey Professional Paper 1769, p. 321–332.

Robinson, J.E., Clynne, M.A., 2012, Lahar hazard zones for 

eruption-generated lahars in the Lassen Volcanic Center, 

California: U.S. Geological Survey Scientific Investigations 

Report 2012–5176–C, 13 p.

Rowan, L.C., Hook, S.J., Abrams, M.J., and Mars, J.C., 2003, 

Mapping hydrothermally altered rocks at Cuprite, Nevada, 

using the Advanced Spaceborne Thermal Emission and 

Reflection Radiometer (ASTER), a new satellite-imaging 

system: Economic Geology and the Bulletin of the Society 

of Economic Geologists, v. 98, p. 1019–1027. 

Rowan, L.C., Schmidt, R.G., and Mars, J.C., 2006, Distribution of hydrothermally altered rocks in the Reko Diq, Pakistan mineralized area based on spectral analysis of ASTER 

data: Remote Sensing of Environment, v. 104, p. 74–87. 

Schilling, S.P., 1998, LAHARZ; GIS programs for automated 

mapping of lahar-inundation hazard zones: U.S. Geological 

Survey Open-File Report 98–638, 79 p. 

Schilling, S.P., 2014, Laharz_py—GIS tools for automated 

mapping of lahar inundation hazard zones: U.S. Geological 

Survey Open-File Report 2014–1073, 78 p., http://dx.doi.

org/10.3133/ofr20141073. ISSN 2331-1258 (online)

Scott, K.M., Vallance, J.W., and Pringle, P.T., 1995, Sedimentology, behavior, and hazards of debris flows at Mount 

Rainier, Washington: U.S. Geological Survey Professional 

Paper 1547, 56 p. 

Scott, K. M., Vallance, J. W., Kerle, N., Macias, J. L., Strauch, 

W., and Devoli, G., 2005, Catastrophic, precipitation-triggered lahar at Casita volcano, Nicaragua—Flow transformations, flow bulking, and future mitigation strategies: Earth 

Surface Processes and Landforms, v. 30, 59–79. 

Siebe, C., Abrams, M., Macias, J.L., and Obenholzner, J., 

1996, Repeated volcanic disasters in Prehispanic time at 

Popocatepetl, central Mexico; Past key to the future?: Geology, v. 24, p. 399–402. 

Tucker, C.J., Grant, D.M., and Dykstra, J.D., 2004, NASA’s 

global orthorectified Landsat dataset: Photogrammetric 

Engineering and Remote Sensing, v. 70, p. 313–322.

Vallance, J.W., and Scott, K. M., 1997, The Osceola mudflow 

from Mount Rainier; Sedimentology and hazards implications of a huge clay-rich debris flow: Geological Society of 

America Bulletin, v. 109, p. 143–163.

Vallance, J.W., 1999, Postglacial lahars and potential hazards 

in the White Salmon River system on the southwest flank of 

Mount Adams, Washington: U.S. Geological Survey Bulletin 2161, 49 p.

Vallance, J.W., 2000, Lahars in Sigurdsson, H., ed., Encyclopedia of Volcanoes: San Diego, Calif., Academic Press, p. 

601–616.

Vallance, J.W., 2005, Volcanic debris flows in Jakob, M. and 

Hungr, O. eds., Debris-flow Hazards and Related Phenomena: Praxis, Springer Berlin Heidelberge, p. 247–274. 

Vallance, J. W., Schilling, S.P., Devoli, G., Reid, M.E., Howell, M.M., and Brien, D.L., 2004, Lahar hazards at Casita 

and San Cristobal volcanoes, Nicaragua: U.S. Geological 

Survey Open-File Report 01–468, 24 pp.

Van Wyk de Vries, B., and Francis, P.W., 1997, Catastrophic 

collapse at stratovolcanoes induced by gradual volcanic 

spreading: Nature, v. 387, p. 387–390.

Van Wyk de Vries B., Kerle N., Petley D., 2000, Sector collapse forming at Casita volcano, Nicaragua: Geology, v. 28, 

p. 167–170.

Voight, B., and Elsworth, D., 1997, Failure of volcano slopes: 

Geotechnique, v. 47, no. 1, p. 1–31.

Watters, R.J., Zimbelman, D.R., Bowman, S.D., and Crowley, 

J.K., 2000, Rock mass strength assessment and significance to edifice stability, Mount Rainier and Mount Hood, 

Cascade Range volcanoes: Pure and Applied Geophysics, v. 

157, p. 957–976.

Zimbelman, D. R., 1996, Hydrothermal alteration and its influence on volcanic hazards—Mount Rainier, Washington, a 

case history:Boulder, University of Colorado, Ph.D. thesis, 

384 p.

Zimbelman, D. R., Rey, R.O., and Breit, G.N., 2005, Origin of 

secondary sulfate minerals on active andesitic stratovolcanoes: Chemical Geology, v. 215, p. 37–60.

Menlo Park Publishing Service Center, California

Manuscript approved for publication February 25, 2015

Edited by Claire M. Landowski

Design and layout by Vivian Nguyen
20 Lahar Inundation Maps of Volcanoes for the ASTER Volcanoes Archive

Mars and others—Alteration, Slope-Classified Alteration, and Potential Lahar Inundation Maps of Volcanoes for the Advanced Spaceborn Thermal Emission and Reflection Radiometer (ASTER) Volcanoes Archive—Scientific Investigations Report 2015–5035

ISSN 2328-0328 (online)

http://www.dx.doi.org/10.3133/sir20155035