2.34 μm

2.20 μm

2.32 μm

1.75 μm

Gypsum

Muscovite

Antigorite

Calcite

1.6 1.8 2.0 2.2 2.4

0.0

0.2

0.4

0.6

0.8

1.0

Wavelength in micrometers (μm)

Scaled reflectance

U.S. Department of the Interior

U.S. Geological Survey

Fact Sheet 2016–3029

July 2016

Hyperspectral Surveying for Mineral Resources in Alaska

Prepared in collaboration with Alaska Department of Natural Resources Division of Geological and Geophysical Surveys, 

University of Alaska Fairbanks, and National Park Service

• Hyperspectral surveys provide information about bedrock and cover materials on the Earth’s surface. 

• Data collection is rapid and can be completed at different spatial scales from broad-regional to detailed hand-specimen scale.

• The surveys provide geologists with information on rock composition that can augment existing geologic mapping.

• The map distributions of mineral classifications also help economic geologists identify areas with mineral deposits.

Background

Alaska is a major producer of base and precious 

metals and has a high potential for additional undiscovered mineral resources. However, discovery is 

hindered by Alaska’s vast size, remoteness, and rugged 

terrain. New methods are needed to overcome these 

obstacles in order to fully evaluate Alaska’s geology 

and mineral resource potential. Hyperspectral surveying is one method that can be used to rapidly acquire 

data about the distributions of surficial materials, 

including different types of bedrock and ground cover. 

In 2014, the U.S. Geological Survey (USGS) began the 

Alaska Hyperspectral Project to assess the applicability of this method in Alaska. The primary study area 

is a remote part of the eastern Alaska Range where 

porphyry deposits are exposed. In collaboration with 

the Alaska Division of Geological and Geophysical 

Surveys, the University of Alaska Fairbanks, and the 

National Park Service, the USGS is collecting and analyzing hyperspectral data with the goals of enhancing 

geologic mapping and developing methods to identify 

and characterize mineral deposits elsewhere in Alaska.

Kilometer to Micrometer Scales

This project applies an integrated approach that 

combines spectroscopic measurements with field and 

laboratory-based geologic investigations. Hyperspectral 

data (also known as imaging spectrometer data) have 

been collected at the following scales: (1) regional, 

(2) outcrop, and (3) hand specimen. The regional 

airborne survey, conducted over two days, used the 

HyMapTM sensor and provides spectral characterization of surface materials across an extensive area 

(1,900 square kilometers [km2]) at 6-meter (m) resolution (fig. 1). A ground-based outcrop-scale survey done 

with the HySpexTM sensor provides higher spatial resolution mineral mapping over a kilometer-scale hillside at 

30-centimeter (cm) resolution (fig. 2). The most detailed 

data were collected at 500-micrometer (μm) spatial 

scale on individual rock samples in the laboratory using 

Corescan’s Hyperspectral Core Imager Mark IIITM imaging spectrometer. Analyses of these measurements reveal 

complex spatial relations of minerals (fig. 3). Scanning 

electron microprobe and X-ray diffraction analyses of 

rock and soil samples are used to validate mineralogical and spectral interpretations of hand specimens. The 

hand specimen and outcrop results are used to improve 

interpretations of the regional hyperspectral dataset.

Figure 1. Regional mineral classification map overlaying a digital elevation model of the 

Orange Hill area, Wrangell–St. Elias National Park and Preserve, Alaska. Colors represent 

the spectrally dominant minerals. Data collected at 6-meter spatial resolution.

What is hyperspectral analysis?

When light hits a material, some of the 

light is reflected back from the material. 

Scientists measure the intensity of 

reflected light with a spectrometer. 

Measuring over a range of wavelengths 

generates a spectrum. The shape of the 

spectrum is like a fingerprint that provides 

information, including presence and 

qualitative abundance of certain minerals 

and the mineral chemistry. Some common 

minerals, such as quartz and feldspars, 

do not have distinctive sharp absorption features in the wavelength region 

of sunlight and must be detected with a 

thermal spectrometer.

Example spectra in the 1.6- to 

2.5-micrometer wavelength region 

showing diagnostic absorption 

features (dips) for calcite, antigorite, 

muscovite, and gypsum.

Orange Hill

NABESNA GLACIER

Nikonda Creek

ALASKA 1,200 METERS

Hymap

survey

Chlorite Not classified

Chlorite + muscovite

Muscovite/illite Kaolinite + muscovite

Muscovite/montmorillonite

Gypsum

Pyrophyllite Vegetation

Calcite

Serpentine

Kaolinite Serpentine or dolomite Snow or ice

Pyrophyllite + kaolinite Carbonate (iron-bearing)

Material class names

EXPLANATION

Printed on recycled paper
Dominant Minerals and So Much More

The hyperspectral surveys provide key pieces of information. Mineral classification 

maps (figs. 1–3) are based on a match to USGS spectral library standards (Clark and others, 

2007) and show the predominant mineral(s) for each spectrum. The USGS is taking steps to 

extract even more information from the spectral data by developing methods to determine 

qualitative abundances of the predominant mineral(s) across the survey area. Subtle changes 

in the shapes of spectral features are also being examined to map differences in the chemical 

composition of select minerals. Generally, relative abundances of predominant mineral(s) 

and observed compositional differences of some minerals may reflect variations in rock 

type. In this study area, muscovite spectral features are empirically observed to change with 

proximity to porphyry copper deposits (fig. 4), as has been shown by studies in other areas 

(for example, Harraden and others, 2013; and Laakso and others, 2015).

60 METERS

Material class names

EXPLANATION

Muscovite/montmorillonite Kaolinite+muscovite Muscovite/illite Gypsum Chlorite+muscovite Chlorite Vegetation Not classified

Figure 2. Outcrop mineral classification map of the western side of the Orange Hill porphyry copper deposit. This mineral map was generated from data 

collected using a HySpexTM spectrometer. Note the different colors that indicate a predominance of clays, muscovite, and gypsum (likely related to porphyry 

mineralization) across the hillside. Data collected at 30-centimeter spatial resolution. Compare to the regional survey in figure 1. 

Figure 3. Hand specimen photograph (top) and mineral classification map (bottom) from

CorescanTM data collected at the Orange Hill deposit. Data collected at 500-micrometer 

spatial resolution.

Figure 4. Orange Hill and Bond Creek areas showing 

systematic changes in wavelength position of muscovite. 

These changes are related to geology and proximity to 

porphyry deposits and occurrences.

Field collections of hyperspectral data at Orange Hill (left) 

and geologic samples at Bond Creek (right).

10 MILLIMETERS

Material class names

Not classified

Chlorite

Chlorite + muscovite

Gypsum

Calcite

Serpentine

Kaolinite + muscovite

Muscovite/montmorillonite

Kaolinite

Muscovite/illite

EXPLANATION

Nabesna Pluton

Igneous

Volcanics

No bedrock

exposure

ORANGE 

HILL

BOND CREEK

NIKE

NEIL

UNNAMED

142°50' 142°45' 142°40' 142°35'

62°15'

62°10'

EXPLANATION

Wavelength position of muscovite feature (micrometer)

No muscovite

Less than 2.1995

2.1995 to 2.2015

2.2015 to 2.2025

2.2025 to 2.2035

2.2035 to 2.2045

Generalized 

geologic contact

Porphyry deposit 2.2045 to 2.2055

Greater than 2.2055

0 2 4 KILOMETERS

0 2 MILES

Base from U.S. Geological Survey 

National Elevation Dataset 5-meter 

digital elevation model

Preliminary Observations

• Regional-scale mineral classification maps generally show lithologic changes 

consistent with previous geologic mapping.

• Mineralogical groupings are consistent at the regional (6 m) to outcrop scales 

(30 cm), with greater detail at the hand-specimen scale (500 μm).

• Differences in wavelength positions for muscovite (different chemistry) can 

correspond to certain geologic units and general proximity to known porphyry 

occurrences.

• Preliminary observations in this study support past studies which indicated 

that hyperspectral data can be used as a tool to identify surface signatures 

of undiscovered mineral resources.

By Raymond F. Kokaly, Garth E. Graham, Todd M. Hoefen, Karen D. Kelley, 

Michaela R. Johnson, and Bernard E. Hubbard

For more information, contact: 

U.S. Geological Survey

Director, Crustal Geophysics and Geochemistry Science Center

Box 25046, MS 973 Denver Federal Center

Denver, CO 80225

http://crustal.usgs.gov

Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement 

by the U.S. Government.

References Cited

Clark, R.N., Swayze, G.A., Wise, R.A., Livo, K.E., Hoefen, T.M., 

Kokaly, R.F., Sutley, S.J., 2007, USGS digital spectral library 

splib06a: U.S. Geological Survey, Digital Data Series 231, 

http://speclab.cr.usgs.gov/spectral.lib06.

Harraden, C.L., McNulty, B.A., Gregory, M.J., and Lang, J.R., 2013, 

Shortwave infrared spectral analysis of hydrothermal alteration associated with the pebble porphyry copper-gold-molybdenum deposit, 

Iliamna, Alaska: Economic Geology, v. 108, no. 3, p. 483–494.

Laakso, K., Rivard, B., Peter, J.M., White, H.P., Maloley, M., Harris, 

J., and Rogge, D., 2015, Application of airborne, laboratory, and 

field hyperspectral methods to mineral exploration in the Canadian 

Arctic—Recognition and characterization of volcanogenic massive sulfide-associated hydrothermal alteration in the Izok Lake 

deposit area, Nunavut, Canada: Economic Geology, v. 110, no. 4, 

p. 925–941.

ISSN 2327-6916 (print) ISSN 2327-6932 (online) http://dx.doi.org/10.3133/fs20163029