National Park Service 

U.S. Department of the Interior 

Natural Resource Program Center 

Lake Clark National Park and Preserve Land Cover 

Mapping Project User’s Guide

Natural Resource Report NPS/LACL/NRTR—1998/001 
ON THE COVER 

View from a ridge just south of The Tusk, looking upstream on the Neacola River. 

Photograph by: Page Spencer, National Park Service. 
Lake Clark National Park and Preserve Land 

Cover Mapping Project User’s Guide

Natural Resource Report NPS/LACL/NRTR—1998/001 

Pacific Meridian Resources 

421 SW 6th, Suite 850 

Portland, Oregon 97204 

National Park Service 

2525 Gambell Street 

Anchorage, AK 99503 

National Park Service 

September 1998 

U.S. Department of the Interior 

National Park Service 

Natural Resource Program Center 

Fort Collins, Colorado 
The Natural Resource Publication series addresses natural resource topics that are of interest and 

applicability to a broad readership in the National Park Service and to others in the management 

of natural resources, including the scientific community, the public, and the NPS conservation 

and environmental constituencies. Manuscripts are peer-reviewed to ensure that the information 

is scientifically credible, technically accurate, appropriately written for the intended audience, 

and is designed and published in a professional manner. 

The Natural Resources Technical Reports series is used to disseminate the peer-reviewed results 

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addresses natural resource management issues; natural resource inventory and monitoring 

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proceedings of technical workshops, conferences, or symposia.

Views, statements, findings, conclusions, recommendations and data in this report are solely 

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endorsement or recommendation for use by the National Park Service. 

Printed copies of reports in these series may be produced in a limited quantity and they are only 

available as long as the supply lasts. This report is also available from the Natural Resource 

Program Center website (http://www.nature.nps.gov/publications/NRPM) on the internet, or by 

sending a request to the address on the back cover. 

Please cite this publication as: 

Golden, M., and P. Spencer. 1998. Lake Clark National Park and Preserve land cover mapping 

project user’s guide. Natural Resource Technical Report NPS/LACL/NRTR—1998/001. 

National Park Service, Fort Collins, Colorado. 

NPS D-38, September 1998 
Table of Contents 

 Title Page 

 I. Introduction 

 II. Project Area 

 A. General Description 

 B. Mapping Extent 

 Figure II-1. Lake Clark Physiographic-Biogeographic Regions 

 Figure II-2. Lake Clark SPOT Scene Locations 

 Figure II-3.

 Lake Clark Mapping Extent 

 III Classification Scheme 

 A. Classification Scheme Development 

 B. Class List and Descriptions 

 C. List of Common Plants

 IV Mapping Methods 

 A. Overview 

 B. Scoping Meeting 

 C. Imagery and Ancillary Data Sets 

 Figure IV-1. IMA and GT Field Data Locations 

 D. Initial Classification 

 E. Field Data Collection 

 Figure IV-3. 1997 Field Training Sites 

 F. Post-Field Processing 

 G. Post-Classification Modeling 

 H. Draft Map and Editing 

 I. Post Accuracy Assessment Editing

 V Results 

 A. Land Cover Map 

 Figure V-1. Lake Clark Land Cover 

 B. Discussion of Results

 VI Accuracy Assessment 

 A. Methods 

 B. Results 

 Table VI-2. Lake Clark Accuracy Assessment Error Matrix (Excel spreadsheat) 

 C. Discussion of Results

 VII Conclusions 

 References Cited

 Appendix A. Background Information Regarding Digital Image Classification 

 Appendix B. Field Forms

 A. IMA 

 B. GT 

 C. 1997 Form

 Appendix C. AA Tally Table
Introduction and Project Background

 

I.A. Introduction.

In August, 1996, the Alaska System Support Office of the National Park Service (NPS) joined with Pacific 

Meridian Resources to develop and produce a digital landcover map of the lands encompassing and 

surrounding Lake Clark National Park and Preserve. This report documents this project, the resulting raster 

land cover map, and the methods and procedures used. In addition to this report, a digital user’s guide (in 

html format) has been produced for the Alaska System Support Office. 

I.B. Project Background

The Alaska Support Office (AKSO) initiated a program to develop landcover maps of Alaska’s National 

Parks in the early 1980’s. The need of fire management professionals for fire fuels information, which can 

be derived from landcover maps, was the initial impetus for developing the program. In 1983, the NPS Fire 

Management Program (FirePro) and the AKSO (then called the Alaska Regional Office) initiated pilot 

projects in Denali and Wrangell-St. Elias National Parks to collect field-based data in support of landcover 

mapping. FirePro crews have continued collecting data through the present in Denali and Wrangell-St. 

Elias, and expanded to include the other "FirePro" parks of Gates of the Arctic, Bering Land Bridge, 

Noatuak, Kobuk Valley, Cape Krusenstern, Yukon-Charley Rivers, Lake Clark, and Katmai. Satellite 

multispectral imagery (Landsat Thematic Mapper and SPOT) has been purchased, from the mid-1980’s 

through 1996. These field and imagery data comprise the major source of information from which the 

landcover maps are derived. Through in-house efforts, interagency partnerships and contracts, the Alaska 

NPS has completed maps for 7 of the 15 units in the state. This report describes the work completed for 

Lake Clark through a teaming effort with Pacific Meridian Resources. 

Since the early years, the program has grown to support a wide variety of resources management needs in 

addition to fire management. Most recently, the Alaska NPS program has received support from the 

national NPS Inventory and Monitoring Program. This program’s purpose is to provide baseline inventory 

information for several "nominal" data themes, of which vegetation is one. The vegetation inventory 

information will be used by scientists, managers, interpreters, park rangers, and planners in better managing 

the resources under NPS care as well as in better serving the public’s information needs. The Inventory and 

Monitoring Program’s goal is that information will be applicable at the local level, while being 

aggregatable for national reporting by using consistent classification schemes and methods. Toward that 

end, the Alaska NPS initiated projects with Pacific Meridian Resources in 1995 to accomplish the 

following tasks: 

 1. Evaluate the National Vegetation Classification System, developed by The Nature 

Conservancy for use in 

 the lower-48, and expand it for mapping Alaska vegetation types. 

 2. Automate much of the FirePro field-based data, 

 3. Conduct a landcover mapping project in Wrangell-St. Elias National Park and Preserve, 

 4. Conduct a landcover mapping project of Lake Clark National Park and Preserve. 

This report will focus on documenting Task 4. 
II. Project Area

A. General Description

Lake Clark National Park and Preserve is a four million acre area in the heart of the Chigmit Mountains on 

the Alaskan Peninsular. The area is a composite of ecosystems representative of many diverse regions 

throughout Alaska. The area encompasses a wide range of biological diversity because it lies at the 

intersection of four different Alaskan bio-geographic regions (Pacific coastal, Interior Alaska, Aleutian, and 

Alaskan arctic-alpine). Also, the climate and microclimate vary greatly within the area due to terrain 

variation and the relative differences of coastal and continental influences. The climate along the eastern 

side of the project (along Cook Inlet) is mild with high precipitation, while to the west more continental 

climates prevail with relatively cold winters, hot summers and low rainfall. 

Most of Lake Clark National Park falls within Gallant’s Alaska Range ecoregion (Gallant, et.al., 1995). 

Two areas are transitional to the Interior Forest ecoregion. These are the areas west of Telaquana Lake and 

the southwestern half of Lake Clark and out the Newhalen River. Racine and Young (1978) describe five 

physiographic-biogeographic regions within Lake Clark National Park and Preserve (see Figure II-1). The 

following paragraphs are taken from their descriptions. 

The Coastal Zone bordering Cook Inlet in the southeast corner of the proposal includes the lowlands 

between the inlet and the ice and snow of the Aleutian Range. Snowline occurs here between 1220 m (4000 

ft) and 1520 m (5000 ft). Pacific coastal forests of Sitka spruce occur here near their northwestern limit in 

North America. Alder thickets cover most of the uplands. Large tidal fluctuations in deep bays (Tuxedni 

and Chinitna Bays) and glacial outwash result in well-developed estuarine salt marsh. Coastal beach 

vegetation is also present as are intertidal communities. Permafrost is rare or absent in the Coastal Zone and 

the climate is warmer and wetter (150-200) cm annual precipitation) than on the west side of the park. 

The Montane Region includes parts of the Alaska and Aleutian Ranges and represents a vast upland of 

alpine tundra, glaciers, permanent snow, ice and rock. Except for a few passes that penetrate the mountains 

where there are forests (Merrill and Lake Clark Passes), the vegetation is sparse and represented mainly by 

alpine tundra and shrub thicket. 

Immediately west of the Montane Region (Alaska and Aleutian Ranges) and penetrating these ranges in 

places along their west side are the Lake Clark-Kontrashibuna and Foothill Lakes Regions. These areas are 

more closely related to Interior and Western Alaska than to the Coastal Region to the east. Precipitation is 

much less (50 cm or 20 in. per year), winters are colder and summers warmer than to the east. The Lake 

Clark-Kontrashibuna Region includes the Lake Clark drainage or watershed and constitutes low elevation 

valleys (around 90 m (295 ft ) and mountain slopes bordering the several lakes. Boreal forest vegetation is 

well-developed here and includes white spruce forest, black spruce muskeg and bogs, as well as deciduous 

paper birch and balsam poplar forests. 

The Foothill Lakes Region occupies the high plains and plateaus of ground moraine just west of the 

mountains. The area is characterized by a series of large, high elevation glacial lakes fed by meltwater 

rivers from the Montane Region. From south to north the main lakes are: Twin Lakes, 603 m (1979 ft ), 

Turquoise Lake, 763m (2504 ft ), Lake Telaquana, 372 m (1219 ft ), and Two Lakes, 345 m (1132 ft ). 

Because of the high elevations and sometimes poorly drained topography over much of this region, various 

forms of alpine tundra vegetation are widespread. However, Boreal Forest vegetation is well represented at 

elevations below 610 m (2000 ft ) on slopes bordering Lake Telaquana, Two Lake and to some extent Twin 

Lakes. 

The Interior Lowlands Region occupies a small area in the northwest corner of Lake Clark National Park 

and Preserve. Drainages here include the Telaquana River and the Stony River which flow west into the 

Kuskokwim River. Elevations of these broad flat valleys are low, usually around 320 m (1050 ft ) and the 

climate here is more interior than in any other part of the Park. Flat topography and poor drainage result in 

the development of black spruce-dominated ecosystems. Much of the Interior Lowland Region west of Two 

Lakes was burned and is in various stages of succession following fire. 
Figure II-1 Lake Clark Physiographic-Biogeographic Regions

(from Racine and Young, 1978) 
II.B. Mapping Extent

A total of eleven SPOT scenes cover Lake Clark National Park and Preserve. (see Section IV.C for scene 

id’s). Initially, the entire extent of the scenes were classified. Figure II-2 presents the extent of the scenes, 

their location within Alaska, and the park and preserve boundary. Aerial photo coverage was very limited 

outside the park boundary. Therefore, review of the classification and subsequent photo based reprocessing 

were limited outside the park and preserve boundary; and accuracy assessment sites (which had been 

delineated on the photography) were non-existent outside the photo coverage. Pacific Meridian Resources 

and NPS felt the classification was much less reliable outside photo coverage. Therefore, after processing, 

the mapping extent boundary was clipped to just outside the park and preserve boundary (Figure II-3). 
 

Figure II-2. Lake Clark SPOT Scene Locations. 

 
Figure II-3. Lake Clark Mapping Extent. 
III Classification Scheme

A. Classification Scheme Development 

As a starting point, the Draft National Vegetation Classification System (NVCS) as modified for Alaska 

(prepared under Task Order 2 of this contract) was reviewed for identifying the classification scheme and 

defining the individual mapping classes. However, the NVCS classes are not mappable directly from 

remotely sensed data and so modifications were allowed. The vegetation notes taken during the field 

reconnaissance in 1996 (see Section VI.C.3) and NPS Ecologist Page Spencer’s knowledge of vegetation 

conditions in and around Lake Clark National Park and Preserve were relied on heavily. Dr. Spencer 

developed an initial list of mapping classes that was reviewed by Page, Sara Wesser, Alan Bennett, and 

Laurel Bennett from NPS and Pacific Meridian Resources. A tentative list of classes was developed. Some 

class definitions were modified during mapping and one class (beetle infested spruce) was dropped due to 

insufficient field data and the inability of the imagery to consistently identify the class. Example 

photographs and descriptions of each class are presented in the next section. he final mapping classes are 

as follows:

1. Closed Coastal Spruce 19. Sedge and Shrub Bog Meadow

2. Closed Interior Spruce 20. Wet Sedge Meadow

3. Closed Birch Forest 22. Emergent Marsh

4. Closed Populus Forest 23. Sedge Dominated Mesic Tundra

5. Closed Mixed Forest 24. Grasslands

6. Open Spruce 25. Gravel and Sparsely Vegetated

7. Spruce Woodland 26. Bedrock

8. Open Mix Forest 27. Ice and Snow

9. Open Populus Forest 28. Clear and Deep Water

10. Open Birch Forest 29. Silty or Shallow/Murky Water

30. Marine Water

11. Closed Alder 31. Marine Mudflats

12. Open Alder 33. Cloud and Cloud Shadow

13. Willow Shrub

14. Low Shrub

15. Dwarf Shrub Tundra

16. Prostrate Shrub Tundra

17. Fellfield

18. Lichen Tundra
B. Class List and Descriptions

1. Closed Coastal Spruce

Closed coastal spruce indicates stands of conifer with total crown cover generally greater than 60% on the 

coastal side of the Alaska Range. Sitka and white spruce (Picea sitchensis and P. glauca) are the dominant 

species, with a deciduous component less than 25% of total crown cover. Sitka spruce is more predominant 

south of Tuxedni Bay, with white spruce becoming predominant north of Tuxedni Bay. The deciduous

species are balsam poplar south of the Big River, and a mixture of balsam poplar and paper birch from the 

Big River north. The understory consists of alder, devil’s club, salmonberry, and a ground cover of mosses 

with scattered grasses and forbs. 

These rich and productive forests are distributed in the major valleys and along the coastal plain of the east

side of Lake Clark National Park and Preserve. Large closed conifer stands grow on the old outwash plain 

east of Crescent Lake, and scattered throughout the valley downstream, and along the coastal plain between

the Red River and East Glacier Creek, and along the floodplain of West Glacier Creek. Stands of dense 

white spruce along Tuxedni Bay and north are becoming infested with spruce bark beetle. 
2. Closed Interior Spruce

Closed Interior Spruce indicates dense spruce stands on the west side of the Alaska Range. Closed spruce

stands generally run from 60% to 80% crown closure of tree species. White spruce is the predominant

overstory in this type, with some scattered black spruce near boggy margins, and balsam poplar and birch 

making up less than 25% of the total crown cover. Understory consists of scattered alder, ferns and forbs, 

and ground cover is a layer of mosses. 

Closed spruce forests occur in small patches along the north shore of Lake Clark, especially at the mouths 

of Kijik River and along Chulitna Bay, up Currant Creek on the south side of Lake Clark, some unburned 

riparian remnant stands along the Stony River, and scattered patches in the Necons River floodplain

northeast of Two Lakes. This type may be spatially confused with open spruce stands, especially on steeper

north facing slopes, and may have some spectral confusion with cloud shadows in the northwest portion of

the classification.
3. Closed Birch Forest

The closed birch forest type are dense stands of Kenai birch with crown closures of 60-100%. Balsam

poplar is often mixed with birch, especially near upper altitudinal limits around Lake Clark and along

riparian zones. Alder often fills in canopy openings and is a major component of the understory. Rose, 

highbush cranberry, grasses and forbs are common in the understory. Ground cover is sparse mosses and

litter. Scattered white spruce make up less than 25% of the tree canopy. 

Closed birch stands are most commonly found along the shores and associated valleys of Lake Clark, and 

on the southeast facing slopes above the Stony River. Scattered stands of closed birch may occur at the east 

entrance to Lake Clark Pass, and down in the valley of West Glacier Creek. Birch seems to be rare to

absent along the Cook Inlet coast between the Big River and the West Glacier Creek.
4. Closed Populus Forest

Closed Populus forests are primarily balsam poplar stands, with scattered aspen clones included in this

class. Balsam poplar grows in riparian zones and in chutes with surface or subsurface water flow along 

upper mountain sides, and occasionally forms timberline stands on southerly facing steep slopes. Crown

closure ranges from 60 to nearly 100%. Birch may be included in these stands, especially on upper slopes. 

Alder is a major component of these stands, filling in canopy openings and extending into the understory. 

White spruce may form up to 25% of the forest canopy, especially in mature riparian stands. Understory in 

dense balsam poplar stands is mostly alder, with scattered rose, some highbush cranberry, grasses, Forbs 

and ferns, and a ground cover primarily litter with scattered mosses. 

Closed balsam poplar forests are primarily on the south facing slopes of Copper Mountain above 

Kontrashibuna Lake, the south facing slopes at the upper end of Lake Clark and the valleys of Miller 

Creek, the lower Tlikakila River, and the lower Kijik River. Small aspen clones occur in the valley of the 

Stony River, and very rarely, along the terraces above Lake Clark west of Port Alsworth.
5. Closed Mixed Forest

Closed mixed forest are stands of primarily white spruce mixed with birch and/or balsam poplar, so that the 

total tree component is 60 to 100% crown closure. Mixes range from 25/75% proportions of deciduous to

conifer, to the reverse at 25/75% conifer to deciduous species. Openings in the overstory are often filled

with alder, which extends to the understory of the stand. A wide variety of understory and ground cover

communities are present, depending on the proportion of tree species and total crown closure. 

Closed (and open) mixed forests are dominant along the shores of Lake Clark and the associated valleys of

Miller Creek, Kijik River, Tlikakila and Chokotonk Rivers, Current Creek and the Tazimina and 

Kontrashibuna Lakes. Mix forest in the northern section of the park are in the upper reaches of the Stony

River, around Tequaluana Lake, the floodplain of the Neacola River, and on the east side in the broad

valleys of the North Fork and Big Rivers, lower Drift River and Crescent River and the north shore of

Tuxedni Bay.
6. Open Spruce

Open Spruce forests cover a wide variety of conifer communities with tree crown closure ranging from 25

to 60%. Deciduous trees (not considering alder), make up less than 25% of the total tree canopy. Open 

spruce is a dominant class in the lower elevations of Lake Clark National Park and Preserve. On the eastern 

Cook Inlet side of the Alaska Range, Sitka and white spruce form open stands, with alder understories and

associated grass and forb ground cover. Extensive stands of open Sitka spruce are on the slopes above 

Cook Inlet from the valley of East Glacier Creek north to the Red River/Saddle Mountain. In the old 

floodplain of East Glacier Creek, open spruce stands are interspersed with small sedge and Sphagnum bogs. 

Open white spruce stands occur in the Crescent River valley and the north side of Tuxedni Bay. These 

stands have a more open understory of alder and willow, with forb and moss ground cover. 

Open spruce in the Stony River valley and west of Telaquana Lake are predominately black spruce 

regrowth from wildfires in the early 1950's. As this area is the southern extent of the interior taiga, some

white spruce may mix into these stands. However, these conifers are often slow growing and stunted. 

Understory is dwarf birch, willow and ericaceous shrubs, with openings of sedge and Sphagnum bog. 

Discontinuous permafrost may be present under these stands. Open spruce in the Chilikadrotna and Little 

Mulchatna drainages west of Snipe and Fishtrap Lakes is primarily "black spruce-like" trees with an 

understory of low shrub and ericaceous shrubs. 

Open spruce stands in Lake Clark Pass are primarily white spruce with alder understory. The easternmost 

stands in this area are experiencing spruce bark beetle infestations. Open white spruce also grows around

Lake Clark east of Port Alsworth/Kijik Lake and associated valleys. Current Creek, Ospook Creek and

upper Tazimina River support open white spruce riparian stands. Extensive open black spruce stands grow

on the flatter country of Keyes Point and Chulitna River on the north and western shore of Lake Clark, and 

on the north facing slopes above Tazimina and Kontrashibuna Lakes. These stands have an understory of

willow, ericecaous shrubs, sedges and mosses.
7. Spruce Woodland

Spruce woodlands are very sparse conifer stands with total tree cover 10-25%. Some deciduous trees, 

particularly mature birch may be present in some stands. Spruce woodlands are intermixed with open 

spruce and their corresponding understory types, such as low shrub, alder or sedge and shrub bog meadow. 

Spruce woodlands in the Stony River and Telaquana Lack country are usually black spruce with wetland

and some low shrub understories. In the Mulchatna and Chilkadrotna drainages, stunted spruce stands have 

dwarf birch and willow understories with ericaceous shrubs and fruticose lichen/moss ground cover. Black 

spruce woodland in the Chulitna River country have sedge and shrub bog understories and frequent

openings. Spruce woodland on the south and west shores of Lake Clark are growing on well-drained lateral 

moraines and glacial outwash plains. Here they are white spruce with lichen and ericaceous ground cover, 

interspersed with birch stands and small sedge and shrub bog meadows. 

Spruce woodlands on the Cook Inlet side of the Alaska Range are concentrated in the outwash fans of the 

Red River, below Hickerson Lake, and the timberline reaches of the North Fork of the Crescent River. 

These are well drained Sitka and white spruce stands with alder understory. 
8. Open Mix Forest

Open mixed forest stands are very similar to closed mix stands, except the crown closure of tree species 

ranges from 25-60%. Mixes range from 25/75% proportions of deciduous to conifer, to the reverse at

25/75% conifer to deciduous species. Open mix stands are intermingled with closed mix stands in broad

swaths through the open slopes and valley bottoms of the study area. As in closed mix, the dominant

conifer species is white spruce, and the deciduous species are birch and balsam poplar. Alder is a major 

component in the understory and canopy openings. Spruce mixes will have a understory with more moss 

and ericaceous ground cover, while deciduous mixes will have a wider variety of shrubs such as rose and 

highbush cranberry, with ferns, forbs and litter for ground cover. 

Open mix is a common cover type throughout the country around Lake Clark and the valleys flowing into

it. Stands higher on the slopes, especially north facing areas, have a little bare rock cliffs and stringers of 

white spruce in the narrow drainages. An unusual community is the open mix regrowth in the fire around

Port Alsworth, which has a understory heavy in Labrador tea. Birch in these regrowth areas seem to be 

major moose browse. Open mix on the Cook Inlet side is concentrated in the valley of Crescent Lake and 

River, especially at the juncture of the North Fork and Lake Fork, Crescent River. Understory in this area is 

willow and alder, with sparse grass and forb and litter ground cover. 
9. Open Populus Forest

Open Populus Forest is almost exclusively balsam poplar, as most of the aspen clones seem to be mapped 

as Closed Populus Forest. Populus balsamiferia has forest cover ranging from 25-60%, with less than 25% 

of the tree cover in conifer. Alder is a common understory and interstial species for this type. Open Populus

grows primarily on floodplains and outwash fans, or occasionally, near timberline in areas with surface or 

subsurface drainages. Mature birch or white spruce may be mixed with balsam poplar. Often the large

sparsely spaced trees are used as nest sites by eagles and other large raptors.

Open Populus forests are scarce on the western side of the Alaska Range in Lake Clark National Park and

Preserve. Some stands occur at the mouth of the Kijik River and around Kijik Lake, on fans from streams 

draining into Tazimina and Kontrashibuna Lakes, scattered along the Stony River. Open Populus stands are 

more common on the eastern side of the study area in the large glacial valleys draining into Cook Inlet.
10. Open Birch Forest

Open birch is dominated with Kenai birch with tree crown closures from 25% to 60%. Balsam poplar and 

white spruce may be minor components of the tree cover in this type. Open birch is heavily infested with

alder in most locations, although dwarf shrub tundra may be the ground cover in the timberline ecotone. 

Open birch is intermingled with closed birch and mixed forest throughout the study area. 

Open and closed birch forest on the north-facing slopes of Tanalian Mountain near Port Alsworth, are 

regrowth stands from a wildfire in the early 1950's. Other open birch stands are the upper limits of forest on

many slopes around Lake Clark and valleys draining into it. Open birch stands also grow on south facing

slopes above the Stony River floodplain. Open birch is uncommon on the Cook Inlet side of the Alaska 

Range. Scattered birch stands grow in the West Glacier Creek valley, then skip most of the coastline 

northward until the east entrance to Lake Clark Pass near Big River Lakes. 
11. Closed Alder

The closed alder type is a dense community with crown cover greater than 75% and generally 100%.

Layers of alder overlap each other in impenetrable thickets, with occasional mountain ash or cow parsnip in 

the less dense spaces. Ground cover is primarily alder leaf litter with scattered grasses. The parasitic plant 

broomrape grows on alder roots. Alder thickets may form large patches surrounded by grassland or dwarf 

shrub tundra, or blanket the mountainsides for miles. Comparisons with historical records indicate that 

alder is spreading down across mountain valleys and closing off previous travel routes.

Alder is a nitrogen fixing species and is an early pioneer on recently exposed soils such as de-glaciated till. 

Alder grown on moderately drained to rocky sites from just about sea level to around 3500'. Open and 

closed alder together are the most extensive vegetated type in Lake Clark National Park and Preserve 

(second only to ice and snow). Even forested areas could be viewed as a dense alder layer with a few trees. 

This community pretty much rings the mountain regions from water to ice with openings for rivers and 

rocky cliffs, especially on the coastal side. On the western slope of the Alaska Range, closed alder grades

from timberline deciduous forest to tundra types above 3500'. 
12. Open Alder 

Open alder is basically smaller patches of Alnus viridis in a matrix of grassland or dwarf shrub tundra. 

Open alder is mapped in areas where the crown cover of alder is 25-75%. As in closed alder, the understory

is sparse grasses and forbs with leaf litter for ground cover. The grassland component is primarily 

Calmagrostis with rich variety of forbs: fireweed, Veratrum, Valeriana, monkshood and ferns. Open alder 

on the Cook Inlet side and some slopes in the extreme western part of the study area are mixed with 

grasslands. Open alder on the western side of the mountains down through the big lake country is usually

mixed with dwarf shrub tundra. This has low growing dwarf birch, blueberry, cranberry, mossberry

(crowberry), Labrador tea, and has a ground cover of mosses and lichens.

Open alder generally occurs at the upper edges of the alder zone where shrubs are grading into tundra

types. As such, it is on most of the upper mountain slopes throughout the park and preserve. The only

vegetated areas of the study area without substantial alder are the Stony River valley south over the 

Telaquana Highlands into the Fishtrap Lake valley, and the Chulitna lowlands. 
13. Willow Shrub

Willow communities make up a small proportion of the vegetated classes, but are important as moose 

habitat, especially in the western mountain valleys of the park. These predominately alpine and riparian

stands have been hand edited to separate them from the willow stands which are part of the low shrub type. 

Willow stands are dominated by Salix alaxensis and S. glauca growing one to over two meters high. They 

commonly grow on recently deglaciated moraines such as those in upper Currant Creeks or on active 

floodplains of flashy mountain streams. Scattered alder are often mixed with these willow stands. Ground 

cover is primarily rocks and dry habitat mosses. In some areas these stands are also important habitat for 

beaver.
14. Low Shrub

Low shrub communities are dominated by a mixture of dwarf birch and several willow species .5-1.5 

meters high. Crown closures range from a low of 25% to densest stands about 80%. Some alder may be 

mixed in with the dwarf birch and willow in this community. A low growing ericaceous layer Labrador tea, 

blueberry, cranberry, and mossberry lies under the taller shrubs and in openings between patches. Ground

cover is mosses with patches of fruticose lichens on dryer locations. 

The low shrub type is concentrated in the center of the western lake country; west of Telaquana and south 

to the Lachbuna lake valley, along the southwestern coast of Lake Clark and Lower Tazamina Lake.
15. Dwarf Shrub Tundra

Dwarf shrub tundra is often understory to types such as low shrub, open alder or spruce woodland. When

no overstory is present, this community is mapped as dwarf shrub tundra. This type is dominated by low 

growing ericaceous shrubs, dwarf birch and occasionally willow; all less than .33 meter high. Dwarf shrub 

tundra has a minimum vegetative cover of 25%, but more commonly has vegetative cover of 50-90%. This

type is often a good berry producer for blueberries, cranberries and mossberries, thus providing fall and

winter food for bears, ptarmigan and voles. Ground cover is mosses and lichens, mostly fruticose lichen 

species commonly called reindeer moss, and foliose Peltigeras. 

Most of the dwarf shrub tundra is distributed in association with low shrub tundra and spruce woodland in

the central lake country on the west side of the Alaska Range. Dwarf shrub tundra is common in the valley 

east of Fishtrap Lake, the upper reaches of what would be the south fork of the Chilikadrotna River, the 

drainages west of Twin Lakes and Turquoise Lake, and mixed with mesic sedge tundra on the Telaquana 

Highlands. Dwarf shrub tundra is widely scattered throughout the remainder of the study area, mostly in

patches on mountain slopes above the alder community and grading into rock and talus. As the terrain 

becomes more extreme and unstable, this type grades into prostrate shrub tundra.
16. Prostrate Shrub Tundra

Prostrate shrub tundra is a shorter, rockier community than dwarf shrub tundra. Vegetation is generally less 

than 6" high and often grows on very well drained, unstable substrates such as active talus, and niches on

bedrock. Prostrate shrub tundra is a micro-patchy community with tundra patches consisting of at least 50% 

cover throughout the unit. Actual tundra patches have more than 70% vegetative cover, usually mountain

avens, dwarf species willow, mossberry, buleberry, cranberry, several species of heather, and fruticose 

lichens like Cladinas and Cladonias. Prostrate shrub tundra is the predominant tundra type in the alpine

zone of the study area. It is distributed through the mountains and rolling uplands above the alder and dwarf 

shrub tundra, and into the rock and talus and glacier regions over 4000'.
17. Fellfield

 Fellfields are very dry rocky tundra communities dominated by mat and cushion plants and lichens. 

Fellfield usually grows on exposed wind-blasted ridges with little snow cover. Vegetation cover ranges 

from 30-70% with broken rock the remainder. Common species are mountain avens, moss campion, 

Diapensia and prostrate growing Labrador tea, blueberry, cranberry, dwarf willows, and mossberry. Lichen

flora is rich in crustose lichens on the rocks, with Allectoria spp, Cetrarias, and some Cladinas woven into

the prostrate vegetation. These areas form a micro-mosaic of communities with pockets of frost fracturing 

and snowbank accumulation zones. 

Fellfield is concentrated on the exposed moraines on the south side of the Telaquana Highlands and the 

corresponding moraines that wrap around the mountain to the south of Turquoise Lake.
18. Lichen Tundra

Lichen tundra is alpine tundra that is dominated by a rich fruticose lichen flora. Total vegetation cover is

greater than 70%, of which 50-70% is lichen cover. Vascular plants are ericaceous shrubs and dwarf birch, 

generally less than 6" high. Lichens are mats of Cetrarias (C. Islandica, nivalis, cucullata), Thamnolia, 

Cladonias and Cladinas with some Stereocaulon on unstable sites. Lichen tundra grows on gentle to 

moderately steep, well drained slopes. As these areas do not accumulate deep snow, they are available for 

winter caribou forage. 

Lichen tundra is sparsely distributed in the mostly unglaciated mountains in the southwestern part of Lake 

Clark Park and Preserve, and in the mountains north of Neacola River/Telaquana Lake. Lichen tundra is

nearly absent in alpine regions on the eastern side of the study area.
19. Sedge and Shrub Bog Meadow

Sedge and shrub bog meadows are a complex of perched wetlands in succession toward forested types.

Sedge tussocks and higher shrub hummocks are interspersed with little pools or streams. Often black

spruce is encroaching around the edges. Sedge tussocks are cottongrass in wetter sites, and Carex on higher

locations with cloudberry, horsetails, sundew and bog rosemary. Shrub hummocks have willow, shrubby 

cinquefoil or Myrica gale with sedges and various forbs. Ground cover in these wetlands is mosses, usually

Sphagnum spp. and litter. Soils are wet to saturated peat. 

Sedge and shrub bog meadows are intermixed with open and woodland black spruce in the lowlands on the 

western side of the park and preserve. These bogs are common in the Stony and Necons River valleys, 

scattered in the poorly drained morainal pockets in the Chilikadrotna Mulchatna drainages, then are 

common further south in the Chulitna River drainage and lower Lake Clark and Tazimina drainages. Sedge

and shrub bog meadows on the east side of the mapping area are on large river valleys of West Glacier 

Creek, outwash of Red River, old beaver pond complexes in the Johnson River, and large open wetlands in

the Crescent River and Polly Creek drainages. Extensive patches of this type are mapped in the outwash 

fans of the Drift, Big, Kustatan, McArthur, and Chakachamna, but the description may not be appropriate 

for these extensive wetlands outside the park and preserve boundary. 
20. Wet Sedge Meadow

Wet sedge meadows are rich wetlands which are usually located along the sides of large floodplains. They 

are intermittently flooded with fresh water, and usually have some standing water. Wed sedge meadows are 

dominated by Carex utriculata and C. aquatalis, and marsh fivefinger with willow in the slightly better 

drained areas. Although not widespread or extensive, these areas are very rich wildlife habitat zones,

especially for moose, bear, swans and other water fowl and beaver.

Moderately large wet sedge meadows are found on the large rivers draining the east side of the study area:

the outwash fan of the Tuxedni Glacier in the Tuxedni River floodplain, in the maze of drainages and 

beaver ponds at the upper end of Crescent Lake and upper North Fork Crescent River, and along the 

Neacola River and depositional area of Kenibuna Lake. On the western side, wet sedge meadows are

smaller and scattered in areas with wet micro topography. Wet sedge meadows are scattered throughout the 

lower Chulitna River, west of Snipe Lake, Stony River valley, as pocket wetlands west of Twin Lakes, and

perched in the old moraines on the south side of the Telaquana Highlands.
21. Salt Marsh

Salt marsh refers to a complex of wetland communities under tidal or marine influence along the Cook Inlet 

coast of the park and preserve. These marshes are dominated by Carex lyngbyaie and C. ramenskii, with

other sedges, forbs and grasses. See work by Jerry Tande and Alan Bennett (Tande, 1996) for more detailed

community descriptions and mapping detail of the salt marshes along the Cook Inlet coast. The salt 

marshes are vital habitat for brown bears, especially in the spring before salmon and berries are available. 

Prominent salt marsh areas are the heads and side shores of Tuxedni and Chinitna Bays and the lagoons 

behind coastal beaches at Spring Point, the fan of the Red River, and along the coast between Silver

Salmon Creek and the mouth of the Johnson River.
22. Emergent Marsh

Emergent marshes are small fresh water wetlands which are flooded throughout the growing season. They 

are usually interspersed with wet sedge meadows in large floodplains. Emergent vegetation growing in

these marshes includes horsetail, bog buckbean, aquatic sedges, and water lilies. These marshes are also 

rich wildlife habitat, especially for moose and nesting sites for waterfowl. 

Emergent marshes are mapped in the lower Tuxedni River, and as scattered pockets at the mouth of Silver

Salmon Creek and along the sides of West Glacier Creek. Emergent marshes fill the old river sloughs of the 

Stony River.
23. Sedge Dominated Mesic Tundra

Sedge dominated mesic tundra is a hummocky tundra of sedges and forbs which spreads across the gentle

rolling slopes of the Telaquana Highlands. Vegetation is dominated by Carex spp and forbs including colts 

foot, Sedum, Arnica sp., dwarf monkshood, and Selaginella and foliose lichen Peltiergia spp. Some dwarf 

shrubs are present, including blueberry, dwarf birch and dwarf willows. Subtle drainages in the area are 

slightly wetter with cottongrass as the sedge component. Mosses and litter make up the ground cover. 

The primary location of sedge dominated mesic tundra is on the rolling uplands between Telaquana and

Turquoise Lakes, named the Telaquana Highlands. This type is probably under mapped in the region due to

clouds and shadows in the area. Other mesic tundra patches are scattered in valley bottoms throughout the 

mountains on the western side of the Alaska Range divide.
24. Grasslands

Grasslands are diverse herbaceous communities of grasses and forbs. Calmagrostis grasslands grow in

large patches within the alder zone and in even larger extents near the upper extents of alder, grading into

rock and tundra types above 4000'. These communities are dominated by Calmagrostis with many large 

forbs. Primary forb species are fireweed, Veratrum, monkshood, Valeriana, Angelica and ferns. Scattered 

mountain ash or highbush cranberry may be present. Calmagrostis grasslands grow in association with 

open alder at the upper ends of large mountain valleys, especially the Tuxedni, Crescent, Drift and 

Tlikakila River drainages. Smaller patches of grasslands ring the mountains north of Lake Clark.

Another important grassland type is the Elymus community that grows along the marine shore just above 

the high tide line. Elymus grows in sand and is subject of occasional salt water flooding or splashing. It is 

crucial for holding beach lands against wave and tidal erosion. Elymus is the dominant species, with cow 

parsnip, beach pea, Honckenya and scattered composites. Elymus grasslands grow along the Cook Inlet 

beaches on the east side of the park and preserve. 

A minor but unusual component of the grassland type is a late snowbank forb community which grows in

small patches on valley slopes of avalanche runout zones and snowbank pockets. Prominent species are

Veratrum, geranium, dogwood, saxifrage and buttercup, with Cassiope tetragona as ground cover.
25. Gravel and Sparsely Vegetated

Gravel and sparsely vegetated is widely distributed through the study area in a variety of forms. Unstable

talus forms debris cones off steep bedrock ridges. Around glaciers the gravel is unsorted till in a variety of 

moraines on the ice and in recently deglaciated areas. These areas may have patches of prostrate shrub 

tundra attempting to colonize the constantly moving surfaces. Active streams and rivers have large areas of 

alluvially deposited gravel. Balsam poplar and willow seedlings are sparse on portions of the floodplains

which have not been subject to recent severe flooding. Coastal beach sands may be mapped as gravel on the 

Cook Inlet shore. And finally, volcanic ash deposits from recent eruptions of Mts Redoubt, Spurr and older 

Iliamna ash deposits are mapped as sparse vegetation where the shrub and tundra communities are 

colonizing the downwind ashfall areas. 
26. Bedrock

The bedrock class indicates barren rock which forms the spine of the Chigmit mountains and various 

outcrops throughout the park and preserve. Small patches of vegetation colonize bedrock fractures and 

sheltered niches at elevations below about 4000’. At higher elevations, bedrock is inhabited by howling

winds and dancing waterfalls. 
27. Ice and Snow

This class maps glacier ice and areas of late lying snow. Functionally, nothing grows there except algae

forming watermelon snow and ice worms. Large expanses of ice and snow blanket the Alaska Range 

mountains down the center of the study area. Occasional bedrock nunataks, moraines and volcanic vents 

penetrate the ice, but otherwise, ice and snow dominate the landscape above 4000' or so, and make large 

glacial excursions into lower elevations.
28. Fresh Water - Clear (Clear and Deep Water)

Clear water maps fresh waters in the park and preserve that are unsedimented, or lightly silty from glacial 

flour. These lakes and ponds are generally rich habitats for aquatic vegetation and native fish species. Most

small lakes deeper than about one meter, and many large lakes are mapped as clear water. Such lakes 

include Lake Clark, the Taziminas, Kontrashibuna, both Twins and Telaquana Lakes. Clear water streams 

generally did not get mapped in this classification. 
29. Water - Silty and/or Shallow (Silty or Shallow/Murky Water)

This class maps rivers and lakes with high sediment load from glacial runoff, and shallow lakes which 

generally freeze to the bottom during winter and thus are not good aquatic habitat. Lakes mapped as silty or

shallow include Little Lake Clark, Turquoise and Two Lakes, shallow portions of the Caribou Lakes and

Crescent Lake. Large braided glacial rivers were also mapped as silty waters, including Tuxedni, Tlikakila, 

Drift, North Fork Big, and Neacola Rivers. Sedimented plumes at the mouths of these rivers into Cook Inlet

are also mapped as extra silty. 
30. Marine Waters (Marine Water) 

Marine water refers to salt water of Cook Inlet. It is mapped exclusively on the eastern shore of the park

and preserve. Important marine bays to Lake Clark habitats include Chinitna and Tuxedni Bays. 
31. Marine Mudflats

Marine Mudflats are those silt and clay flats subject to diurnal tidal flooding. The tide stage during image 

acquisition shows Mudflats at the heads of Chinitna, Tuxedni Bays and Squarehead Cove. These area are 

rich clam beds which are used by bears during low tides in spring
32. Beetle infested spruce 

This class was not mapped in the final classification due to lack of field support data and the rapidly 

moving infestation between image date of 1995 and field work in 1997. Spruce bark beetles are primarily 

infesting closed and open spruce stands along the Cook Inlet coast and associated valleys from the south 

shore of Tuxedni Bay and north, and are moving west through Lake Clark Pass. As of summer 1997, spruce 

bark beetle infested trees were evident several miles west of the North Fork and Tlikakila River junction, 

and about half way down the Chokotonk River which flows into Little Lake Clark. 
33. Cloud and Cloud Shadow

This class accounts for both clouds and cloud shadows present over the study area during data collection on 

August 20 and 21, 1995. There is a flotilla of cumulus clouds in the northwest part of the study area in the 

Stony River valley west of Telaquana and Turquoise Lakes, scattered cumulus in the upper valleys of

Current Creek, Tlikakila River, upper Kijik River, and both sides of upper Twin Lake. A dense valleyfilling fog clogs the entire drainage of the Telaquana River between the lake and Telaquana Pass, and lays 

along the front of the mountain slope between Telaquana and Turquoise Lakes. Several con trails and their 

associated shadows traversed the southwest portion of the study area, but have been largely edited out and

remapped to their appropriate landcover type. 

Despite rigorous hand-editing, some confusion may remain with prostrate shrub tundra, talus/sparsely 

vegetated and wetland classes. 
C. List of Common Plants 

Common Name or term used Scientific Name (Caswell, 1998) 

 in Class Descriptions terms in ( ) are as used in Hultén (1968)

Sitka spruce Picea sitchensis 

white spruce Picea glauca 

black spruce Picea mariana 

Kenai birch Betula kenaica 

balsam poplar Populus balsamifera 

aspen Populus tremuloides 

alder Alnus viridis (Alnus crispa) 

mountain ash Sorbus scopulina, S. sitchensis 

dwarf birch Betula glandulosa, B. nana 

riparian willow (in Willow shrub class) Salix alaxensis, S. pulchra, S. barclayi 

alpine willow (in Willow shrub class) Salix brachycarpa, S. glauca 

low shrub willow (in low shrub & 

 open/woodland conifer) Salix fuscescen, S. pulchra 

dwarf willows (in tundra classes) Salix arctica, S. reticulata 

moss campion Silene acaulis 

Honckenya Honckenya peploides 

Arnica Arnica frigida 

colts foot Petasites frigidus 

Diapensia Diapensia lapponica 

dogwood Cornus canadensis 

mossberry (crowberry) Empetrum nigrum 

Labrador tea Ledum palustre, L. groenlandicum 

heathers Cassiope spp, Arctostaphylos spp. Luetkea pectinata, 

 Phyllodoce aleutica, Harrimanella stelleriana 

Casiope Cassiope tetragona Cassiope tetragona 

Ericaceous shrubs Vaccinium spp., Arctostaphylos spp, Ledum, Emptrum 

bog rosemary Andromeda polifolia 

blueberry Vaccinium uliginosum 

cranberry Vaccinium vitis-idaea 

bog buckbean Menyanthes trifoliata 

geranium Geranium erianthum 

devil's club Oplopanax horridus (Echinopanax horridum) 

Calamagrostis Calamagrostis canadensis 

Elymus Leymus mollis (Elymus arenarius) 

highbush cranberry Viburnum edule 

Veratrum Veratrum viride 

fireweed Epilobium angustifolium 

broomrape Boschniakia rossica 

beach pea Lathyrus japonicus (L. maritimus) 

buttercup Ranunculus sp. 

monkshood Aconitum delphinifolium 

rose Rosa acicularis 

mountain avens Dryas octopetala 

cloud berry Rubus chamaemorous 

shrubby cinquefoil Pentaphylloides floribunda (Potentilla fruticosa) 

 marsh fivefinger Comarum palustre (Potentilla palustris) 

Salmonberry Rubus spectabilis 

Sedum Sedum integrifolium (S. rosea) 

saxifrage Saxifraga sp. 

cottongrass Eriophorum spp. 

sedges Carex spp. 

sundew Drosera rotundifolia 

Angelica Angelica lucida 
cow parsnip Heracleum maximum (H. lanatum) 

Valeriana Valeriana capitata 

water lily Nuphar polysepala 

Myrica gale Myrica gale 

Selaginella Selaginella sibirica 

horsetails Equisetum arvense 

Peltigerias 

fruticose lichens Cladinas, Cladonias, Alectoria, Thamnolia, 

 Sterocaulon, Cetrarias (C. islandica, C. nivalis, 

C. cucullata) 

crustose lichens Umbilarica spp. and many others 

Sphagnum Sphagnum spp. 

 
IV Mapping Methods

A Overview

The Lake Clark Mapping Project was a joint endeavor between the National Park Service and Pacific 

Meridian Resources. The primary data sources were SPOT multispectral images, Alaska NHAP color infrared photography, and extensive field data. All digital processing was conducted using ERDAS Imagine 

software on a SUN Unix workstation. All image processing was conducted by Mike Golden from Pacific 

Meridian Resources. 

A total of 31 land cover classes (plus cloud cover) were identified for the Lake Clark mapping project. This 

project integrated digital image classification, photointerpretation, ground-based vegetation data, and 

knowledge of the vegetation of the park by NPS and Pacific Meridian Resources personnel. Initial mapping 

was accomplished using automated computer classification of the SPOT imagery. Subsequent iterations 

used the imagery as a geographic base and relied on photointerpretation and ground based vegetation data. 

The map was further improved by the use of spatial models developed by the NPS and Pacific Meridian 

Resources. A flowchart of the steps involved in producing the land cover map is presented in Figure IV-1. 

Detailed descriptions of the steps involved in producing the land cover map are presented in the sections 

that follow. 

A discussion of general image processing for land cover mapping is presented in Appendix A. 

B. Scoping Meeting.

On December 3 and 4, 1996, Pacific Meridian Resources met with The National Park Service for a project 

kick-off and scoping meeting. Present at the meeting were Jeff Campbell and Mike Golden from Pacific 

Meridian Resources; George Dickison, Sara Wesser, Page Spencer, Joel Cusick, and Beth Koltun from the 

NPS Alaska Support Office in Anchorage; and Alan Bennett from Lake Clark National Park. The purpose 

of the meeting was to provide Pacific Meridian Resources with background information on Lake Clark NP 

and the Park’s data needs, discuss available ancillary data sets, begin organizing communication and 

logistics for field work, and refine the list of mapping classes for the project. 
Figure IV-1. Lake Clark Mapping Project Flowchart. 

 
C. Imagery and Ancillary Data Sets

C.1. SPOT imagery data. The primary data source used to produce the land cover map was SPOT 

multispectral imagery. A total of eleven scenes, spanning three satellite paths were purchased by the NPS 

(see Figure II-2). Table VI-1 lists the scene identification numbers for each scene by path. Paths 442 and 

445 were acquired on Aug. 20, 1995; scenes from path 446 were acquired on Aug. 21, 1995. 

Table IV-1 SPOT Scene Identification

Path Scene Number Scene ID

Path 442 442-224 34422249508212137142X

442-225 34422259508212137212X

442-226 34422269508212137292X

442-227 34422279508212137372X

Path 445 445-224 34452249508212137121X

445-225 34452259508212137201X

445-226 34452269508212137281X

445-227 34452279508212137351X

Path 446 446-225 34462259508202156351X

446-226 34462269508202156431X

446-227 34462279508202156511X

The scenes were terrain corrected and projected to Albers (Alaska) projection using the following 

parameters and NAD27 datum: 

Projection Albers 

Units METER Spheroid CLARK1866 

Parameters: 

1 standard parallel 55 0 0.000 

2 standard parallel 65 0 0.000 

central meridian 154 0 0.000 

latitude of projection’s origin 50 0 0.000 

false easting (meters) 0.00000 

false northing (meters) 0.00000.

st

nd

Because paths 442 and 445 were collected at the same time (same satellite overpass, different sensors 

aboard the satellite), all parameters (sun angle, atmospheric conditions, satellite orientation, etc.) are 

identical except for sensor calibration. Therefore, the eight scenes in the two paths could be calibrated and 

merged into a single data set. Because the satellite orientation was identical, the registration between the 

two paths was nearly perfect. Path 442 was calibrated to path 445. Using the overlap between the paths, for 

each reflectance value in path 445, a histogram of corresponding reflectance values in path 442 was 
generated. This was done for each of the three spectral bands (up to 255 histograms for each spectral band). 

The histograms were examined and found to be normally distributed. It was hypothesized that the variance 

was due to within-path mis-calibration between individual detectors. A difference image for the overlap 

area was produced to examine this hypothesis. The difference image had distinct stripping in the path 

direction, verifying that various detectors were consistently brighter or darker than other detectors on the 

sensor. From the histograms, a "look-up" table was generated and applied to path 442. The resulting scene 

was merged with path 445 to produce a single image file from the original 8 scenes. This file was initially 

processed as a single file. The three scenes in path 446 were joined and processed as a second image file. 

Close inspection of the registration between path 445 and path 446 identified a mistregistration of these 

data relative to several other data layers in the Lake Clark digital data set (particularly 1:65000 

hydrography). The misregistration was not consistent across the SPOT scenes so a simple shift would not 

correct the misregistration. Pacific Meridian Resources and NPS agreed that requesting SPOT to re-terrain 

correct the images would probably not solve the problem, since the DEM data used was of too coarse a 

scale (1:250,000). Instead, Pacific Meridian Resources performed a precision correction using ground 

control points (gcp) selected on screen. These points were converted to an ArcInfo link coverage, and the 

images were "rubber-sheeted" in GRID using the ADJUST command. Three iterations of slecting gcp’s/ 

adjusting/reviewing were performed. This work was conducted concurrently with image classification, so 

the classified image files were adjusted along with the original image files. The final files could then be 

mosaiced without an obvious stitchline, and registered well with the hydology layer. 

In addition to the three spectral bands of SPOT multispectral imagery, two derrived bands were generated. 

A ratio band was created by ratioing band 2 (red visible) by band 3 (near infra-red) and scaled within the 

range of 8-bit data (0-255). This particular band combination has been shown to assist in minimizing the 

impact of shadowing in satellite image classification. A texture band was generated using Image Processing 

Workbench (IPW) software (Woodcock, et.al., 1993). IPW’s texture algorythm (3x3, adaptive window) 

was used to produce the texture band. It was anticipated that the texture band would help discriminate 

between low density forested sites (particularly low density spruce) and non-forested sites. The two derived 

bands were added to the original three spectral bands to produce images with 5 bands. 

C.2. 1:65,000 Color Infra-red Photography. Color infra-red photography (1,60,000) from the Alaska High 

Altitude Photography (AHAP) program was the secondary data set used to produce the classified image 

map. Nine-by-nine inch prints covering the Park and Preserve were available. 

 

C.3. FirePro data. Two sets of FirePro data were available for the Lake Clark Mapping Project. Twentyfour photos had been selected by NPS as Intensive Measurement Areas (IMA). On each of the photos, IMA 

polygons were delineated and vegetation data was collected for 499 polygons in the field by FirePro crews 

during the summers of 1983, 1984, 1986,and 1991. These IMA polygons were the primary source of 

training site data for classifying the imagery. Accuracy assessment polygons were also selected from the 

IMA polygons. Additionally, NPS had selected 200 Ground Truth (GT) points which were pin-pricked on 

photos and vegetation information collected in the field throughout the Park and Preserve. Both the IMA 

and GT data were entered into a FoxPro database as part of Task 3 of this contract. ArcInfo polygon and 

point coverages of the locations of IMA polygons and GT points were also generated as part of Task 3 and 

were used extensively in this project. The locations of IMA and GT sites are presented in Figure IV-2. An 

example of the IMA field form and the GT field form is presented in Appendix B. 
Figure IV-2. IMA and GT field data locations.
VI. Mapping Methods (cont.)

C.4. Field ‘96 notes. During the summer of 1996, Page Spencer conducted aerial reconnaissance of the 

Park and Preserve. She recorded extensive field notes on hard copy image plots and field notebooks. The 

purpose was twofold; 1) to gather basic vegetation information to help define mapping classes, and 2) to 

gather site specific vegetation data for use in the mapping. The site specific notes were later transferred to 

ArcView shape files and plotted over the imagery and later over interim draft map products. These notes 

were also used extensively in the mapping project. Mike Golden joined Page for two days of 

reconnaissance August 20 and 21, 1996. 

C.5. Arcview project coverages. The NPS supplied an Arcview 2 project for Lake Clark containing 

numerous physical, cultural, and baseline data sets. Of particular use to this project were: 

1:63360 park, preserve, and wilderness boundary – used for orientation on screen and hard copy 

map outputs.

1:63360 photorevised hydrography for the park – used for orientation and hard copy map outputs. 

Used to refine the terrain correction of the SPOT imagery.

1:25000 digital elevation models – used to produce raster coverages of slope, aspect, and 

elevation. These coverages were used in post classification modeling.

1:63360 quads of National Wetlands Inventory for part of the park – used to review the 

classification of wetland classes.

D. Initial Classification

The first step in the classification of the imagery involved reviewing and labeling all the available training 

sites. For each IMA polygon and GT site, the field data and field photo were examined. Sites were labeled 

according to the classification scheme described in Section III. Sites were flagged as unacceptable as 

training sites or accuracy assessment sites if the vegetation amounts fell near class breaks in the 

classification scheme (generally within 10% of the defining covertype percent). For example, for the break 

between open spruce (<60% cover) and closed spruce (>60%), sites between 50% and 70% would not be 

used. Sites were also eliminated if they did not appear homogenous (in terms of class) across the entire 

training site or were too narrow or too small to generate acceptable training site statistics. After labeling, 

the IMA polygons were stratified by class and one-half of each class was randomly selected as accuracy 

assessment sites (see Sec. VI.A). These sites were not used in the classification or reprocessing of the 

imagery. 

Image classification was performed separately on the two image files (mosaiced paths 442 and 445, 

referred to as the "interior image" throughout the remainder of this document; and path 446, referred to as 

the "coast image"). The same procedures were performed on each image. An initial unsupervised 

classification with 100 clusters was performed on each image. Visual assessment of this classification 

identified several spectral classes representing single classes (e.g., snow, or marine water, or rock, etc). 

These classes were masked out into a separate file and were not considered in subsequent classification 

iterations. 

Next, the remaining IMA polygons (those not chosen for accuracy assessment or eliminated as 

unacceptable) were used as supervised training sites, and the location and spectral signature of each site 

was digitized on the imagery using the aerial photography as a guide. To accomplish this task, the image 

analyst located a "seed" pixel within each IMA polygon using Erdas Imagine’s region growing tool. The 

seed pixel was allowed to "grow" into adjacent spectrally similar pixels based on thresholding parameters 

established by the analyst. The goal in "seeding" training sites is to produce a distribution of pixel digital 

number (dn) values that adequately cover the spatial extent of the site and are normally distributed with a 

fairly small standard deviation (typically 3 dn). The final group of spectrally similar pixels grown within 

each IMA polygon represented the supervised training site for the polygon. Spectral signatures for each site 

were then saved and labeled according to the mapping class definition. Site labels included the site 

identification and the mapping class number and text label. Additional information deemed useful by the 
analyst was often also attached to the training site label. For example, if the training site had a high

standard deviation in a particular spectral band this information could be added to the site label.

A second unsupervised classification (200 clusters for the interior image, 150 clusters for the coast image) 

was performed on the portions of the image that had not previously been labeled from the initial 

unsupervised classification. The unsupervised signatures were combined with the supervised signatures

into a single signature set. The spectral means of the signatures were then exported into a SAS data set and 

multivariate clustering of the means was performed (the parameters used were Euclidean distance,

complete linkage, normalization). Results were plotted in a tree diagram. Figure IV-3 presents a small 

portion of the spectral clustering for the interior image. The clustering produced three possible groupings of

classes: 

 1. groups of informationally similar supervised signatures and unsupervised signatures; 

 2. groups of informationally dissimilar supervised signatures.

 3. groups of unsupervised signatures (with no supervised signatures). 

Figure IV-3 Portion of Spectral Clustering of Means 

Grouping one represents classes which are informationaly consistent and spectrally homogeneous. We

would anticipate that these signatures would consistantly classify pixels correctly. The second grouping

(informationally dissimilar signatures) present the biggest challenge to the image analyst. These signatures 

are spectrally similar but represent different mapping classes. We would anticipate that these signatures

would produce class confusion if used in a computer classifcation. These classes need additional spectral

analysis, further clustering, or ancillary information to separate the classified pixels into the appropriate 

class. Some signatures from this second grouping need to be re-seeded or discarded as unusable signatures. 

The third grouping represents spectral classes which were not accounted for by the supervised signatures. 

These signatures must be identified and labeled through photointerpretation and/or field verification.

The clustering of supervised and unsupervised signatures is a very useful tool for examining spectral

signatures. However, it does not definitively identify which signatures should be used in subsequent

classification iterations. It is best used to identify, and focus attention on, those signatures that need 

additional analysis. The final decision on which signatures to use, which to eliminate, and which

classification technique to use must be based on the analyst’s knowledge and expert judgement.

After identifying "problem" signatures (grouping 2) from the spectral signature clustering, scatter diagrams

and ellipse plots were used to assess the signatures. Scatter diagrams plot the dn values of the signatures in

two dimensional plots (multiple plots of two-band combinations). Over these plots the standard deviation

values are plotted, forming an ellipse. Recall that the spectral clustering described above used the spectral 

mean of each band. Signatures could have spectral means which were similar, but have multivariate 

distributions which did not overlap. These signatures would not necessary cause class confusion in

computer classifications and ellipse plots would identify those signatures. On the other hand, signatures 

whose ellipses overlap are truly spectrally confused. Another analysis tool used was Imagine’s quick alarm

tool. Single or multiple signatures can be used in a parallel-piped classification on screen to quickly

identify which pixels would be classified by that signature. After examining scatter diagrams, ellipse plots, 
and quick alarms, numerous signatures were identified as truly "problem" signatures. These signatures were 

dropped from the signature set, but kept in a list of sites for future processing. 

From the spectral analysis described above, a single set of supervised and unsupervised training site was 

used in a maximum likelihood classification. The supervised training sites were assigned a color based on 

their class label; the unsupervised training sites were assigned colors to match their appearance on a false 

color infra-red image. The resulting classified image was visually reviewed for accuracy using the training 

sites, GT sites, and the field notes. The next step was to determine the appropriate label (or labels) for the 

unsupervised signatures. Using the classified image on screen, areas of homogeneous unsupervised class 

pixels were identified and transferred to the aerial photography These areas would then become supervised 

training sites by visiting them in the field and recording the vegetation data. The NPS and Pacific Meridian 

Resources identified sixteen areas to concentrate this effort. For each area, each unsupervised cluster was 

highlighted on screen and new training sites were delineated. . A total of 243 training sites were delineated. 

By concentrating in just sixteen areas, field logistics could effectively be planned and field costs 

minimized. These areas were selected to cover as broad a range of vegetation types as possible and cover 

areas of the park that were not already covered by IMA plots, GT plots, or field notes from 1996. 

Hard copy plots at 1:63360 of the classification was produced for each 15-minute quad covering the park 

and preserve. Hydrology was also plotted on these maps to help in orienteering. Plots of the unclassified 

imagery was also plotted. These plots were taken to the field, along with the aerial photos, and the newly 

delineated training sites. 

E. Field Data Collection

Between July 14 and July 22, 1997, Page Spencer from NPS and Mike Golden (the analyst from Pacific 

Meridian Resources) performed field data collection and general reconnaissance within the park and 

preserve. A helicopter was used for all transportation to and from field locations, using Port Alsworth as a 

base. The purpose of field work was to: 

 (1) collect additional training site vegetation data across the park. Most of the field sites were described 

entirely from aeiral reconnaissance from approximately 100’ to 500’ above the ground. Vegetation was 

recorded on a standardized field data collection form. An example form can be found in Appendix B. 

When practical and/or required, the helicopter landed and the crew collected vegetation data from the 

ground. 

 (2) perform an initial field-based review of the preliminary land cover classification. While flying 

between training site locations, and to and from Port Alsworth, notes were taken on the field map denoting 

areas correctly classified and areas needing reclassification. 

 (3) gain a better understanding of the vegetation types within the park and preserve, and how that 

vegetation varied across the park. Special notice was taken to the distribution of classes across the park and 

general aspect/elevation ranges of classes (e.g., the upper elevation range of prostrate shrub tundra in 

different parts of the park, etc.). 

 (4) gather additional vegetation cover type notes. While flying additional notes were taken on the 

photos and field maps detailing the mapping class at specific sites. These notes would augment the notes 

taken during the reconnaissance in 1996. 

 (5) calibrate the photo interpretation. Whenever the helicopter shut down (for training sites, lunch 

breaks, etc), the crew examined stereo photo pairs and compared them to the corresponding vegetation 

cover in the area. This helped calibrate the analyst for photo interpretation and image interpretation later 

back in the office. 

The locations of the training sites collected in 1997 are presented in Figure IV-4. 
Figure IV-4. 1997 Field Training Sites. 
F. Post-Field Processing

After the field data collection, all the new training sites were labeled and entered into a FoxPro database. 

These sites were digitized and the spectral signatures for each training site were added to the training sites 

used in the previous interim classification. Another iteration of spectral analysis (clustering of signatures 

means, quick alarms, and ellipse plots described above) was performed. Again, a list of any signatures 

eliminated due to spectral confusion was maintained for future processing. 

A second classification was run on the signature set developed after the spectral analysis. This 

classification was reviewed on screen, again using all available information (except accuracy assessment 

site data). At this point, the individual training site classes were not recoded to the mapping classes. 

Rather, the original training site class was maintained, and the appropriate mapping class color was 

assigned to each training site. The review indicated that while many pixels were misclassified, the spatial 

patterns in the pixels closely matched the vegetation patterns in the photos and imagery. It was decided 

that the most efficient way to reprocess and refine the classification was to rely on photo interpretation and 

spectral class labeling rather than additional iterations of masking, spectral analysis, and automated 

classification. This was particularly true for discriminating between alder shrubs and the broadleaf forest 

classes. 

The photo interpretation/spectral labeling was conducted primarily on a watershed by watershed basis. 

Individual watersheds (or comparable areas) were clipped from the image and processed individually, then 

overlain back into the classified image. The classification was compared to the photography, and field data 

and notes; and individual classes were highlighted on screen. Individual classes were then re-labeled where 

appropriate, often confined to specific portions of the watershed being reprocessed. Those training sites 

eliminated during spectral analysis were also examined at this point for re-introduction into the classified 

map. Particular attention was paid to sites that were collected within the area being re-processed at that 

time. These training sites were applied to a parallel-piped classifier on a class by class basis. Several 

iterations with varying parameter settings were often necessary to accurately classify the appropriate 

pixels. The final iteration was then saved and overlain back into the classified image. Quality control was 

maintained by taking copious notes, comparison to the field data and notes, and an index image map used 

to track which watersheds had been re-processed and the boundaries of each processing area. 

The final step in the reprocessing of the image involved simple manual editing of the pixels. Once again, 

the field data and notes, as well as knowledge of vegetation variation across the park, guided the decisions 

on map edits.
G. Post-Classification Modeling

Much of the remaining class confusion involved particular classes across wide areas in the images. These 

problems were best corrected using spatial models. Page Spencer and Mike Golden developed the models 

described below. Users of the final map should pay particular attention to the parameters of these models, 

particularly before determining elevational and aspect thresholds for particular species based on the map. 

Elevation/aspect thresholds may be due to the models, rather than a real threshold found in the field. 

 

Prostrate Shrub Tundra (pst) versus Saltwater Marsh. Below 100’ elevation, pst was recoded to salt marsh. 

Within the coast imagery (path 446), there had been consistent spectral confusion between pst and salt 

marsh. The saltwater pixels in high elevations had been corrected in the reprocessing stage. This model 

corrected the pst pixels that should have been labeled as salt marsh. 

Prostrate Shrub Tundra versus Gravel and Sparsel Vegetated (gsv). Between elevations of 100’ and 1000’, 

pst was recoded to gravel and sparsely vegetated (gsv). This corrected the pixels in stream beds classified 

as prostarte shrub. Between elevations of 1000’ and 1500’, spectral confusion between pst and gsv was 

corrected through manual editing. Above 1500’, pst was not recoded to gsv. 

Prostrate Shrub Tundra versus Rock. For prostrate shrub tundra an upper elevational limit was set. For the 

interior image (paths 442 and 445) above 3500’, pst was recoded to rock. This was based on field notes, 

and review of the imagery and photos. For the coast image (path 446), the upper limit for pst was 4500’. 

Above 4500’, pst was recode to rock. 

Rock versus Marine Mud. Along the coast, pixels of marine mud were often being labeled as rock. This 

model recoded rock to maine mud below an elevation of 100’. 

Rock versus Gravel and Sparsely Vegetated. In the interior image (paths 442 and 445), gravel and sparsely 

vegetated (i.e. talus) was overclassing in high elevation areas of rock. Above 4000’ elevation on southwest 

to northeast aspects, several of the classes from the first unsupervised classification were recoded from 

gravel and sparsely vegetated to rock. 

Alder versus Broadleaf Classes. In the interior image, there was consistent confusion between alder and 

broadleaf forest classes in several of the training sites. An elevation of 1800’ was used to separate these 

classes according to Table IV-2. 

 

Table IV-2. Alder versus broadleaf spatial model

 Training 

Site #

Below 1800 

recode to: 

Above 1800 

recode to:

79 Closed birch Closed alder

145 Open mix Open alder

155 Open mix Closed alder

157 Open mix Closed alder

172 Closed birch Closed alder

174 Closed birch Open alder

 Gravel in Lakes. In the interior image, pixels of water in many lakes and ponds were 

being classed as gravel and sparsely vegetated. Training site number 186 was the primary 

training sites responsible for this confusion. To correct this, on slopes less than or equal 

to one percent, training site 186 was recoded to clear water. 
Water classed on shadowed slopes. A consistent spectral confusion in most any image classification is 

water and steep shadowed slopes (both appear very dark in the imagery). Aspect and elevation were used 

to correct this. First, pixels classed as water on the shadowed slopes were examined to determine the 

appropriate class. In some areas the appropriate class was ice and snow; in other areas the appropriate 

class was rock. An Imagine area-of-interest (aoi) file was used to identify the appropriate areas. The pixels 

of water were then recoded if the aspect was between 0 and 30, or greater than 290, and the slope was 

greater than 50 percent. 

Wetland classes mis-classed on high elevation sites. On high elevation sites, often pixels were often misclassified as wet sedge or sedge-shrub bog. The imagery, photos, and field notes were reviewed and the 

appropriate classes identified. On slopes greater than 15 percent and elevations above 2953 feet (900 

meters), wet sedge was recoded to prostrate shrub tundra. On slopes greater 15 percent and elevations less 

than 2953 feet, wet sedge was recoded to open alder. For sedge-shrub bog pixels, if the slope was greater 

than 23 percent and the elevation greater than 2953 feet, the pixels were recoded to prostrate shrub tundra. 

For slopes greater than 23 percent and elevations below 2953 feet, sedge-shrub bog pixels were recoded to 

open alder.

H. Draft Map and Editing

After reprocessing and post-classification modeling, quad-based maps of the classified images were plotted 

at a scale 1:63360 scale for each of the 15’ quadrangles within the project area. The draft maps were given 

to the NPS for review and comments. During January, 1998, the draft maps were reviewed by Page 

Spencer and Lee Fink, Leon Alsworth, John Branson, and John Bundy from Lake Clark National Park. 

Comments were written directly on the draft maps as site specific comments and as general comments 

covering broad areas of the park and preserve. 

The comments from the draft map review were then incorporated into the classified map. At the same 

time, the field notes and training sites were again reviewed and incorporated into the map. This process 

involved manual editing of the map on a quad by quad basis. The resulting map was then recoded from 

the training site pixel classes to the thirty-two final mapping classes. This map was delivered to NPS and 

used in accuracy assessment, described in Section VI. 

I. Post Accuracy Assessment Editing

The accuracy assessment analysis was conducted and the results reviewed by Pacific Meridian Resources 

and NPS. While the accuracy was relatively high for land cover maps produced from remotely sensed data, 

Pacific Meridian Resources and NPS agreed to use the accuracy assessment results to further improve the 

map. In particular, accuracy assessment had identified class confusion between willow shrubs and low 

shrub. Based on these results, the willow class was redefined. Much of what was previously willow was 

redefined as low shrub (see Section III for final definitions of these classes). These changes were then 

incorporated into the map through an area recode and review of the photos and field data and manual 

editing. Since the accuracy assessment results would no longer be valid for the newly edited map, the 

accuracy assessment sites were now available for improving the classification. Those sites, and 

surrounding areas, misclassified were corrected during this editing stage. The results of the post accuracy 

assessment editing constitute the final map for the project. 
V Results

A. Land Cover Map

Figure V-1 presents the final classified map for the Lake Clark Mapping Project. Table 

V-1 presents the pixel totals and acre totals for the entire mapping project. Table V-2

presents the pixel totals and acre totals for just Lake Clark Park and Preserve. 

 

 

Table V-1. Summary of Land Cover Classification

Mapping Class Number of Pixels Acres % of Cover

1. Closed Coastal Spruce 117402 11604 0.19

2. Closed Interior Spruce 86562 8556 0.14

3. Closed Birch Forest 275580 27238 0.45

4. Closed Populus Forest 155081 15328 0.25

5. Closed Mixed Forest 772483 76352 1.27

6. Open Spruce 1785237 176453 2.93

7. Spruce Woodland 1386918 137083 2.27

8. Open Mix Forest 1529295 151156 2.51

9. Open Populus Forest 118371 11700 0.19

10. Open Birch Forest 171797 16980 0.28

11. Closed Alder 5319544 525784 8.72

12. Open Alder 4357817 430727 7.15

13. Willow Shrub 335266 33138 0.55

14. Low Shrub 1743986 172376 2.86 

15. Dwarf Shrub Tundra 2055457 203161 3.37

16. Prostrate Shrub Tundra 5941335 587242 9.74

17. Fellfield 32442 3207 0.05

18. Lichen Tundra 655564 64796 1.08

19. Sedge and Shrub Bog Meadow 1360306 134453 2.23

20. Wet Sedge Meadow 349707 34565 0.57

21. Salt Marsh 161973 16009 0.26

22. Emergent Marsh 121551 12014 0.20

23. Sedge Dominated Mesic Tundra 117744 11638 0.20

24. Grasslands 605439 59842 0.99

25. Gravel and Sparsely Vegetated 9462587 935282 15.52

26. Bedrock 3354784 331587 5.50

27. Ice and Snow 11048456 1092029 18.12

28. Clear and Deep Water 1319905 130459 2.16

29. Silty or Shallow/Murky Water 781081 77202 1.28

30. Marine Water 32341079 320348 5.32

31. Marine Mudflats 81051 8011 0.13

33. Cloud and Cloud Shadow 2132378 210764 3.50

Total 60980369 6027083 100.00
 Table V-2. Summary of Land Cover Classification within Park/Preserve 

Boundary. 

 

Mapping Class Number of Pixels Acres % of Cover

1. Closed Coastal Spruce 89022 8799 0.22

2. Closed Interior Spruce 61335 6062 0.15

3. Closed Birch Forest 211658 20921 0.52

4. Closed Populus Forest 101091 9992 0.25

5. Closed Mixed Forest 494787 48906 1.21

6. Open Spruce 1237395 122307 3.03

7. Spruce Woodland 974615 96333 2.39

8. Open Mix Forest 1034434 102246 2.54

9. Open Populus Forest 89645 8861 0.22

10. Open Birch Forest 162199 16032 0.40

11. Closed Alder 3265566 322776 8.01

12. Open Alder 3065567 303007 7.52

13. Willow Shrub 229126 22647 0.56

14. Low Shrub 1247201 123276 3.06

15. Dwarf Shrub Tundra 1413138 139678 3.46

16. Prostrate Shrub Tundra 4243441 419431 10.40

17. Fellfield 32282 3191 0.08

18. Lichen Tundra 447943 44276 1.10

19. Sedge and Shrub Bog Meadow 699602 69150 1.72

20. Wet Sedge Meadow 193090 19085 0.47

21. Salt Marsh 30029 2968 0.07

22. Emergent Marsh 18472 1826 0.05

23. Sedge Dominated Mesic Tundra 96961 9583 0.24

24. Grasslands 411158 40640 1.01

25. Gravel and Sparsely Vegetated 7247245 716333 17.77

26. Bedrock 2600282 257017 6.38

27. Ice and Snow 8097950 800419 19.85

28. Clear and Deep Water 1197250 118339 2.94

29. Silty or Shallow/Murky Water 340099 33616 0.83

30. Marine Water 3414 337 0.01

31. Marine Mudflats 3650 361 0.01

33. Cloud and Cloud Shadow 1446301 142956 3.55

Total 40785955 4031372 100.00
Figure V-1. Lake Clark Land Cover. 
B. Discussion of Results

The Lake Clark Mapping Project was a joint endeavor between the National Park Service and Pacific 

Meridian Resources. The primary data sources were SPOT multispectral images, Alaska NHAP color 

infra-red photography, extensive field data and notes, and input from NPS personnel most familiar with the 

park. 

This project differed from most image-based land cover mapping in that it relied more heavily on 

photointerpretation. The imagery provided the geographic base for the mapping. While the initial 

classifications were supervised and unsupervised computer classifications of the imagery, most subsequent 

processing was performed on a watershed by watershed basis guided by photointerpretation. Two factors 

made this the most efficient and effective way to produce the map. First, SPOT imagery’s limited spectral 

bands (green, red, infra-red) limited the ability of automated classification algorithms to discriminate 

detailed vegetation classes consistently across wide spatial extents. Results of the spectral analysis 

revealed that relying on computer classification alone would not produce acceptable results. However, the 

spatial resolution of the SPOT imagery did allow better visual image interpretation and the spatial 

vegetation patterns were evident in the computer classifications. Secondly, there was a wealth of field 

based vegetation data and notes to guide the image and photo interpretation. Between the IMA, GT, and 

the 1996 and 1997 field notes, nearly every drainage within the park and preserve boundary had some type 

of field data. This data was critical in reviewing computer classifications, in guiding the reprocessing, and 

the final editing. Without this data, results from this approach would have been disappointing, if not 

unacceptable. 

Another factor contributing to the success of this project was the local knowledge of NPS staff involved in 

the project. At the beginning of the project, the proposed mapping classes accurately reflected the 

vegetation classes found in the park, as well as those classes mappable with the imagery, photos, and data 

available. Also, the effort of NPS personnel during the draft map review greatly enhanced the quality of 

the final map product. 

While the spatial resolution of the imagery is 20 meters (each pixel is 20 meters by 20 meters), the map 

should not be used at inappropriately large scales. Because much of the mapping was based on the 

1:60,000 photography (training sites were delineated on the photos, visual review of the map on screen was 

compared to photos, and much of the reprocessing was based on photointerpretation), the appropriate scale 

of this map is that of the photos (1:60,000). Use of the map at larger scales is highly discouraged
VI Accuracy Assessment

A. Methods

A1. Background. Quantitative accuracy assessment (aa) of maps produced from remotely sensed data 

involves the comparison of a map with reference information. The purpose of quantitative accuracy 

assessment is the identification and measurement of map errors. There are two primary motivations for 

accuracy assessment: (1) to determine the overall accuracy or reliability of the map and the accuracy of 

individual classes, and (2) to understand the errors in the map. While more attention is often paid to the 

first motivation, understanding the errors is arguably the most important aspect of accuracy assessment. 

For any given class on the map, it is critical to know the probability of the site being labeled correctly and 

what other classes (and their probability) might truly be found there. Quantitative accuracy assessment can 

answer those questions. 

Usually funding limitations preclude the assessment of every spatial unit on the map. Because comparison 

of every spatial point is impractical, sample comparisons are used to estimate the accuracy of maps. These 

samples are commonly referred to as accuracy assessment sites. The standard method of reporting 

accuracy assessment results is the error matrix, a square matrix tallying sites by reference label (in the 

matrix rows) and by map label (in the matrix columns) (Congalton, 1991). Quantitative accuracy 

assessment is laborious and expensive, requiring many accuracy assessment sites to be statistically valid. A 

generally accepted rule of thumb suggests a minimum of 50 accuracy assessment sites per mapping class 

(Congalton, 1991). Rarely does time and budget constraints allow for this level of analysis and much fewer 

sites per class are often selected. Such compromises between statistical rigor and budgets are required in 

most quantitative accuracy assessments. These compromises must be considered when interpreting the 

results. 

A more detailed description of general accuracy assessment topics is presented in Appendix A. 

Accuracy assessment procedures involve four basic steps: (1) the selection of accuracy assessment sites, 

(2) labeling and rating of the accuracy assessment site labels, and (3) reporting the accuracy assessment 

results in an error matrix, and (4) analyzing the results of the assessment. 

A2. Selection of Accuracy Assessment Sites. As described in Section IV.D., after the IMA sites were 

reviewed and labeled, one-half of the sites for each class were randomly selected (by class) and set aside as 

accuracy assessment sites. Some sites were obscured by clouds on the imagery and were subsequently 

eliminated. The IMA sites had all been located in vegetated polygons, so the NPS supplemented them with 

sites selected in non-vegetated classes. These sites were selected by visual scanning of the photos and 

selecting sites representative of the particular non-vegetated class. After reviewing and eliminating cloud 

cover sites, a total of 216 sites were available for accuracy assessment. Table VI-1 tallies the sites by class. 

Each aa site location had been delineated on the aerial photography. The aa sites were then digitized on 

screen over the imagery. 
 Table VI-1. Accuracy assessment sites by class

Mapping Class Number 

of Sites Mapping Class Number of Sites

1. Closed. Coastal Sp. 1 17. Fellfield 0

2. Closed Interior Sp. 1 18. Lichen Tundra 1

3. Closed Birch 2 19. Sedge&Shrub Bog 10

4. Closed Populus 2 20. Wet Sedge Meadow 7

5. Closed Mix 5 21. Salt Marsh 2

6. Open Spruce 16 22. Emergent Marsh 3

7. Spruce Woodland 12 23. Sedge Dominated Mesic 

Tundra 0

8. Open Mix 12 24. Grasslands 1

9. Open Populus 3 25. Gravel & Sparsely Vegetated 20

10. Open Birch 3 26. Bedrock 10

11. Closed Alder 24 27. Ice and Snow 10

12. Open Alder 16 28. Clear Water 10

13. Willow Shrub 8 29. Silty and/or Shallow Water 3

14. Low Shrub 10 30. Marine Water 3

15. Dwarf Shrub Tundra 9 32. Marine Mudflats 4

16. Prostrate Shrub 

Tundra 8

 

 

A3. Site Labeling and Rating. Each aa site is assigned two class labels: a reference label, and a map 

label. The reference label was assigned after reviewing the field vegetation data for the site and using the 

mapping class definitions described in Section III-B. During the review, sites that fell near class 

breakpoints were not considered acceptable as accuracy assessment sites. The reference label represents 

the overall covertype class for the entire site. 

For the accuracy assessment map label, the digitized site locations were overlain on the cover type map and 

a summary of pixels within each site was generated. Rarely does a single pixel class fill an accuracy 

assessment site completely. Rather, aa sites are usually composed of a mixture of pixel classes. In order to 

compare reference labels to map labels, a map label, or range of map labels, must be assigned to the aa site 

based on the distribution of pixel classes falling within the site. Assigning labels to a mixture of pixel 

classes can be a subjective process subject to variation in human interpretation, and can have profound 

effects on accuracy assessment results (see discussion in Appendix A). To account for this variation, a 

simple form of fuzzy set theory (Gopal and Woodcock, 1994) was adapted for assigning map labels, 

described in the following paragraphs. 

Pacific Meridian Resources and the NPS developed general labeling “rules” (or guidelines) for assigning 

map labels from the pixel summaries. Using these guidelines, for each aa site all possible map labels were 

rated as “best”, “acceptable”, or “unacceptable”. If a single pixel class constituted two-thirds of the 

polygon, that class was assigned a “best” rating. For pixels with a forested label, the mid-point of the 

crown cover class was used to compute a weighted average composite crown cover call for the site. The 

weights used were: closed - 80%, open - 42.5%, and woodland - 17.5%. Likewise for alder pixels, the 

midpoints of closed (87.5%) and open (50%) were used to compute a composite alder crown cover 

estimate. The computed calls were then used to guide the rating of the map labels. The computed crown 

cover was not necessarily a hard rule for rating labels, but rather a guide. 
After assigning a “best” label for the site, the remaining possible map labels were assigned either 

“acceptable” or “unacceptable” ratings. Typically, if the calculated crown cover was within 10% of a class 

break, the adjacent label would be rated as “acceptable”. After determining “acceptable” labels, all other 

possible labels were rated as “unacceptable”. Pacific Meridian Resources and the NPS worked jointly on 

rating all the aa map labels. 

This approach for developing map labels from the pixel has been developed and applied in numerous 

Pacific Meridian Resources mapping projects, including the other mapping project under this contract 

(Wrangell-St. Elias). This approach has been found to be the most consistently reliable method for 

developing map labels for a single polygon based on it’s composition of individual pixels within the 

polygon. Polygon-based labels are necessary for comparison to the accuracy assessment reference site 

label that also describes the polygon as a whole, rather than the individual components making up the 

polygon. While computer algorithms may be developed to “calculate” a best label or a series of acceptable 

labels, rarely is there a better substitute for human visualization, knowledge of the mapping classes, and 

labeling of the site based on the composition of pixels making up the accuracy assessment site. 

A.4. Accuracy Assessment Table and Matrix Generation. The results of the site labeling and rating 

were tallied in an Microsoft Access database table (see Appendix C). Each site was entered as a row in the 

table, with fields for the site identification, the reference label, ‘best’ map label, and the individual class 

label ratings. For each possible map label ratings, a zero was entered for ‘unacceptable’ and a 1 for 

“acceptable”. The records in the table were then tallied in the error matrix presented in the next section. 

B. Results

Table VI-2 (presented as an Excell spreadsheet) contains the error matrix for the cover type map. Each 

accuracy assessment site was tallied in the matrix in the appropriate column (based on the best map label) 

and row (based on the reference label). Non-diagonal cells in the matrix contain two tallies. The first 

number represent those sites where the reference label matched an “acceptable” map label. These sites are 

considered a “match” for estimating overall accuracy. The second number in the cell represents those sites 

where the reference label matched an “unacceptable” map label. Overall accuracy was estimated as the 

percentage of sites where the best or acceptable map label(s) matched the reference label. The overall 

accuracy of the cover type map is estimated to be 83% (180/216). Individual class accuracies can be 

estimated by summing the 

number of matches in its row (or column) and dividing by its row total (or column total). Class accuracy 

by row represents “producer’s” class accuracy. That is, from a map producer’s viewpoint, when we know 

what the class label should be, what is the proportion of sites correctly mapped. Conversely, class accuracy 

by column represents “user’s” class accuracy. When a site is mapped a particular class on the map, user’s 

class accuracy estimates the percent of time the site is mapped correctly. Individual class accuracy 

estimates range from a high of 100% for several classes to a low of 0% (0/2 for Closed Populus producer’s 

accuracy). However, recall the discussion earlier regarding compromises between sample size and budget 

contraints. Many of the individual classes had very few accuracy assessment sites. Some classes had no 

accuracy assessment sites. Because of the small number of accuracy assessment sites per class, the 

estimated user’s accuracy and producer’s accuracy for individual classes are not statistically valid. Very 

little can be concluded from an estimate of 100% based on 1 sample (e.g., Closed Birch user’s accuracy) or 

an estimate of 0% based on just two samples (e.g., Closed Populus producer’s accuracy).
Table V-2. Lake Clark Accuracy Assessment Error Matrix 
C. Discussion of Results

Recall from Sections IV.H and IV.I that these accuracy assessment results are for the map prior to the post 

accuracy assessment editing. These results were used to further improve the map. While we can assume 

that the final editing improved the map, treads identified in the accuracy assessment are important to note 

and understand. 

Overall accuracy of the map was estimated at 83%. As with the review of most land cover classification 

error matrices, however, the most interesting and informative information can be acquired through a careful 

examination of the off-diagonal elements in the matrix. In following paragraphs, discussion of individual 

class accuracies will focus on user’s map accuracy. This is the more important measure from a map user’s 

perspective. 

One indication of trends within a class is the difference in number sites of that class in the reference data 

and in the map data. More sites in the map data indicates that the map tends to overestimate that class. 

Conversely, more sites in the reference data indicates that the map underestimates that class. For most 

classes, the row and column totals are comparable. The map seems to overestimate the amount of open 

alder (21 map labels versus 16 reference labels). From the matrix we can see that closed alder and willow 

account for most of the overclassed sites. We see the same trend in prostrate shrub tundra (12 map labels 

versus 8 reference labels). In this case, dwarf shrub tundra and low shrub account for most of the 

overclassed sites. For the willow class, we see the opposite; the map is underclassing willow (no map 

labels versus 8 reference labels). From a producer’s viewpoint (2 of 8 sites correct), we can see that the 

mis-classed sites were a variety of classes. This result prompted the decision to proceed with the post 

accuracy assessment editing. 

Another trend can be seen if we look at just forest cover density. The map tends to estimate forest crown 

cover lower than the reference data both for conifer and broadleaf species. On possible explanation of this 

trend is the difference in how individuals estimate crown cover (FirePro field crew versus the image 

analyst). 

Information on individual class accuracy can be categorized into three groups: 1) areas of high 

classification accuracy, 2) areas of class confusion, and 3) classes under sampled in the analysis. Each of 

these categories are discussed below. 

 

Areas of high classification accuracy. 

Open forested classes all had relatively high classification accuracy (from 86% for woodland spruce, to 

94% for open spruce) with the exception of open Populus. The spruce forest appeared distinct on both the 

imagery and the photos, and the accuracy reflects the reliable discrimination of these sites. 

Closed and open alder were mapped at accuracies of 92% and 76%, respectively. If we combine both 

classes, overall accuracy of alder is 91% (41/45). Some confusion exists between the alder classes and 

open birch and open Populus. This is not surprising since all four classes have similarly high reflectance in 

the infra-red band in the imagery. Also when the birch and Populus trees are small they appear very similar 

on the photos to the alder. Much of the confusion between alder and the broadleaf tress seems to occur 

when the trees are fairly small. 

Wet sedge meadow was also mapped at a high accuracy (87.5%). The wetland classes as a group (sedgeshrub bog, wet sedge meadow, emergent marsh, and salt marsh) were accuracy mapped at 83% (20/24). As 

with the spruce stands, the wetland classes were discernable on the imagery (when using specific image 

enhancements designed for wetlands) and this contributed to their relatively high accuracy. 

Finally, all of the non-vegetated classes had high estimated accuracy. Of those non-vegetated sites misclassified, all were confused with other non-vegetated sites. 

 
Areas of class confusion. 

Open Populus was the one forested class with low user’s accuracy (33%, 1/3). However, this class only 

had three sites and is also listed with those sites under sampled. Examining user’s class accuracy, the misclassed sites were closed Populus. Examining producer’s class accuracy shows us that open Populus is 

sometimes confused with alder. This is not surprising since alder is typically the understory component in 

open Populus sites. 

As described above, the willow class had the most confusion with other classes. As part of the post aa 

editing, willow was re-defined and this mitigated some of the confusion. The final edits also paid particular 

attention to the willow class. Willow remains one of the most difficult classes to map. It is often confused 

with alder on the map as well as on the photos. 

Low shrub user’s class accuracy is estimated at 64%. This accuracy should be higher in the final map 

because of the redefinition of the willow and the final edits. It is sometimes confused with dwarf shrub 

and/or prostrate shrub in the map. Likewise dwarf shrub (user’s accuracy of 67%) is sometimes confused 

with low shrub and prostrate shrub. 

Sedge-shrub bog user’s accuracy is estimated at 58%. However, producer’s accuracy is 90%. This implies 

that where sedge-shrub bog is found on the ground, it has been mapped correctly; however, the map is 

identifying additional areas incorrectly as sedge-shrub bog. Sixteen percent of the time the correct class 

was another wetland type (wet sedge meadow or salt marsh). The rest of the confusion was with open 

spruce, alder, and prostrate shrub (a surprising result). 

Classes undersampled in the analysis. 

None of the mapping classes met the rule of thumb of 50 accuracy assessment sites per class. Some of the 

class had so few samples that conclusions are hard to draw about their class accuracy. This was due to 

budget constraints. Accuracy assessment sites were drawn from the existing IMA polygons (one-half of 

the sites per class), supplemented with non-vegetated photo sites not visited in the field. Table VI-3 lists 

those sites that had few or no accuracy assessment sites. 

 Table VI-3. Undersampled Classes in Accuracy Assessment Analysis

Mapping Class Number of Sites 

by Reference Label

Number of Sites 

by Map Label

Closed coastal spruce 1 0

Closed interior spruce 1 0

Closed birch 2 1

Closed Populus 2 0

Open Populus 3 3

Open birch 3 1

Fellfield 0 0

Lichen Tundra 1 0

Salt Marsh 2 1

Emergent Marsh 3 3

Sedge Dominated Mesic Tundra 0 0

Grasslands 1 0
VII. Conclusion

The land cover of Lake Clark National Park and Preserve was mapped using SPOT mulit-spectral imagery, 

color infra-red photography, and extensive field data and notes. The mapping extent extended to just 

outside the park and preserve boundary. Areas outside the park were restricted due to limited photo 

coverage. A total of 31 land cover classes (plus cloud cover) were identified. While the spatial resolution 

of the imagery is 20 meters (each pixel is 20 meters by 20 meters), the map should not be used at 

inappropriately large scales. Because much of the mapping was based on the 1:60,000 photography 

(training sites were delineated on the photos, visual review of the map on screen was compared to photos, 

and much of the reprocessing was based on photointerpretation), the appropriate scale of this map is that of 

the photos (1:60,000). Use of the map at larger scales is highly discouraged. 

This project integrated digital image classification, photointerpretation, ground-based vegetation data, and 

knowledge of the vegetation of the park by NPS and Pacific Meridian Resources personnel. Initial 

mapping was accomplished using automated computer classification of the SPOT imagery. Subsequent 

iterations used the imagery as a geographic base and relied on photointerpretation and ground based 

vegetation data. The map was further improved by the use of spatial models developed by the NPS and 

Pacific Meridian Resources. 

Overall accuracy of the land cover map is estimated at 83%. However, due to the limited number of 

accuracy assessment sites per class, conclusions regarding individual class accuracies are dificult. 

Estimates of individual class accuracy are not statistically valid. Additional editing of the map after the 

accuracy assessment analysis improved the map above that reported.
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Appendix A

Background Information on Vegetation Mapping through Processing of Satellite Imagery

 

A. Vegetation Mapping through Processing of Satellite Imagery 

 With the launch of Landsat-1 in 1972, a new dimension was added to the capability of acquiring and 

analyzing natural resource data. Today, satellite imagery presents a more consistent, readily updateable, 

less expensive source of data for vegetation mapping. Several types of digital satellite imagery are 

currently commercially available and are increasingly used for mapping vegetation and other natural 

resource features. A digital satellite image is composed of pixels, or picture elements, which are the 

smallest spatial unit in the image. The spectral reflectance of each pixel in each band of an image is 

represented by a digital number (DN). Some imagery, such as SPOT panchromatic data, is composed of 

only one band. Other imagery, such as Landsat Multispectral Scanner (MSS), Landsat Thematic Mapper 

(TM), or SPOT multispectral, has multiple bands; each band representing a particular wavelength of 

energy. Spatial resolution also varies with the type of imagery. SPOT multispectral data, for example, has 

20 meter pixels while Landsat TM pixel size is 30 meters and MSS data has a spatial resolution of 80 

meters. 

 

Early in the Landsat era, it was found that forest site types could be mapped faster and often with lower 

costs than when employing many traditional methods such as basic photo-interpretation (Hame, 1986). 

Use of satellite imagery to map forest vegetation led to the recognition that there are several advantages of 

utilizing digital satellite data. These include the ability to classify a very large land area: 

(1) with great consistency; (2) despite poor accessibility; and (3) much more quickly and cost effectively 

than using traditional classification approaches, e.g. photo-interpretation (Talbot and Markon, 1986). 

 Initial vegetation and landscape characterizations using satellite imagery were limited to relatively low 

resolution Landsat Multi-Spectral Scanner (MSS) data. Fleming and Hoffer (1977) initiated much of the 

work related to classifying forest stand characteristics using MSS data including identifying various 

coniferous and deciduous forest species and defining a general classification break for spruce-fir crown 

closure. Another example of the use of Landsat MSS imagery to map forest land cover is a project in the 

Olympic National Park (Cibula and Nyquist, 1987). 

 

More recently, vegetation classification studies implementing digital satellite data have utilized higher 

spatial, spectral, and radiometric resolution Landsat Thematic Mapper (TM) data. Several studies have 

shown that the higher information content of TM data results in significant improvements in classification 

accuracy for more distinctive classes. For instance, classification accuracies of forest species (Horler and 

Ahern, 1986) and forest stand characteristics (Peterson et al., 1986, Williams and Nelson, 1986) were 

shown to improve with TM data, as opposed to MSS data. 

 Forest stand characteristics and tree species in all of the national forests and national parks in the Pacific 

Northwest have been mapped using Landsat TM data in order to assess the extent and distribution of 

potential old growth forests remaining in those areas (Green, 1990, and Green and Congalton, 1990). In 

this study conducted by Pacific Meridian Resources, forest tree size, stand structure, crown closure and tree 

species were mapped in order to identify portions of forest land that met USFS. Region 6 criteria for 

potential old growth based on the above characteristics. The project produced potential old growth 

classification accuracies of greater than 80% for the study area as a whole (Teply and Green, 1991). 

Similar studies utilized Landsat TM data in the classification of old growth forests in New Mexico 

(Gonzales, et al., 1992), and private and public forestland in the State of Washington (Bernath et al., 1992). 

 

In the classification of digital data, the means of obtaining training statistics for identifying various spectral 

classes within a study area are often crucial to the success of the classification. The process of multispectral classification is often thought of as being performed using either of two methods for obtaining 

training statistics: supervised or unsupervised (Jensen, 1986). Additional approaches based on 

modification of these two basic methods have proved more successful than one or the other. Two hybrid 

techniques, termed multi-cluster blocks and mono-cluster blocks respectively, have been found to produce 

a higher classification accuracy than either the unsupervised (clustering) or supervised approaches 
(Fleming et al., 1975; and Fleming and Hoffer, 1977). 

 

A methodology for combining unsupervised classes and supervised training fields for use in a classification 

process has been described by Chuvieco and Congalton (1988). Utilizing the cluster analysis statistical 

technique, training statistics generated from both supervised and unsupervised approaches are grouped 

together. The merging shows two major benefits. (1) It aids in the labeling of the unsupervised classes by 

grouping them with a known supervised training field while (2) it also helps to identify classes that possess 

no spectral uniqueness, which greatly restricts them from being accurately classified (Chuvieco and 

Congalton, 1988). This approach of obtaining training statistics for the classification of Landsat TM data 

has been shown to be very effective. Various mapping projects conducted by Pacific Meridian Resources 

in the Pacific Northwest have successfully utilized this method of training statistic derivation (Green and 

Congalton, 1990, and Teply and Green, 1991). This combined approach was used in this project. 

 

When an image is registered to the ground, each pixel contains x,y coordinates allowing for both the 

spectral and spatial analysis of the image. The spectral and spatial properties of satellite imagery can be 

used to map land cover using methods similar to those used in photo interpretation. The key to image 

classification (as well as to photo interpretation) is to learn when the image is a good predictor of 

vegetation type, and when it is not. 

 

Typically, photo interpreters assess photo color, tone, and texture to classify an aerial photograph. Often 

the color, tone, and texture of the photograph is insufficient information for the accurate classification of 

the photo. Thus, photo interpreters also rely on their knowledge of ecological relationships between 

vegetation, location, and position. Photo interpretation, while an extremely useful tool, is expensive, often 

inconsistent, and unable to account for the biodiversity inside a vegetation type polygon. 

 

The integration of GIS and image processing has allowed for the full implementation of photo 

interpretation techniques into image classification. Color, tone, and texture are represented in the image. 

Locational and positional information is available in ancillary GIS coverages such as elevation, slope, and 

aspect. Knowledge of ecological relationships is enhanced through other ancillary information such as 

research plot data and hydrology. 

 

The keys to the successful use of satellite imagery to map vegetation are analysis techniques and tools that 

emphasize (1) understanding the relationships between image variation and vegetation variation, and (2) 

the integration into the mapping process of (a)field information, (b) analog and digital ancillary data, (c) 

statistical analysis of the spectral and ancillary data, and (c) efficient classification, reprocessing, and 

editing techniques. 

 

B. Development of Database of Vegetation Characteristics 

 Satellite imagery can be used as a basis for developing many of the desired vegetation data themes through 

the integration of various ancillary data sources. Remotely sensed data (be it satellite imagery or aerial 

photography) can be used to collect information about vegetation conditions because there is a high 

correlation between variation in the imagery and variation in vegetation. 

 

Fortunately, most of the characteristics which cannot be directly mapped from the imagery, can be mapped 

through the development of relationships between the imagery, fire history, and/or landform 

characteristics. For example, vegetation height cannot be mapped directly from aerial photos or reflective 

satellite imagery. Vegetation height is, however, highly correlated with vegetation species compositions 

which can be estimated from remotely sensed data. The challenge is to determine what types of 

information are most effective and cost efficient for the development of the desired vegetation database. 

A more specific discussion of the application of these concepts to this vegetation landcover classification 

project is contained in the methods sections of this report. 

 C. Accuacy Assessment Background 
 Quantitative accuracy assessment of maps produced from remotely sensed data involves the comparison of 

a map with reference information (often erroneously referred to as "ground truth"). The purpose of 

quantitative accuracy assessment is the identification and measurement of map errors. There are two 

primary motivations for accuracy assessment: 

 - to understand the errors in the map (so they can be corrected), and 

 - to provide an overall assessment of the reliability of the map (Gopal and Woodcock, 1992). 

 Usually funding limitations preclude the assessment of every spatial unit on the map. Because comparison 

of every spatial point is impractical, sample comparisons are used to estimate the accuracy of maps. 

Accuracy assessment requires (1) the design of unbiased and consistent sampling procedures, and (2) 

rigorous analysis of the sample data. 

 In the past, thematic maps, such as forest type and vegetation cover maps, have often been prepared 

directly from aerial photography with no rigorous assessment of reliability. During the 1980's, discussion 

of accuracy assessment of maps derived from remotely sensed data was initiated (Smith and Kovalick, 

1985; Congalton and Mead, 1983; Story et al., 1984; Congalton, l991). While published research on 

accuracy assessment is now extensive, much less accuracy assessment has been accomplished on actual 

mapping projects. 

 

Experience with accuracy assessment indicates that the following factors are critical to successful design 

and implementation of map accuracy assessment: 

 1. The classification rules used to produce the map being assessed must be rigorous and well-defined. 

 2. Accuracy assessment procedures must be unbiased and consistent. Sites used to train the photo 

interpreter or image processing system cannot be used for accuracy assessment. Existing ground or photo 

data can be used only if it is reinterpreted under accuracy assessment procedures. 

 3. The sample design must be cost efficient. Because accuracy assessment can require a large number of 

sample sites, cost efficient design is imperative. 

 4. The accuracy of the reference data must be evaluated. In the past, reference data has been assumed to 

be 100 percent accurate. Pacific Meridian's experience has shown that differences between reference data 

and mapped data are often caused by factors other than map error (Congalton and Green, 1993). Photo 

interpretation often varies, especially in heterogeneous vegetation. It is important, therefore, that variation 

in reference data be quantified before being used to assess map accuracy. 

 5. The imagery or photos used to assess the accuracy of the maps must be of the same general vintage as 

those originally used in map classification. The greater the time period between the media used in map 

classification and that used in assessing map accuracy, the greater the likelihood that differences are due to 

change in vegetation (from succession, disease, etc.) rather than from misclassification. 

 In the 1970's, researchers, notably Hord and Brooner (1976), van Genderen and Lock (1977), and Ginevan 

(1979), proposed criteria and techniques for testing map accuracy. In the early 1980's more in-depth 

studies were conducted and new techniques were proposed (Rosenfield et al., 1982; Congalton et al., 1983; 

and Aronoff, 1985). As a result of this work and work by others, the error matrix (Story and Congalton, 

1986) became the standard medium for reporting remotely sensed data classification accuracies. 

 Error matrices compare map data to reference data (usually from photo interpretation or ground visits). 

Because field data are expensive to collect, the accuracy assessment of maps created from satellite imagery 

is usually performed by comparing the map to photo interpreted reference sites (Congalton and Green, 

1993; Maus et al, 1992; Bernath et al., 1992; Czaplewski, 1992). 

 A major assumption of the standard error matrix is that the label from the reference information represents 

the "true" label of the site and that all differences between the remotely sensed map classification and the 

reference data are due to classification and/or delineation error. Unfortunately, error matrices can be 

inadequate indicators of map error because they are often confused by non-map error differences 

(Congalton and Green, 1993). The confusion in the error matrix is a function of: 

 - Registration differences between the reference data and the remotely sensed map classification 

 - Digitizing errors 
 - Data entry errors 

 - Changes in land cover between the date of the remotely sensed data and the date of the reference data 

 - Mistakes in interpretation of reference data 

 - Variation in classification and delineation of the reference data due to inconsistencies in human 

interpretation of vegetation 

 If these sources of confusion are not controlled, then the error matrix should not be used to assess map 

accuracy. Confused error matrices have been shown to be biased estimators of accuracy (i.e., they 

systematically underestimate the accuracy of the map) (Congalton and Biging, 1992; Congalton and 

Green, 1993; Gopal and Woodcock, 1992). Thus, the error matrix is really a difference matrix and is only 

representative of error if the other sources of confusion have been accounted for. As Congalton and Green 

(1993) state, “It is critical that these differences be considered in any accuracy assessment." 

 Most of the sources of confusion can be controlled with rigorous quality control. Pacific Meridian 

Resources' methods include documented procedures, extensive training, and the consistent use of forms to 

categorize data. However, the classification systems applied to this project represent discrete 

classifications of phenomena that are continuous in nature with few distinct boundaries. In situations 

where classification system breaks represent artificial distinctions along a continuum, variation in human 

interpretation is often very difficult to control and, while unavoidable, can have profound effects on 

accuracy assessment results (Congalton, 1991; Congalton and Green, 1993). 

 

Several researchers have noted the impact of the variation in human interpretation on map results and 

accuracy assessment (Gong and Chen, 1992; Lowell, 1992; Congalton and Biging, 1992). Gopal and 

Woodcock (1992) state, “The problem that makes accuracy assessment difficult is that there is ambiguity 

regarding the appropriate map label for some locations. The situation of one category being exactly right 

and all other categories being equally and exactly wrong often does not exist." Gopal and Woodcock 

instituted the phrase "fuzzy set" logic to refer to this phenomenon. Lowell (1992) calls for "a new model of 

space which shows transition zones for boundaries, and polygon attributes as indefinite". As Congalton 

and Biging (1992) conclude in their study of the validation of photo interpreted stand type maps, "The 

differences in how interpreters delineated stand boundaries was most surprising. We were expecting some 

shifts in position, but nothing to the extent that we witnessed. This result again demonstrates just how 

variable forests are and the subjectiveness of photo interpretation." 

 However, photo interpretation has been used for over 60 years to effectively classify land cover type. 

Resource managers have long accepted maps made in this manner. The long history of reliance on photo 

interpretation for the production of resource maps is a testimony to both the value of the technique and the 

acceptance of some levels of confusion. While it is difficult to control variation in human interpretation, it 

is possible to measure the variation, and to use the measurements to compensate for differences between 

reference and map data that are caused not by map error but by variation in interpretation. There are two 

options available to control the variation in human interpretation to reduce the impact of this variation on 

map accuracy. One is to measure each reference site precisely to reduce variance in reference site labels. 

This method can be prohibitively expensive, usually requiring extensive field sampling. The second option 

incorporates fuzzy logic into the reference data to compensate for non-error differences between reference 

and map data. While the photo interpreter is an integral part of the process, an objective and repeatable 

method to capture the impacts of human variation is required. 
Appendix B

Field Forms
A. IMA Data Form. 

 
B. GT Data Form
C. 1997 Data Form 

Appendix C. 

Accuracy Assessment Tally Table 





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