Land Cover Mapping of the National Park 

Service Northwest Alaska Management Area 

Using Landsat Multispectral and Thematic 

Mapper Satellite Data

By Carl J. Markon and Sara Wesser

Open-File Report 00-51

U.S. Department of the Interior 

U.S. Geological Survey
LAND COVER MAPPING OF THE NATIONAL PARK SERVICE NORTHWEST

ALASKA MANAGEMENT AREA USING LANDSAT MULTISPECTRAL AND

THEMATIC MAPPER SATELLITE DATA

By Carl J. Markon1 and Sara Wesser2

1 Raytheon SIX Corp., USGS EROS Alaska Field Office, 4230 University Drive, 

Anchorage, AK 99508-4664. E-mail: markon@usgs.gov. Work conducted under contract 

#1434-CR-97-40274

2 National Park Service, 2525 Gambell St., Anchorage, AK 99503-2892
Land Cover Mapping of the National Park Service Northwest Alaska 

Management Area Using Landsat Multispectral Scanner and Thematic

Mapper Satellite Data

ABSTRACT

A land cover map of the National Park Service northwest Alaska management area was 

produced using digitally processed Landsat data. These and other environmental data were 

incorporated into a geographic information system to provide baseline information about the 

nature and extent of resources present in this northwest Alaskan environment.

This report details the methodology, depicts vegetation profiles of the surrounding 

landscape, and describes the different vegetation types mapped. Portions of nine Landsat 

satellite (multispectral scanner and thematic mapper) scenes were used to produce a land cover 

map of the Cape Krusenstern National Monument and Noatak National Preserve and to update an 

existing land cover map of Kobuk Valley National Park Valley National Park. A Bayesian 

multivariate classifier was applied to the multispectral data sets, followed by the application of 

ancillary data (elevation, slope, aspect, soils, watersheds, and geology) to enhance the spectral 

separation of classes into more meaningful vegetation types. The resulting land cover map 

contains six major land cover categories (forest, shrub, herbaceous, sparse/barren, water, other) 

and 19 subclasses encompassing 7 million hectares. General narratives of the distribution of the 

subclasses throughout the project area are given along with vegetation profiles showing common 

relationships between topographic gradients and vegetation communities.

INTRODUCTION

In 1980, the Alaska National Interest Lands Conservation Act (Public Law 96-487) was 

enacted to provide and conserve additional federal public lands in Alaska. As part of the Act, the 

National Park Service (NPS) was instructed to manage and protect the natural and cultural 

resources in the area now encompassing the Cape Krusenstern National Monument, Noatak 

National Preserve, and Kobuk Valley National Park. To accomplish the overall management 

activities in this area, the NPS recognized a need for maps showing the location and extent of 

land cover. Originally, only broad or regional land cover classes (Kuchler 1966; Selkregg 1975) 

or use-specific land cover classifications (Soil Conservation Service 1984) existed for the newly 

formed management areas. Parts of Cape Krusenstern National Park and Kobuk Valley National 

Park were' previously mapped by the NPS using Landsat thematic mapper (TM) data (Faeo, 

1993; Wesser, 1994), but the northwest part of Cape Krusenstern National Park was not 

previously mapped and no medium-scale (1:100,000 to 1:250,000) land cover information 

existed for Noatak National Preserve.

After the previous Cape Krusenstern National Park and Kobuk Valley National Park 

mapping efforts were completed, more recent Landsat data (1985-92) became available for the 

entire northwest Alaska management areas. The NPS wanted to update existing maps using the
new satellite data, as well as produce a new medium-scale land cover map of Noatak National 

Preserve and complete the unmapped areas of Cape Krusenstern National Park. As a result, the 

NFS established a cooperative program with the U.S. Geological Survey (USGS). This program 

followed other successful mapping efforts (Markon and Wesser, 1997).

The basis for the cooperative program was founded on previous work by the USGS in the 

northwest Alaska management area (Bering Land Bridge National Preserve, Markon and Wesser, 

1997) and the USGS Multiresolution Land Characterization (MRLC) Program. The MRLC 

involves development of regional-scale land cover information from the analysis of remotely 

sensed data for support of management and research over Federal, State, and privately owned 

lands as well as national and international global change studies (Vogelmann and others, 1998, 

Vogelmann, Sohl, and Howard, 1998, Loveland and Shaw, 1996). The NFS was interested in the 

MRLC process as a possible additional tool to meet future management needs. The NFS also 

wanted to produce a seamless data base for the entire northwest Alaska management area (Bering 

Land Bridge National Preserve, Kobuk Valley National Park, Cape Krusenstern National Park, 

and Noatak National Preserve) using similar data types and methodology. This report 

summarizes the results of the land cover mapping project under the cooperative agreement 

through the MRLC program for the Kobuk Valley National Park, Cape Krusenstern National 

Park, and Noatak National Preserve areas.

PROJECT AREA

The three NPS management units are located in northwest Alaska (fig. 1). The Cape 

Krusenstern National Park and Noatak National Preserve areas lie entirely above the Arctic 

Circle, and the Kobuk Valley National Park is bisected by it. The northern border of Cape 

Krusenstern National Park lies just south of the Wulik River and is bordered on the west by the 

Chukchi Sea. To the south lies Kotzebue Sound, and the eastern border lies west of and parallels 

the Noatak river. The area consists primarily of moist and wet tundra in the lowlands and hills, 

with dryer tundra in the mountains. Large lagoons and wetland complexes are common along the 

western and southern coasts, and sand dunes are common along the southwest coast of Cape 

Krusenstern. Bedrock geology consists primarily of basic (limestone, dolomite, and marble) 

Devonian and Silurian rocks in the southern half and more acidic (sandstone, graywacke, and 

quartzite) Mississippian or Devonian rocks to the north (Biekman, 1980). Soils consist of loamy, 

near-level to rolling Histic Pergelic Cryaquepts (cold, wet soils with a thick organic layer and 

minimal weathering) in low coastal areas along the western and southern coasts to more gravelly 

Pergelic Cryaquepts or Pergelic Cryorthents (cold, wet soils with a thin organic layer and a 

minimal to small amount of leaching) in the central and northern lowlands and uplands 

(including low mountains; Rieger and others, 1979).

The Noatak National Preserve area is predominantly bordered on the north by the Delong 

Mountains, on the south by the Baird Mountains, on the west by the Noatak river (south of 

Kikmiksot Mountain), and on the east by the Schwatka Mountains; the bulk of the preserve lies 

within the Noatak River drainage. Conifers occupy much of the land in the lower wide valleys in 

the western part of the preserve, with high mountain peaks, wide, treeless, hilly valleys, and
Northwest Alaskan Management Area

162 W

Noatak National Preserve

0 . . , , River Valley 

Sisiak Creek _ J Cape Krusenstern 

National Monument

Noatak River

Kobuk National Park

Mountain

Igisukruk 

Kobuk River

Mouth of * 

Noatak River

Figure 1. Location of National Park Service Northwest Alaska management areas.

4
plains in the east. Histic Pergelic Cryaquepts occur over much of the lower slopes along rivers, 

and Pergelic Cryaquolls (cold, wet, somewhat calcareous, more fertile soils with high organic 

carbon) and Typic Cryoborolls (slightly warmer fertile soils) occur on the upper slopes (Rieger 

and others, 1979). River flood plains and associated lower valley slopes in the northern part of 

the preserve are composed of moraines and modified moraines. Coarse- and fine-grained 

surficial deposits occur in the central part of the preserve, dominated by the Noatak River. To 

the east, roughly half of the preserve is composed of undifferentiated eolian, colluvial, fluvial, 

marine, and glacial deposits (Karlstrom and others, 1964).

Contiguous with the south and southeast boundary of the Noatak National Preserve is the 

Kobuk Valley National Park. The Kobuk Valley National Park is bounded roughly by the Baird 

Mountains to the north, the Kallarichuk Hills to the west, the Waring Mountains to the south, and 

the Jade Mountains to the east. The Kobuk Valley National Park River bisects the southern third 

of the park. The Great Kobuk Valley National Park and Little Kobuk Valley National Park Sand 

Dunes are prominent features south of the Kobuk Valley National Park River. Much of the park 

is covered by evergreen and deciduous forests, tall and low shrubland, and tundra. Surficial 

geology includes complex older coastal deposits of interstratified alluvial and marine sediments, 

which include glacial drift and small pockets of eolian silt up to 1.5 meters thick (Karlstrom and 

others, 1964). Soils are primarily composed of loamy, near-level to rolling Histic Pergelic 

Cryaquepfs north of the Kobuk Valley National Park River and sandy Pergelic Cryorthods (cold, 

wet soils with slow leaching) and Pergelic Cryaquepts with dune intrusions to the south (Rieger 

and others, 1979).

Selkregg (1975) and Rieger and others (1979) report the following general vegetation 

types within the northwest Alaska management area: high brush, low brush bog and muskeg, 

moist tundra, alpine tundra and barren ground, and wet tundra. Upland and bottomland spruce 

forests and cottonwood-birch forests occur throughout the Kobuk Valley National Park and in the 

west and southwest parts of Noatak National Preserve, although the extent of deciduous forest in 

Noatak National Preserve is very small in comparison.

The growing season for the area as a whole is generally from mid-May to late August, 

with initial green-up beginning about mid-May in the southern part to mid-June in the northern 

part (Markon and others 1995). The three management areas cross three ecoregions (from north 

to south): Arctic Foothills, Brooks Range, and Interior Forested Lowlands and Uplands (Gallant 

and others, 1995).

METHODOLOGY

Data Base Design

Land cover mapping of the three management areas was performed in a raster digital data 

base format with all data georeferenced to an Albers Equal-Area Conic projection. Minimum 

data resolution for each picture element, or pixel, was 30 m by 30 m. A general schematic 

diagram of the data base development and mapping process is shown in figure 2.
Purchase 

satellite 

data

 ^

Radiometric 

and 

corrections

geometric

 ^> sample 

Systematic 

of 

image

* Matches aerial photographs used.

Ident

analys

Elevation data and derivatives:

- slope 

-illumination

-aspect 

USGS digital data droducts:

-surficial 

-soils 

geology

National Park 

Service

IMA's in 

data 

Arc/Info 

base

 ^-

 

^^

+~

Ancillary data georeferenced to themultispectral data

Data ext 

fore 

IM 

within

 ^ Sfltfil

scei

A* 

ach 

raction 

each 

J

ic

lite 

Tabulation 

of

class 

attributes

Ji

Spectral stratificatio 

and recombination

Identification and

analysis of spectral

classes

M

dif

G

cl

m

Figure 2. Schema for the National Park Service, northwest areas land cover mapping project.

6
Data Acquisition

Parts of nine Landsat scenes (table 1) were used to provide complete coverage of the 

three management areas and surrounding lands. Of the nine scenes, seven were Landsat TM 

mid-summer data sets and two were Landsat multispectral scanner (MSS) data sets obtained in 

late winter. Winter MSS data have proved to be efficient in separating conifer crown closure 

classes (for example, close, open, woodland; Fleming, 1988) and were used in the Noatak 

National Preserve and Kobuk Valley National Park areas.

Table 1. Landsat scene identification, path/row, and dates used for mapping the National Park 

Service northwest Alaska management areas

Sensor

MSS

MSS

TM

TM

TM

TM

TM

TM

TM

Identification

LM5080013008410390

LM5081012008411090

LT 5078012008518710

LT 5078013008520310

LT 50790120086181 10

LT 4080012009221310

LT 4080013009221 3 10

LT 508 10 120090222 10

LT 5081013009022210

Path

80

81

78

78

79

80

80

81

81

Row

13

12

12

13

12

12

13

12

13

Date

April 12, 1984

April 19, 1984

July 06, 1985

July 22, 1985

June 30, 1986

July 31, 1992

July 3 1,1 992

August 10, 1990

August 10, 1990

Prior to digital analysis of the satellite data, NPS personnel selected 54 intensive mapping 

areas (BVIA; Cape Knisenstern National Park - 16, Noatak National Preserve - 26, Kobuk Valley 

National Park - 12) and field visited them during a 4-year period (1988-91). These sample areas, 

which represent land cover types within the project area, correspond to 1:60,000-scale colorinfrared aerial photographs (circa 1979-85), and they were used as training blocks, as described 

by Fleming (1975, 1988), Talbot and others (1986), and Wesser (1994). On each sample area 

photograph, photointerpreted polygons were subjectively drawn around what appeared to be 

homogeneous plant communities. This resulted in a total of 657 polygons (Cape Krusenstern 

National Park - 119, Noatak National Preserve - 380, KOVA - 158). A ground survey of each 

polygon determined vegetation cover, structure, and general species composition information 

using procedures similar to those described by Talbot and Markon (1988) and Faeo (1993).

Preprocessing

Each Landsat scene was reviewed to ensure adequate project area coverage and confirm 

the presence and location of clouds or bad data. The data used for this project were free of line 

drops and cloud cover was minimal. Satellite scenes from the same path and date were 

mosaicked and treated as a single data set during the initial image analysis; the other scenes were 

analyzed individually.

Georeferencing of each Landsat scene was done at the EROS Data Center in Sioux Falls,
S. Dak. The process was started by selecting recognizable points collocated on the Landsat 

scenes and USGS 1:63,360-scale topographic maps. These points were used to define a secondorder, least squares polynomial transformation relating coordinates in an Albers Equal-Area 

Conic projection to the row and column locations within each scene. Displacement effects 

caused by differences in terrain elevation also were addressed. Mean residual errors associated 

with the second-order transformation resulted in a registration accuracy within +/- 1.0 pixel. 

Dimensions and georeferenced coordinates for the project area section are shown in table 2.

Ancillary digital data obtained for the project area included 1:250,000-scale digital 

elevation data (and the derivatives, slope, aspect, and illumination), 1:1,000,000-scale soils, 

l:2,500,000-scale bedrock geology, and l:250,000-scale watersheds. The soils, geology, and 

watershed data sets were digitized and converted to georeferenced raster formats coregistered to 

the raster Landsat data. Although these data are at much coarser resolutions than the Landsat TM 

data, they were the best ancillary information available. In addition to these data, the NFS 

supplied a digitized preserve boundary and other point and polygon data containing vegetation 

information from the IMA field studies.

Table 2. Registration parameters used for the National Park Service northwest Alaska 

management area land cover mapping project

Number of Lines: 9,936

Data type: Byte 

Zone Code: 62

Latitude of first standard parallel: 

Latitude of second standard parallel: 

Longitude of the central meridian: 

Latitude of projection origin:

Corner Coordinates:

Upper Left Corner : 

Upper Right Corner : 

Lower Left corner : 

Lower Right corner:

Pixel Size: 30 X 30 

Pixel Units: meters 

Pixel Increment: 1 row by 1 column

Number of Samples: 15,244 

Number of Bands: 1

Projection Code: (3) Albers Equal-Area Conic

Semimajor Axis: 6.3782064E+06 

Semiminor Axis: 6.768657997291 IE-03

5.50000+07 

6.50000+07 

-1.54000+08

5.00000+07

2.129820E+06-4.77540E+05 

2.129820E+06-2.02500E+04 

1.831770E+06-4.77540E+05 

1.831770E+06-2.02500E+04
Digital elevation data were originally acquired from the Defense Mapping Agency 

(DMA). The DMA generated the data by digitizing hypsographic features (contour lines, ridge 

lines, lake boundaries, and point data) from USGS map series (1:63,360 through 1:250,000 scale) 

and converting them to rectangular grid values with a 3-arc-second by 3-arc-second interval. 

Slope, aspect, and illumination were computed from the digital elevation data (Ailts and others, 

1990). Slope was expressed as percentage slope (that is, units of rise per 100 units of run). 

Aspect values ranged from 0 to 360 (1-degree increments) in a clockwise direction from true 

north and were reassigned to values ranging from 0 to 180 (2-degree increments) for conversion 

to 8- bit data. Illumination was based on the Sun's elevation and azimuth at the time each 

Landsat scene was acquired. The computed illumination data resulted in values from 0 to 255, 

indicating a relative amount of radiance to the ground surface; low values indicated shadows and 

high values indicated sunny slopes.

Development of Spectral Statistics and Image Classification

Statistics used to generate the preliminary classification were derived individually for 

each summer satellite scene using a two-step, unsupervised and partially supervised classification 

process (Swain and Davis, 1978). The first step involved sampling the spectral variability in the 

satellite scene by extracting every 10th row and column from bands 1-5 and 7. The sample 

output was then used to produce 40 to 50 (number subjectively predetermined) discrete spectral 

clusters on the basis of brightness value of each pixel in each band using an isodata algorithm. 

Each cluster was defined in terms of descriptive statistics (means, standard deviations, 

covariance matrices between bands) for all bands (Swain and Davis, 1978). Redundant or 

overlapping statistics in the initial statistical data set were manually removed or combined, on the 

basis of descriptive statistics, to form a final set of cluster statistics that provided an independent 

estimate of the spectral characteristics of each scene being analyzed. Landsat band 6, the thermal 

band, was not used because of its lower pixel resolution (120 m) and its poor information content 

in relation to vegetation type and condition.

A Bayesian maximum likelihood classification algorithm (Swain and Davis, 1978) was 

used to apply the cluster statistics to the entire Landsat scene. The algorithm used a complex 

mathematical decision rule for evaluating the pixel brightness values compared to the descriptive 

statistic values obtained for each cluster. Each pixel was then assigned to one of the spectral 

clusters on the probability that the pixel belonged to the spectral class of that cluster. The result 

was a spectral classification where each pixel fell into a spectral class.

The spectrally classified satellite scene was then evaluated and each class assigned to a 

land cover class name on the basis of interpreted color-infrared aerial photographs and field data 

descriptions. Spectral class inconsistencies within and between geographical areas were noted 

for possible refinement.

The second step in the classification process occurred after the class labeling described 

above. A spectral class may include two different vegetation types that are spectrally distinct but 

not sampled sufficiently during the initial sample to allow for identification of some classes (for 

example, linear habitats falling between sample units). If these areas were known to exist in a 

specific class, spectral stratification was used (Ailts and others, 1990). The problem class (from 

the preliminary classified image) was used as a pattern to extract multispectral information from
the original satellite image (bands 1-5 and 7). This produced a small, multispectral image 

containing spectral qualities for only that class. The isodata algorithm was then used on this 

smaller image to produce two to four new spectral clusters. These new clusters were then 

incorporated into the original classification and labeled as described above.

Postclassification Refinement Using Ancillary Data

Correct identifications of land cover types from preliminary classifications are often low 

because of similar spectral response from (1) the presence of similar vegetation type, (2) the 

effects of shadow and water on the overall reflectance of the vegetation, and (3) subtle changes in 

vegetation type owing to elevation, slope, or aspect. To improve classification accuracy, 

ancillary data were applied to distinguish classes that were known to be misclassified. For 

example, slope and aspect were used to separate shadow from water, and elevation and aspect 

were used to separate lowland shrub types from upland shrub types. The winter Landsat MSS 

data were used to differentiate three conifer crown closure classes and to separate closed conifer 

from other land cover types. Also, the winter MSS data were used to separate some closed 

conifer types that were spectrally similar to water and some tall shrub types with tall dead stems. 

This was accomplished by viewing band 4 of the winter MSS data on a display screen and 

recording the digital numbers corresponding to features shown on aerial photographs and 

described in field data. For example, digital numbers from 0 to 46 were found to represent 

closed conifer, 47 to 80 open conifer, and 81 to 89 conifer woodland. These ranges of values 

were then renumbered to one, two, and three, respectively, and made into a mask. The mask was 

then matched with areas mapped as conifers in the original spectral data. Those conifer classes 

that corresponded to one on the mask (digital values of 0 to 46) were then identified as closed 

conifer; this was repeated for those areas with mask values of two and three.

Digital and Hard-Copy Products

Final digital products for the three management areas include; original georeferenced 

Landsat TM and MSS images, land cover, elevation, slope, aspect, and preserve or park 

boundary. All digital data were stored on transferable media (CD-ROM, tape, and ftp site). 

Color hard-copy maps of the land cover were produced at a scale of 1:500,000.

Assessment of Final Land Cover Map

A subjective assessment of the land cover map was made using a random 10-percent 

sample of the polygons used during the ground sampling and classification process. For each 

polygon selected we recorded the land cover type previously identified by the field crew, along 

with the types and quantity of land cover pixels falling in the polygon from the final map. Fieldbased vegetation descriptions were used to assign relative levels of classification uncertainty, or 

"fuzzy labels" (table 3; also see Gopal and Woodcock, 1994), to each land cover class occurring 

in each polygon. For example, a polygon identified as open low shrub-birch/ericaceous in the 

field may have the following combination of classes associated with it: (1) 10 pixels of open low 

shrub - birch/ericaceous, (2) 5 pixels of open low and dwarf shrub tussock tundra, (3) 1 pixel of 

clear water, and (4) 1 pixel of wet herbaceous, for a total of 17 pixels. Fuzzy labels given to each 

of these could be 5, 4, 1, and 1, respectively. Because each polygon often contains two or more

10
land cover classes, the normal set of outputs from a fuzzy logic accuracy assessment was not 

produced (Gopal and Woodcock, 1994; Markon and Wesser, 1997; Muller and others, 1998). 

Instead, two tables were produced, one that showed the total number of pixels by fuzzy label, and 

the other summing the number of pixels occurring in each land cover class by fuzzy label (similar 

to the Match/Mismatch table as reported by Gopal and Woodcock).

Table 3. Fuzzy label values and descriptions used in assessing land cover types 

Fuzzy Label_________________Description______________________

1 Absolutely wrong. Land cover type has no relationship to actual 

vegetation type.

2 Wrong. There is very little relationship between the land cover type and 

the vegetation type described.

3 Somewhat correct. Land cover type matches some of the attributes of

actual vegetation. Should be used with caution depending on application 

of map.

4 Mostly correct. Land cover type could be used for most applications 

(difference probably due to subject cut points for height or percentage 

cover of a plant type).

5 Absolutely correct. Land cover exactly matches vegetation description.

RESULTS

Land cover Map

The land cover mapping process for the three management areas resulted in six major 

land cover categories (forest, shrub, herbaceous, sparse/barren, water, other), which were further 

separated into 19 classes (table 4). Class values were numbered to maintain consistency with 

other mapping projects. A narrative of each class is found in appendix A, along with class codes 

corresponding to hierarchical vegetation classification by Viereck and others (1992); a color 

hard-copy example of the final land cover map is found in the back of a limited number of copies 

of this report. Class summaries below are described first for the entire project and then for each 

management unit.

Project Area

For the entire project area (table 5), open low and dwarf shrub tussock tundra was the 

largest class, covering a little more than 1.1 million hectares, or 16 percent. The second largest 

class was open low shrub-birch/ericaceous, with 1.07 million hectares (15 percent), closely 

followed by clear water (a little less than 931,00 hectares, or 13 percent). Much of the water 

class (approximately 82 percent) occurs in the ocean, and a very small amount occurs in 

mountainous areas (owing to shadow effects), which could not be removed with elevation data or 

its derivatives, slope and aspect. The fourth largest class was open low shrub-alder/willow, with

11
about 661,000 hectares, followed by moist or dry herbaceous with over 516,000 hectares. It 

should be noted that these two classes may be confused if visited in the field because variations 

in the amount of graminoids present may be high in the tussock type.

Table 4. Land cover classes and class values used for the National Park Service's northwest 

Alaska management areas land cover map

Major Category Subclass Class Value

Forest Closed needleleaf forest 

Open needleleaf forest 

Needleleaf woodland

Shrub Tall open and closed alder/willow 8

Closed low shrub - alder/willow 9

Closed low shrub - birch/ericaceous 10

Open low shrub - alder/willow 11

Open low shrub - birch/ericacious 12

Open low and dwarf shrub tussock tundra 13

Dwarf shrub tundra/dwarf shrub peatland 14

Open dwarf shrub - talus/lichen 17

Herbaceous Moist or dry herbaceous 

Wet herbaceous

19

20

Sparse/barren Sparsely vegetated

Barren

Snow/Ice/Cloud

22

23

24

Water

Other

Clear water 

Turbid water

Shadow

25

26

27

12
Table 5. Hectare summaries for each class for the entire National Park Service northwest Alaska 

management area and surrounding lands (LCC = land cover class number, Npixels = number of 

pixels)

LCC Class Description Npixels Hectares

1

2

3

8

9

10

11

12

13

14

17

19

20

22

23

24

25

26

27

Closed needleleaf forest

Open needleleaf forest

Needleleaf woodland

Tall open and closed alder/willow

Closed low shrub-alder/willow

Closed low shrub-birch/ericaceous

Open low shrub-alder/willow

Open low shrub-birch/ericaceous

Open low and dwarf shrub tussock tundra

Dwarf shrub tundra/dwarf shrub peatland

Open dwarf shrub-talus/lichen

Moist or dry herbaceous

Wet herbaceous

Sparsely vegetated

Barren

Snow/ice/cloud

Clear water

Turbid water

Shadow

Total

1,222,932

2,974,111

2,438,090

1,702,662

2,992,922

1,175,742

7,344,120

11,937,475

12,497,862

4,755,835

2,608,643

5,740,292

1,194,115

3,033,807

4,896,944

108,720

10,341,271

226,391

773,478

77,965,412

110,064

267,670

219,428

153,240

269,363

105,817

660,971

1,074,373

1,124,808

428,025

234,778

516,626

107,470

273,043

440,725

9,785

930,714

20,375

69,613

7,016,887

1.57%

3.81%

3.13%

2.18%

3.84%

1.51%

9.42%

15.31%

16.03%

6.10%

3.35%

7.36%

1.53%

3.89%

6.28%

0.14%

13.26%

0.29%

0.99%

The barren class makes up over 440,000 hectares (over 6 percent) of the final map, 

occurring primarily in the De Long, Baird, Waring, and Schwatka Mountains, and the Mulgrave 

and Igichuk Hills. The sparsely vegetated class covered approximately one-half of the area 

(230,000 hectares).

The dwarf shrub tundra/dwarf shrub peatland was found on slightly more than 428,000 

hectares (roughly 6 percent). This class may be underrepresented throughout the project area 

because of complex species mixing in this vegetation type. Open alder/willow communities 

occur throughout the area, but varying amounts of graminoid, dwarf birch, and ericaceous shrubs 

are often present in the understory, making it difficult to separate them from the open low shrub - 

dwarf birch/ericaceous shrub class.

Forests occur throughout the central and eastern parts of the project area. Open 

needleleaf is the most extensive, with over 267,000 hectares (4 percent), followed by needleleaf 

woodland, with 219,000 hectares (or 3 percent). Closed needleleaf forest was the smallest 

forested class, with 110,00 hectares (2 percent). Although open mixed forest is found in the 

Kobuk Valley National Park area, it could not be adequately distinguished from other vegetation 

classes.

Other prominent classes that have less than 5 percent coverage of the project area include 

closed low shrub-alder/willow, with a little more than 269,000 hectares, open dwarf shrub-talus 

lichen, with close to 235,000 hectares, and moist or dry herbaceous, with about 107,000 hectares.

13
Cape Krusenstern National Monument

Within the Cape Krusenstern National Monument (table 6), four vegetation classes cover 

close to 70 percent of the area. Open low shrub-birch/ericaceous occupied the largest area, with 

almost 85,000 hectares (32 percent), followed by open low and dwarf shrub tussock tundra, with 

about 55,000 hectares (20 percent). These two types were well distributed throughout the

Table 6. Hectare summaries for each class for Cape Krusenstern National Preserve (LCC = land 

cover class number, Npixels = number of pixels)

LCC Class Description Npixels Hectares

1

2

3

8

9

10

11

12

13

14

17

19

20

22

23

24

25

26

27

Closed needleleaf forest

Open needleleaf forest

Needleleaf woodland

fall open and closed alder/willow

Closed low shrub-alder/willow

Closed low shrub-birch/ericaceous

Open low shrub-alder/willow

Open low shrub-birch/ericaceous

Open low and dwarf shrub tussock tundra

Dwarf shrub tundra/dwarf shrub peatland

Open dwarf shrub-talus/lichen

Moist or dry herbaceous

Wet herbaceous

Sparsely vegetated

Barren

Snow/ice/cloud

Clear water

Turbid water

Shadow

Total

696

3,746

1,506

127,179

93,220

165,724

94,069

943,655

607,519

113,197

74,662

324,377

91,155

41,414

78,537

0

202,312

6,386

564

2,969,918

63

337

136

11,446

8,390

14,915

8,466

84,929

54,677

10,188

6,720

29,194

8,204

3,727

7,068

0

18,208

575

51

267,293

0.02%

0.13%

0.05%

4.28%

3.14%

5.58%

3.17%

31.77%

20.46%

3.81%

2.51%

10.92%

3.07%

1.39%

2.64%

0.00%

6.81%

0.22%

0.02%

monument, occurring on low rolling hills and lower mountain slopes. Moist or dry herbaceous 

covered a little over 29,000 hectares (11 percent) and was most prevalent near the coast in 

lowland areas, especially in the northern part of the monument. Clear water was the fourth 

largest class mapped (7 percent). Closed low shrub-birch/ericaceous was fairly common in the 

region, with almost 15,000 hectares (6 percent), followed by tall open and closed alder/willow 

(about 11,500 hectares), occurring primarily in drainages and water tracks on upper slopes.

Dwarf shrub tundra/dwarf shrub peatland occurred on a little more than 10,000 hectares 

(just under 4 percent). Open low shrub-alder/willow occurred on roughly 8,500 hectares (3 

percent), primarily along water courses and lower hill slopes, although it also fringed conifer 

types on middle and upper slopes.

Wet herbaceous sites were less prominent in the monument, covering about 8,200 

hectares (3 percent) and occurring in low areas, especially within and near the coastal lagoons. It 

should be noted that both the wet herbaceous and moist or dry herbaceous types also may occur 

in upper mountain valleys in the north.

All other classes covered less than 3 percent of the area.

14
Noatak National Preserve management area

The Noatak National Preserve has both preserve and wilderness designations. Although 

table 7 provides total hectares by land cover class for both, the following summarizes the land 

cover types over the entire management area.

Four land cover classes occupied more than 50 percent of the land area (1.62 million 

hectares) of the Noatak National Preserve. Open low shrub-dwarf shrub tussock tundra covered 

the largest area (20 percent), being a prominent cover type on the many rolling hills and lower 

mountain slopes. Associated with this class and occurring on the same type of terrain was open 

low shrub- birch/ericaceous covering 17 percent. Vegetatively, these two types are very similar, 

the main difference being the amount of tussock-forming graminoid present (that is, Carex 

bigelowii or Eriophorum vaginatum). Transitions between the two are often subtle and difficult 

to map. The third largest class was open low shrub- alder/willow (14 percent). This class was 

found primarily on mid-slopes of hills and mountains with low ericaceous shrub types occurring 

both below and above. In some areas, this class had an understory dominated by dwarf birch and 

ericaceous plants, which in some areas may be mapped as an open low shrub-dwarf 

birch/ericaceous type.

The fourth class was moist or dry herbaceous (11 percent). In many cases, this class was 

difficult to separate from wet herbaceous, which may be the reason for such low coverage (less 

than 1 percent). These two types are often difficult to separate, both in situ and by remote 

sensing. Local, short-term climate conditions may change the appearance of a moist herbaceous 

site to that of a wet herbaceous site simply by the amount and duration of rainfall occurring 

shortly before satellite images were obtained or the field crew visited the site.

The fifth largest class mapped in the area was barren, with close to 216,000 hectares (9 

percent), occurring primarily in the mountains but also found in the lower reaches of the Noatak 

River. Dwarf shrub tundra/dwarf shrub peatland was somewhat common throughout the 

preserve with about 209,000 hectares (8 percent), especially in the east, where it occurred on 

mountain slopes, lower valley reaches, and bottomlands.

Sparsely vegetated and open dwarf shrub- talus/lichen were present, but not in large 

amounts (about 5 percent of the total area for both). Both of these types commonly occur in 

transition areas between more densely vegetated areas on lower slopes and barren areas on upper 

slopes.

Closed low shrub-alder/willow is fairly common throughout the preserve, although in 

small amounts (less than 3 percent), occurring primarily in drainages along stream and river 

shorelines, and in larger floodplains. It can often be mapped incorrectly as tall open and closed 

alder/willow (which covered about 2 percent of the area) because the only prominent separation 

is height (greater than 1.5 m). In the lower Noatak River area, either of these classes also may 

contain aspen (Populus tremuloides) and cottonwood (Populus balsamifera).

Kobuk Vallev National Park

Among the most prominent differences between the Kobuk Valley National Park and the 

other two management areas are the extensive forests and the presence of active sand dunes. 

Open needleleaf forest occupies a little less than 88,00 hectares (table 8), closely followed by

15
Table 7. Hectare summaries for each class for the Noatak National Monument (LCC = land cover class number, N

LCC 

pixels)

Class Description

1 [Closed needleleaf forest

2 bpen needleleaf forest

3 Needleleaf woodland

8 [Tall open and closed alder/willow

9

11

10

Closed low shrub-alder/willow

Closed low shrub-birch/ericaceous

Open low shrub-alder/willow

12 bpen low shrub-birch/ericaceous

13 bpen low and dwarf shrub tussock tundra

14 Dwarf shrub tundra/dwarf shrub peatland

17 bpen dwarf shrub-talus/lichen

19 Moist or dry herbaceous

20 VVet herbaceous

22 Sparsely vegetated

23 Barren

27

26

25

24

Clear 

Snow/ice/cloud

water

Turbid water

Total

Shadow

Preserve Wilderness

Npixels Hectares %

88,733

37,717

279,563

759,397

436,140

136,161

2,857,120

1,328,565

2,122,341

5,281,952

4,337,606

3,807,229

179,535

2,399,858

1,416,502

56,281

174,138

158,779

265,499

26,123,116

7,986

3,395

68,346

39,253

12,254

' 25,161

257,141

119,571

191,011

475,376

390,385

342,651

16,158

215,987

127,485

5,065

15,672

14,290

23,895

2,351,080

1.07

2.91

1.67

0.52

0.34

0.14

20.22

16.60

14.57

5.09

8.12

10.94

0.67

0.61

1.02

0.22

9.19

5.42

0.69

Npixels Hectares %

72,267

38,193

55,002

67,332

79,127

22,892

285,667

107,245

206,188

585,728

755,765

452,654

55,924

363,813

190,624

47,963

1,572

24,767

3,412,723

6,504

3,437

4,950

6,060

7,121

2,060

18,557

52,716

68,019

40,739

9,652

25,710

5,033

32,743

17,156

4,317

141

2,229

307,145

2.12

1.12

1.61

1.97

2.32

0.67

17.16

22.15

13.26

5.59

1.64

8.37

3.14

6.04

10.66

0.73

0.05

1.41

Total 

Npixels H

60,609

351,830

797,590

491,142

203,493

167,860

3,142,787

1,435,810

2,328,529

5,867,680

5,093,371

4,259,883

235,459

2,763,671

1,607,126

56,281

198,905

160,351

313,462

29,535,8392,

16
Table 8. Hectare summaries for each class for the Kobuk National Park (LCC = land cover 

class number, Npixels = number of pixels)

LCC Class Description Npixels Hectares

1

2

3

8

9

10

11

12

13

14

17

19

20

22

23

24

25

26

27

Closed needleleaf forest

Open needleleaf forest

Needleleaf woodland

Fall open and closed alder/willow

Closed low shrub-alder/willow

Closed low shrub-birch/ericaceous

Open low shrub-alder/willow

Open low shrub-birch/ericaceous

Open low and dwarf shrub tussock tundra

Dwarf shrub tundra/dwarf shrub peatland

Open dwarf shrub-talus/lichen

Moist or dry herbaceous

Wet herbaceous

Sparsely vegetated

Barren

Snow/ice/cloud

Clear water

Turbid water

Shadow

Total

296,623

977,484

947,304

221,394

762,473

206,281

456,073

954,588

1,333,111

447,772

134,591

134,234

10,828

202,337

362,087

13

262,282

1,369

147,347

7,858,191

26,696

87,974

85,257

19,925

68,623

18,565

41,047

85,913

119,980

40,299

12,113

12,081

975

18,210

32,588

1

23,605

123

13,261

707,237

3.77

12.44

12.05

2.82

9.70

2.63

5.80

12.15

16.96

5.70

1.71

1.71

0.14

2.57

4.61

0.00

3.34

0.02

1.88

needleleaf woodland, with a little more than 85,000 hectares. Small amounts of closed 

needleleaf forest were mapped (less than 28,000 hectares), occurring along and within the 

Kobuk River floodplain. The largest class mapped was open low and dwarf shrub tussock 

tundra, with a little less than 120,000 hectares (17 percent); it is ubiquitous throughout the 

Park. Closed low shrub-alder/willow was less common, covering more than 68,000 hectares 

(10 percent), occurring along all stream and river courses, most low to middle slopes of 

mountains, and high hills, and often revegetating burned forest areas. Open low shrub-alder 

willow occupied similar sites, with about 41,000 hectares (6 percent). Of the alder/willow 

types, tall open and closed alder/willow was the least prominent, with just under 20,000 

hectares (3 percent). As with the open class, this type also occurred on mid-mountain slopes 

and along water courses.

Dwarf shrub tundra/dwarf shrub peatland was the seventh largest class mapped, with 

a little more than 40,000 hectares (6 percent), occurring primarily on upper slopes in the 

northern part of the park. Open low shrub-birch/ericaceous covered about one-half as much 

area, less than 19,000 hectares (3 percent), occurring primarily in the Kobuk River valley.

Barren areas covered over 32,000 hectares (5 percent), owing to the extensive 

mountains in the northern half of the park.

Phvtogeographic patterns

Vegetation patterns across the landscape are based on a number of physiographic, 

edaphic, climatic, and topographic processes. A series of vegetation profiles and site 

descriptions based primarily on topographic characteristics were obtained for Cape

17
Krusenstern National Park and Noatak National Preserve areas during brief helicopter 

overflights and a limited number of landings in 1995, 1996, and 1997. Because of constraints 

in flight time and landing restrictions, only general representations of the many and varied 

conditions for the two management areas are given; similar overflights were not performed 

for Kobuk Valley National Park. In general, the following descriptions begin at the Noatak 

River delta, proceed north through the Cape Krusenstern National Park and western Noatak 

National Preserve, northward to the Kelly River, and then eastward through the Noatak River 

watershed and tributaries culminating in the Cutler River drainage.

Just north of the village of Kotzebue, the Noatak River forms a small delta as it 

empties into Kotzebue Sound. On the east side of the delta, mud flats quickly transition from 

mud to graminoid to open shrub types, followed by open needleleaf forest and woodland 

(primarily ofPicea glaucd). The west side of the delta is somewhat different in that there are 

much fewer trees and shrubs, possibly indicating a younger or more active flood plain.

To the north, as the landscape rises up out of the active floodplain and delta area into 

Cape Krusenstern National Park, conifers are more prevalent, forming small stands of forests, 

especially on the upper hillslopes. In the lower rolling hills, low shrub-graminoid tussock 

tundra is prominent on all slopes and aspects, with open and closed alder (Alnus spp.) or 

willow (Salix spp.) thickets in drainages and around lake and pond ridges. Alder also is 

found on the lower and upper slopes of mountains, with other types of communities on the 

mid-slopes. A common topographic sequence (fig. 3) is alder on the toe slope, forming a 

transition zone between tussock tundra below and needleleaf forest above, extending through 

the needleleaf forest and beyond the treeline to the upper slope. Above this, the alder thins 

out into low and dwarf shrub communities (Betula nana, Cassiope tetragona, Dry as spp., 

and Arctostaphylos rubra, to name a few), occurring with various graminoids and forbs. The 

upper slope finally culminates with a lichen-barren talus community and a few scattered 

dwarf and prostrate shrubs.

In the central part of Cape Krusenstern National Park is a small valley situated 

between the Igichuk Hills to the south and the Mulgrave Hills to the north; it is the site of a 

westward lobe of a middle Pleistocene glacier that covered the Noatak valley (Coulter and 

others, 1965). This area consists of rolling hills less than 200 meters high, with small ponds 

and lakes in the east and central part and numerous small wetlands to the west. Open low 

ericaceous shrubs (for example, Vaccinium uliginosum, V. vitis-idaea, Ledum palustre, 

Empetrum nigruni) dominate the hills along with areas of low shrub-graminoid tussock 

tundra. Bordering small streams, ponds, and lake shorelines are open tall and low willow, 

often with an ericaceous shrub understory.

Between the lower Noatak Canyon and Kelly River is a wide valley that has broad, 

low rolling hills less than 100 m high, gently sloping toward the Noatak River with numerous 

lakes, ponds, and wetlands. In this area, three different types of shrub communities occur, 

depending on local hydrology. In areas of moderate to swift flowing water away from the 

main active Noatak River floodplain, found in stream channels or in areas of sheet wash, low 

willow dominates. In other areas, where the water is slow moving or is found in small 

pockets owing to a shallow thaw layer, dwarf birch and other ericaceous shrubs dominate. In 

areas near the active floodplain of the Noatak River, where the substrate is generally wanner

18
Lichen - barren tallus

Open low and dwarf shrub

Open and closed willow or alder

Open needleleaf forest 

with willow and/or alder

Open and closed alder

Open low ericaceous shrub

Low shrub graminoid 

tussock tundra

Inland

Open tall and low

willow or alder 

Figure 3. Typical vegetation profile from mouth of Noatak River to Igisukruk Mountain in southeast Cape Krusens

19
and more gravelly, tall willows such as Salix alaxensis are more common, as are open and 

closed needleleaf forests.

To the north, approximately where the Noatak River bends to the east, the valleys 

generally become narrower, the terrain slightly higher, and the vegetation patterns more 

complex because of topography, proximity to active floodplains, and recent fire history (fig. 

4). Along the larger rivers, cottonwood-willow forests can occur, along with needleleaf 

forests that expand outward from the river. Beyond the needleleaf forests, open low shrub 

(willow and ericaceous species) communities are common. Some of these shrub 

communities are a result of tundra or forest fires and are dominated by willows, dwarf birch 

(Betula glandulosa, B. nand), and ericaceous plants. In other areas, these same woody 

species may be associated with graminoid tussocks but not necessarily with fires.

Further away from the thermal influence of the river, a number of different plant 

communities may occur. Dwarf shrub peatland may be found in low shallow basins with low 

shrub graminoid tussock communities on the surrounding low, rolling hills. Occasionally, 

there are small knolls commonly associated with water bodies and streams in which open 

needleleaf forests or closed alder/willow shrubs occur. These knolls often have low shrub 

graminoid tussocks on the opposite side away from the water body. Small depressions are 

numerous throughout the valley and will have moist or wet herbaceous types, often with 

scattered willows.

As the landscape begins a quick ascent up the toe slope of a nearby mountain, the low 

shrub graminoid tussocks give way to low willow-ericaceous shrub communities. Open 

needleleaf forests occur again about mid-slope, often associated with alder or willow. Above 

the needleleaf forests, alder or willow communities are present and eventually give way to 

ericaceous shrub and then finally to lichen talus at the mountain crest.

The next major river valley to the east is the Kugururok River. An interesting feature 

of the lower reaches of this river drainage is the dissimilarity between the east and west 

valley slopes. Distances between the river and nearby mountain peaks are shorter on the 

west side than on the east side. The east side is primarily represented by broad, low rolling 

hills with dwarf shrub- graminoid tussock or by graminoid-dominated tussock tundra with 

tall and low stands of willow in small drainages. On the west side, closed stands of 

needleleaf forest are common in the floodplain and may continue up to mid-slope. Low 

shrubs and dwarf shrub-graminoid tussock communities occur in the surrounding hills and 

open tundra areas as well as the lower mountain slopes. As with the east side, tall and low 

stands of willow occur in smaller drainages. In some areas, moist graminoid communities 

occur above the treeline and lichen talus is common on upper slopes.

Upriver from the Kugururok is the Kaluktavik River. This relatively small drainage is 

notable in that needleleaf forests are absent; however, large groves of cottonwood (Populus 

balsamiferd) are present. Since these stands are a few kilometers upstream from the river's 

confluence with the Noatak River, it is not known if the seed source was brought in by wind 

or by animal-borne vectors, or if the stands are relics from larger forests that may have been 

there in the past (Bob Gall, NFS, oral communication). Small cottonwood groves (10-30 

trees) also can be found along the Noatak River, up to and including its confluence with the 

Nimiuktuk River.

20
Lichen - tallus mountain summit and upper slope

Tallus - ericaceous shrub

Alder - willow mix (mid-slope) 

with scattered tallus

Open needleleaf forest 

on lower slope

Old burn with graminoid 

tussock tundra

Low ericaceous shrub - 

graminoid tussock tundra

Toe-slope with low 

ericaceous shrub

Old burn with mix 

ericaceous shrub 

I 

I 

Open needleleaf forest ^--^^ 

on small hill Dwarf shrub 

peatland

Wet and moist 

sedge meadows

Tall alder-willow communities 

on stream and pond banks

Tal

pop

Away from river Tow

Figure 4. Vegetation profile of the Kelly River area.

21
The Nimiuktuk River flows through a broad valley in the north central part of the 

Noatak National Preserve. Numerous low, sloping hills occur throughout the valley, along 

with occasional rock outcrops. The prominent plant community covering the hills is dwarf 

shrub-graminoid tussock tundra, although some mid-slopes have open alder stands. Lower 

parts of hillslopes often level out with graminoid tussock communities and graminoid 

marshes in depressions or in low-centered polygons. Water tracks occur in this area and are 

often dominated by dense stands of dwarf birch. The Nimiuktuk River floodplain is usually 

broad, especially in the lower reaches, and is typically dominated by low and tall willows 

and, occasionally, alders on upper terraces (fig. 5). Northeast of the Nimiuktuk River is the 

Anisak River. The headwaters of the Anisak River are in a narrow valley with alder 

communities common on the mid- and upper-slopes and willow, dwarf birch, and ericaceous 

plants on the lower slopes. The slightly braided river floodplain is often dominated by open 

and closed tall and low willows.

Continuing down river, graminoid marshes occur on level terraces on either side of 

the valley, with dwarf birch and ericaceous shrubs occupying drier sites and lower slopes; 

alder is still present on upper slopes. The Anisak River eventually flows into a wide valley 

(part of glacial Lake Noatak) consisting of broad, low hills. In the Desperation Lake area 

(fig. 6), these hills are dominated by low willow/birch and dwarf shrub-graminoid tussock 

tundra, with some hillcrests consisting primarily of Dry as spp., lichens, and talus. Wet 

graminoid marshes are common along the Anisak River and on some low hills in water 

tracks. Alder is no longer common in this area, but open low willow communities are. These 

types of conditions continue eastward into the Amuik River area and to the south where 

broad valleys of low rolling hills occur. In general, alder is sparse and insignificant in this 

area, occurring on river and stream cut banks, at times following stream courses up to 500 m 

in elevation. Low and dwarf shrub- graminoid tussock types are more common, with willow 

thickets along streams and rivers or in some moist areas.

The central part of the Noatak National Preserve is composed of steep hills and 

rugged mountains. The area where the Noatak River cuts through is often referred to as the 

Grand Canyon of the Noatak River. The river course in this area occurs in a transition zone 

where trees are a relatively common occurrence to the west but are absent in the east. On the 

western end of the canyon (hills east of Kaluktavik River), white spruce (Picea glauca), 

balsam cottonwood (Populus balsamifera), and paper birch (Betula papyrifera} all occur. 

The paper birch was not observed in other areas in the western part of the Noatak National 

Preserve, although some 'tree-like' plants seemed to be a hybrid of Betula papyrifera and B. 

glandulosa (a shrub). The spruce and cottonwood grow in small stands or singly, whereas 

the birch is normally found singly. Willows are common on lower slopes and in drainages, 

and alder is common on steep upper slopes. In the central part of the canyon (Sisiak Creek 

area; fig. 7), the spruce eventually disappears. Cottonwoods are common in small groves on 

the river floodplain and up on the first terrace above the river (rising approximately 50 m in 

elevation); birch trees also are present.

West of the Sapun Creek confluence with the Noatak River, the trees disappear, and 

the vegetation consists primarily of tall and low shrubs along the drainages, with low and 

dwarf shrubs on the nearby rolling hills and lower mountain slopes (similar to figure 7

22
Lichen - tallus upper 

slope and 

Dryas 

summit

- dwarf shrub and 

tallus upper slope

Dwarf shrub tundra

Upper midslope alder 

with dwarf shrub understory

Low and dwarf shrub - 

graminoid tussock

Low and dwarf shrub- graminoid 

tussock tundra

Lower mid slope alder

Dwarf birch 

water track

Graminoid marsh 

and/or meadow

Small tallus/ 

rock outcrop

Low willow and 

graminoid 

birch/ericaceous 

community Low shrub 

community

tussock 

graminoid 

Tall willow 

popular gro

N

Tall al

comm

pond, 

river

Figure 5. Typical vegetation profile of lower Nimiuktuk River valley.

23
Tallus - Dry as - forb 

ridge top

Low willow - birch and dwarf shrub 

graminoid tussock

Open low willow often with 

graminoid tussock

Moist or wet graminoid 

in water tracks

Mois

gram

Figure 6. Vegetation profile in the Desperation Lake area of northern Noatak National Monument.

24
Tallus lichen - dwarf 

shrub upper slope

Low and dwarf 

shrub upper slope

Dwarf shrub - 

graminoid terrace

Open alder with 

low shrub graminoid 

understory

Tall shrub with 

scattered deciduous 

trees

Low and dwarf shrub 

graminoid tussock tundra

Graminoid marsh 

in old river oxbow

S

d

Tall 

alon

cour

Figure 7. Vegetation profile in the Sisiak River area along the Noatak River.

25
without tree species).

The eastern part of the Noatak National Preserve consists of broad, low rolling hills 

(to 500 m) surrounded by tall, rugged mountains (to 1,100 m) and was once covered by 

glacial Lake Noatak. Tall, rounded summits in the southern part of the area drained by the 

Coulter River contain striations indicative of old shorelines. Vegetation communities in this 

area are much like those of the Desperation Lake area to the north. Talus-lichen and dwarf 

shrubs occur on the higher rounded hilltops. Dry to moist graminoid-forb (Equisetum sp., 

Carex sp.) communities are common on upper and middle slopes, as are low shrub and dwarf 

shrub communities. The most common vegetation type is the low and dwarf shrubgraminoid tussock, which can extend uninterrupted for many kilometers. Most of the stream 

banks and small drainages are lined with low willows, with some high (+ 10m) noneroded 

banks vegetated by tall willow and occasionally alder. Surrounding mountain summits and 

escarpments are normally sparsely vegetated or barren, especially above 400 m.

Map Assessment

A 10-percent sample of all polygons occurring within the Cape Krusenstern National 

Park, Noatak National Preserve, and Kobuk Valley National Park areas resulted in 31 

polygons containing 7,158 pixels (appendix B). Table 9 shows the total number of pixels 

found by fuzzy label type. Assuming homogenous plant cover within each polygon and

Table 9. Number of pixels by fuzzy label for all classes found in the sample polygons 

Fuzzy Label Number of Pixels Percent

1

2

3

4

5

434

608

1,594

2,337

2,185

6

8

22

33

31

acceptable fuzzy labels of 4 and 5, overall agreement between the mapped pixels occurring in 

the polygons and field observations was 64 percent. If a fuzzy label of 3 also were 

acceptable, then the agreement would increase to 86 percent. However, the acceptance of a 

fuzzy label of 3 may have more severe consequences than the acceptance of a 4 and 5. To 

consider a fuzzy label of 3 acceptable would depend upon the land cover class type, the 

ultimate use of the data, and intimate knowledge of the relationship between the class type 

and actual vegetation occurring in the field. Whereas the difference between a 4 and a 5 may 

be due to a percentage cover or height cut point, a value of 3 may represent areas that have 

entirely different plant species or combinations of plant species. For example, a pixel may be 

mapped as open low shrub- alder/willow but actually be an open low shrub-birch ericaceous. 

Although these two classes may be similar in terms of cover, they may be very different in 

terms of soils, nutrients, or wildlife use.

Table 10 provides a somewhat more detailed assessment by looking at individual

26
Table 10. Map assessment using weighted fuzzy labels for a 10-percent sample of the IMA polygons used during t

process

LCC Description

Number 

Pixels

Fuzzy 

5 Mismatch

Remaining Fuzzy 

4 Mismatch

Remaining Fuzzy Rema

3 Mism

19

17

14

13

12

11

10

9

8

3

2

1

27

26

25

23

22

20

Closed needleleaf forest

Dpen needleleaf forest

Needleleaf woodland

fall closed alder/willow

Closed low shrub-alder/willow

Closed low shrub-birch/ericaceous

Dpen low shrub-alder/willow

Dpen low shrub-birch/ericaceous

Dpen low and dwarf shrub tussock tundra

Dwarf shrub tundra/dwarf shrub peatland

Dpen dwarf shrub-talus/lichen

Moist or dry herbaceous

/Vet herbaceous

Sparsely vegetated

Dlear 

Barren

water

Turbid water

Shadow

Total

555

112

543

86

262

229

80

1,431

1,104

182

349

127

98

981

201

797

7

14

7,158

0

97

185

19

218

98

10

238

597

285

82

13

5

0

114

224

0

0

2.185

0

119

559

834

819

457

102

325

67

165

44

0

68

125

114

93

0

0

3.891

207

57

157

41

31

0

635

101

314

104

250

206

82

2

24

112

7

7

2,337

49

10

8

3

118

0

245

730

569

251

18

91

346

0

7

44

13

32

2,534

49

57

0

0

0

100

1

175

587

421

6

166

8

0

1

23

0

0

1594

2

61

0

0

0

0

14

14

25

9

18

1

70

5

0

21

0

0

98

27
Table 10. Continued

Fuzzy Remaining Fuzzy Remaining 

LCC Description 2 Mismatch 1 Mismatch

10

9

8

3

2

1

27

26

25

23

22

20

19

17

14

13

12

11

Closed needleleaf forest

Dpen needleleaf forest

vleedleleaf woodland

Fall closed alder/willow

Closed low shrub-alder/willow

Closed low shrub-birch/ericaceous

Dpen low shrub-alder/willow

Dpen low shrub-birch/ericaceous

Dpen low and dwarf shrub tussock tundra

Dwarf shrub tundra/dwarf shrubjDeatland

Dpen dwarf shrub-talus/lichen

Moist or dry herbaceous

/Vet herbaceous

Sparsely vegetated

Dlear 

Barren

water

Turbid water

Shadow

Total

2

17

0

0

0

0

57

143

63

170

10

141

0

0

0

5

0

0

608

0

0

0

0

44

0

85

80

2

13

0

39

0

0

0

0

0

0

263

10

8

3

0

44

0

85

80

2

13

0

32

90

39

0

21

0

7

434 0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

28
Table 10. Continued

Number Fuzzy Remaining Fuzzy Remaining Fuzzy Rem

LCC Description Pixels 5 Mismatch 4 Mismatch 3 Mism

19

17

14

13

12

11

10

9

8

3

2

1

27

26

25

23

22

20

Closed needleleaf forest

Open needleleaf forest

Meedleleaf woodland

fall closed alder/willow

Closed low shrub-alder/willow

Closed low shrub-birch/ericaceous

Dpen low shrub-alder/willow

Dpen low shrub-birch/ericaceous

Dpen low and dwarf shrub tussock tundra

Dwarf shrub tundra/dwarf shrub peatland

Dpen dwarf shrub-talus/lichen

Moist or dry herbaceous

/Vet herbaceous

Sparsely vegetated

Dlear 

Barren

water

Turbid water

Shadow

Total

80

262

229

86

555

112

543

1,431

1,104

981

201

797

14

182

349

127

98

7.158

7

31%

0%

0%

63%

64%

10%

5%

0%

41%

30%

42%

26%

18%

9%

40%

22%

37%

81%

0%

69%

100%

100%

37%

36%

90%

95%

100%

59%

70%

58%

74%

82%

91%

60%

78%

63%

19%

100%

33%

100%

50%

13%

32%

65%

2%

65%

50%

39%

7%

23%

37%

0%

38%

66%

60%

18%

39%

37%

0%

50%

24%

4%

25%

93%

35%

9%

31%

51%

52%

45%

91%

22%

12%

3%

1%

61%

22%

0%

0%

13%

2%

0%

1%

17%

3%

22%

41%

38%

0%

89%

10%

0%

0%

0%

61%

25

92

18

9

10

13

45

2

1

12

0

15

50

12

29
Table 10. Continued

Fuzzy Remaining Fuzzy Remaining 

LCC Description 2 Mismatch 1 Mismatch

9

8

3

2

1

27

26

25

23

22

20

19

17

14

13

12

11

10

Closed needleleaf forest

Dpen needleleaf forest

Needleleaf woodland

Fall closed alder/willow

Closed low shrub-alder/willow

Closed low shrub-birch/ericaceous

Dpen low shrub-alder/willow

Dpen low shrub-birch/ericaceous

Dpen low and dwarf shrub tussock tundra

Dwarf shrub tundra/dwarf shrub peatland

Dpen dwarf shrub-talus/lichen

Moist or dry herbaceous

/Vet herbaceous

Sparsely vegetated

Dlear 

Barren

water

Turbid water

Shadow

Total 8%

0%

0%

0%

1%

0%

0%

14%

5%

7%

10%

6%

31%

2%

3%

0%

0%

0%

0%

..

..

8%

12%

..

8%

14%

..

..

-

6%

__

50%

12%

..

25%

92%

4%

6%

0%

50%

12%

0%

25%

92%

4%

1%

2%

0%

8%

14%

0%

8%

12%

3%

1%

0%

..

~

~

~

-

--

~

~

~

..

~

~

~

--

~

~

-

..

~

30
classes. All map classes are represented, although some only minimally. The fuzzy columns 

represent the number of pixels found for each respective class that received that fuzzy value. 

The "Remaining Mismatch" column represents the remainder of pixels that received a fuzzy 

value less than the preceding higher fuzzy value. Successive mismatch columns are reduced 

by the sum of previous fuzzy column amounts. For example, for land cover closed low 

shrub-alder/willow (class 9), the "Remaining Mismatch" column following "Fuzzy 4" 

represents the sum of all pixels in the class (543) minus the number of pixels with a fuzzy 

value of 5 (218), minus the number of pixels with a fuzzy value of 4 (207). Thus, class 9 

had an accuracy of approximately 78 percent (lower half of table 10) if fuzzy values of 4 and 

5 were acceptable.

Using fuzzy values of 4 and 5, the four classes that had the highest agreement with the 

polygon data were open needleleaf forest (class 2, 99 percent), needleleaf woodland (class 3, 

97 percent), open dwarf shrub-talus/lichen (class 17, 91 percent), and barren (class 23, 96 

percent). Those with the worst agreement were wet herbaceous (class 20, 7 percent), 

followed by closed low shrub-birch/ericaceous (class 10, 8 percent).

DISCUSSION

The northwest areas land cover map was derived using parts of nine different satellite 

scenes, over a wide range of seasonal dates (30 June to 8 August), and using field data 

collected by different crews. This caused major problems in mapping such an extensive area, 

including unresolved classes at scene boundaries, spectral class overlap, and inconsistency in 

field data collection.

Differences in vegetation phenology and natural disturbances alter the way data are 

recorded by the sensor, making seamless data merging between satellite scenes difficult. In 

northwest Alaska, changes in vegetation cover across the landscape can be subtle, and slight 

changes in data acquisition dates may make a difference in how a particular spectral class is 

ultimately labeled. Early data acquisition dates (that is, June) may capture shrub vegetation 

in its initial leaf-out condition, thereby allowing dead, previous-year understory vegetation to 

show. In contrast, data acquired in July will pick up full shrub leaf-out conditions. Thus, the 

same vegetation community will have two very different spectral responses and may 

ultimately be assigned to different land cover classes. This is especially true in the Noatak 

National Preserve and in the central and northern parts of the Cape Krusenstern National 

Monument. Small changes in shrub and graminoid cover can occur within 30 to 120 m (1-4 

pixels), depending on soil moisture, slope, and aspect (all of which affect the type of spectral 

class being identified). As an example, alder was fairly common throughout the area, 

although it was found most often on steeper hillslopes or mid-slopes of mountains. In many 

cases, the spectral class of open alderstands would be the same as those of open low shrubdwarf birch/ericaceous cover because the understory vegetation would be dominated by 

dwarf birch and ericaceous plants. Alder also occurred on aspects away from the dominant 

sun angle (effects of shading), producing a spectral signature close to that of a dwarf 

birch/ericaceous type.

These types of problems can be somewhat expected, as a spectral classification is 

basically the grouping of spectral responses into abstract classes of shared characteristics 

(Norwine and Greegor, 1983). To alleviate this problem, good field information is needed

31
for the areas in question, but was lacking in parts of this study because field information was 

not always collected near the time of satellite data acquisition.

Another difficulty of this mapping project was the use of three different crews to 

collect the in situ field information over a period of 4 years (1988-91). As a result, some of 

the field calls by different individuals were not consistent across the project area. In addition, 

the aerial photographs used for spectral class identification that accompanied the field data 

span a period of 8 years (1977-85) and predate the satellite data. Although this is not a major 

problem, some of the photographs showed fire scars that were completely revegetated on the 

satellite images. Burned areas were not visited during the field campaigns, and assumptions 

had to be made as to what land cover type was identified by the satellite data. Also, field data 

were collected for the identification and description of mapped classes and not necessarily for 

accuracy assessments, thereby prompting the use of "fiizzy labels" and the polygon data.

The vegetation descriptions in appendix A are broad in scope and often meager in 

detail. This is primarily because (1) field data collection focused on general vegetative cover 

instead of detailed species descriptions and (2) very little time was allowed for botanical data 

collection. Also, field data procedures stressed that dominant species or microcommunity 

(for example, tussock) cover add up to 100 percent in an effort to catagorize what the satellite 

sensor would detect. This left out the opportunity to record other (possibly important) 

species and site conditions that may have been present. It would be useful to obtain more 

complete species information in the future for each of the vegetation types listed to better 

relate how cover types change throughout the management area.

There was a problem with comparable resolution between data sets. The Landsat TM 

data have a pixel resolution of 30 meters, making these data very useful for landscape-scale 

projects (1:60,000 to 1:100,000). However, the digital elevation data, an important 

component for separating mixed classes and its derivatives, slope and aspect, were derived 

from data with an initial pixel resolution of 90 to 100 meters. This resulted in much broader 

changes in spectral and land cover class discrimination than desired. For example, valleys 

that could be identified on the Landsat data and contained classes to be modified would 

disappear after application of the elevation data. This also was a problem, albeit to a lesser 

degree, with the bedrock geology data (1:2,500,000 scale) and soils data (1:1,000,000-scale). 

Similar problems can occur when using winter Landsat MSS data with a nominal pixel 

resolution of 80 meters, but appeared to be minimal when used for conifer stratification in 

this study.

Map accuracies are an important result of any mapping project. They allow users to 

judge the usefulness of individual classes and the map as a whole. The accuracy of the final 

land cover from this project was lower (64 percent) than what is normally considered 

acceptable (80 percent or greater). The low accuracy is partially due to the use of pixel labels 

that occurred within the sampled polygons, a procedure that is not entirely valid but is used 

elsewhere and is perhaps the only logical means of obtaining a sense of the map's usefulness. 

Even though the polygons appeared to be homogeneous on the photograph, the influences of 

subtle vegetation changes across the polygon, in combination with aspect and slope effects on 

lighting, often lead to individual pixels being classed differently. Also, the collection of field 

data within those polygons was not designed to be used in an accuracy assessment. Only a 

small part of each polygon was visited (not the entire polygon), with the assumption that the 

entire polygon was characterized by the site visited. Therefore, it is not known what

32
vegetation actually occurred within any given pixel in any given polygon.

The use of this project's accuracy assessment information is further compounded in 

that the sampled polygons may have been used for spectral class identification. Although 

some believe that, ultimately, this should make no difference on the final accuracy (Mike 

Fleming, USGS, oral commun.), others believe that it may and that the two data sets should 

be kept separate. For this type of accuracy assessment to be valid in the future, polygons 

used for the accuracy assessment should be kept independent of spectral class identification, 

and each pixel within the sample polygon should be sampled.

CONCLUSIONS

Federal land management agencies in Alaska have been using Landsat data for 

mapping land cover since the early to mid-1970's (Markon, 1995), the National Park Service 

being one of the initial users of the data (Dean and Heebner, 1982). This most recent land 

cover map and the resulting digital data base produced for the National Park Service's 

Northwest Management Area provides information that can be used by regional and local 

resource planners, managers, and researchers. Although many management personnel prefer 

higher resolution data products than the Landsat TM provides, it is currently the most 

available and cost effective way to obtain land cover information for large areas such as the 

Northwest Alaska management area.

The final land cover map may have several different applications, depending on the 

user and purpose. Some users may want to use the entire map, others may only be interested 

in one or a few classes. Although the accuracy assessment provides some information about 

the usefulness of the mapped data, users may want to perform more detailed analysis of the 

data, especially if they are interested in individual classes. For others, however, general 

information may suffice. For example, a wildlife biologist may only be interested in locating 

areas that generally contain tall and low shrub, regardless of shrub type and association with 

other vegetation communities. Since shrub types are generally easy to separate from 

nonshrub types, the current information may be sufficient.

The northwest area is a land rich in historical uses by Native Americans, containing 

many significant archeological sites. The land cover data base may be manipulated to show 

various land cover types important to animals or indigenous peoples, or it may be used in 

conjunction with terrain data to determine possible historical land use sites or travel routes. 

The fact that the data base is georeferenced also allows other data to be included for further 

manipulation, enhancement, or updating.

LITERATURE CITED

Ailts, B., Akkerman, D., Quirk, B., and Steinwand, D., 1990, LAS 5.0 -- An image

processing system for research and production environments, in American Congress 

on Surveying and Mapping (ACSM)/American Society for Photogrammetry and 

Remote Sensing (ASPRS) Annual Convention, Denver, Colorado, March 1990, 

Proceedings: Bethesda, Md., ACSM/ASPRS, v. 4, p. 1-12.

Beikman, H.M., 1980, Geological map of Alaska: U.S. Geological Survey Map.

33
Coulter, H.W., Hopkins, D.M., Karlstrom, T.N.V., Pewe, T.L., Wahrahaftig, C., and

Williams, J.R., 1965, Map showing extent of glaciation in Alaska: U.S. Geological

Survey Miscellaneous Geologic Investigations, Map 1-415, scale 1:2,500,000. 

Craighead, J.J., Craighead, F.L., Craighead, D.J., and Redmond, R.L., 1988, Mapping arctic

vegetation in northwest Alaska using Landsat MSS imagery: National Geographic

Research, v. 4, p. 496-527 

Dean, F.C., and Heebner, D.K., 1982, Landsat based vegetation mapping of Mount McKinley

National Park region, Alaska: Alaska Cooperative Park Studies Unit, University of

Alaska, Fairbanks. 

Faeo, V., 1993, Cape Krusenstern National Monument, Alaska, satellite land cover map

user's guide: National Park Service, Alaska Regional Office, Anchorage, Alaska, 25

PFleming, M..D., 1975, Computer-aided analysis of Landsat data: A comparison of three

approaches including modified clustering approach: West Lafayette, Ind., Purdue

University Laboratory for Applications of Remote Sensing, LARS Information Note

072475, 9 p. 

___ 1988, An integrated approach for automated cover type mapping of large inaccessible

areas in Alaska: Photogrammetric Engineering and Remote Sensing, v. 54, no. 3, p.

357-362. 

Gallant, A.L., Binnian, E.F., Omernik, J.M., and Shasby, M.B., 1995, Ecoregions of Alaska:

U.S. Geological Survey Professional Paper 1567, 73 p. 

Gopal, S., and Woodcock, C., 1994, Theory and methods for accuracy assessment of thematic

maps using fuzzy sets: Photogrammetric Engineering and Remote Sensing, v. 60, no.

2, p. 181-188. 

Karlstrom, T.N., Coulter, H.W., Fernald, A.T., Williams, J.R., Hopkins, D.M., Pewe, T.L.,

Drewes, H., Muller, E.H., and Condon, H., 1964, Surficial Geology of Alaska: U.S.

Geological Survey Miscellaneous Geological Investigations Map 1-357, two map

series, scale 1:1,584,000. 

Kuchler, A. W., 1966, Potential natural vegetation of Alaska: National Atlas of the United

States of America, Washington, D.C., U.S. Geological Survey, p. 92. 

Loveland, T.R., and Shaw D.M.., 1996, Multiresolution land characterization: Building

collaborative partnerships, in Scott, J.M.., Tear, T., and Davis, F., Gap Analysis: A

Landscape Approach to Biodiversity Planning, Proceedings of the ASPRS/GAP

Symposium, Charlotte, N.C., National Biological Service: Moscow Idaho, p. 83-89. 

Markon, C.J., 1995, History and use of remote sensing for conservation and management of

Federal lands in Alaska, USA: Natural Areas Journal, v. 15, p. 329-338. 

Markon, C.J., Fleming, M.D., and Binnian, E.F., 1995, Characteristics of vegetation

phenology over the Alaskan landscape using AVHRR time series data: Polar Record,

v. 31, p. 179-190. 

Markon, C.J., and Wesser, S., 1997, The Bering Land Bridge National Preserve land cover

map and its comparability with 1995 field conditions: U.S. Geological Survey OpenFile Report, 97-103, 27 p.

34
Muller, S.V., Walker, D.A., Nelson, F.E., Auerback, N.A., Bockheim, J.G., Guyer, S., and

Sherba, D. 1998, Accuracy assessment of a land-cover map of the Kuparuk River

Basin, Alaska: considerations for remote regions: Photogrammetric Engineering and

Remote Sensing, v. 64, p. 619-628. 

Norwine, J. and Greegor, D.H., 1983, Vegetation classification based on advanced very high

resolution radiometer (AVHRR) satellite imagery: Remote Sensing of Environment,

13, p. 69-87. 

Rieger, S., Schoephorster, D.B., and Furbush, C.E., 1979, Exploratory soil survey of Alask:,

U.S. Dept of Agriculture, Soil Conservation Service, 213 p. 

Selkregg, L.L., 1975, Alaska regional profiles, northwest region: Anchorage, Alaska,

University of Alaska, Arctic Environmental Information and Data Center, 265 p. 

Soil Conservation Service, 1985, Range survey of the Seward Peninsula reindeer ranges,

Alaska:. U.S. Dept. of Agriculture, Soil Conservation Service, Anchorage, Alaska,.

65 p. 

Swain, P.H., and Davis, S.M., 1978, Remote sensing: the quantitative approach: McGraw

Hill, Inc., New York, N.Y., 396 p. 

Talbot, S.S., Fleming, M..D., and Markon, C.J., 1986, Intermediate-scale vegetation mapping

in Kanuti National Wildlife Refuge, Alaska using Landsat MSS digital data: The

American Society for Photogrammetry and Remote Sensing and The American

Congress on Surveying and Mapping, Fall 1986, Proceedings, p. 392-406. 

Talbot, S.S. and Markon, C.J., 1988, Intermediate-scale vegetation mapping of Innoko

National Wildlife Refuge, Alaska , using Landsat MSS digital data: Photogrammetric

Engineering and Remote Sensing, v. 54, no. 3, p. 377-383. 

U.S. Congress, 1980, Alaska National Interest Lands Conservation Act (ANILCA), Public

Law 96-487: Washington, D.C. 

Viereck, L.A., Dyrness, C.T., Batten, A.R., and Wenzlick, K.J., 1992, The Alaska vegetation

classification: U.S. Forest Service, Pacific Northwest Research Station, General

Technical Report PNW-GTR-286, 278 p. 

Vogelmann, I.E., Sohl, T., Campbell, P.V., and. Shaw, D.M., 1998, Regional land cover

characterization using Landsat thematic mapper data and ancillary data sources:

Proceedings of the 3d EMAP Symposium, April 8-11, 1997, Albany, N.Y.,

Environmental Monitoring and Assessment. 

Vogelmann, I.E., Sohl, T., and Howard, S.M., 1998, Regional characterization of land cover

using multiple sources of data: Photogrammetric Engineering and Remote Sensing v.

64, p. 45-57. 

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Environments, University of Alaska, Fairbanks, Alaska, p. 50.

35
APPENDIX A

Class Descriptions for

the Northwest Areas Management Area

Land Cover Map

Closed Needleleaf (class 1)

Closed needleleaf forests are dominated by white spruce (Picea glaucd) on welldrained sites and along drainages, or black spruce (Picea mariand) on lowland sites in the 

Kobuk Valley National Park. Crown closure follows that of Viereck and others (1992)3 at 60 

to 100 percent. Understory layers may consist ofAlnus sp., Salix sp., Betula glandulosa, 

Spirea beauverdiana, Ledum palustre, Arctostaphylos uva-ursi, Vaccinium uliginosum, and 

various species of moss (particularly of the genus Sphagnum). Forbs and gramminoids may 

include Pyrola sp., Equisetum silvaticum, E. arvense, Lupine sp. Aster sp., Calamagrostis 

canadensis, and Carex sp. Mosses may include Sphagnum sp. and Hylocomium sp.

Viereck3 classes: lAlj, lAlk, IA11

Open Needleleaf Forest (class 2)

This class is similar to the closed needleleaf forest with the major difference being 

that crown closure is 25 to 59 percent. Understory vegetation is also similar, except that 

other ericaceous plants may be found (such as Empetrum nigrum), as well as numerous forbs. 

In some areas, lichens also are prominent.

Viereck classes: IA2e, IA2f, IA2g

Needleleaf Woodland (class 3)

Needleleaf woodland extends the conifer forest continuum to a more sparse cover 

with 10- to 24- percent crown closure. Species mix is similar to the class above, with a 

higher cover of shrubs in the understory. In the lower part of the Kobuk Valley National 

Park, the understory may be dominated by lichens and have few shrub or forb species present. 

In lowland areas, this type also may include Betula. nana, Petasitesfrigidus, Empetrum 

nigrum, Rubus chamaemorus, Vaccinium vitis-idaea, and Carex bigelowii, to name a few.

Viereck classes: IA3c, IA3d, IA3e

3Viereck, L.A., Dyrness, C.T., Batten, A.R., and Wenzlick, K.J., 1992, The Alaska 

Vegetation Classification: U.S. Forest Service, Pacific Northwest Research Station, General 

Technical Report, PNW-GTR-286, Portland, Oregon, 278 p.

36
Tall Open and Closed Alder/Willow (class 8)

This type occurs primarily on upper hill slopes, mid-mountain slopes, or along rivers, 

streams, and small drainages. Shrub canopy is greater than 75 percent, with heights at or 

exceeding 1.5 m. Alders (Alnus sp.) are more common on upper slopes or along small 

drainages; willows (Salix alaxensis, S. planifolia.) are more ubiquitous in this class. 

Understory vegetation may consist of graminoids (Carex sp., Calamagrostis canadensis), 

forbs (Petasites sp., Epilobium angustifolium), and mosses, depending on canopy closure. 

This type also may represent sites that contain an open tall alder/willow component but have 

a high understory cover of low willow (Salix sp.) and low or dwarf birch (Betula glandulosa 

or B. nana). In addition to having the traditional shrub species, this type may also include 

small stands or clumps of trees (B. papyrifera and Populus balsamifera) that were too small 

to map, or were too similar spectrally to nearby alders and willow to be described.

Viereck classes: IBlc, IBld, IB2a, IB2b, IB2c, IIBla, HBlb, IIBlc, IIBld, HB2a, IIB2b, 

HB2dIIB2e

Closed Low Shrub-Alder/Willow (class 9)

This class is often found on well-drained sites, such as stream and river banks, lake 

shores, and south-facing slopes. It is dominated by either alder (Alnus sp.) or willow (Salix 

sp.), with more than 75-percent cover that is 20 to 150 cm tall; on some sites, Alnus may 

obtain heights up to 2 m. Low birch (Betula glandulosa, or shrub birch) may occur mixed 

with the alder or willow, later grading into the closed low shrub-birch/ericaceous type as the 

permafrost thaw layer becomes shallower. Associated species may include Vaccinium 

uliginosum, Potentillafruticosa, Spiraea beauvardiana, various forbs, grasses 

(Calamagrostis canadensis), mosses (Sphagnum}, and lichens.

Viereck classes: IlClb

Closed Low Shrub-Birch/Ericaceous (class 10)

This class is dominated by either low or dwarf shrub birch (Betula glandulosa or B. 

nana), along with other types of ericaceous plants, such as Ledum decumbens and L. palustre, 

Vaccinium uliginosum, V. vitis idaea, and Empetrum nigrum. Plant cover for the birch and 

other ericaceous species combined is greater than 75 percent, with heights greater than 20 cm 

but less than 150 cm. Associated species may include Potentillafruticosa, Salix sp., various 

forbs, grasses, mosses, and lichens. This class is often found on upland tundra sites and hill 

slopes that are underlain by permafrost soils or on old beach dunes (with Elymus arenarius)', 

or it may also dominate small stream banks.

Viereck classes: IlCla, IIClc

37
Open Low Shrub-Alder/Willow (class 11)

This class is similar to the closed low shrub-alder/willow classes except that 

alder/willow cover is less than 75 percent and is often found on southern aspects of low, 

rolling hills. Stands of open alder may be found on mid slopes of mountains (up to 600 m), 

upper slopes of rounded hills, and steep mid-slopes of hills. An under-story ofBetuIa sp and 

ericaceous species is common. Occasionally Equisetum sp. And various graminoids and 

forbs also are present.

Viereck classes: IIC2g, IIC2h, IIC2i, HC2k, IIC21

Open Low Shrub-Birch/Ericaceous (class 12)

This class is similar to the closed low shrub-birch/ericaceous class except that the 

shrub cover is less than 75 percent. It is found on various land forms, including foot slopes 

of mountains, lower to upper slopes of low, rolling hills, and tundra areas of broad, low 

relief. Low and dwarf types ofSalix are often present in small amounts; Carex bigelowii 

tussocks also may be found.

Viereck classes: IIC2c, IIC2d, HC2e, HC2f

Open Low and Dwarf Shrub Tussock Tundra (class 13)

This type is very similar to the open low shnib-birch/ericaceous, the main difference 

being that fewer low shrubs and a greater abundance of dwarf shrubs (less than 20 cm high) 

are associated with the graminoid tussocks. Shrub species are the same as other 

birch/ericaceous types. The graminoid tussocks may be formed by Eriophorum vaginatum or 

Carex bigelowii.

Viereck classes: IIC2a, IIC2b

Dwarf Shrub Tundra/Dwarf Shrub Peatland (class 14)

This shrub type is dominated primarily by shrubs less than 20 cm tall, such as dwarf 

birch (Betula glandulosa and/or B. nand) and dwarf ericaceous shrubs (Cassiope tetragona, 

Arctostaphylos uva-ursi, Vaccinium uliginosum, V. vitis-idaea, Ledum palustre, Dryas 

integrifolia or D. octopetala, Rubus chamaemorus, and Salix sp). Tundra communities are 

most common on upper slopes of mountains in the west or on hill and mountain slopes in the 

east. Peatland communities are common in low areas and also may include Oxycoccus sp. 

Mosses (primarily Sphagnum sp.) are common in peatland types; however, lichens 

(Stereocaulon sp., Thamnolia sp., and other fruiticose and crustose species) are more 

common in the tundra sites.

Viereck classes: IIDla, IIDlb, IID2b, Hd3a

38
Open Dwarf Shrub - Talus/Lichen (class 17)

This class consists of plants less than 20 cm high with 25 to 74 percent of the cover 

being dwarf shrubs and associated with talus and (or) lichen-covered talus. It is common on 

dry upper hill and mountain slopes or on other sites prone to windblown conditions. Various 

lichens (for example, Cetraria, Thamnolia, and Stereocaulen) are usually present in large 

amounts. Plant species may include Dryas integrifolia and D. octopetala on dry river 

terraces or ridge tops, in combination with Empetrum nigrum, Betula nana, Vaccinium vitisidaea, and Arctostaphylos uva-ursi at the base of steep slopes, on solifluction lobes, and in 

areas of late snowpack. Various grasses and sedges (Carex sp.) also are common. Other 

plants present to a lesser extent may include various species ofSalix, Potentilla, Hedysarum, 

Rhododendron, Artemisia, Oxytropis, and Papaver. Some hill slopes also may contain 

Equisetum sp.

Viereck classes: IIDlc

Moist/Dry Herbaceous (class 19)

This type is dominated by sedges (primarily Carex aquatilis and C. bigelowii), 

normally with greater than 60 percent cover. Most sites will have other grass or grass-like 

plants (Eriophorum sp., Luzula sp., andJuncus sp.), as well as scattered shrubs (Salix sp., 

Arctostaphylos uva-ursi, and Dryas octopetala) and forbs (Valeriana capitata, Pedicularis 

sp., and Equisetum sp.) In some areas, C. bigelowii orE. vaginatum will form tussocks. 

Mosses and lichens also may be present in varying amounts in the understory.

These sites can occur on a variety of sites and slopes; however, they are commonly 

found on well-drained hill slopes that have been burned, and in low, broad basins, and on 

coastal plain tundras. Site moisture is subjective, but there is not standing water. However, 

some sites will have standing water throughout the spring thaw, but will become dryer as the 

summer progresses.

Viereck classes: fflAla, IIIA2a, HIA2d, IIIA2h, fflA2i

Wet Herbaceous (class 20)

Wet herbaceous is similar to the moist/dry herbaceous class in species composition 

but may also contain Arctophila fulva, depending on location. Hippuris vulgaris and 

Potentilla palustris may be present in limited amounts. This class is found primarily in low 

basins and on coastal plain tundra areas where water has been impounded. The chief 

difference between the wet herbaceous class and the previous class is that there is standing 

water at the wet sites throughout the growing season.

Viereck classes: fflASa, fflASc, HIA3f

39
Sparse Vegetation (class 22)

Areas that have more than 5 percent but less than 25 percent, vascular plant cover are 

mapped as sparse vegetation. Sites may include mountain or ridge tops, rounded or steep 

talus slopes, or fluvial gravel and sand bars. Vascular plants present in mountainous areas 

may include Silene acaulis, Diapensia sp., Cassiope tetragona, Rhododendron 

camtschaticum, Vaccinium uliginosum, Salix sp., Dryas octopetala, Arctostaphylos rubra, 

and Oxytropis sp. Fluvial areas may contain Epilobium angustifolium, Dryas integrifolia, D. 

drummondii, and species ofDraba and Carex. Crustose and fruitocose lichens (Cladina sp., 

Cladonia sp.) may be present in the mountainous areas and on talus slopes as small patches 

with greater than 25- percent cover, but the area as a whole would still be considered sparse 

vegetation.

Viereck classes: None

Barren (class 23)

Barren areas consist primarily of sand, gravel, rocks, and boulders of various sizes 

and are often associated with active floodplains, hill summits, and mountaintops. Vascular 

plant cover is normally less than 5 percent; however, varying amounts of crustose lichens 

may be found.

Viereck classes: None

Snow/Ice/Cloud (class 24)

This type was minimal in the project area, consisting primarily of lingering snow 

patches in the mountains.

Viereck classes: None

Clear Water (class 25)

This class includes lakes, ponds, rivers, and offshore water bodies with little to no 

particulate matter.

Viereck classes: None

Turbid Water (class 26)

Turbid water comprises lakes, ponds, rivers, and offshore waters that have high 

particulate matter.

Viereck classes: None

40
Shadow (class 27)

The shadow class represents those areas obscured from the sensor by mountainous 

terrain (for example, steep north-facing slopes). Vegetation may or may not occur in these 

areas, depending on a combination of slope, aspect, and elevation. This class also includes 

those areas that are indicated as shadow because the resolution of the digital elevation data 

used to modify some spectral classes was more coarse than the Landsat data (see discussion 

section).

Viereck classes: None

41
APPENDIX B

Polygon information used for assessment of final map classes 

from Noatak National Monument

Polygon Number Fuzzy Polygon Number Fuzzy 

ID LCC of Pixels Label ID LCC of Pixels Label

2007

2007

2007

2007

2007

2007

2007

2022

2022

2022

2022

2022

4008

4008

4008

4008

4008

4008

7010

7010

7010

7010

7010

7010

7010

10002

10002

10002

10002

10002

10002

10002

10002

10002

10002

9

11

12

13

14

19

20

8

11

12

13

19

11

12

13

14

19

20

8

11

14

17

19

22

23

2

3

8

9

11

12

13

14

19

20

1

47

17

50

128

31

1

2

126

15

20

1

1

17

61

2

10

1

3

4

5

6

48

10

15

3

4

1

36

66

69

16

4

1

1

1

2

5

5

4

4

3

5

4

3

3

3

1

3

3

1

4

5

1

1

1

3

2

5

4

1

1

1

1

1

1

3

1

4

5

12003

12003

12003

12003

12003

13005

13005

13005

13005

13005

13005

13005

13005

15001

15001

15001

15001

15001

15001

15001

15001

15001

15003

15003

15003

15003

15005

15005

15005

15005

15005

15005

15005

15005

17007

13

14

17

19

23

11

12

13

14

17

19

22

23

9

10

11

12

13

14

20

25

26

9

13

14

19

9

11

12

13

14

19

25

26

11

7

23

101

9

1

9

16

6

44

60

93

5

4

13

2

47

25

15

1

2

23

7

10

121

4

7

7

16

24

107

1

275

1

7

2

3

3

4

1

4

1

1

2

2

5

2

4

4

5

5

5

4

4

4

4

4

4

4

5

4

4

1

2

3

5

2

4

1

1

4

LCC refers to the different land cover classes shown in Table 4.

42
Polygon information used for assessment of final map classes 

from Noatak National Monument - continuned

Polygon Number Fuzzy Polygon Number Fuzzy 

ID LCC of Pixels Label ID LCC of Pixels Label

17007

17007

17007

17007

17013

17013

17013

17013

17013

17013

24006

24006

24006

24006

24011

12

13

14

19

8

9

13

17

23

25

11

12

13

19

11

36

319

172

50

9

29

4

8

4

23

1

57

158

37

3

4

3

4

4

5

5

2

2

2

3

3

3

5

4

5

24011

24011

24011

24011

25009

25009

25009

25009

25009

25009

25009

25013

25013

25013

12

13

14

19

11

12

13

14

19

22

23

14

22

23

13

74

2

81

15

63

24

85

1

17

7

79

11

73

4

4

1

4

2

2

2

5

3

4

4

3

4

4

43
Polygon information used for assessment of final map classes 

from Cape Krusenstern National Preserve

Polygon Number Fuzzy 

ID LCC of Pixels Label

1001

1001

1001

1001

2007

2007

2007

2007

2007

2007

7005

7005

7005

7005

7005

8002

8002

8002

8002

8002

8002

8002

8002

8002

8002

8002

13006

13006

13006

13006

13006

13006

13006

13006

13006

13006

14007

14007

14007

14007

14007

14007

14007

14007

14007

14007

14007

12

13

19

20

8

10

12

13

19

20

13

14

19

22

23

8

10

11

12

13

14

17

19

20

22

25

9

10

11

12

13

14

17

19

20

25

2

3

8

9

11

12

13

19

20

23

25

2

23

115

3

6

100

266

13

3

3

18

153

4

29

224

8

2

44

43

30

32

22

23

41

3

114

172

6

2

51

26

5

2

8

22

1

3

5

9

39

29

26

15

28

24

8

20

5

5

4

5

1

3

5

5

3

1

2

5

1

1

5

5

5

5

3

3

3

5

1

1

5

5

5

5

5

4

2

2

1

3

1

4

4

4

4

4

4

4

4

4

1

3

1

44
Polygon information used for assessment of final map classes 

from Kobuk National Park

Polygon Number Fuzzy Polygon Number Fuzzy 

ID LCC of Pixels Label ID LCC of Pixels Label

23

23

23

23

23

23

25

25

25

25

25

36

36

36

36

36

36

36

36

42

42

42

42

42

42

46

46

46

2

3

8

9

11

14

2

3

9

11

12

1

2

9

11

13

14

17

19

1

2

3

13

14

23

1

2

3

6

43

43

17

3

3

12

97

57

16

1

46

26

2

4

4

1

2

65

31

159

52

1

3

1

3

20

57

5

4

4

2

2

2

4

5

3

4

3

3

4

4

2

2

2

2

3

4

5

4

2

2

2

3

5

4

46

46

46

75

75

75

75

75

77

77

77

77

77

122

122

122

127

127

127

127

127

149

149

149

149

149

149

149

8

9

13

11

12

13

14

19

11

12

13

14

19

3

9

13

10

12

22

23

27

9

11

12

13

14

22

27

5

24

60

85

56

125

17

88

2

43

97

9

3

4

132

23

2

256

49

12

1

4

33

8

14

24

3

6

4

4

2

2

4

5

3

3

5

4

3

4

1

1

4

3

2

3

4

4

4

5

4

3

3

3

1

4

45