Prepared in cooperation with the Bureau of Land Management

Color Shaded-Relief and Surface-Classification Maps 

of the Fish Creek Area, Harrison Bay Quadrangle, 

Northern Alaska

By John C. Mars, Christopher P. Garrity, David W. Houseknecht, 

Lee Amoroso, and Donald C. Meares

Explanatory text to accompany

Scientific Investigations Map 2948

2007

U.S. Department of the Interior

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

DIRK KEMPTHORNE, Secretary

U.S. Geological Survey

Mark D. Myers, Director

U.S. Geological Survey, Reston, Virginia: 2007

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Suggested citation:

Mars, J.C., Garrity, C.P., Houseknecht, D.W., Amoroso, Lee, and Meares, D.C., 2007, Color shaded-relief and surfaceclassification maps of the Fish Creek area, Harrison Bay quadrangle, northern Alaska: U.S. Geological Survey Scientific Investigations Map 2948, available only online at http://pubs.usgs.gov/sim/2007/2948/.
iii

Contents

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

Data and Methods .........................................................................................................................................1

Landsat 7 ETM+ Data ...........................................................................................................................1

IFSAR Data.............................................................................................................................................5

Color Shaded-Relief, and Shaded-Relief Surface-Classification Maps......................................5

Interpretation..................................................................................................................................................8

Surface-Classification Map ................................................................................................................8

Shaded-Relief Surface-Classification Map....................................................................................13

Conclusions...................................................................................................................................................14

References Cited..........................................................................................................................................14

Figures

1. Landsat Multispectral Scanner false-color composite image of the Fish Creek 

study area, National Petroleum Reserve, Alaska (NPRA) .....................................................2

2. Graph showing U.S. Geological Survey laboratory spectra of live vegetation, dead 

vegetation, quartz sand, and montmorillonite..........................................................................3

3. Graph showing Landsat 7 ETM+ image-reflectance spectra from the NPRA study 

area of water, ice, and vegetation and sediment....................................................................3

4. Landsat 7 ETM+ false-color composite images of the eastern part of NPRA....................4

5. Landsat 7 ETM+ image-reflectance spectrum of green vegetation from NPRA...............5

6. Images used to identify landforms and associated spectra .................................................6

7. An example of matched filtering results on two lake-sediment image spectra from 

NPRA...............................................................................................................................................7

8. Block diagrams of the Nuiqsut area in eastern NPRA...........................................................8

9. Block diagrams of freeze-thaw lakes in eastern NPRA.........................................................9

10. Offset and nonoffset averaged image spectra of spectral units from NPRA 

Landsat 7 ETM+ dataset............................................................................................................10

11. Photograph of water, sandy mud, and dead and green grass taken in the western 

part of the NPRA study area .....................................................................................................11

12. Laboratory and field spectra from NPRA................................................................................12

13. Photographs of vegetation on dunes in NPRA ......................................................................13

14. Photograph of a wind blowout feature in NPRA that consists of greater than 

95 percent quartz sand ..............................................................................................................14

15. Photograph of a slightly muddy sand deposit at a drained freeze-thaw lake and 

blowout feature in the western part of the NPRA study area.............................................14

16. Photograph of dead and green vegetation cover in a river valley in the eastern 

part of the NPRA study area .....................................................................................................14
iv

Sheets

1. Color shaded-relief map

2. Surface-classification map

3. Combined shaded-relief and surface-classification map
1

U.S. Geological Survey, Reston, VA 20192

2

U.S. Geological Survey, Flagstaff, AZ 86001

3

Bureau of Land Management, Fairbanks, AK 99709

Color Shaded-Relief and Surface-Classification Maps 

of the Fish Creek Area, Harrison Bay Quadrangle, 

Northern Alaska

By John C. Mars,1 Christopher P. Garrity,1 David W. Houseknecht,1

Lee Amoroso,2 and Donald C. Meares3

(sheet 3). Remote sensing datasets that were used to compile 

the maps include Landsat 7 Enhanced Thematic Mapper+ 

(ETM+), and interferometric synthetic aperture radar (IFSAR) 

data. In addition, a 1:250,000-scale geologic map of the Harrison Bay quadrangle, Alaska (Carter and Galloway, 1985, 

2005) was used in conjunction with ETM+ and IFSAR data.

Data and Methods

Landsat 7 ETM+ Data

The Landsat 7 ETM+ radiance-at-the-sensor data were 

acquired on June 6, 2003, and consist of (1) six bands at 30 m 

resolution in the 0.4 to 2.5 micrometer (μm) region, (2) one 

band at 90 m resolution centered at 11.45 μm, and (3) one 

panchromatic band at 15 m resolution. The thermal-infrared 

and panchromatic bands were not used in this study. The 

Landsat 7 ETM+ scene was calibrated to reflectance using 

an ENVI (Environment for Visualizing Images) reflectance 

algorithm (Research Systems Incorporated, 2000). Evaluation 

of the reflectance data indicated that values in bands 1 through 

4 were anomalously high; therefore, a dark-object subtracton 

method (Crain, 1971) was used to correct for the optical scattering of light in bands 1 through 4. A subset of the calibrated 

reflectance data was then extracted to cover the NPRA study 

area.

Spectral analysis of training areas was used to define 

spectral map units, which are referred to in this report as 

“spectral units.” Landsat 7 ETM+ data were used to identify 

specific materials, or mixtures of materials, on the basis of 

their spectral characteristics and field data obtained from the 

study area in July 2004. Laboratory spectra (resampled to 

Landsat 7 ETM+ bandpasses) of typical materials found at 

NPRA (such as green vegetation, quartz sand, dead vegetation, 

and montmorillonite clay) have distinct spectral signatures that 

can be mapped using spectral shape-fitting algorithms (fig. 2). 

Image spectra used to define spectral units contain mixtures of 

green vegetation, quartz sand, dead vegetation, and clay and 

Introduction

The northeastern part of the National Petroleum Reserve 

in Alaska (NPRA) has become an area of active petroleum 

exploration during the past five years. Recent leasing and 

exploration drilling in the NPRA requires the U.S. Bureau of 

Land Management (BLM) to manage and monitor a variety of 

surface activities that include seismic surveying, exploration 

drilling, oil-field development drilling, construction of oilproduction facilities, and construction of pipelines and access 

roads. BLM evaluates a variety of permit applications, environmental impact studies, and other documents that require 

rapid compilation and analysis of data pertaining to surface 

and subsurface geology, hydrology, and biology. In addition, 

BLM must monitor these activities and assess their impacts on 

the natural environment. Timely and accurate completion of 

these land-management tasks requires elevation, hydrologic, 

geologic, petroleum-activity, and cadastral data, all integrated 

in digital formats at a higher resolution than is currently available in nondigital (paper) formats.

To support these land-management tasks, a series of 

maps was generated from remotely sensed data in an area of 

high petroleum-industry activity (fig. 1). The maps cover an 

area from approximately latitude 70°00' N. to 70°30' N. and 

from longitude 151°00' W. to 153°10' W. The area includes 

the Alpine oil field in the east, the Husky Inigok exploration 

well (site of a landing strip) in the west, many of the exploration wells drilled in NPRA since 2000, and the route of a 

proposed pipeline to carry oil from discovery wells in NPRA 

to the Alpine oil field. This map area is referred to as the “Fish 

Creek area” after a creek that flows through the region.

The map series includes (1) a color shaded-relief map 

based on 5-m-resolution data (sheet 1), (2) a surface- classification map based on 30-m-resolution data (sheet 2), and 

(3) a 5-m-resolution shaded relief-surface classification map 

that combines the shaded-relief and surface-classification data 
2 Color Shaded-Relief and Surface Classification Maps of the Fish Creek Area, Harrison Bay Quadrangle, Northern Alaska Teshekpuk Lake

Beaufort

Sea

F si h

Creek

oC vl il el

vi R

r e

11,13

14, 15

16

70°30'70°00'

151°00' 153°00'

70°30'70°00'

151°00' 153°00'

ALASKA

MAP LOCATION

0 20 10

KM

Figure 1. Landsat Multispectral Scanner false-color composite image (red=6, green=5, blue=4) of the Fish Creek study area, National Petroleum Reserve, Alaska (NPRA). Numbered points represent locations of photographs in this report (figs. 11, 13, 14, 15, 16). NPRA border is shown in white. Ice in some of the freeze-thaw lakes appears white in the image. Shallow or turbid water in some of the freeze-thaw lakes appears light green in the image. Meter grid based on Alaska Albers Equal Area coordinate system.
thus have spectral signatures that consist of multiple spectral 

features.

Freeze-thaw lakes still contained a substantial amount 

of ice as well as water when the image was acquired in June 

2003. ETM+ reflectance image spectra of sediment, vegetation, water, and ice show that bands 1 through 4 have low 

reflectance whereas band 5 (1.65 μm) reflectance for ice and 

water are lower, than the band 5 reflectance for vegetation and 

sediment (fig. 3). Thus, on the basis of water, ice, and vegetated sediment spectra, a threshold of ETM+ band 5 was used 

to map the freeze-thaw lakes and other water and ice bodies 

(fig. 4).

An image spectrum from the study area and a resampled 

spectrum of green vegetation from a spectral library both indicate a chlorophyll absorption feature at 0.66 μm (figs. 2 and 

5). A Landsat 7 ETM+ band ratio of 4:3 produces an image 

with high digital number (DN) values where there are relatively strong chlorophyll absorption features; thus, the green 

vegetation spectral unit was mapped by applying a threshold to 

an ETM+ band ratio 4:3 image. Areas classified as the green 

vegetation spectral unit contained more than 50 percent green 

vegetation on the basis of field observations.

In order to map additional surficial units, a false-color-composite (red=band 7, green=band 4, blue=band 2) ETM+ image 

was assessed to select image spectra (fig 6A). Due to high spectral 

contrast, an ice and water mask was applied to the false-colorcomposite image to improve spectral variability. Spectral units 

other than green vegetation and water were defined by examining 

the spectral characteristics of image spectra (fig. 6B) associated 

with specific geomorphic features such as dunes, river bars, and 

lake shorelines (fig. 6A). Selection of specific landforms was 

based on inferred sediment types associated with the depositional 

environment that produced the landform, and from the 1:250,000-

scale engineering geologic map of the study area (Carter and 

Galloway, 1985, 2005; Boggs, 1995; fig. 6A). Approximately 20 

image spectra were selected from the false-color-composite iceand water-masked image. Interpreted spectral units using this process include (1) vegetated, dry sand from linear ridges; (2) clean 

sand from active dunes around freeze-thaw lakes; (3) muddy sand 

from sand bars in rivers; and (4) wet, vegetated, sandy mud from 

lake sediments in freeze-thaw lakes (fig. 6A, B).

Matched filtering, an algorithm for detecting target spectra in the presence of spectral mixtures (Harsanyi and Chang, 

QUARTZ SAND

0.5 1.0 1.5 2.0

REFLECTANCE (OFFSET FOR CLARITY)

MONTMORILLONITE

DEAD VEGETATION

GREEN VEGETATION

10% REFLECTANCE

LANDSAT 7 ETM+ BANDS

1 2 3 4 5 7

WAVELENGTH (MICROMETERS)

Figure 2. Graph showing U.S. Geological Survey laboratory 

spectra (Clark and others, 2003) of live (green) vegetation, dead 

(brown) vegetation, quartz sand, and montmorillonite, resampled 

to Landsat 7 ETM+ bandpasses.

WATER

ICE

VEGETATION AND

SEDIMENT

BAND 5

0.5

15

10

25

20

35

30

40

1.0 1.5 2.0

DIGITAL NUMBER (OFFSET FOR CLARITY)

LANDSAT 7 ETM+ BANDS

1 2 3 4 5 7

WAVELENGTH (MICROMETERS)

Figure 3. Graph showing Landsat 7 ETM+ image-reflectance 

spectra from the NPRA study area of water (blue line), ice (green 

line), and vegetation and sediment (red line). Band 5 illustrates 

more spectral contrast between water and ice and vegetation and 

sediment than all other Landsat 7 ETM+ bands.

Data and Methods 3
A

KM

0 10

70°30'

70°00' 70°00'

70°30'

153°00' 150°00'

153°00' 150°00'

B

KM

0 10

70°30'

70°00' 70°00'

70°30'

153°00' 150°00'

153°00' 150°00'

Figure 4. Landsat 7 ETM+ (red=7, green=4, blue=2) false-color composite images of the eastern part of NPRA. A. Unmasked image. 

B. With water and ice masked out using a band 5 threshold. Ice and water have high and low reflectance, respectively, which tends 

to obscure land features that have moderate reflectance. Thus, it is easier to see land features in the water and ice masked image B

than the unmasked image A.

4 Color Shaded-Relief and Surface Classification Maps of the Fish Creek Area, Harrison Bay Quadrangle, Northern Alaska
Figure 5. Landsat 7 ETM+ image-reflectance spectrum of green 

vegetation from NPRA illustrating low reflectance for band 3 

(0.66 μm) and high reflectance for band 4 (0.82 μm). The 0.66 μm 

absorption feature is caused by chlorophyll. DIGITAL NUMBER BAND 4

15

10

25

20

30

0.5 1.0 1.5 2.0 BAND 3

LANDSAT 7 ETM+ BANDS

1 2 3 4 5 7

WAVELENGTH (MICROMETERS)

1994; Farrand and Harsanyi, 1997), was used with the image 

spectra to produce a series of gray-scale images (figs. 6B and 

7). The images were qualitatively assessed for spatial coherence and accuracy (fig. 7). Four images were selected and 

interpreted to represent mixtures of sediment, water, and vegetation on the basis of (1) their spectral properties, (2) similar 

distribution in relation to lithologic units of the geologic map 

(Carter and Galloway, 1985, 2005), (3) a 5-m-resolution digital-terrain model of the IFSAR data, and (4) the water-masked 

false-color-composite RGB Landsat 7 ETM+ image (figs. 2, 3, 

5, and 6; sheet 1). A threshold was applied to each gray-scale 

image to remove noise, poor matches, and similarly mapped 

pixels. The remaining pixels in the gray-scale images were 

converted to vectors. The vector spectral units for each processed image were then combined on a Landsat band 5 image 

to produce a provisional surface-classification map.

The provisional surface-classification map was tested in 

the field for consistency and accuracy of the spectral units with 

respect to surficial material assignments such as sediment, 

water, and vegetation content. A FieldSpec Pro4 field spectrometer was used to collect reflectance spectra in the field and 

was used to collect reflectance spectra from field samples in 

the laboratory. The field spectrometer collects reflectance data 

at 1-nanometer spacing from 0.35 μm to 2.5 μm. A comparison of field spectra with lab spectra from selected calibration 

sites consisting primarily of windblown quartz sands indicated that no additional calibration of the ETM+ dataset was 

necessary. The field and lab spectra also were compared to the 

image spectra for evaluation of material content and accuracy 

of spectral units.

IFSAR Data

The IFSAR data used in the study were collected by the 

STAR-3i5 airborne synthetic aperture radar system. STAR-3i 

is a high-resolution, single-pass, across-track IFSAR system, 

which uses two apertures to collect data from the surface. The 

path-length difference between the apertures for each image 

point, along with the known aperture distance, is used to determine the topographic height of the terrain. The IFSAR system 

is capable of collecting data with a vertical accuracy of less 

than 1 m and a horizontal accuracy of less than 3 m.

Data are delivered as three core products: orthorectified 

radar images (ORRIs), digital surface models (DSMs), and 

digital terrain models (DTMs). ORRIs are 8-bit grayscale 

GeoTIFF images that show the radar reflectance intensity of 

various earth surface materials. These images are commonly 

used to identify and extract drainage networks and cultural 

features such as pipelines, roads, and buildings. The ORRIs 

used in this study had a pixel size of 1.25 m and a horizontal 

accuracy of 2.5 m. The DSMs, or “first-return” elevation data, 

display the first surface on the ground that the radar strikes. 

These images consist of measured points collected by the sensor, including the z-values of structures (for example, heights 

of building and towers) and vegetation (for example, trees and 

crops). These elements are removed from the DSM through 

filtering techniques to create a DTM. The DTMs, or “baldearth” elevation data, are similar to digital elevation models 

(DEMs) in that nonterrain elements are absent (fig. 8). Unlike 

the regular array of elevation values that are characteristic of 

a DEM, however, a DTM defines topographic elements by 

irregularly spaced breaklines, or abrupt changes in surface 

smoothness, such as shorelines, roads, streams, and slope 

breaks. The result is a more accurate depiction of the terrain, 

which is useful for contouring, triangulated irregular network 

(TIN) calculations, and other terrain modeling (fig. 9).

Color Shaded-Relief, and Shaded-Relief 

Surface-Classification Maps

Computer-based analytical hillshading has become a 

widely used tool to visualize three-dimensional topography 

4

Fieldspec Pro—Analytical Spectral Devices, Inc., 5335 Sterling Drive, Suite 

A, Boulder, CO 80301 USA

5

Star-3i—Intermap Technologies, Inc., 8310 South Valley Highway, Suite 400 

Englewood, CO 80112-5847 USA

Data and Methods 5
6 Color Shaded-Relief and Surface Classification Maps of the Fish Creek Area, Harrison Bay Quadrangle, Northern Alaska

A

Vegetated

Stable Dunes

Active Dunes Lake Sediments Vegetated Fluvial Sands

Stable Dunes

Lake Sediments

VEGETATED

STABLE DUNES

ACTIVE DUNES

LAKE SEDIMENTS

0.5 1.0 1.5 2.0

DIGITAL NUMBER (OFFSET FOR CLARITY)

LANDSAT 7 ETM+ BANDS

1 2 3 4 5 7

FLUVIAL SANDS

B WAVELENGTH (MICROMETERS)

153°10'

70°15'

153°00'

153°10' 153°00'

KM

0 2

KM

0 2

152°25' 152°15'

152°25' 152°15'

70°15'

Figure 6. Images used to identify landforms and associated spectra. A, Water- and ice-masked Landsat 7 ETM+ false-color composite 

(red=7, green=4, blue=2) images used to identify landforms such as lakes, active and vegetated dunes, and fluvial systems. B, Image 

spectra selected from the identified landforms in part A.
Figure 7. An example of matched filtering results on two lakesediment image spectra from NPRA (in the far left of the imagery 

shown on plates 1-3). White pixels represent a good match of the 

input spectrum to image spectra and black pixels represent a 

poor match. A, Matched filter image has poor spatial coherence 

in mapping lake sediments and has incorrectly mapped the Pik 

Dunes. B, Matched filter image has correctly mapped the lakes 

sediments and has good spatial coherence.

A

Pik Dunes

70°15'

153°15' 153°05'

KM

0 2 Lake

Sediments

B

Pik Dunes

70°15'

153°15'

153°15' 153°05'

153°05'

KM

0 2 Lake

Sediments

motion in the aircraft caused visible ripples in the image. A 

regular banding pattern was apparent along the sensor swath 

boundaries when the elevation data were viewed at small 

scales. Even with these flaws, the DSMs and DTMs derived 

from the IFSAR system proved to be an excellent data source 

for generating the shaded-relief and surface-classification 

images (Garrity, 2004).

Large-scale (1:20,000) shaded-relief images were generated from DTM data in order to identify the location of surficial objects with greater accuracy. The initial image rendering 

revealed potential challenges related to the portrayal of surface 

features in an area devoid of any significant relief. When rendered with no vertical exaggeration, the shaded-relief image 

was essentially flat. Using a 5× vertical exaggeration, the 

shadows appeared blocky at the desired map scale. To give the 

landscape images a more natural appearance, DTM surfaces 

were slightly “bump-mapped” (Garrity, 2004). Because the 

images were used for surface delineation, a random height 

map was unsuitable for fear of compromising data integrity. 

Instead, the corresponding DSM was used as a height map 

to generate the bumped surface. The natural “roughness” of 

the DSM gave the hillshaded DTM a subtle texture without 

obscuring the DTM surface with unnecessary detail.

Removal of the jagged shading was accomplished by 

generalizing the raw data grids, isolating their shaded pixels, 

and then merging the generalized shaded layer with the original grid. In order to generalize the raw data grids, softening 

techniques were applied in ArcInfo Workstation6 using focal 

functions. The amount of softening was regulated by adjusting 

the neighborhood configuration (shape, size) in the focal command. In all instances, a very small neighborhood was used 

and care was taken not to overgeneralize the softened grids. 

The isolation of shaded pixels within the generalized grids 

was accomplished in Adobe Photoshop7 by using the “Curves” 

tool; the remaining pixels were converted to white. Finally, 

the adjusted softened hillshade and the original hillshade 

were merged by multiplying the two images in Photoshop. 

Multiplying the images caused the converted white pixels of 

the softened image to drop out, which in turn resulted in a 

softening effect that was restricted to the darkest shades of the 

merged image. The final resulting hillshade image has a more 

natural appearance, devoid of any jagged shading.

The 30-m-resolution surface-classification map was 

resized to match the dimensions of the 5-m-resolution DTM 

hillshade image in ERDAS Imagine8. A median filter was 

applied to the 30-m-resolution dataset to minimize pixelization 

when the image is viewed at full resolution. Datasets were fused 

(merged) using raster arithmetic operators in ArcGrid6 and then 

downsampled appropriately based on the desired map scale.

6

ArcInfo Workstation and ArcGrid—Environmental Systems Research 

Institute, Inc., (ESRI), 380 New York Street, Redlands, CA 92373 USA

7

Photoshop—Adobe Systems Inc., 345 Park Avenue, San Jose, CA 95110-

2704 USA

8

ERDAS Imagine—Earth Resources Data Analysis System, Leica Geosystems 

Geospatial Imaging, LLC, 5051 Peachtree Corners Circle, Norcross, GA 

30092-2500 USA

on a two-dimensional surface. Unlike manually produced 

shaded-relief maps, analytical hillshade images often reveal 

imperfections in the elevation data used to render the image. 

The IFSAR data contained flaws in some areas because excess 

Data and Methods 7
8 Color Shaded-Relief and Surface Classification Maps of the Fish Creek Area, Harrison Bay Quadrangle, Northern Alaska

Interpretation

Surface-Classification Map

The spectral units of the ETM+ surface-classification 

map are as follows: water (blue); green vegetation (green): 

dry, vegetated sand (yellow); wet, vegetated, sandy mud (red); 

clean sand (white); and muddy sand (cyan) (sheet 2). In addition, unclassified pixels (black) are shown on the classification 

map.

The average spectrum of the wet, vegetated, sandy mud 

spectral unit (red on the surface-classification map) has a 

strong band 3 absorption feature, a high reflectance in band 5, 

a low reflectance in band 7, and a relatively low albedo when 

compared to other spectral units (fig. 10). Field observations 

indicate that the wet, vegetated, sandy mud spectral unit consists of approximately 45 to 50 percent water, approximately 

45 to 50 percent dead or senescent (brown, no chlorophyll 

present) and live (green, chlorophyll present) grass, and less 

than 5 percent sandy mud at the surface (fig. 11). Field spectra 

resampled to ETM+ bandpasses are very similar to the image 

spectra that were taken from parts of the image classified as 

wet, vegetated, sandy mud. The field spectra illustrate a strong 

(0.7 μm) chlorophyll absorption feature, and low reflectance in 

the 2.0 μm to 2.5 μm region due to cellulose absorption from 

dead and live vegetation, and from water (fig. 11; red curve on 

fig. 12). The field spectra also have a lower albedo than other 

field spectra due to the presence of water (figs. 3 and 11). 

Figure 8. Block diagrams of the Nuiqsut area in eastern NPRA. A, Digital surface model (DSM) draped with an orthorectified radar 

image (ORRI). B, Digital terrain model (DTM) draped with an ORRI. The DSM displays all of the measured points collected by the sensor, 

as illustrated by the irregular elevation spikes in the diagram. The nonterrain elements are removed through filtering in order to create a 

DTM. The DTM simulates a “bald-earth” and is traditionally preferred over a DSM for topographic mapping purposes (Garrity, 2004).

A

B
Thus, the spectral characteristics of the wet, vegetated, sandy 

mud spectral unit are due to a mixture of live (green) and dead 

or senescent (brown) vegetation (low reflectance in bands 3 

and 7, respectively), and water (relatively low albedo compared to other spectral units).

The average spectrum of the dry, vegetated sand spectral 

unit has a slight chlorophyll absorption feature, high band 5 

reflectance, low band 7 reflectance, and the highest albedo of 

all of the spectral units (fig. 10). Field data indicate that the 

dry, vegetated sand unit consists of approximately 20 percent 

live (green) vegetation, 75 percent dead (brown) vegetation, 

and less than 5 percent bare sand (fig. 13). In some areas (such 

as on parabolic dunes), the dry, vegetated sand spectral unit 

consists of up to 20 percent lichen and up to 20 percent bare 

sand (fig. 13). A field spectrum illustrates a 0.7-μm (ETM+ 

band 3) chlorophyll absorption feature. The field spectrum 

also illustrates high reflectance in the 1.4- to 1.8-μm region 

and low reflectance in the 2.0- to 2.5-μm region, which is 

due to cellulose absorption (figs. 12A and 10, purple curve). 

Quartzose sand also has high reflectance in the 1.4 to 1.8 

μm region (figs. 2 and 10), which is partially responsible for 

the high band 5 reflectance of image spectra from the dry, 

vegetated sand spectral unit. The spectral characteristics of the 

dry, vegetated sand spectral unit are due to small amounts of 

live (green) vegetation mixed with large amounts of dead or 

senescent (brown) vegetation, sand, and lichen.

Spectral comparisons of an averaged field spectrum 

(n=15) resampled to Landsat 7 ETM+ bandpasses and the 

average image spectrum of the dry, vegetated sand spectral 

unit indicate that band 5 and band 3 reflectances are lower for 

field spectra; however, the overall spectral shapes are similar (fig. 12B). The field spectrum of dry, vegetated sand is 

similar to the field spectra taken from an area that is classifed 

as wet, vegetated sandy mud (fig. 12). The lower band 3 and 

band 5 reflectance values of the field spectrum resampled to 

Landsat 7 ETM+ bandpasses are due to the seasonal changes 

in vegetation. The field spectra were recorded in midsummer 

when there was more live (green) vegetation than in early 

Figure 9. Block diagrams of 

freeze-thaw lakes in eastern 

NPRA. Both are digital terrain 

models (DTMs) draped with 

orthorectified radar images 

(ORRIs). A, DTM draped with a 

grayscale ORRI. B, DTM draped 

with colorized ORRI. The colors 

were derived from Landsat 7 

ETM+ image data which were 

processed to identify surface 

materials.

A

B

Interpretation 9
10 Color Shaded-Relief and Surface Classification Maps of the Fish Creek Area, Harrison Bay Quadrangle, Northern Alaska GREEN VEGETATION CLEAN SANDS WET, VEGETATED, SANDY MUD 0.5 1.0 1.5 2.0

DIGITAL NUMBER (OFFSET FOR CLARITY)

LANDSAT 7 ETM+ BANDS

1 2 3 4 5 7

SLIGHTLY MUDDY SANDS

DRY, VEGETATED SAND

30252015

GREEN

VEGETATION

CLEAN

SANDS

WET, VEGETATED,

SANDY MUD

SLIGHTLY MUDDY

SANDS

DRY, VEGETATED SAND

0.5 1.0 1.5 2.0

DIGITAL NUMBER (NO OFFSET)

LANDSAT 7 ETM+ BANDS

1 2 3 4 5 7

WAVELENGTH (MICROMETERS) WAVELENGTH (MICROMETERS)

A

B

Figure 10. Offset and nonoffset averaged image spectra of spectral units from NPRA Landsat 7 ETM+ dataset . 

A, Averaged image spectra offset for comparison of spectral 

features. 

B, The same averaged image spectra not offset in order to compare albedo.
Figure 11. Photograph of water, sandy mud, and dead and green 

grass taken in the western part of the NPRA study area. The area 

shown in the photograph is mapped as wet, vegetated, sandy 

mud and is shown by red pixels on sheets 2 and 3. See figure 1 for 

location of this photograph. Lens cap in lower left of photograph is 

for scale. Photograph by John C. Mars.

summer when the Landsat 7 ETM+ data were acquired. Green 

vegetation contains more chlorophyll and water than dead or 

senescent (brown) vegetation, and results in lower reflectance 

in bands 3 and 5 (figs. 2, 12, and 13). The increase in green 

vegetation later in the summer indicates that the acquisition of 

data by visible to short-wave infrared detectors needs to occur 

in early summer when vegetation is still senescent.

The average image spectrum for the clean sand spectral 

unit has a small chlorophyll absorption feature, high reflectance in bands 5 and 7, and lower albedo when compared to 

the average dry, vegetated sand spectrum (fig. 10). The lower 

albedo of the clean sand average image spectrum may be due 

to surface moisture at the time of the Landsat data acquisition. 

Field observations indicate that the clean sand spectral unit 

primarily consists of quartz sand (approximately 95 percent) 

and minor amounts (less than 5 percent) of live (green) vegetation, silt, and clay (fig. 14). Quartz is spectrally flat and has 

high reflectance in the 1.5- to 2.4-μm region (fig. 2). Laboratory spectra of sand samples from dune blowout features indicate high reflectance, from 1.5 to 2.5 μm, with slight 2.20 μm 

and 2.35 μm absorption features due to muscovite (fig. 12). 

The high reflectance of bands 5 and 7 in the average spectrum 

of the clean sand spectral unit is due to the high percentage of 

quartz (greater than 95 percent; figs. 2 and 12).

The slightly muddy sand spectral unit has a more intense 

absorption of band 7 than the clean sand spectral unit, and a 

less intense chlorophyll absorption and higher albedo than the 

wet, vegetated, sandy mud spectral unit (fig. 10). Laboratory 

spectra of all of the sands from the study area have a 2.2-μm 

absorption feature that is typically associated with either 

muscovite, illite, or montmorillonite (figs. 2 and 10). Field 

observations also indicate that there is slightly more (greater 

than 5 percent) silty mud and green vegetation in the muddy 

sand spectral unit than in the clean sand spectral unit (fig. 15) 

but less than in the wet, vegetated, sandy mud spectral unit. 

The greater percentage of clay and (or) muscovite and green 

vegetation accounts for the deeper band 7 absorption feature in 

the slightly muddy sand unit than observed in the clean sand 

spectral unit.

Some pixels did not correlate with any of the spectral unit 

classifications. Field investigations in June 2004 indicated that 

some of the nonclassified areas contained equal amounts of 

green and dead or senescent vegetation and up to 10 percent 

standing water (fig. 16). It was not possible to determine if 

this is a separate classification unit. Many of the nonclassified 

pixels have different spectral signatures; therefore, several 

different spectral classes may be grouped in the nonclassified 

category.

A single spectral unit, a combination of spectral units, or 

a combination of spectral units and nonclassified pixels form 

patterns that define eolian, fluvial, deltaic, and marine depositional facies (sheet 2). The dry, vegetated sand spectra primarily correspond to deposits of eolian and marine sand located 

in the northwestern part of the study area. The wet, vegetated, 

sandy mud spectral unit primarily defines the muddier lake 

sediments of freeze-thaw lakes. Clean sand and muddy sand 

spectral units correlate with recent sand bodies, including 

eolian blowout features, fluvial sand bars, and deltaic deposits. A mixture of (1) dry, vegetated sand, (2) green vegetation 

spectral units, and (3) unclassified pixels define flood-plain 

deposits in the eastern part of the study area.

On the basis of both field and spectral data, most of the 

spectral characteristics observed in the NPRA imagery are due 

to water, quartz sand, silty mud, live (green) vegetation, and 

dead or senescent (brown) vegetation. The primary spectral 

controls on the dry, vegetated sand are mixtures of live (green) 

and dead or senescent (brown) vegetation. The freeze-thaw 

lake sediment’s spectral characteristics are primarily due to 

mixtures of live (green) vegetation, dead or senescent (brown) 

vegetation and water. The clean sand spectral unit’s characteristics are due primarily to quartz sand, and the muddy sand 

Interpretation 11
12 Color Shaded-Relief and Surface Classification Maps of the Fish Creek Area, Harrison Bay Quadrangle, Northern Alaska Figure 12. Laboratory and field spectra from NPRA. 

A, (1) Laboratory spectrum of sand from the Pik Dunes; the 2.2 μm absorption feature is caused by less than 5 percent 

muscovite content of the sand. (2) An averaged spectrum produced from 20 field spectra of lake vegetation, sediment, and water; the red line represents the wet, vegetated, sandy mud spectral unit; vegetation spectral characteristics include the 0.66 μm absorption feature, which is caused by chlorophyll and the 2.165 μm and 2.3 μm absorption features, which are caused by cellulose. (3) An averaged spectrum using 10 field spectra collected from an eolian dune that was covered by green and dead vegetation, bare soil, and lichen; the purple line represents the dry, vegetated sand spectral unit; chlorophyll and cellulose absorption features due to vegetation are present in the spectra. 

B, (1) 

Spectrum 1 from part 

A resampled to Landsat 7 ETM+ bandpasses. (2) Spectrum 2 from part 

A resampled to Landsat 7 ETM+ bandpasses. (3) Spectrum 3 from part 

A resampled 

to Landsat 7 ETM+ bandpasses.

10% REFLECTANCE

1–Sand from the Pik Dunes

2.2 μm feature

(muscovite)

2.35 μm feature

(muscovite)

2–Water, mud, and vegetation

from a freeze-thaw lake

3–Green and dead vegetation, bare soil,

and lichen from a vegetated dune

0.5 1.0 1.5 2.0

A WAVELENGTH (MICROMETERS)

REFLECTANCE (OFFSET FOR CLARITY)

1–Sand from the Pik Dunes

2–Water, mud, and vegetation

from a freeze-thaw lake

3–Green and dead vegetation, bare soil,

and lichen from a vegetated dune

10% REFLECTANCE

0.5 1.0 1.5 2.0

LANDSAT 7 ETM+ BANDS

1 2 3 4 5 7

B

REFLECTANCE (OFFSET FOR CLARITY)

WAVELENGTH (MICROMETERS)
spectral unit’s characteristics are due primarily to quartz with 

small mixtures of silty mud and vegetation.

Shaded-Relief Surface-Classification Map

The shaded-relief surface-classification map was produced by integrating the 5-m resolution IFSAR data with the 

ETM+ surface-classification map (sheet 3). The colors for the 

classification scheme are the same as for the surfaceclassification map; however, light-gray to black hillshading 

from the DTM has been added to enhance surface features.

Specific spectral units, or combinations of spectral units, 

tend to correlate with specific topographic features such as 

eolian and coastal ridges and river valleys. A combination of 

shaded-relief data with the pixels representing dry, vegetated 

sand, sandy mud, and nonclassified pixels define the east- to 

northeast-trending ridges that are capped by parabolic dunes 

in the western part of the study area (sheet 3). Pixels classified 

as dry, vegetated sand primarily cover the ridges and dunes; 

pixels that are nonclassified and pixels classified as wet, sandy 

mud dominate the inter-dune and inter-ridge areas (sheet 3).

River valleys with profiles of less than 10 m dominate 

the eastern part of the study area (sheet 3). Meandering 

channels are slightly incised and the sediments in the active 

channels are classified primarily as clean or slightly muddy 

sands. Adjacent to the active fluvial channels are low-profile 

water- and sediment-filled abandoned channels and freezethaw lakes; these areas are classified as water, or as wet, 

vegetated, sandy mud (sheet 3). Bordering the active and 

abandoned channels are low-relief terraces that are classified 

as dry, vegetated sand (sheet 3). Field investigations indicate 

that reworked windblown fluvial deposits cap some of the 

low-relief terraces. Flanking the low-relief terraces are broad, 

flat, interfluvial plains that are classified as either wet, sandy 

mud, dry, vegetated sand; or green vegetation; these areas also 

include nonclassified pixels (sheet 3). The broad plains contain 

an abundance of sandy mud and water-filled freeze-thaw lakes. 

Some of the lakes and lake deposits tend to form groups of 

northeast-trending, topographically low areas and may indicate 

locations of older abandoned stream and river channels.

The shaded-relief surface-classification map illustrates a 

close relationship between surficial vegetation and topography, 

which may be linked to the availability of water for vegetation 

and the depth to permafrost. The DTM shows that the eolian 

and coastal ridges, which cover the western part of the study 

area, are up to 500 m wide, average 4 km in length, and have 

vertical profiles of approximately 18 m (sheets 1 and 3). Up to 

60 percent of the vegetation on these dunes is either senescent 

or dead (brown to light-gray in color; fig. 14). Vegetation 

types on eolian and coastal ridges include sedges, lichens, 

and mosses (fig. 13). Field investigations show that the flood 

plains and river valleys in the eastern part of the study area 

contain up to 80 percent green vegetation, consisting of 

sedges, dwarf willows and alders, perennials, and mosses (fig. 

16). Thus, field observations suggest that there is significantly 

more moist vegetation and standing water in the river valleys 

than on the eolian and coastal ridges. Shallow pits dug in the 

field indicate that the depth to permafrost is 0.5 to 1.2 m in the 

eolian and coastal dune areas, and 0.2 to 0.4 m in the eastern 

fluvial-dominated areas. The differences in depth to permafrost may be due either to better drainage of the eolian and 

coastal dune sediments, or to a greater exposed surface area of 

the dunes, which would increase the rate of melting permafrost. Because of the extremely dry conditions of less than 10 

cm of precipitation per year (NOAA, 2005), water perched 

above the permafrost is a major source of water for vegetation. 

Thus, an increased depth to permafrost would create more arid 

surface conditions that favor stressed vegetation and organisms, such as lichens, that thrive in relatively arid conditions.

Figure 13. Photographs of vegetation on dunes in NPRA. A, 

Photograph of moss lichen and bare soil on the crest of a parabolic 

dune situated on top of a larger eolian dune in the western part of 

the NPRA study area. Scale at the lower right of photo contains 

vertical dashed lines with centimeters on the left and inches on the 

right. B, Photograph of green and dead sedge on an eolian dune. 

The areas in which parts A and B were photographed are classified 

as vegetated sand spectral units which is the yellow unit on the 

surface classification and shaded-relief surface-classification 

maps, illustrated on sheets 2 and 3, respectively. Lens cap in lower 

left of the photograph is for scale. The location of the photograph is 

shown in figure 1. Photograph by John C. Mars.

A

B

A

B

Interpretation 13
14 Color Shaded-Relief and Surface Classification Maps of the Fish Creek Area, Harrison Bay Quadrangle, Northern Alaska

from lake deposits due to the increase in green vegetation. 

Image spectra of dry, vegetated sand acquired in early summer, however, indicates less green vegetation and has distinct 

spectral features that can be mapped.

The IFSAR and Landsat 7 ETM+ data provide complementary information that enhances the ability to map subtle 

land-cover variations in the NPRA coastal plain. The DTM 

permits identification of landforms using elevation data. The 

surface-classification map is composed of spectral units that 

define different types of sediment, such as wet, sandy mud, 

muddy sand, and clean sand. The spectral units of the surfaceclassification map also define areas of green vegetation, dead or 

senescent vegetation, and water during the early summer. The 

spectral units of the surface-classification map form distinct patterns that highlight geomorphic features such as sediment-filled 

lakes, dune and ridge complexes, and river valleys. The shadedrelief surface-classification map provides a better definition 

of topographic features than the individual datasets due to the 

relationship of topographic features, sediment types, and vegetation cover to water availability; water availability may be due to 

variations in the depth to permafrost for specific landforms.

References Cited

Boggs, Sam, Jr., 1995, Principles of sedimentology and stratigraphy, second edition: Englewood Cliffs, N.J., Prentice 

Hall Inc., 774 p.

Carter, D.L., and Galloway, J.P., 1985, Engineering geologic 

maps of northern Alaska, Harrison Bay quadrangle: U.S. 

Geological Survey Open-File Report 85-256, 47 p., scale 

1:250:000.

Conclusions

Image and field spectra indicate that the acquisition of 

visible and short-wave infrared remote-sensing imagery must 

be done during early summer. In mid- to late summer, green 

vegetation densities increase and obscure some of the spectral 

variability. In this study, the field spectra of dry, vegetated 

sand acquired in mid-summer resemble field spectra taken 

Figure 14. Photograph of a wind blowout feature in NPRA that 

consists of greater than 95 percent quartz sand. Note that there 

are some 1-m-high dunes with vegetation in the background. The 

area in the photograph is classified as the clean sand spectral 

unit, which is the white unit on the surface-classification and 

shaded-relief surface-classification maps, illustrated on sheets 

2 and 3, respectively. The location of the photograph is shown in 

figure 1. Photograph by John C. Mars.

Figure 15. Photograph of a slightly muddy sand deposit (less 

than 95 percent quartz sand) at a drained freeze-thaw lake and 

blowout feature in the western part of the NPRA study area. The 

area in the photograph is classified as the slightly muddy sand 

spectral unit, which is the white unit on the surface-classification 

and shaded-relief surface-classification maps, illustrated on 

sheets 2 and 3, respectively. Notebook for scale. The location of 

the photograph is shown in figure 1. Photograph by John C. Mars.

Figure 16. Photograph of dead and green vegetation cover in a 

river valley in the eastern part of the NPRA study area. Most of the 

green vegetation is dwarf willow. Notebook for scale. This area is 

not classified as a specific spectral unit and is illustrated as the 

black unit on sheets 2 and 3. The location of the photograph is 

shown in figure 1. Photograph by John C. Mars.
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using airborne interferometric synthetic aperture radar 

(IFSAR) North Slope, Alaska, in Soller, D.R., ed., Digital 

Mapping Techniques ‘04—Workshop Proceedings: U.S. 

Geological Survey Open-File Report 2004-1451, p. 47–52.

Harsanyi, J.C., and Chang, C., 1994, Hyperspectral image 

classification and dimensionality reduction—An orthogonal 

subspace projection approach: IEEE Transactions on Geoscience and Remote Sensing, v. 32, p. 770-785.

National Oceanic and Atmospheric Administration, 2005, Precipitation data tables; Ashville, N.C., National Climate Data 

Center, available only online at http://www.ncdc.noaa.gov/oa/

climate/online/ccd/nrmpcp.txt.

Research Systems Incorporated, 2000, 3.4 ENVI users guide: 

Boulder, Colo., Research Systems Inc., 864 p.

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