National Park Service 

U.S. Department of the Interior 

Natural Resource Program Center 

Denali National Park and Preserve 

Landcover Mapping Project 

Volume 1: Remote Sensing Data, Procedures and Results 

Natural Resource Technical Report NPS/DENA/NRTR—2001/001 
ON THE COVER 

Graphic representing Landcover Map progress (photo on the ground, as it appears on satellite image and as represented in the 

landcover map 

Graphic by EarthSat Corp. 
Denali National Park and Preserve 

Landcover Mapping Project 

Volume 1: Remote Sensing Data, Procedures and Results 

Natural Resource Technical Report NPS/DENA/NRTR—2001/001 

Jennifer L. Stevens 

Todd Helt 

Earth Satellite Corporation, 

6011 Executive Blvd, Suite 400, 

Rockville, Maryland 20852. 

Keith Boggs 

Ann Garibaldi 

Alaska Natural Heritage Program, Environment and 

Natural Resources Institute, 

University of Alaska Anchorage, 

707 A Street, 

Anchorage, AK 99501. 

Jess Grunblatt 

National Park ServiceAlaska Support Office, 

2525 Gambell St, 

Anchorage, AK 99503. 

March 2001 

U.S. Department of the Interior 

National Park Service 

Natural Resource Program Center 

Fort Collins, Colorado 
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sending a request to the address on the back cover. 

Please cite this publication as: 

Stevens, J. L. K. Boggs, A. Garibaldi, J. Grunblatt, and T. Helt. 2001. Denali National Park and 

Preserve landcover mapping project Volume 1: Remote sensing data, procedures and results. 

Natural Resource Technical Report NPS/DENA/NRTR—2001/001. National Park Service, Fort 

Collins, Colorado. 

NPS D-387, March 2001 

PREFACE 

The final documentation for the Denali Landcover Mapping Project is presented in two volumes. 

Volume 1: “Remote Sensing Data, Procedures and Results” provides information on the 

technical aspects of map development so that Users may better interpret and understand the 

map’s strengths and limitations. Volume 2: “Landcover Classes and Plant Associations” 

provides detailed ecological information (plant associations, ecoregion and climate influences, 

etc.) for the study area. Users are encouraged to review both documents in order to fully 

understand the strengths, limitations and ecological implications of the landcover products. 

AUTHORS 

Jennifer Stevens is an imagery / geospatial analyst at Earth Satellite Corporation, 6011 Executive 

Blvd, Suite 400, Rockville, Maryland 20852. 

Keith Boggsis a vegetation ecologist at the Alaska Natural Heritage Program, Environment and 

Natural Resources Institute, University of Alaska Anchorage, 707 A Street, Anchorage, AK 

99501. 

Jess Grunblatt is the Land Cover coordinator at the National Park Service-Alaska Support Office, 

2525 Gambell St, Anchorage, AK 99503. 

Todd Helt was the Program Manager of the National Park Service Landcover Mapping Contract 

at Earth Satellite Corporation, 6011 Executive Blvd, Suite 400, Rockville, Maryland 20852. 

Ann Garibaldi was a vegetation ecologist at the Alaska Natural Heritage Program, Environment 

and Natural Resources Institute, University of Alaska Anchorage, 707 A Street, Anchorage, AK 

99501. 

ACKNOWLEDGEMENTS 

The authors wish to thank and acknowledge the National Park Service-Alaska Support Office for 

financial support of this project. We especially thank Jess Grunblatt (NPS-AKSO) for reviewing 

the document and for his help in all stages of the project. 

We are also very appreciative to Denali Park staff for assisting with logistic support during the 

1999 field season, for their positive comments and review of the draft map, and particularly for 

their enthusiasm for the landcover products. We especially thank Carl Roland, Denali Park 

Plant Ecologist, for his thoughtful input during various stages of the project. 

 

 

ABSTRACT 

Stevens, J. L. K. Boggs, A. Garibaldi, J. Grunblatt, and T. Helt. 2001. Denali National Park 

and Preserve landcover mapping project Volume 1: Remote sensing data, procedures and

results. Natural Resource Technical Report NPS/DENA/NRTR—2001/001. National Park 

Service, Fort Collins, Colorado. 

As part of the Inventory and Monitoring Program of the National Park Service (NPS), the Alaska 

Support Office (NPS-AKSO) commissioned Earth Satellite Corporation (Rockville, MD) and the 

Alaska Natural Heritage Program (University of Alaska Anchorage) to produce digital and 

hardcopy landcover maps and supporting documents for Denali National Park and Preserve 

(Denali NP&P). Using Landsat Thematic Mapper multi-spectral imagery as the primary data 

source and SPOT XS data as a secondary source, landcover was mapped at intermediate scales 

(1:63,360 - 1:100,000) with 25 classes following a modified version of the Alaska Vegetation 

Classification system at levels III and IV (Viereck et al. 1992). The study area was 

approximately 9.5 million acres and included the Park and Preserve as well as a surrounding 10 

mile buffer. 

The development of the Denali NP&P landcover map involved collection and analysis of fieldverified datasets and the analysis and interpretation of remotely sensed imagery (satellite and 

aerial photography). A supervised digital image processing approach using the Maximum

Likelihood (ML) classifier was performed on both Landsat TM and select SPOT XS scenes 

covering the study area. The base landcover map derived from the ML spectral process was then 

refined through iterative modeling using environmental and spectral data layers such as 

illumination, slope, elevation, Normalized Difference Vegetation Index (NDVI), and individual 

channels of the Landsat TM data. The digital image processing was guided by visual 

interpretation of the Landsat TM and SPOT XS data, review of available field data, spot 

checking of 1:60,000 high altitude color infrared photography, and input from key NPS 

personnel familiar with the Park. 

In addition to the detailed landcover map of 25 mapping classes, a more general 16 class 

landcover map was also created by combining ecologically and/or spectrally similar classes. 

Accuracy assessments of the detailed (25 class) landcover map and the general (16 class) 

landcover map were performed to evaluate the reliability of the remote sensing techniques and to 

provide users of the map with an indication of relative confusion among mapping classes. 

The results, based on a ~2 acre minimum mapping unit (MMU) dataset, showed an estimated 

overall accuracy of 81.6% for the detailed landcover map and 84.8% for the general landcover 

map. The overall accuracy provides an average classification accuracy, while the producer’s 

and user’s accuracies provide more detailed information about the errors of omission 

(producer’s error) and commission (user’s error) for the individual mapping classes. 
 

The area (acreage) and accuracies for the general landcover types in the Denali NP&P project 

area were as follows: 

General Landcover Classes 

PARK Park and Buffer Park and Buffer Park and Buffer 

 Area (acres) Area (Acres) Producer's % User's % 

Needleaf Forest 601,786.8 1,088,427.1 92.0 84.6 

Stunted Spruce 853,984.8 1,283,661.2 80.1 88.1 

Broadleaf 97,062.9 253,607.6 79.8 62.0 

Spruce-Broadleaf 149,929.4 509,643.9 59.4 88.8 

Tall Shrub 355,788.1 729,465.2 93.8 90.2 

Low Shrub (drier sites) 624,734.2 985,523.4 83.5 78.3 

Low Shrub (moister sites) 279,625.9 484,598.9 83.2 81.6 

Dwarf Shrub 420,740.9 625,249.4 79.2 80.4 

Herbaceous - shrub 114,555.7 228,214.9 81.7 87.9 

Wet herbaceous 38,426.7 74,171.7 68.0 87.4 

Sparse - Bare 881,606.6 1,100,182.7 94.6 88.1 

Snow - Ice 932,789.6 1,049,705.7 99.7 99.6 

Water 92,093.3 169,427.3 97.6 98.7 

Burn 172,528.5 296,213.5 NA NA 

Indeterminate 413,616.9 674,481.1 NA NA 

Totals 6,029,270.4 9,552,573.4 --- ---

Keywords: Alaska, Denali National Park and Preserve, landcover mapping, remote sensing, 

satellite imagery. 
TABLE OF CONTENTS 

 Page 

1.0 Introduction 1 

1.1 Background 1 

1.2 Scope of Project 1 

1.3 Description of Project Area and Environment 2 

2.0 Methods and Materials 4 

2.1 Overview of the Landcover Classification System 4 

2.2 Preprocessing of Satellite Imagery 8 

2.21 Scene Selection 8 

2.21a. Radiometric quality (Striping) issues 20 

2.22 Orthorectification of the Satellite Data 21 

2.23 Imagery Processing Blocks and Mosaic Design 21 

2.24 CIR Photography 25 

2.25 FirePro Database 25 

2.3 Spectral Training Database Development 25 

2.4 Goals and Preparation for the 1999 Field Season 28 

2.41 Image Map Production for Sampling Design 28 

2.42 Image Map Production for 1999 Field Work 29 

2.5 Field Data Collection Procedures 29 

 2.51 FVA ‘Fly Over’ Polygons 29 

 2.52 Ground Site Descriptions 30 

 2.53 Incidental Observations 31 

2.6 Supplementing the Spectral Signature Database with the 1999 Field data 33 

2.7 Supervised Maximum Likelihood Classification, Signature Evaluation and 

Mapping Class Refinement 33 

2.8 Spectral and Environmental Modeling 37 

2.81 Model Development and Implementation 37 

2.82 Summary of Key Data Layers used for Modeling and Editing 43 

2.83 Models and Edits to Reduce Spectral Confusion Between Mapping Classes 44 

2.84 Defining and delimiting the Southside Herbaceous Shrub Class 55 

2.9 Raster Editing and Filtering Approach 57 

2.10 Quality Control and Evaluation 58 

3.0 Results and Discussion 59 

3.1 Project Results 59 

3.2 Distribution of Landcover within Denali NP&P 

62 3.3 Accuracy Assessment 

64 

3.31 Discussion of Accuracy of Landcover Classes 77 

3.4 Discussion of Strengths and Limitations 80 

4.0 Literature Cited 81 
5.0 Appendices 

5.1 Key to Landcover Classes 83 

5.2 Landcover Mapping Class Descriptions 85 

5.3 1999 Field Season Forms 115 

5.4 Number of Signatures for each Mapping Class 117 

5.5 Confusion Matrices for Final Spectral Classifications 131 

5.6 Modeling Reports 145 

5.7 Glossary of Terms 159 

List of Tables 

Table 1. Target Mapping Classes 4 

Table 2. Final (25) Detailed Mapping Classes 6 

Table 3. Final (16) General Mapping Classes 7 

Table 4. Landsat TM and SPOT XS differences 8 

Table 5. Landsat TM and SPOT XS spectral characteristics 8 

Table 6. Imagery list 10 

Table 7. Imagery processing blocks 23 

Table 8. Confusion Matrix of TM Block1 39 

Table 9. Confusion Matrix of SPOT Block 41 

Table 10. Modeling Parameters 51 

Table 11. Area and Percent Distribution and Area of Detailed Mapping Classes 62 

Table 12a. General Mapping Class List 63 

Table 12b. Area and Percent Distribution and Area of General Mapping Classes 64 

Table 13. Confusion Matrix for the Detailed Landcover Map 67 

Table 14. Confusion Matrix for the General Landcover Map 69 

Table 15. Confusion Matrix for the Detailed (Northside) Landcover Map 73 

Table 16. Confusion Matrix for the Detailed (Southside) Landcover Map 75 

List of Figures 

Figure 1. Location of the Denali NP&P study area 

3 

Figure 2. Landsat TM scene, August 15, 1988 – 70/15 11 

Figure 3. Landsat TM scene, August 15, 1988 – 70/16 12 

Figure 4. Landsat TM scene, July 21, 1985 – 71/15 13 

Figure 5. Landsat TM scene, June 29, 1991 – 70/16 14 

Figure 6. Landsat TM scene, June 20, 1991 – 71/16 15 

Figure 7. Landsat TM scene, September 8, 1999 – 69/15 16 

Figure 8. Landsat TM scene, September 8, 1999 – 69/16 17 

Figure 9. SPOT XS Mosaic, September 4, 1996 18 

Figure 10. SPOT XS Mosaic, September 6, 1994 19 

Figure 11. Example of satellte data striping 20 

Figure 12. Imagery Mosaic Design 24 

Figure 13. Example of the spectral database development process 27 
Figure 14. Locations of FVA transects 32 

Figure 15. Example of data striping effects on the classification 35 

Figure 16. Example of spectral confusion 47 

Figure 17. Area of Herbaceous / Shrub reclass 56 

Figure 18. Detailed Landcover Map 60 

Figure 19. General Landcover Map 61 

Figure 20. Detailed Landcover Map shown with the Northside-Southside divide 71 
 

 

 

 

 

 

CHAPTER ONE 

INTRODUCTION 

1.1 BACKGROUND 

The Alaska Support Office (AKSO) of the National Park Service (NPS) sought to develop a 

digital landcover map of Denali National Park and Preserve (Denali NP&P). The project was 

part of the Inventory and Monitoring Program of the NPS, a program designed to provide 

reliable and consistent scientific information to assess the status and trends of national park 

ecosystems. Vegetation mapping is conducted under the inventory portion of the program in 

order to provide basic information immediately useful to the parks for making resource 

management decisions and designing monitoring programs. The primary intent of the landcover 

map was to provide baseline information about the landcover resources in Denali. Other intended 

uses included providing information useful for modeling wildlife habitat, fire fuel availability 

and the occurrences of sensitive species. 

The general approach and methods for producing the Denali NP&P landcover map was to derive 

approximately 30-35 endpoint classes from Landsat TM and SPOT XS imagery through spectral 

classification, environmental site modeling and raster editing procedures. Ultimately however, 

25 endpoint classes were found to be both spectrally discernible and ecologically meaningful. 

1.2 SCOPE OF THE PROJECT 

A project team consisting of resource managers from NPS-AKSO and Denali NP&P and 

representatives from Earth Satellite Corporation (Rockville, MD) and the Alaska Natural 

Heritage Program (Anchorage, AK) met in May 1999 to discuss, plan and develop logistics for 

the Denali NP&P landcover mapping project. Their discussions included project objectives and 

goals, user needs and requirements, classification system development and mapping classes, 

plans and logistics for the 1999 field season, and evaluation of available datasets (satellite 

imagery, air photos, vegetation data, etc.) for developing the landcover map. 

Landcover for Denali NP&P was mapped at intermediate scales (1:63,360 - 1:100,000) with 25 

classes, using a classification based primarily on vegetation structure and form (tree, shrub or 

herbaceous), and to lesser extent genera and species information. The mapping classification 

follows, with modifications, The Alaska Vegetation Classification system at levels III and IV 

(Viereck et al., 1992). The landcover classification key and mapping class descriptions used for 

this mapping effort are provided in Appendices 5.1 and 5.2 

The usefulness of the map classification to land managers can be increased dramatically when 

the landcover classes are linked to more detailed descriptions of plant communities such as those 

given in plant associations of the National Vegetation Classification System (NVCS; Grossman 

et al., 1998). NVCS plant associations are generally equivalent to Level V of Viereck et al., 

1992. Plant associations for the Denali NP&P study area are described in Volume 2: Landcover 

Classes and Plant Associations. 

1
 

 

Development of the Denali NP&P landcover map included the following stages: 1.) review of 

all existing vegetation data for the study area, 2.) collection and analysis of ground truth or fieldverified data, 3.) analysis and interpretation of remotely sensed imagery (satellite and aerial 

photography), 4.) application and evaluation of spectral classifications, 5.) development and 

application of models using ancillary digital data to distinguish among confused landcover 

(spectral) classes, and 5.) numerous qualitative and quantitative evaluation steps through out the 

process. 

1.3 DESCRIPTION OF PROJECT AREA AND ENVIRONMENT 

The Denali NP&P Landcover Project Area includes approximately 6 million acres from the Park,

and an additional 3.5 million acres from a 10 mile buffer surrounding the Park Boundary. Both 

the Park and the 10 mile buffer, a total of 9.5 million acres, were the primary area of interest for 

development of the landcover maps and supporting documentation. Figure 1 shows the general 

location of the Denali NP&P study area. 

The Park encompasses a diversity of vegetation and land forms in a landscape that is largely 

unchanged by human development. South of the Alaska Range, coastal influences dominate 

producing higher rainfalls and denser forests. North of the Alaska Range interior climate 

prevails with boreal forests transitioning to arctic and alpine tundra. Permafrost is a strong 

influence in this area of the Park. The Alaska Range itself is an immense region dominated by 

glaciers, snowfields and rock escarpments. 

The decision to use satellite imagery (both Landsat Thematic Mapper (TM) and SPOT 

multispectral (XS)) for the project was based on the vast project area, the level of mapping 

information requested by NPS, the spatial and spectral characteristics of the available 

sensors/data, and the time and budget resources available for the project. 

2
 

Figure 1. Location of the Denali NP&P study area 

3
 

 

 

 

 

 

 

 

CHAPTER TWO 

2.0 METHODS and MATERIALS 

2.1 Overview of the Landcover Classification System 

An initial list of over fifty Target Mapping Classes (Table 1) consisting of landcover types 

believed to potentially occur within the study area was developed. This list was developed from

review of the Denali Fire Program (FirePro) vegetation data as well as from vegetation literature 

reviews of the study area (refer to Volume 2). The target mapping classes were used to help 

guide field collection efforts, and were later combined or dropped (based on ecological and/or 

spectral imagery considerations) to form the 25 endpoint mapping classes used in the final 

detailed landcover map (Table 2). A landcover class key and descriptions of the final 25 

(detailed) landcover classes are provided in Appendices 5.1 and 5.2. For additional discussion 

on the process of combining the Target classes down to the 25 endpoint class level, refer to 

section 2.7 of this Volume. The detailed landcover map was further aggregated to create a 

general 16 landcover classes (Table 3) which may be useful for broad-scale summaries or 

analyses of the landcover types found in the study area . 

Table 1. Post-Field Target Mapping Classes for Denali NP&P. Following the field season and review of 

existing data, there were 56 target landcover classes that were tested for ‘spectral map-ability’ . However, 

based on satellite imagery resolution issues and ecological considerations, this list was refined to the final 

detailed (25) mapping classes shown in Table 2.

Final 

Code Target Mapping Class 

Canopy

Cover Height of vegetation / site factor

1 Open White Spruce 25-59 

2 Open Black Spruce 

3 Open Stunted Spruce (~black 

spruce) 

25-59 

4 Woodland White Spruce 10-24 > 15’ 

5 Woodland Stunted Spruce 

Ericaceous 

10-24 <15’, >25% Ericaceous 

6 Woodland Stunted Spruce Sedge 10-24 < 15’, >25% Sedge 

7 Closed Birch 60-100 > 50% Birch 

8 Closed Aspen 60-100 > 50% Aspen 

9 Closed Balsam Poplar 60-100 > 50% B. Pop. 

10 Closed Mixed Deciduous 60-100 -

11 Open Birch 25-59 > 50% Birch 

12 Open Aspen 25-59 > 50% Aspen 

13 Open Balsam Poplar 25-59 > 50% B. Pop. 

14 Open Mixed Deciduous 25-59 -

15 Woodland Broadleaf 

16 Closed Mixed 60-100 -

17 Open Mixed 25-59 -

18 Woodland Mixed 10-24 -

19 Closed Tall Alder -Willow 75-100 > 1.5m (5’), >75% Alder 

20 Closed Tall Alder 75-100 > 1.5m (5’), >75% Willow 

21 Closed Tall Willow 75-100 > 25-75% of each 

22 Open Tall Alder 25-74 > 1.5m (5’) 

23 Open Tall Willow 25-74 > 1.5m (5’) 

4
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Final 

Code Target Mapping Class 

Canopy

Cover Height of vegetation / site factor

24 Closed Low Alder-Willow 75-100 8”–5’(1.5m) 

25 Closed Low Shrub Birch-Willow 

26 Closed Low Willow 

27 Closed Low Shrub BirchEricaceous Shrub 75-100 8”–5’(1.5m) 

28 Closed Low Shrub Birch 75-100 8”–5’(1.5m) 

29 Open Low Alder-Willow 25-74 8”–5’(1.5m) 

30 Open Low Alder 25-74 8”–5’(1.5m) 

31 Open Low Shrub Birch/Ericaceous 

Shrub-Willow 

25-74 8”–5’(1.5m) 

32 Open Low Willow 25-74 8”–5’(1.5m) 

33 Open Low Mixed Shrub-Tussock 25-74 8”–5’(1.5m), >25% tuss 

34 Open Low Shrub Bog 

35 Open Low Shrub Birch-Ericaceous 

Shrub 

25-74 8”–5’(1.5m) 

36 Dryas Dwarf Shrub >50% Dryas, <8”, >25% Rock 

37 Mixed Dwarf Shrub >50%MixDwfShrb, <25% Sedge, <25% Rock 

38 Mixed Dwarf Shrub-Sedge >50%MixDwfShrb, >25% Sedge, <25% Rock 

39 Mixed Dwarf Shrub-Lichen >50%MixDwfShrb, >25% Tussock, <25% Rock 

40 Mixed Dwarf Shrub-Rock >50%MixDwfShrb, <25% Tussock or sedge, 

>25%Rock 

41 Dry/Mesic Grass >25% Herb, <25% Shrub, <25% Tussock 

42 Dry/Mesic Grass-Forb >25% Herb, >25% Grass, 50% Forb, <25% 

Tusscok 

43 Dry/Mesic Sedge-Forb >25% Herb, >25% Sedge, 50% Forb, <25% 

Tussock 

44 Tussock Mesic, >25% 

45 Wet Herbaceous Emergent Vegetation., No Tussock 

46 Aquatic 

47 Sparse Vegetation 15-25% Total Vegetation Cover 

48 Mosaic/Patterned Ground < 15% Vegetation Cover 

49 Bare Ground 

50 Snow/Ice 

51 Shadow/Indeterminate 

52 Silty Water 

53 Clear Water 

54 Human Development 

55 Burn Site 

56 Cloud 

Table 1 cont. Post-Field Target Mapping Classes for Denali NP&P. 

5
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 2. Target mapping classes (right) were combined to form the final 25 landcover classes (left). 

Endpoint

Class Final Mapping Class Name

Target 

Class* Target Mapping Class Name 

1 Dense Open Spruce 1 Open White Spruce

2 Open-Woodland Spruce 2 Open Black Spruce 

3 Stunted Spruce 3 

5 

6 

Open Stunted Spruce (~black spruce) 

Stunted Spruce Ericaceous 

Stunted Spruce Sedge 

4 Broadleaf 7 

11 

8 

12 

10

14

9 

13 

Closed Birch

Open Birch

Closed Aspen

Open Aspen 

Closed Mixed Deciduous 

Open Mixed Deciduous 

Closed Balsam Poplar 

Open Balsam Poplar 

5 Spruce-Broadleaf 16

17 

Closed Mixed 

Open Mixed

6 Alder 19 

20 

22 

30

24 

29 

Closed Tall Alder -Willow

Closed Tall Alder 

Open Tall Alder 

Open Low Alder 

Closed Low Alder-Willow

Open Low Alder-Willow

7 Willow 21 

23 

26 

Closed Tall Willow

Open Tall Willow

Closed Low Willow 

8 Closed Low Birch Shrub 28 Closed Low Shrub Birch 

9 Low Shrub Birch / Ericaceous / Willow 25 

27 

31 

35 

32

Closed Low Shrub Birch-Willow

Closed Low Shrub Birch-Ericaceous Shrub 

Open Low Shrub Birch/Eric Shrub-Willow

Open Low Shrub Birch-Ericaceous Shrub 

Open Low Willow 

10 Low Shrub-Sedge 33 Open Low Mixed Shrub-Tussock 

11 Peatland (Southside only) 34 Open Low Shrub Bog 

12 Herbaceous-Shrub (Southside only) ** 

13 Mixed Dwarf Shrub 37 

38 

36 

39 

Mixed Dwarf Shrub 

Mixed Dwarf Shrub-Sedge 

Dryas Dwarf Shrub 

Mixed Dwarf Shrub-Lichen

14 Mixed Dwarf Shrub-Rock 40 Mixed Dwarf Shrub-Rock

15 Dry- Mesic Herb 41

42 

43 

44 

Dry-Mesic Grass 

Dry-Mesic Grass-Forb 

Dry-Mesic Sedge-Forb

Tussock

6
 

 

 

 

 

 

Endpoint

Class Final Mapping Class Name

Target 

Class* Target Mapping Class Name 

16 Wet Herb 45 Wet Herbaceous 

17 Aquatic 46 Aquatic 

18 Sparse 47 Sparse Vegetation 

19 Bare 49 Bare Ground

20 Snow-Ice 50 Snow-Ice 

21 Shadow 51 Shadow-Indeterminate 

22 Silty 52 Silty Water 

23 Clear 53 Clear Water 

24 Burn 55 Burn site 

25 Cloud 56 Cloud 

*Target classes listed in Table 1 that were not verifed either during the 1999 field season or in the NPS 

FirePro vegetation database were not included (ex., Target Class #4 Woodland White Spruce).

** The Herbaceous-Shrub class was not defined until the later stages of classification and map 

development. Refer to section 2.84, “Defining and Delimiting the Southside Herbaceous-Shrub Class”. 

Table 3. The detailed 25 mapping classes were aggregated to create the general (16 

class) landcover map product. 

General Mapping Class Name 

1. Needleleaf (Dense-Open Spruce, Open–Woodland Spruce) 

2. Stunted Spruce 

3. Broadleaf 

4. Spruce-Broadleaf 

5. Tall shrub (Alder, Willow) 

6. Low Shrub (Closed Low Shrub Birch, Low Shrub Birch Ericaceous Willow) 

7. Lowland Low Shrub (Low Shrub-Sedge, Peatland) 

8. Mixed Dwarf Shrubs 

9. Dry-Mesic Herbaceous and Herbaceous-Shrub 

10. Wet Herbaceous 

11. Sparse Vegetation and Barren Ground 

12. Snow-Ice 

13. Silty and Clear Water and Aquatic Herbaceous 

14. Burn 

15. Clouds 

16. Indeterminate - Shadow (Cloud and Terrain Shadow) 

7
 

 

2.2 Preprocessing of Satellite Imagery (Selection Criteria, Issues, Pre-Processing) 

Both Landsat TM and SPOT XS imagery were used to derive the Landcover map. Although 

there were advantages and disadvantages to both image data sources, Landsat TM imagery was 

ultimately selected to serve as the base imagery, and the SPOT XS imagery was used to 

supplement or enhance the Landsat TM derived classification where appropriate. Tables 4 and 5 

outline some of the spectral and spatial differences between Landsat TM and SPOT XS data. 

Table 4. Key Spectral and Spatial Differences between Landsat TM and SPOT XS Imagery

Landsat TM SPOT XS 

Spatial Resolution 28.5 m 20 m

Number of Spectral Bands 6 3 

Average Spatial (Geographic) Extent 600 sq km 60 sq km

Table 5. Spectral ranges (per band) of Landsat TM and SPOT XS Imagery. Units are in 

micrometers. (Jensen, 1996) 

Landsat TM * SPOT XS** 

Band 1 (Blue) 0.45 – 0.52 0.50 - 0.59

Band 2 (Green) 0.52 – 0.60 0.61 - 0.68

Band 3 (Red) 0.63 – 0.69 0.79 - 0.89

Band 4 (Reflective Infrared) 0.76 – 0.90 Not Applicable 

Band 5 (Mid Infrared) 1.55 – 1.75 Not Applicable 

Band 7 (Mid Infrared) 2.08 – 2.35 Not Applicable 

* Landsat TM data also has a thermal band (Band 6) that was not used in the mapping effort 

** SPOT data also contains a Panchromatic Band, which was not acquired or integrated into the 

mapping effort 

2.21 Scene Selection 

The selection criteria for both the Landsat TM and the SPOT XS data were similar, and generally 

were based on consideration of image date, atmospheric quality and radiometric quality: 

Image Date (i.e., the date of image acquisition) was critical to the mapping effort. Only scenes 

that were acquired during peak vegetation season (late June to early September) were considered. 

Additionally, more recently acquired scenes were given preference over older image date scenes. 

Once a scene met the image date criteria, it was evaluated for atmospheric quality. Generally, 

acceptable scene quality was related to a low cloud occurrence and/or low to zero atmospheric 

haze. Cloud cover and associated cloud shadows obstruct the spectral reflectance of the 

vegetation beneath it, while atmospheric haze may distort or diminish the observed spectral 

values. 

8
Scene selection was also based on its radiometric quality. However, radiometric quality could 

not always be easily or accurately ascertained when selecting and ordering the imagery remotely. 

Both the Landsat TM and the SPOT XS data were affected with a radiometric quality issue 

known as “striping” or “banding”. Section 2.21a provides additional information on the 

radiometric quality issue of striping. 

The Denali NP&P Landcover Project Area could have potentially been covered with just four 

Landsat TM scenes (assuming they were cloud free) or approximately 15+ SPOT XS scenes. 

However, the limited availability of cloud-free imagery for the study area during the summer 

seasons necessitated the integration of multiple TM scenes. Imagery searches of the U.S. 

Geological Survey Earth Resources Observation Systems (EROS) Data Center (for Landsat TM 

data), the SPOT DALI browser and NPS-AKSO archives resulted in the selection of six Landsat 

TM scenes (Figures 2-7) and 16 SPOT XS scenes (Figures 8 & 9) to be used for map 

development. A list of all the Landsat TM and SPOT XS satellite imagery that were used to 

develop the land cover map is provided in Table 6. 

9
Table 6. List of all Landsat TM and SPOT XS used to develop the Denali NP&P landcover map. 

Six Landsat TM scenes formed the primary image base for the classification, while the remaining 

scenes were used to update the classification where appropriate (i.e., as cloud and snow fill-in 

areas). 

Status Data Type Acquisition Date Path Row 

Primary Landsat TM 5 8/15/88 70 15 

Primary Landsat TM 5 8/15/88 70 16 

Primary Landsat TM 5 7/21/85 71 15 

Primary Landsat TM 5 6/20/91 71 16 

Primary Landsat TM 5 6/29/91 70 16 

Primary Landsat TM 7 9/08/99 69 16 

Alternate Landsat TM 7 9/08/99 69 15 

Alternate SPOT-XS 9/04/96 445 218 

Alternate SPOT-XS 9/04/96 445 219 

Alternate SPOT-XS 9/04/96 445 220 

Alternate SPOT-XS 9/04/96 445 221 

Alternate SPOT-XS 9/04/96 446 218 

Alternate SPOT-XS 9/04/96 446 219 

Alternate SPOT-XS 9/04/96 446 220 

Alternate SPOT-XS 9/04/96 449 221 

Alternate SPOT-XS 9/06/94 442 218 

Alternate SPOT-XS 9/06/94 442 219 

Alternate SPOT-XS 9/06/94 442 220 

Alternate SPOT-XS 9/06/94 442 221 

Alternate SPOT-XS 9/06/94 445 218 

Alternate SPOT-XS 9/06/94 445 219 

Alternate SPOT-XS 9/06/94 445 220 

Alternate SPOT-XS 9/06/94 445 221 

Alternate SPOT-XS 9/06/94 445 222 

10
Figure 2. Primary Landsat TM Scene Path 70 Row 15 – August 15, 1988. The scene is displayed in band 

combination 4,5,3. 

11
Figure 3. Primary Landsat TM Scene Path 70 Row 16 – August 15, 1988. The scene is displayed in band 

combination 4,5,3. 

12
Figure 4. Primary Landsat TM Scene Path 71 Row 15 – July 21, 1985. The scene is displayed in band 

combination 4,5,3. 

13
Figure 5. Primary Landsat TM Scene Path 70 Row 16 – June 29, 1991. The scene is displayed in band 

combination 4,5,3. 

14
Figure 6. Primary Landsat TM Scene Path 71 Row 16 – June 20, 1991. The scene is displayed in band 

combination 4,5,3. 

15
Figure 7. Primary Landsat TM Scene Path 69 Row 16 – September 08, 1999. The scene is displayed in 

band combination 4,5,3. 

16
Figure 8. Alternate Landsat TM Scene Path 69 Row 15 – September 08, 1999. The scene is displayed in 

band combination 4,5,3. 

17
Figure 9. Alternate SPOT XS Mosaic– September 04, 1996 (Mosaic is comprised of the eight 1996

SPOT scenes listed in Table 6). The mosaic is displayed in band combination 3,2,1. 

18
Figure 10. Alternate SPOT XS Mosaic– September 06, 1994 (Mosaic is comprised of the nine 1994

SPOT scenes listed in Table 6). The mosaic is displayed in band combination 3,2,1. 

19
2.21a. Radiometric quality (Striping) issues: 

Landcover types found in the Denali NP&P study area (and in many areas of Alaska) make 

images acquired there more susceptible to a radiometric anomaly known as ‘striping’ or 

‘banding’. “Striping” in satellite imagery appears as stripes in the images that have a lighter or

darker tone than in adjacent stripes. Data striping is related to a slight malfunctioning of the 

sensor’s scanning instruments. It typically occurs after the scanners ‘sweep’ from a large 

expanse of very bright radiance values (such as snow, ice or desert sand) to immediate adjacent 

areas of significantly lower radiance values (such as water or vegetation). In effect, there is a 

delayed calibration in one or more of the detectors, which slightly alters the observed spectral 

values for that that sweep, and causes a noticeable striping effect in the imagery. 

Data striping occurred in both the Landsat TM and SPOT XS data available for the Denali NP&P 

Project Area. Unfortunately, its impact on the 1994 SPOT XS data was significant enough to 

preclude its use in the spectral analysis. Although many of the Landsat TM scenes also had 

some areas of striping, their enhanced spectral resolution helped mitigate the phenomenon. 

Typically only TM Bands 1 – 3 were affected by striping, thus leaving Bands 4, 5 and 7 available 

for spectral analysis. An example of a ‘data- striping’ area in one of the Landsat TM scenes is 

shown in Figure 11. 

Figure 11. Example of ‘Striping’ or ‘Banding’ affecting Landsat TM imagery (p70r 15 and 16-

August 1988). The raw pixel values were affected. (Band2 shown here) 

20
 

2.22 Orthorectification of the Satellite Data - Development of the Image Base 

Prior to further processing, the TM and SPOT imagery were orthorectified to create a stable and 

geometrically accurate base map. USGS Digital 1:63,360 Elevation Models (DEM) of the 

project area were obtained and mosaiked to complete a parkwide DEM database. Horizontal 

controls were selected from a 1:63,360 USGS topographic digital raster graphic (DRG) file for 

the area. TM and SPOT imagery were co-registered so that information extracted from either 

could be accurately overlayed with the other. Ideally, in the interest of both accuracy and 

efficiency, this would have been best accomplished by processing all 23 individual scenes 

simultaneously. However, the imagery was received and processed piecemeal as project goals 

and objectives became better defined over time. First the eight September 4, 1996 SPOT XS 

scenes were orthorectified using the horizontal and vertical controls described above. Next the 

nine September 6, 1994 scenes were received and processed using the previously mentioned 

controls and were additionally tied into the 1996 SPOT mosaic. Finally, the Landsat TM images 

were received and were processed together in a single, photogrammetric block adjustment 

process using the previously mentioned controls as well as the 1996 SPOT mosaic. All in all, 

the image to image registration between all 23 scenes (total number of both TM and SPOT 

scenes) was quite good. Image co-registration between all the individual Landsat TM scenes and 

between the Landsat TM scenes and the 1996 SPOT Mosaic averaged two pixels (+- 57.0 m). 

Image co-registration between the 1994 SPOT mosaic with the other image data sets was also 

approximately 2 pixels in the central section of the Park, however the co-registration between the 

1994 SPOT XS mosaic and the Landsat TM images diminished to approximately 4+ pixels in the 

most western reaches of the study area. As previously stated, the poor radiometric quality of the 

1994 SPOT XS imagery pre-empted its use for vegetation spectral analysis. It was used however 

to identify fire impacted areas. Qualitative evaluation of the resulting orthorectification TM 

image base with other layers such as the 1:63,360 derived hydrology vector coverage, were also 

very positive. 

2.23 Imagery Processing Blocks and Mosaic Design 

Once all of the images were orthorectified and co-registered with one another, ‘Image 

Processing Blocks’ and the study wide mosaic design were formed. Again, the Landsat TM 

imagery was selected to serve as the base imagery for classification, and the SPOT XS imagery 

was used to supplement or enhance the Landsat TM derived classification where appropriate. 

Satellite scenes from different sensors (Landsat 5, Landsat 7 and SPOT) and/or different dates 

were processed separately. Similarly, individual scenes from the same sensor and date were 

mosaicked and processed together as an ‘Image Processing Block’. This resulted in a total of five 

TM processing blocks and two SPOT processing blocks. 

Table 7 identifies these image processing blocks. 

Although the resulting landcover classifications from each of the Image Processing Blocks were 

not mosaicked together until after all of the appropriate spectral and modeling procedures were 

conducted on each of them, development of the Mosaic Design (i.e., how to piece the blocks 

together) was developed in conjunction with the formation of the Image Processing Blocks. 

21
 

Mosaic design criteria were similar to the image selection criteria. Emphasis was placed on 

using the most recent and/or the image date with the best vegetation phenology as the top layer. 

Cloud obstructed areas were cut out and replaced with either alternate TM imagery or with the 

1996 SPOT data. 

Generally, most of the Landsat TM image dates (late June-August) corresponded to prime 

vegetation biomass and phenology (with the exception of path 69 row16 - September 8, 2000). 

Although the early September Imagery (both SPOT and TM) were not as ideal for vegetation 

mapping, they did tend to have less cloud cover, and they provided useful information on some

vegetation species (primarily birch and willow) that had already begun to senesce by early 

September. Additionally, the more recent SPOT datasets (1994 and 1996) were used to extract 

updated fire burn information, which was identified to be of significant interest to Park Staff. 

The early September 1996 SPOT data also had a higher snow line than the late June Landsat TM 

scene (path 70 row 16 – June 29, 1991). Therefore, mapping class information for elevations 

greater than 784 m on the Park’s southside was extracted from the SPOT spectral classification 

in order to update areas still covered in snow and ice in the late June Landsat TM scene. The 

final image mosaic design for the Denali NP&P landcover map is shown in Figure 12. 

22
Table 7. Imagery Processing Blocks used to develop the Denali NP&P Landcover Map. 

Landsat TM data was the Primary Image Base for the Landcover Map 

Block Name Composed of: 

Path/Row - Date 

Comments / Issues 

Landsat TM Block 1 P70 / R15 – August 15, 1988 

P70 / R16 – August 15, 1988 

Block was clipped to exclude 

clouds occurring on and south of 

the Alaska Mountain Range. Block 

had desireable vegetation 

phenology / spectral reflectance . 

Some ‘striping effects’ in the 

Wonder Lake area 

Landsat TM Block 2 P71 / R15 – July 21, 1985 Individual scene was clipped to 

remove clouds and haze occurring 

on the Eastern section of the image. 

Landsat TM Block 3 P70 / 16 – June 29, 1991 Vegetation phenology earlier than 

ideal. Snow line still fairly low. 

Some areas effected by striping 

Landsat TM Block 4 P71/ R16 – June 20, 1991 Vegetation phenology thought to be 

earlier than ideal, however spectral 

classification results were positive. 

Landsat TM Block 5 P69 / R16 – Sept. 8, 1999 Acquired from the Landsat 7 

sensor. Vegetation phenology later 

than ideal. Snow line fairly low. 

SPOT XS data was used to to augment the Base Landsat TM Classification 

1996 SPOT Mosaic 

Block 

All eight September 4, 1996 

scenes listed in Table 4 were 

used to make the 1996 SPOT 

Mosaic Block 

Mosaic was later split along the 

Alaska Range to form Northside 

and Southside SPOT spectral 

processing blocks. The Northside 

SPOT classification was used to fill 

in scattered clouds occurring on 

TM Block1, and the Southside 

SPOT classification was used to fill 

in the low snow line on TM Blocks 

3 and 5 

1994 SPOT Mosaic 

Block 

All nine September 6, 1994 

scenes listed in Table 4 were 

used to make the 1994 SPOT 

Mosaic Block 

Due to poor radiometric quality, the 

1994 SPOT data was not used for 

spectral analysis. It was however 

used to identify fire scars, senesced 

birch and willow and other 

landcover types during the 1999 

field season 

23
 

 

 

 

 

 

 

 

 

Figure 12. The Imagery Mosaic Design for the Denali NP&P Study area. 

 

24
 

2.24 CIR Photography 

A set of color infrared-stereo pair aerial photography was also available for the Park. These 

1:63,360 scale photographs were acquired through Alaska’s High Altitude Program in the early 

1980’s (1979-1984). Although these photographs provided better spatial detail, the prints were 

of variable quality. Where they were of good quality, these prints were useful for identifying and 

delineating different vegetation / mapping class types (broadleaf versus needleleaf, etc.), and 

were used to help classify and edit the satellite imagery classification. 

2.25 FirePro Database 

Over the past 15 years NPS AKSO had collected an extensive database in support of fire history 

and succession studies, landcover mapping and fire fuel modeling. Under the AKSO Landcover 

Mapping program, the spatial coordinates or locations of the Fire Program (FirePro) data points 

were digitized, and the tabular data were automated for use in landcover mapping projects. The 

FirePro database represent(ed) the best available Park-wide vegetation or landcover information 

within the Denali NP&P project area at the inception of this mapping project. However, it is 

worth noting that a field data collection effort currently in progress by the Natural Resources 

Conservation Service (NRCS) will also become a valuable ecological data resource for the Park. 

The NRCS database, when completed in the next few years, will contain species level 

information that will be used to develop soil and seral stage associations. The NRCS database 

could potentially provide a powerful means to link and/or further refine the mapping classes 

derived from this Landcover mapping effort to a more detailed, localized scale. 

The Denali NP&P FirePro database however, was used extensively in this landcover mapping 

project. This database was comprised of two ARC/INFO coverages (with approximately 840 

georeferenced features), 1:60,000 high altitude aerial photos, 35-mm photo slides, and field 

forms. The FirePro data included detailed vegetation composition and information on dominant 

plant communities in sampled sites, and tree cores and cross sections to aid in determining when 

the site burned. Since 1987, remote sensing specialists in the AKSO GIS office have delineated 

polygons of homogeneous signature and representative vegetation types on aerial photos, which 

the FirePro crews then sampled. The sampled areas were qualitatively selected so as to 

approximate the full range of spectral and vegetative variation in the park. Summaries of the 

FirePro database were utilized during development of the classification system for the Denali 

NP&P landcover project. 

2.3 Spectral Database Development : Modifying the FIREPRO Database 

In order to directly integrate the FirePro database into the landcover project, it was necessary to 

generate a spectral signature for each FirePro site. To accomplish this, all available data for each 

FirePro site (species composition, percent cover, aerial photos, site photographs, and field forms)

were integrated into an ESRI ArcView project that had enhanced data integration and display 

features. The enhanced ArcView Project, known as the Field Data Viewer, was specifically 

developed by NPS and EarthSat to accommodate the FirePro and other field-related data sets. 

All available information for a particular FirePro site was displayed in an ArcView Dialog Box 

25
(Figure 13). All FirePro data were then reviewed by an image analyst and botanist to determine 

the appropriate mapping class. A new spectral signature polygon was then screen digitized onto 

the Landsat TM image base (to capture only the spectrally relevant (homogenous) portion of the 

polygon), and assigned the appropriate mapping class label. FirePro sites that showed 

inconsistencies in the database, photos, or field-forms were generally not used as spectral 

signatures. The FirePro derived signature database went through multiple revisions and 

refinements. In general, the FirePro Intensive Mapping Area (IMA) polygon coverage was more 

useful for deriving signatures then the FirePro Grounth Truth (GT) point coverages. The point 

coverages however did provide useful reference information for general map development and 

editing. In all, about 535 FirePro IMA polygons were evaluated, resulting in the development of 

approximately 400+ spectral signatures distributed throughout the Denali NP&P project area. 

These signatures were then supplemented with field data collected during the 1999 field season 

to create the final spectral database used for both classifying and evaluating the Landcover Map. 

26
Figure 13. The Dialog Box to the left displays the photos and the tabular data for the original FirePro polygon (light bl

signature polygon (dark blue) that better reflects the site data and the Landsat TM imagery was delineated. 

 

27
 

2.4 Goals and Preparation for the 1999 Field Season 

The primary goals of the 1999 fieldwork were to allow the Project Team to better familiarize 

themselves with the distribution of vegetation and landcover types within the Park, and to collect 

samples (in addition to those derived from the FirePro database) required to create the landcover 

map products. Image maps and a Sampling Design were developed for the 1999 field season to 

assist with the placement of field verification transect areas (FVAs). Field verification transect 

areas were similar in concept to FirePro Intensive Mapping Areas, and represented general 

locations targeted for 1999 field season sampling. 

2.41 Image Map Production for Sampling Design and FVA Transect Placement 

From the onset of the Project in the Spring of 1999, a decision was made by the Project Team to 

try and integrate the best available imagery into the landcover map. This meant remaining open 

and flexible to integrating any new satellite scenes (either SPOT or Landsat ) acquired of the 

study area during the 1999 summer growing season. This effectively meant that the imagery that 

would serve as the base image map was not known at the time of the 1999 field season. So 

although Landsat TM data later served as the primary image base for the Landcover Map, the 

SPOT imagery was used for the sampling design (FVA transect placement) and 1999 fieldwork. 

While this was disadvantageous from a project design and imagery processing point of view 

(ideally the Project Team should have had the final TM image base map to work with in the 

field), it helped prevent delaying map production for another season (year) while waiting for new 

1999 satellite acquisitions. 

To begin with, a 1:125000 scale SPOT image mosaic (both the 1994 and 1996 datasets) of 

Denali NP&P was produced and used as the base map for the placement of the FVA transects. 

Next, ecoregions as defined by the U.S. Geological Survey (USGS) and U.S. Environmental 

Protection Agency (US EPA) (Gallant et al., 1995) that occurred within Denali NP&P were 

overlaid on the SPOT mosaic for coarse scale stratification purposes. Other relevant vector 

coverages, such as Park boundary and the FirePro points and polygons were also included. 

Finally, a systematic sampling scheme was developed by overlaying a grid on top of the SPOT 

mosaic. The grid was large enough (10 mile spaced intervals) to ensure placement of more than 

30 FVAs across the vegetated portion of Denali NP&P, with slight overlap into the bareground 

and glacier landcover classes. 

Within each FVA, a transect or discontinuous transect was laid out on the SPOT mosaic to 

encompass the FVA’s full variation of environmental factors. These environmental variables 

included ecoregion, aspect, slope and distinct SPOT spectral signatures. In the mountains, each 

transect spanned an entire valley, from ridgetop to ridgetop. In the remaining regions, transects 

that encompassed the environmental variability were often too long to fully sample. 

Movement of some of the FVA transects was based upon having area-weighted representative 

examples of FVAs within each ecoregion. FVAs that overlaid Firepro locations were moved to 

an edge of the Firepro IMA. All aspects (north, south, east, west) on mountain slopes were 

included. Both large and small valleys within the Alaska Range were sampled. Private inholdings were avoided whenever possible. 

28
 

 

Again, the intent of this transect-polygon sampling approach was to target areas for sample 

collection to be used in spectral training signature generation during automated image 

classification and for ecological charactrization of mapping types). This approach provided a 

complete picture of the occurrences of mapping classes for areas over all slope/aspect /ecoregion 

positions in the FVA. This transect “snapshot” was important for establishing realistic 

parameters in the environmental and spectral models used to refine the landcover map. 

Locations of the 1999 FVA transects are shown in Figure 14. 

2.42 Image Map Production for the 1999 Field work 

Following development and delineation of the sampling design (section 2.41), image maps (tiles) 

of the SPOT data at 1:63,360 inch to a mile (ITM) scale were produced to support field data 

collection and subsequent processing. (The TM images used to derive the LC map were not 

acquired until after the 1999 field season). Histogram equalization stretches were applied to the 

SPOT data sets to enhance vegetation discrimination. Image maps had the appropriate layers for 

fieldwork (sampling design, Denali NP&P boundary and buffered boundary, private lands, ITM 

and QM quad boundaries and latitude/longitude graticules). FirePro samples were also included 

as annotation on these working field maps to improve their interpretability during fieldwork. In 

addition, available CIR prints (9” x 9”) of select FVA transect areas were also identified, 

scanned and enlarged to 1:~21,000 in order to fully exploit all available resources during the 

field season. 

2.5 1999 Field Data Collection Procedures 

The field season was from approximately July 13, 1999 to August 6, 1999. Personnel from

EarthSat, AKNHP and NPS participated in the field season. The primary purpose of field data 

collection was specifically to provide additional training signatures for the automated image 

processing required to develop the landcover map. Similarly, it was also important to obtain 

more specific site information that could be used to describe landcover types and plant 

associations. To support these objectives, field data were collected at three levels of detail: FVA 

‘fly over’ polygons, ground site descriptions, and incidental observations. 

2.51 FVA ‘Fly Over’ Polygons 

The image analyst and botanist arrived at the target FVA transect area by helicopter, and then 

flew a reconnaissance of the transect. After landing at one end of the FVA transect (typically on 

a high vantage point), the image analyst and the botanist surveyed the potential sample sites and 

compared it to the CIR enlargements and SPOT image map of the area. Polygons that were 

sufficiently large (>10 acres) and distinct/recognizable on the CIR photo and SPOT image were 

delineated on both the CIR photo field map (when available) and the SPOT imagery. On 

average, the crew delineated approximately 20 polygons within the FVA vicinity for sampling. 

These polygons were then sampled by hovering and/or slow fly-by in the helicopter. Again, 

between 15-25 polygons were visited at each FVA for a total of approximately 330 samples 

29
 

(Figure 14). Great care was taken to minimize helicopter impacts (i.e., hover height and 

duration were minimized), and wildlife were avoided throughout the entire Project Area. 

Information collected at “Fly-Over’ polygons was recorded on field forms (Appendix 5.3) and 

included the minimum data required to assign one of the 55 Target Mapping class calls for each 

sampled polygon. This included total cover % for the various landcover classes, dominant 

species % cover, and photo records. Canopy cover was ocularly estimated (Brown 1954) for the 

dominant species, and defined as the percent of the ground in the polygon covered by the gross 

outline of an individual plant's foliage (canopy), or the outline collectively covered by all 

individuals of a species or life-form within the polygon (Daubenmire 1959). Total cover for 

each sample area was expected to sum to 100%. 

2.52 Ground Site Descriptions 

The purpose for this level of data collection was to support classification system refinement, to 

provide a more thorough understanding of the mapping classes (and therefore improve class 

descriptions), and to provide the required information for linking the landcover map to plant 

communities. Landcover class descriptions were initially derived from literature descriptions, 

Firepro data and the Viereck et al. (1992) classification. Ground site descriptions collected 

during the 1999 field season provided additional species information for the landcover class 

descriptions (vegetation structure, plant dominance type, site moisture level, and soil 

characteristics). 

To gather this information, The Project Team’s ecologist was dropped off at each FVA, and 

sampled as many distinct landcover class types/polygons (on foot) as possible in the time 

allowed on the ground. Ground time was largely dictated by the time it took for the fly-over 

crew to delineate and then sample an FVA’s polygons. Selection of ground sites to sample 

within a polygon were made similar to the approach termed "subjective sampling without 

preconceived bias" as described by Mueller-Dombois and Ellenberg (1974). Site selection was 

based on homogeneous vegetation. Sample sites (homogeneous land cover units) were chosen 

with regard to their position in any classification, extant or envisioned, or by applicability to 

specific management considerations. 

A GPS point and photo record were taken at each Ground Site area. Additionally, the following 

site variables were also recorded: slope, aspect, landform, surficial geology, hydrologic regime

(dry, mesic, wet, aquatic), depth of soil peat, A, B and C horizons, depth to permafrost, and 

depth to water. A sample Ground site form is available in Appendix 5.3. Other recorded 

variables included air photo number, polygon code, landcover class name, landcover class code, 

date, surveyors, photos, latitude and longitude, environmental comments and disturbance 

comments. Although Ground Site Description areas could not be used to generate training 

signatures for the automated map development, they did provide useful information for map 

modeling and editing purposes, in addition to the value they added to the landcover and mapping 

class descriptions. 

30
2.53 Incidental Observations 

In addition to targeted FVA polygons, opportunistic or incidental polygon areas of homogeneous 

or known vegetation class types identified while in flight from one FVA to the next were also 

delineated and recorded. These incidental observations were used primarily for editing the map 

and qualitative assessment of its accuracy. These observations typically included a general 

vegetation call, such as “alder”, without providing any indication of height, canopy cover, 

understory, etc. They also often represented broad landscapes rather than particular sites. 

Photographs were often collected for these sites, and in some cases GPS points were recorded. 

The purpose of these sites was to provide a ‘reality check’ on the Landsat TM derived landcover 

map, and to provide additional general information for editing the map. 

Additional information related to Sampling Design and Field Data Collection is also available in 

Volume 2: Landcover Classes and Plant Associations. 

31
Figure 14. Locations of 1999 Field Transects and Sample Sites 

32
 

 

 

 

2.6 Supplementing the Spectral Signature Database with the 1999 Field Data 

Following the 1999 field season, collected data were entered into a Microsoft Access database, 

and the spatial extents of the polygons were digitized and attributed in an ArcView project. This 

process was similar to the process of modifying the FirePro polygons (recall section 2.3). 

Delineation of spectral signature polygons from the 1999 FVA data was based on field 

annotations made on the SPOT and CIR imagery and from the field collected GPS points. As 

with the FirePro derived signatures, signatures from the 1999 FVA data were reviewed by both 

an image analyst and botanist, and delineated on the appropriate (orthorectified) TM image base 

map. The FVA polygons were utilized to generate approximately 330 additional spectral 

signatures. A total of 426 signatures had been derived from the FirePro database. Thus making 

the total number of signatures defined for Denali NP&P, from both the FVA and the FirePro 

databases to be approximately 755. However, numerous and extensive review of these 

signatures (in simulated 4,5,3 (RGB) color) with regard to spectral homogeneity, size, 

appropriateness of assigned mapping class and output from preliminary spectral classifications 

resulted in the elimination of about 100 of these (Section 2.7). In the end, 656 signatures were 

eligible for use in the final automated map classification. The number of training signatures for 

each mapping class (by image processing block) can be found in Appendix 5.4. 

2.7 Supervised Maximum Likelihood Classification, Signature Evaluation and Mapping 

Class Refinement 

The spectral signature database derived from the field verified data had two potential uses; 1.) to 

be used as “training” data for the automated spectral classification and 2.) for evaluating the 

resulting classification (i.e., accuracy assessments). Originally it was hoped that there would be 

enough spectral signatures derived from the field databases to use a portion for training, while 

using the remaining portion for later accuracy assessments. However, the entire spectral 

database (650+ signatures) was used for training in order to obtain the best spectral classification 

possible. 

Training signatures provide the statistical (spectral) criteria that the processing software 

(ERDAS, Imagine) uses to classify all of the pixels in a satellite scene. For every land cover 

mapping polygon delineated on the imagery, the spectral statistics (typically the mean and 

covariance matrix) for the pixels under that polygon were extracted. 

These training signatures were then used as input into a supervised classification of the TM 

scenes using the Maximum Likelihood (ML) classifier. Based on the statistical spectral 

characteristics of the training signatures, the ML classifier assigned every pixel in the scene to 

one of the land cover mapping types. 

ML classifications were run on the 5-band (bands 2,3,4,5,7) TM image datasets and on 3-band 

(bands 4,5,7) datasets for areas of the TM imagery that were affected by ‘striping’ (recall Section 

33
2.21). Figure 15 shows the differences between a spectral classification of a 5-band imagery 

data set affected by striping versus a spectral classification run of the 3-band imagery data set. 

The channels most affected by striping (Bands 2 and 3) were eliminated from the data set prior to 

running the ML classifier. 

34
Figure 15. Spectral classification output from an area on TM block 3 that was affected by striping (left). The impacted chann

were eliminated from the data set and the classification was re-run on bands 4, 5 and 7 (right). 

35
Each image processing block was treated as a separate stratum for the automated image 

classification. This situation was ideal for maximizing vegetation discrimination within 

individual processing blocks. Mapping classes were generally evaluated with a confusion matrix 

between the supervised classification output and the training data for that block. This provided 

mapping class level information (i.e. how good the training data were at mapping themselves). 

Initially, the ML classifier was run on TM block 1 and the 1996 SPOT imagery block using the 

55 Target Mapping Classes used in the field. 

Confusion matrices for both the TM Block1 and the 1996 SPOT blocks were generated (Tables 8 

and 9) and evaluated by NPS, EarthSat and AKNHP. Identifying which mapping classes were 

better captured in the respective data sources, as well as which classes were poorly represented in 

both data types helped guide the evolution to the final 25 endpoint classes. In general, mapping 

classes were combined or dropped if they lacked sufficient signatures (five signatures per 

processing block was considered the minimum number needed), or if there was too much 

spectral confusion with another (ecologically) similar class. For example, there was significant

confusion between Open Low Alder and Open Low Alder Willow (i.e., neither the TM nor the 

SPOT spectral resolution was fine enough to reliably discern between the two classes, so they 

were combined to be included in the same mapping class). Mapping classes were evaluated and 

combined as necessary throughout the entire map development process. However, mapping 

classes that were unique (such as closed low shrub birch or peatland) or were considered 

important to wildlife (willow) were kept separate even if they consistently exhibited spectral 

confusion with other mapping classes. 

Generally, before making the final decision to combine or discard a particular mapping class, the 

spectral signatures used to define that mapping class were first evaluated within an ArcView 

Project. Individual signatures were interactively reviewed and evaluated based on the imagery, 

the vegetation species information and zonal statistical criteria established by NPS and EarthSat. 

Signatures for each processing block were reviewed and used to classify the training data sets. 

In general, signatures for training sites where the assigned land cover mapping class was not 

equal to the majority occurring class based on the ML algorithm were either deleted or revised 

based on additional reviews of the site data, photos, and the imagery’s spectral characteristics. 

In addition, signatures from training sites whose assigned land cover mapping class equaled the 

majority class, but had a majority percent lower than 50% were either deleted, redrawn or 

relabeled based on additional reviews of the site data, photos, and the imagery’s spectral 

characteristics. 

Upon completion of each [ArcView] signature review process, the ML classification was rerun, 

and both the confusion matrices and the signatures were again evaluated in the same manner. 

This iterative review process was implemented a minimum of two-three times for every Landsat 

TM and the 1996 SPOT image processing block. Interim versions of the ML classifier output and 

training signatures for every TM image processing block were evaluated by NPS, enabling 

integration of their input and comments into the final supervised classification run. Only after 

multiple signature reviews and ML classifier runs (and after the classifier output had been 

reviewed by both NPS and EarthSat) did the output become final, and thus ready to move on to 

the modeling stage. Confusion matrices for the final ML spectral classification (for each of the 

36
 

 

 

 

Imagery Processing Blocks) are provided in Appendix 5.5. 

2.8 Spectral and Environmental Modeling 

2.81 Model Development and Implementation 

Upon iterative refinements and finalization of the supervised ML spectral classification, 

landcover characterization in each Landsat TM image processing block was further improved 

through modeling. Spectral and environmental data layers were specifically generated and 

utilized for improving the spectral classification. Modeling was conducted at the image 

processing block level and was implemented in multiple stages. The primary goal of the 

beginning stages was to first identify confused classes in the individual Landsat TM processing 

block spectral classifications, and then to determine what, if any ancillary data from the 

environmental/spectral modeling database could be used for consistently separating and 

accurately mapping the confused classes. Classes requiring modeling were selected based on 

their low class accuracy on the training data confusion matrices and/or were flagged during the 

multiple spectral classification evaluations and reviews. Once the spectral confusion between 

certain mapping classes were identified, the next steps involved formulating ecologically-based 

modeling rules appropriate to the specific image processing block (i.e., specific to that area of the 

Park) that could resolve the confusion. 

An ecological/biophysical justification was required for every model implementation. For 

example, in many of the processing blocks, water, which generally has a low spectral reflectance 

(or a high absorbance) was often spectrally confused with terrain shadowed areas. Since the ML 

classifier was not able to always distinguish between water and shadow spectral values, there 

were instances where the shadow class was incorrectly mapped in place of water bodies, and 

instances of water being incorrectly mapped high in the Alaska Mountain Range. In order to 

correct the misclassification of water on shadowed hillsides, a simple ecological model based on

the assumption that water does not generally occur in areas of significant slope was developed . 

In order to employ this model, the slope data layer was generated for the particular processing 

block, and then the appropriate slope thresholds were established. For a discussion of the 

various model layers that were generated, refer to Section 2.82. For a general discussion of 

mapping classes that were typically spectrally confused with one another (and therefore required 

modeling and/or editing), refer to Section 2.83. 

The process of parameter-threshold testing was one of the final modeling steps. 

Initial modeling parameters were established based on interactive review of the spectral 

classification with each of the modeling data layers and the Landsat TM data. Field data and 

other available vegetation data were also referenced along with the image and modeling data in 

establishing and refining these parameters. After each model run, the results were reviewed and 

compared with the satellite imagery, the field data, and other available data for the project area. 

Based on these reviews, parameters were refined and the models re-run until the model outputs 

were determined to best represent the particular landcover class(es) being modeled. Once the 

model output passed this iterative evaluation process, the Landsat TM processing block was 

mosaicked into the thematic landcover database. 

37
38
 

Table 8. Original Confusion Matrix for TM Block 1 and the Target Mapping Classes. Landcover types that were ecologically similar, but exhibited poor spectral discrimination were combined.

 Reference / Training Data 

Image Classification 

sed Mixed

Open

M xed

Open White Spruce Open Black Spruce Open Stunted Spruce (~black spruce) Woodland White Spruce Woodland Stunted Spruce Ericaceous Woodland Stunted Spruce Sedge Closed Birch CloCl Cl e c ou Open Birch OpeOp i i s Woodland Broadleaf Clo i Woodland Mixed Closed Tall Alder -Willow Closed Tall Alder Closed Tall Willow Open Tall Alder Open Tall Willow Closed Low Alder-Willow Closed Low Shrub Birch-Willow Closed Low Willow Closed Low Shrub Birch-Ericaceous Shrub Closed Low Shrub Birch Open Low Alder-Willow Open Low Alder Open Low Shrub Birch/Ericaceous Shrub-Willow Open Low Willow Open Low Mixed Shrub-Tussock Open Low Shrub Bog Open Low Shrub Bi

Dryas 

sed Aspen osed Bals m P pla n Aspen en Balsa Po ar a o r osed Mix d De idu s m pl Open M xed Dec duou

Theme 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 

1 2092 142 59 0 20 4 0 0 0 0 0 0 13 0 0 67 53 0 0 0 0 8 18 1 1 0 7 5 5 1 1 0 6 0 3 0 

2 230 877 285 0 102 5 0 0 0 0 0 0 7 0 0 25 15 0 0 0 0 0 0 0 0 0 0 21 0 0 0 0 0 0 12 0 3 140 198 1716 0 339 49 0 0 0 0 0 0 1 0 0 14 48 0 0 0 0 0 2 0 0 0 1 0 0 0 0 0 0 0 1 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 12 33 201 0 964 129 0 0 0 0 0 0 13 0 0 6 51 0 0 0 0 6 1 0 26 0 3 47 0 0 5 0 2 0 26 0 6 7 3 193 0 121 263 0 0 0 0 0 0 2 0 0 0 10 0 0 0 0 0 0 0 0 0 4 50 0 0 0 0 32 0 5 0 7 0 0 0 0 0 0 986 0 0 81 6 0 36 0 0 0 3 0 35 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

10 25 5 2 0 1 0 196 0 0 214 11 0 18 0 0 0 2 0 8 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 41 0 0 6 11 0 7 0 0 0 0 0 12 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 13 3 15 2 0 9 0 73 0 0 36 6 0 771 0 0 3 51 0 9 7 0 0 2 23 0 0 10 0 5 0 1 0 29 0 3 0 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16 40 10 22 0 13 0 1 0 0 0 0 0 2 0 0 186 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17 171 19 27 0 45 1 15 0 0 1 0 0 32 0 0 11 749 0 0 4 0 1 2 2 16 0 0 0 6 0 0 0 1 0 2 0 18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 49 0 0 8 1 0 7 0 0 0 0 0 148 25 0 70 0 1 0 0 0 0 9 0 0 0 0 0 0 0 20 0 0 0 0 0 0 1 0 0 4 0 0 12 0 0 0 2 0 13 164 0 68 3 6 1 0 9 0 15 3 0 0 0 0 10 0 21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 22 1 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 24 29 0 450 0 0 16 0 0 0 0 3 4 0 0 0 26 0 23 11 1 52 0 4 0 0 0 0 0 0 0 17 0 0 0 23 0 0 1 0 1 119 0 1 0 3 0 3 1 0 0 1 0 1 0 24 0 0 0 0 0 0 0 0 0 0 0 0 27 0 0 0 14 0 1 6 0 0 0 51 0 0 0 0 1 0 0 0 0 0 0 0 25 3 0 0 0 3 1 0 0 0 1 0 0 3 0 0 0 2 0 0 1 0 11 0 0 520 0 29 15 1 0 35 0 37 0 64 0 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 27 2 0 0 0 8 11 0 0 0 0 0 0 50 0 0 0 0 0 1 6 0 7 2 0 40 0 296 12 0 0 35 0 112 0 37 0 28 0 2 1 0 21 8 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 3 0 8 277 0 0 18 0 10 0 12 0 29 0 0 0 0 1 0 0 0 0 0 0 0 7 0 0 0 0 0 1 3 0 8 0 4 1 0 0 0 18 2 0 0 0 0 1 0 30 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 39 0 0 0 0 0 0 2 23 0 0 0 0 2 0 31 0 0 0 0 11 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 2 0 0 15 0 9 4 0 0 230 0 55 0 53 0 32 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 156 4 0 0 0 33 5 0 0 0 4 19 0 0 0 0 0 0 128 0 0 0 8 0 0 0 0 1 0 0 23 0 58 18 0 0 66 6 2426 0 286 0 34 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 35 7 13 6 0 43 6 0 0 0 0 0 0 15 0 0 1 8 0 0 14 0 27 0 0 145 0 15 17 4 0 99 2 120 0 1311 0 36 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 2 0 0 0 55 37 0 1 0 0 2 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 2 0 0 0 0 0 2 0 0 0 8 1 0 0 2 38 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 19 0 1 0 39 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 14 4 0 1 1 40 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 41 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 1 0 0 5 0 0 0 0 0 0 0 9 0 11 0 42 0 0 0 0 0 0 16 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 43 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 44 0 1 0 0 1 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 11 41 0 8 1 45 0 4 14 0 7 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 46 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 47 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 48 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 49 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 50 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 51 18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 52 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 53 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 54 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 56 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Col. Tota 2767 1324 2580 0 1724 496 1378 0 0 355 35 0 1209 0 0 313 1043 0 252 270 0 706 149 89 817 0 452 468 69 33 494 200 2913 0 1876 59 

Prod. % 75.6 66.2 66.5 N/A 55.9 53.0 71.6 N/A N/A 60.3 17.1 N/A 63.8 N/A N/A 59.4 71.8 N/A 58.7 60.7 N/A 63.7 79.9 57.3 63.6 N/A 65.5 59.2 26.1 69.7 46.6 78.0 83.3 N/A 69.9 93.2 

Theme 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 
40
 

 

 

 

 

 

 

 

 

 

 

Table 9. Original Confusion Matrix for 96 SPOT block (North of the Alaska Range only) and the Target Mapping Classes. Landcover types that were ecologically similar, but exhibited poor spectral discrimination were co

After comparing the matrices and mapped output for both the TM and SPOT data, the TM classification was ultimately selected to serve as the base classification map.

 Reference / Training Data 

Open B ack Spruce l

Open S unt d Sp uce (~bla k

Open White Spruce

t e r c uce)

Woodland White Spruce

Woodland Stunted Spruce

Ericaceous 

Woodland Stunted Spruce Se

Closed Birch

Closed Aspen 

Closed Balsam Poplar

Closed Mixed Deciduous 

Open Birch 

Open Aspen

Open Balsam Poplar

Open Mixed Deciduous 

Woodland Broadleaf 

Closed Mixed

Open Mixed

Woodland Mixed 

Closed Tall Alder -Willow 

Closed Tall Alder 

Closed Tall Willow 

Open Tall Alder 

Open Tall Willow 

Closed Low Alder-Willow 

Closed Low Shrub Birch-Willow 

Closed Low Willow 

Closed Low Shrub Birch-Ericaceous

Shrub 

Closed Low Shrub Birch 

Open Low Alder-Willow 

Open Low Alder 

Open Low Shrub Birch/Ericaceous

Shrub-Willow 

Open Low Willow 

Open Low Mixed Shrub-Tussock 

Open Low Shrub Bog 

Open Low Shrub Birch-Ericaceous

Shrub 

Dryas Dwarf Shrub 

Mixed Dwarf Shr

Mixed 

Theme 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 

1 309 177 3 0 0 0 0 0 0 0 0 0 0 0 0 0 104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 44 4031 475 0 368 52 0 0 0 0 0 0 0 0 0 3 1562 0 0 1 0 0 3 0 0 1 7 0 0 0 0 0 0 0 0 0 2 0 3 0 173 1028 0 169 15 0 0 0 0 0 0 0 0 0 4 123 0 0 0 0 0 0 0 0 0 15 0 0 0 0 4 0 0 201 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 264 1229 0 2388 160 0 0 0 1 0 0 13 0 0 0 66 0 0 0 1 3 5 0 0 3 126 20 5 0 0 0 29 0 15 0 0 0 6 0 10 303 0 188 571 0 0 0 3 0 0 5 0 0 1 35 0 0 0 1 8 0 5 0 0 179 0 10 4 0 0 108 0 5 0 0 0 7 0 0 0 0 0 0 119 0 0 10 0 0 0 0 0 0 7 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 1 17 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

10 0 0 0 0 6 19 9 0 0 175 0 0 1 0 0 0 1 0 4 0 0 29 0 2 0 0 18 0 0 7 0 0 2 0 0 2 22 0 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 13 0 0 0 0 49 0 4 0 0 0 0 0 1144 0 0 19 23 0 2 2 0 0 0 6 0 2 2 32 0 0 0 12 0 0 0 8 28 1 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16 0 2 0 0 0 0 0 0 0 0 0 0 2 0 0 39 30 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17 32 374 43 0 58 37 21 0 0 0 0 0 23 0 0 21 2651 0 14 2 0 7 0 0 0 0 0 14 0 3 0 0 0 0 0 0 0 0 18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0 0 0 2 0 0 10 0 0 2 33 0 352 45 0 6 0 13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 0 24 425 0 43 0 13 0 0 0 0 4 0 0 0 0 0 0 0 0 0 21 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 5 5 0 0 0 161 13 0 0 0 0 20 0 0 0 1 0 22 0 0 0 0 2 8 0 0 0 9 0 0 3 0 0 2 4 0 20 36 0 604 0 138 0 0 0 0 54 11 0 0 3 0 0 0 0 0 23 0 10 0 0 21 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 4 2 246 0 0 15 297 128 0 0 46 0 14 0 11 4 24 8 24 0 0 0 0 3 0 0 0 0 0 0 0 1 0 0 0 3 0 7 19 0 29 0 369 0 0 2 0 4 0 0 0 0 0 3 0 0 0 25 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 988 43 44 24 0 0 123 3 4 0 332 42 3 0 26 0 1 0 0 18 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 23 279 103 0 0 0 1 0 25 0 66 1 15 8 27 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 7 6 21 0 548 9 1 0 20 1 12 0 352 5 2 0 28 0 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 6 0 74 2 219 1278 0 0 82 1 5 0 424 0 0 3 29 0 0 0 0 0 12 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 89 0 16 0 0 1 0 133 9 0 0 1 0 0 0 5 0 30 0 0 0 0 0 3 0 0 0 4 0 0 0 0 0 0 7 0 0 0 0 9 0 9 0 0 0 0 2 30 0 0 0 0 0 0 0 0 31 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 44 1 50 10 0 0 940 0 0 0 12 0 1 0 32 0 0 1 0 0 0 0 0 0 0 0 0 13 0 0 0 0 0 0 0 0 0 0 0 2 0 6 0 0 0 0 272 0 0 502 26 6 0 33 0 0 0 0 31 71 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 16 4 5 2 66 2151 23 13 0 3 0 2258 0 236 0 0 7 34 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 35 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 33 1 138 18 0 0 2 2 6 0 1273 0 0 0 36 0 0 0 0 0 0 0 0 0 1 0 0 24 0 0 0 1 0 0 0 0 0 4 0 9 3 0 0 0 0 0 13 0 0 0 272 20 1 37 0 3 0 0 0 0 11 0 0 20 0 0 76 0 0 0 5 0 0 0 0 1 17 0 1 42 6 1 3 0 1 7 0 0 0 69 466 15 38 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 20 0 0 26 33 6 0 0 2 0 20 0 3 6 31 84 39 0 0 0 0 0 0 1 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 5 0 0 4 1 110 0 0 0 0 0 0 0 3 8 3 40 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 20 0 0 0 0 0 0 0 0 0 0 21 33 7 41 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 16 0 1 0 0 0 0 0 1 0 0 0 0 0 42 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 1 0 0 0 2 0 33 0 0 1 0 75 0 4 0 0 0 22 0 0 1 5 0 43 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 44 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 10 0 3 41 94 11 1 0 60 0 48 0 324 0 15 4 45 0 7 0 0 3 0 0 0 0 0 0 0 24 0 0 0 3 0 0 0 0 0 5 0 7 0 18 1 0 0 0 34 0 0 593 1 8 0 46 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 47 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 4 0 48 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 49 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 50 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 51 2 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 52 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 53 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 54 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Col. Tota 387 5058 3082 0 3321 959 170 0 0 225 0 0 1347 0 0 96 4665 0 424 532 17 889 345 588 1244 529 4295 1698 234 64 1280 349 2578 0 4352 469 716 141 

Prod. % 79.8 79.7 33.4 #DIV/0! 71.9 59.5 70.0 #DIV/0! #DIV/0! 77.8 #DIV/0! #DIV/0! 84.9 #DIV/0! #DIV/0! 40.6 56.8 #DIV/0! 83.0 79.9 47.1 67.9 71.3 62.8 79.4 52.7 12.8 75.3 56.8 46.9 73.4 77.9 87.6 #DIV/0! 29.3 58.0 65.1 59.6 

Theme 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 

Image Classification 
42
 

 

Since modeling was conducted at the image processing block level, the modeling layers/steps 

that were applied varied based on classification issues specific to the individual processing block. 

Terrain shadow modeling for example, was used extensively in the eastern and central portions 

of the study area, but not at all in the flat boreal forest areas found in the western section of the 

Park. In cases where the same ecological model / concept was applied to two different 

processing blocks, the final parameters used for the respective blocks often varied depending on 

the specifics of the imagery and the particular processing area. 

Appendix 5.6 provides the final modeling reports for each of the five Landsat TM image 

processing blocks identified in Section 2.23. Section 2.82 identifies all spectral and 

environmental data layers that were produced and evaluated for use in the modeling process. 

Again, not all modeling layers were appropriate (and were therefore not implemented on all 

processing blocks). 

2.82 Summary of Key Data Layers Used for Modeling and Editing 

The key modeling layers identified as most useful were slope, illumination (shaded relief), 

elevation, Transformed Normalized Difference Vegetation Index (TNDVI), and Landsat TM 

Band 5. 

The TNDVI (Transformed Normalized Difference Vegetation Index) was particularly useful for 

refining the Dwarf Shrub classes, Sparsely Vegetated, and Bare Ground classes since it is a 

measure of relative “greenness”. 

Spectral reflectance of the TNDVI layer was less affected by terrain shadow, which was 

particularly important for discriminating these classes in alpine areas. NDVI was also utilized to 

identify and extract burn information from the SPOT and TM data layers. 

Burn areas were identified and mapped by establishing thresholds within the NDVI layers 

created from the 1994 and 1996 SPOT imagery and from TM image processing blocks 1 and 4. 

The thresholds for the upper and lower NDVI values for each image block were defined through 

interactive review with the satellite imagery and the associated thematic NDVI layers. Once the 

range of NDVI values corresponding to burn areas (i.e., no or very low ‘greenness’) was 

established, a burn mask that was constrained by a Park provided fire polygon coverage, was 

extracted. Two map products were provided, one with the burn mask digitally overlaid on the 

landcover map (i.e. replacing the wet herbaceous with the burn category) and one that left the 

wet herbaceous as it was spectrally mapped by the ML classifier. The burn mask was also 

provided as a separate product layer. However users should take note, that in addition to 

legitimate burn areas, some areas of legitimate wet herbaceous (i.e., wet herbaceous that was not 

burned) as well as some areas of water and aquatic herbaceous areas fell into the thresholded 

range of NDVI values and were included in the burn mask. To help alleviate the 

misclassification of legitimate water (both silty and clear) and aquatic herbaceous to burn, an 

additional water and aquatic herbaceous mask was also generated. This water mask was digitally 

overlaid on top of the burn mask in order to restore the overrepresentation of burn area back to 

water and/or aquatic herbaceous. 

43
 

 

 

 

 

 

 

 Illumination/shaded relief was useful for refining upslope versus floodplain and aspect 

differences. This layer was generated from the ITM DEM (Albers), and the sensor-to-surface 

orientation parameters of sun elevation and solar azimuth for the Landsat TM image acquisition 

date and path/row. Because of the various illumination conditions dependent on local aspect, 

slope and neighboring terrain, and the limited direct relationship between illumination and 

vegetation, this layer was determined to best represent the modeling as a continuous variable (as 

opposed to discrete aspect categories such as S, SE, SW, etc.). Thresholds were established for 

identifying areas of Full Terrain Shadow and Partial Terrain Shadow. It was assumed that no 

reliable or consistent information could be extracted from areas of full terrain shadow. 

Ecological and modeling assumptions were only applied to areas of partial terrain shadow. 

Band 5 - This was the 8-bit unsigned thematic image file for Landsat TM Band 5 (middle IR). 

Band 5 was useful for establishing thresholds for mapping classes based on moisture and/or the 

amount of standing water. Band 5 was also used extensively (with AOI delineations) to refine 

water classes. 

Slope was useful for discriminating floodplain from upslope areas. It was particularly useful for 

water and shadow separation. The raw slope layer (based primarily on 1:63,360 level DEMs) 

contained too many contour line relics to be useful for modeling. Several different approaches 

and scales to eliminating relics were investigated, and it was determined that a 5x5 smoothing 

(low pass) filter removed most problems while preserving the essential detail for 

upslope/floodplain modeling. 

Elevation from the DEM was used to limit the occurrence of many classes at higher elevations. 

It was used to model Needleleaf, Broadleaf, Spruce–Broadleaf, Water and Wet Herbaceous 

classes out of high elevation areas. 

2.83 Models and edits to reduce spectral confusion between mapping classes 

Models and parameters applied to each individual Landsat TM scene (block) are detailed in 

Table 10. The climatic conditions and ecological communities found in Denali NP&P study area 

north of the Alaska Range vary from the climatic conditions and ecological communities found 

south of the Alaska Range. The climatic condition in the lowlands north of the Alaska Range is 

subarctic-continental with short, warm summers and long, cold winters. While the climate of the 

lowlands south of the Alaska Range is affected by both maritime and subarctic-continental 

influences, being warmer and moister in winter and cooler and moister in summer than the 

northern half of the mountain range. 

An ecological/biophysical justification was required for all model implementations. Typical 

examples of confusion between mapping classes, and some of the models and/or edits 

implemented to remedy that confusion are described below: 

Spruce / Water confusion. Spruce (both Dense Open Spruce and Open-Woodland Spruce) and 

Water areas were confused, particularly along lake/pond edges. Many of the ponds south of the 

McKinley gravel bar (i.e. south of Wonder Lake) incorrectly had a Dense Open Spruce or Open 

44
Spruce perimeter around them. This spectral confusion problem was corrected primarily through 

interactive editing with Landsat TM Band 5, the SPOT imagery and the CIRs (Figure 16). 

Spruce / Shadow confusion. Shadow and Spruce, particularly dense open spruce were sometimes 

spectrally confused. This problem, when it occurred, was more common on the east side, over to 

and including the Kantishna Hills (i.e, in areas of significant terrain relief). Areas of

misclassified Spruce sometimes occurred at high elevations (well above the boreal extent). 

Similarly, shadow pixels were often mixed with needle-leaf mapped areas, particularly when 

they occurred on slopes with north or west aspects. Both Dense-Open Spruce and Shadow 

occurring on the hillsides of the northern Kantishna Hills were either modeled or edited to OpenWoodland Spruce. However, in other areas of the Park, it was necessary to consult ancillary data 

sources to determine whether it was Open–Woodland Spruce, Alder or Broadleaf occurring on 

the hillsides. In general, in the northeast section of the Study area (over towards Healy), it was 

often Broadleaf occurring on the shadowed hillside, while in the southern Kantishna Hills, it 

was Alder. 

Dense Open Spruce / Open-Woodland Spruce / Stunted Spruce confusion. Confusion between 

the different needleaf classes occurred when the percent of needleaf canopy for an area 

approached the upper limit for that mapping class. For example, in the Western section of the 

Park (west of the Kantishna Hills), it was difficult to spectrally differentiate between OpenWoodland Spruce and Stunted Spruce when the percent composition of stunted spruce was 40% 

or greater, or when the there was 15% or more Larix spp. mixed in with the Stunted Spruce 

areas. For the areas east of the Kantishna Hills, the needleleaf classes were sometimes confused 

with one another in areas of terrain shadow. The illumination layer was used to model Dense 

Open Spruce occurring in terrain shadow to either Open-Woodland Spruce, terrain shadow or to 

some other appropriate class. 

Stunted Spruce / Closed Low Shrub Birch / Low Shrub Birch-Ericaceous-Willow confusion. 

Throughout most of TM image processing block1, there were many instances, particularly in 

areas of low illumination (partial shadow), where Closed Low Shrub Birch-Ericaceous-Willow 

areas were spectrally misclassified as Stunted Spruce (or as closed low shrub birch to a lesser 

extent). The illumination model layer was used to remap areas of Stunted Spruce in partial 

terrain shadow areas to Closed Low Shrub Birch-Ericaceous-Willow. Areas of Closed Low 

Shrub Birch occurring in low illumination areas were remapped to Low Shrub Birch-EricaceousWillow via raster editing and review of available field data, the CIR photography and/or the 

examination of the mapping classes occurring in adjacent, but better illuminated areas. In other 

words, determination of the specific low shrub class was based on the context of the site (review 

of the mapping classes of surrounding areas). 

Stunted Spruce / Wet Herbaceous confusion. Spectral confusion between Wet Herbaceous and 

Stunted Spruce occurred throughout the study area, usually within a drainage feature.

45

Figure 16. Selected box areas in both the Landsat TM imagery (left) and a draft version of the landcover classification (right) 

water, terrain shadow and spruce were misclassified (spectrally confused) with one another. Such areas were modeled or raste

appropriate class. 

Landsat TM Imagery (Bands 4,5,3) Landcover Classification: Shadow =Black, Wate

Dark Green

47
If available CIRs indicated there were spruce present, then these areas were usually left as Openwoodland stunted spruce. There was no systematic way, other than visual inspectection, to check 

for and remedy this confusion. 

Spruce-Broadleaf and Broadleaf confusion. There was significant spectral confusion between 

Broadleaf and Spruce-Broadleaf, particularly on the Southside. Spruce-Broadleaf was fairly 

extensive on the southside, but many of those areas contained high percentages of Broadleaf 

(balsam poplar and/or paper birch). So although these areas technically keyed to the SpruceBroadleaf class, they had significant spectral confusion with (i.e., similarities to) the Broadleaf 

class. Similarly, the Spruce-Broadleaf also had confusion with the Open–Woodland Spruce class 

in areas where the Spruce-Broadleaf contained high percentages of conifer. Unfortunately, 

although a surficial geology / floodplain layer may have been useful for separating Spruce–Balsam 

poplar from Spruce–Paper Birch, there was no such layer readily available. Additionally, the 

Project Team did not come up with any satisfactory models to better separate the Broadleaf from

the Spruce–Broadleaf . Instead, separation relied on interactive editing with the imagery, field data 

and CIRs. 

Broadleaf / Alder / Willow confusion. Spectral confusion between the tall shrub class (mainly 

Alder, but also Willow to a lesser extent) and the Broadleaf class was observed in the northern 

portions of the study area. In upslope or higher elevation areas (particularly the North east section 

of the study area) Alder was often spectrally confused with Paper Birch and/or Aspen Stands. In 

the area between the Toklat and Teklanita Rivers, willow was spectrally confused with the 

broadleaf forest mapping class. The confusion between the Tall Shrub and Broadleaf classes may 

have been exacerbated by the uneven ratio of Tall Shrub to Broadleaf signatures in the training 

database. Although there was a fair amount of Balsam poplar signatures, there were relatively few 

Paper Birch / Aspen signatures in the database. In contrast, there were often numerous Alder 

and/or Willow signatures available for each processing block. The 1994 and 1996 SPOT data were 

relied on to strengthen the broadleaf forest class. Both of these image data sets had an early 

September image date, and Paper Birch and Aspen had already begun to senesce in both of them. 

These senesced areas showed up as distinctly recognizable bleached out whitish or pinkish areas 

on both data sets (particularly on south and/or east aspect locations). A mask of these areas was 

made and overlaid on top of the existing mosaic. Additionally, elevation models were used in 

some areas to remap broadleaf forest occurring at high elevations to either Alder or Willow. 

Willow and young or open balsam poplar occurring on gravel bars also exhibited a significant 

degree on confusion. 

Low Shrub Sedge / Dry-Mesic Herbaceous confusion. There was some confusion between DryMesic Herbaceous and Low Shrub-Sedge on both the North and the South side of the Alaska 

Mountain Range. In general, these two classes had ecological, and therefore spectral similarities. 

Since there were significantly more Low Shrub-Sedge signatures than Dry-Mesic Herbaceous 

signatures on the Northside, and since the Low Shrub-Sedge Signatures tended to have larger 

spatial extents, the ML classifier mapped Low Shrub-Sedge much more aggressively than DryMesic Herbaceous on the Northside processing blocks (particularly TM Processing Blocks 1 and 

4). Arguably however, the lack of large or numerous Dry-Mesic Herbaceous signatures on the 

Northside may be indicative of that it is does not have extensive occurrence there. Since no 

concerns were raised at any of the Draft Map reviews regarding the patterning of Low Shrub-Sedge 

48
 

 

and / or Dry-Mesic Herbacous classes on the Northside, no attempt was made to model or edit 

between these two classes. On the Southside however, there were concerns that both classes were

over classified, particularly on hillsides. There were significantly more Dry-Mesic Herbacous 

signatures available from the Ground truth data for the southside. Many of these occurred in TM 

Processing Block #5 (the Broad Pass area). The spectral classification was relied on heavily for 

discriminating between these two classes. 

Dry-Mesic Herbaceous/Dwarf Shrub confusion. In the higher elevation valleys, Dry-Mesic 

Herbaceous areas were found adjacent to Dwarf Shrub areas. This confusion was particularly 

problematic when the herbaceous content of the Dwarf Shrub areas was close to the 25% upper 

threshold for this mapping class. The spectral classification was relied on heavily for 

discriminating these two classes. Other modeling layers (such as NDVI) were ineffective at 

separating the two classes, and image interpretation and review of CIR photos was also not very 

effective at reducing the confusion. 

Dwarf Shrub/Dwarf Shrub-Rock/Sparse Vegetation/Bare Ground confusion. The “greenness” as 

indicated by NDVI was used to separate the Dwarf Shrub/Dwarf Shrub-Rock/Sparse 

Vegetation/Bare Ground classes. This layer was particularly useful because it enabled partial 

penetration of terrain shadow. NDVI thresholds were established and used to model (distinguish) 

these four mapping classes. 

Wet Herbaceous/Sparse Vegetation/Bare Ground confusion. Wet Herbaceous tended to be 

misclassified along river channels and roads. Wet Herbaceous that occurred along river channels 

or roads was usually edited to either the Sparse Vegetation or the Bare Ground class based on 

imagery interpretation. 

Wet Herbaceous/Shadow confusion. Wet Herbaceous was significantly overclassified in terrain 

shadow areas. The shaded relief and slope data layers were used to model erroneous Wet 

Herbaceous on ridge tops and hillsides to the Sparse Vegetation or Shadow classes, respectively. 

Wet Herbaceous/Peatland confusion. Wet Herbaceous and Peatland confusion occurred on the 

valley bottoms of the Southside. Although it was hoped that TM Band 5 could be used for 

thresholding out the very wet sites within the Peatland mapping class, investigations found it to 

not be very effective. In general, Band 5 alone, was not enough to consistently and accurately 

distinguish between these two classes. Minimal modeling and editing were applied to the Wet 

Herbaceous and Peatland classes on the Southside (i.e., their distribution and occurrence is 

primarily the result from the ML classifier). 

Wet Herbaceous/Burn confusion. Large areas of burn on the Park’s west side were spectrally 

mapped by the ML classifier as wet herbaceous. Since there were no burn samples collected or 

included as signatures, there was no possible way for the ML classifier to map the burn class. 

Interestingly, many of the more recent or the very intense burns (as indicated by the scarring on the 

raw imagery) were spectrally classified to Wet Herbaceous, while many of the older, or less 

intense burns were spectrally mapped to Willow or Low Shrub-Sedge. Moreover, many of the 

recent and/or more intense burns probably scorched the ground surface’s top organic layer, 

exposing the permafrost layer beneath it. This could conceivably create legitimate Wet 

49
Herbaceous-like conditions, such as close to saturated conditions and the colonization of 

herbaceous emergent vegetation species. 

Willow/Low Shrub-Sedge/Burn confusion. As described above, older or less intense burns (i.e., 

those that had regrowth) tended to be spectrally mapped as willow or low shrub sedge. These 

areas were not included as part of the burn mask because the NDVI layer indicated that there was 

some ‘greenness’ associated with them. Since there were no samples collected in the burn areas 

during the 1999 field season, it was difficult to say with any certainty which vegetation mapping 

class would be the most appropriate for these areas (in reality, different burn areas would be at 

different regrowth / successional stages and therefore have different site characteristics and plant 

associations). Unfortunately, the Project Team did not have the opportunity or resources to visit 

and/or sample burn areas following the spectral classification results. Thus, without the benefit of 

any empirical data in hand, both the Willow and Low Shrub-Sedge mapping classes spectrally 

mapped in obvious old burn areas seemed reasonable, and were not changed to the burn or any 

other mapping class. General or incidental observations made of various burn areas during the 

1999 field season (i.e., before the spectral classification results were known) indicated that some

(but not all) of the burn sites tended to be wet. Typical vegetation species included (but were not 

limited to) cotton grass, willow and/or young balsam poplar or aspen. 

Herbaceous-Shrub/Dwarf Shrub /Dry-Mesic Herbaceous/Willow confusion. The HerbaceousShrub class (which is limited to the Park’s southside) is a composition of three other mapping 

classes; Dwarf Shrub, Dry-Mesic Herbaceous and Willow. User’s should therefore expect some

confusion amongst all four classes on the hillslopes of the Park’s southside. (Refer to section 2.84 

and Appendix 5.2 for more information about the Herbaceous-Shrub class). 

Water/Shadow confusion. In areas of high terrain and extensive terrain shadows, erroneous water 

often appeared in areas of shadow or partial shadow. A combination of illumination, slope, and 

the spectral classification was used to ensure that overclassified water areas were remapped to 

shadow based on the principal that water bodies only occur on low slope areas, and not in terrain 

shadow areas. The illumination and spectral classification were again used to enhance the Shadow 

class. 

Cloud or Snow/Bare confusion. Scatterings of either snow or cloud pixels were spectrally mapped 

at higher elevations or on gravel bars. They were usually reclassed to the Bare Ground or Sparse 

Vegetation class. 

50
Table 10. Models and modeling parameters applied to the Imagery Processing Blocks. Refer to Appendix 5.6 for additional modeling details

Image Processing Block 1 - Landsat TM p70r15/16 - August 15, 1988Model Layer /Name Confused Class(es) Separable by ... IF Parameter 1 AND Parameter 2 THEN Output equals ...

Terrain Shadow All classes Illumination IF illum >= 69 THEN Shadow /Indeter

Partial Terrain Shadow Wet Herbaceous Illumination 70 < illum <= 134 THEN Mixed Dwarf Sh

Mixed Spruce-Deciduous Illumination 70 < illum <= 134 THEN Alder 

Dense Open Spruce Illumination 70 < illum <= 134 THEN Shadow /Indeter

Open-Woodland Spruce Illumination 70 < illum <= 134 THEN Alder 

Open-Woodland Stunted Spruce Illumination 70 < illum <= 134 THEN Low Shrub Birc

TNDVI to correct overclassification of floodplain dwf shrb rock and upslope bare

Dwarf Shrub Rock Slope, TNDVI IF Slope <= 12 AND TNDVI > = 124 THEN Sparse Vegetati

Bare ground Slope, TNDVI IF Slope >= 13 AND TNDVI > = 124 THEN Sparse Vegetati

Correct overclassification of water on partially shadowed upslopes

Shadow/Indeterminate Illumination, Slope IF illum < 135 AND Slope > 16 THEN Bare Ground

Silty Water Illumination, Slope IF illum < 135 AND Slope > 16 THEN Bare Ground

Correct overclassification of water and wet herb on upslopes

Clear Water Slope Slope > 11 THEN Bare Ground

Silty Water Slope Slope > 11 THEN Bare Ground

Wet Herbaceous Slope Slope > 11 THEN Mixed Dwarf Sh

Image Processing Block 2 - p71r15 - July 21, 1985

No models applied 
Table 10 cont. Models and modeling parameters applied to the Imagery Processing Blocks. Refer to Appendix 5.7 for additional modeling details

Image Processing Block 3 - p70r16 - June 29, 1991 (and Upper Elevation SPOT - September 4, 1996)Model Layer /Name Confused Class(es) Separable by ... IF Parameter 1 AND Parameter 2 THEN Output equals ...

Correction of overclassification of wet herb in upslope areas

Wet Herbaceous Slope IF Slope > = 12 THEN Mixed Dwarf Sh

Corrects error in terrain shadow

Open-Woodland Spruce Illumination IF 80 < illum<=157 THEN Shadow/IndeterOpen-Woodland Stunted Spruce Illumination IF 80 < illum<=157 THEN Low Shrub BircMixed Spruce-Deciduous Illumination IF 80 < illum<=157 THEN Alder Shrub

Balsam Poplar Illumination IF 80 < illum<=157 THEN Willow Shrub

Open Low Shrub (Peatland) Illumination IF 80 < illum<=157 THEN Shadow/Indeter

Correction of terrain shadow andoverclassification of water

Silty Water Illumination, Slope IF 80 < illum<=136 AND Slope >=17 THEN Shadow/Indeter

Clear Water Illumination, Slope IF 80 < illum<=136 AND Slope >=17 THEN Shadow/Indeter

Subsets Alpine (upper elevation) SPOT Spectral Classifcation

Elevation IF DEM >=784 THEN Use Spot classif

(else use TM cla

Corrects shadow effects in SPOT alpine subset based on Slope

Dense Open Spruce IF Mapping Class = 1 THEN Shadow/Indeter

Open-Woodland Spruce IF Mapping Class = 2 THEN Shadow/IndeterOpen-Woodland Stunted Spruce IF Mapping Class = 3 THEN Shadow/Indeter

Silty Water Slope IF Slope > = 16 THEN Shadow/Indeter

Clear Water Slope IF Slope > = 16 THEN Shadow/Indeter

Wet Herbaceous Slope IF Slope > = 16 THEN Shadow/Indeter

Additional correction of upslope water

Silty Water Slope IF Slope >= 17 THEN Bare Ground

Clear Water Slope IF Slope >= 17 THEN Bare Ground

Correct overclassification of wet herb on upslope areas

Wet Herbacous Slope IF Slope >= 12 THEN Mixed Dwarf Sh

Correct overclassification of low shrub sedge elevation, AOI IF If Elev <~ 600 THEN Herb Shrub
Table 10 cont. Models and modeling parameters applied to the Imagery Processing Blocks. Refer to Appendix 5.7 for additional modeling details

Image Processing Block 4 - p71r16 - June 21, 1991Model Layer /Name Confused Class(es) Separable by ... IF Parameter 1 AND Parameter 2 THEN Output equals ...

Correct barren to ice, mixed dwf shrub, dwfshrub-rock or sparse based on TNDVI

Bare Ground TNDVI IFBare Ground TNDVI IF

Bare Ground TNDVI IF

Bare Ground TNDVI IF

Correct shadow and upslope water 

Silty Water Slope and IlluminationIF

Clear Water Slope and IlluminationIFAll Classes

TNDVI <= 109125 <=TNDVI <= 130

131 <=TNDVI <= 143

144 <=TNDVI <= 200Slope >= 17Slope >= 17

illum >= 130

THEN Snow/IceTHEN Sparse Vegetati

THEN Mixed Dwarf Sh

THEN Mixed Dwarf Sh

THEN Bare GroundTHEN Bare Ground

THEN Shadow/Indeter

Image Processing Block 5 - p69 r16 - Sept. 8, 1999Model Layer /Name Confused Class(es) Separable by ... IF Parameter 1 AND Parameter 2 THEN Output equals ...

Identify terrain shadow/indeterminate

All classes Illumination IF illum >= 87

Integrates bare& Ice from SPOTclassification andcorrects ice/snow shadowed areas in TM classification

Snow /ice Bare Ground IF Tm map class = sno

wAND

IF Tm map class = Silty ANDIF Tm map class = ClearAND

IF SPOT map class = Snow

Correct overclasification of water on upslopes

Silty Water Slope IF Slope > 16

Clear Water Slope IF Slope > 16

THEN Shadow/Indeter

SPOT map class =THEN Bare GroundSPOT map class =THEN Bare Ground

SPOT map class =THEN Bare Ground

THEN Snow/Ice

THEN Shadow/Indeter

THEN Shadow/Indeter
54
2.84 Defining and delimiting the Southside Herbaceous-Shrub Class 

The Herbaceous-Shrub class was not defined until the final phases of the mapping project. It was 

developed to correct extensive over classification of the Low Shrub-Sedge class on the hill slopes 

of the Southside. Since Herbaceous-Shrub was not identified as a potential target or endpoint 

mapping class during the 1999 fieldwork or during the review and attributing of the firepro 

vegetation database, there were no Herbaceous-Shrub training signatures in the spectral database 

for the ML Classifier to utilize for spectrally mapping the Herbaceous-Shrub class. Instead, the 

mapped extent of the Herbaceous-Shrub class was the result of reclassing the Low Shrub-Sedge 

class on the hill slopes (at or above the occurrence of dense alder) on the Southside through 

modeling and editing. 

The extensive overclassification of the Low Shrub-Sedge class was the result of two issues: 

1.) the mis-assignment of 3-4 polygons from the firepro database to the Low Shrub-Sedge class;

and 2.) the poorer spectral resolution of the SPOT XS data. The firepro vegetation species list for 

the 3-4 mis-assigned signatures included a mix of species types, such as alder or willow (low and 

dwarf), ericaceous (low and dwarf), significant graminoid component (usually 35% or more) and 

some sedge. (Refer to Appendix 5.2 for a description of the Herbaceous-Shrub Class). The 

species composition for these sites did not neatly key into any of the previously defined mapping 

classes, but in lieu of tossing them from the spectral database (spectral training signatures based on 

actual field data are considered a valuable commodity!), they were assigned to the Low ShrubSedge class. In general the grass/forb component was significantly higher in the subalpine 

herbaceous and shrub types South of the Alaska range. Consequently a new mapping category 

was established. 

Supervised ML classifications (using the spectral database that contained the 3-4 signatures 

described above) were run on both the Landsat TM and SPOT XS imagery available for the Park’s 

Southside. In general, these areas were spectrally mapped in the TM data as a mix of Dwarf 

Shrub, Dry-Mesic Herbaceous and some Low Shrub-Sedge. These same areas were predominantly 

spectrally mapped as Low Shrub-Sedge in the SPOT XS data. So while the TM data results 

appeared to be more ‘pixelated’, the results indicated that the TM data were better able to discern 

the true variability of landcover types occurring in these areas. And although the SPOT 

classification results of uniformly classifying these areas as Low Shrub-Sedge appeared ‘cleaner’, 

it actually provided less insight into the heterogeneous nature of these areas. A complicating 

factor however was that poor herbaceous development was expected in the early (June 29, 1991) 

and late (September 8, 1999) TM image dates (as evidenced by their low snow lines). 

As discussed in Section 2.23, the advantage of integrating the 1996 SPOT data set into the mosaic 

design was that the SPOT data had a higher snow line than the corresponding TM data. The SPOT 

classification was therefore incorporated in the study wide mosaic for elevations greater than 784 

m on the Park’s southside. While this alleviated concerns that vegetation hadn’t ‘greened’ up by 

the late June TM date, it also introduced the SPOT’s overclassification of the Low Shrub-Sedge 

into the landcover mosaic. 

A proposed remedy for correcting the extensive Low Shrub-Sedge classification on the Southside 

hill slopes, was to expand the Low Shrub-Sedge mapping class description to include graminoids 

55
and forbes. However, there was resistance to ‘diluting’ the Low Shrub-Sedge class to 

accommodate the Southside, especially since the Low Shrub-Sedge class appeared to be accurately 

defined and mapped on the Northside. In addition lower elevation valley bottoms on the Southside 

were typically sedge dominated. 

In general, areas of Low Shrub-Sedge at or above the alder line (generally elevations of 600 m and 

higher) on the Park’s southside (below Broad Pass) were reclassed to the Herbaceous-Shrub 

mapping class (Figure 17). 

Figure 17. The red dashed area indicates where Low Shrub-Sedge was reclassed to HerbaceousShrub on the Southside of the study area. 

56
 

 

 

 

2.9 Raster Editing and Filtering Approach 

Raster editing is the process of changing, adding, or deleting raster attributes or data values of 

thematic and continuous raster data. Raster editing is used in image classification to correct bad or 

noisy data values by recoding or reassigning pixels to another class. In the Denali NP&P 

landcover project, raster editing was focused on classes that could not be separated effectively 

through models, but could be positively identified through image interpretation or CIR 

photography, and/or review of the Denali NP&P Landcover (spectral) Database (FirePro, 1999 

field data, annotations/notes). 

During preliminary product development, the landcover map was first reviewed at small and 

intermediate scales (1:250,000, and 1:100,000 respectively) to identify obvious problems. More 

detailed editing was conducted at approximately 1:63:360 or larger scale throughout the Denali 

NP&P project area. In most cases, areas requiring edits were analyzed to determine whether 

problems could be resolved through further modeling. Many edits were completed through this 

approach (further refinement of and application of models on a local or ecoregion basis). 

Priority for raster editing was focused along mosaic scene boundaries, and within the park 

boundary (where virtually all ground reference data were located). Substantial editing was also 

required within the 10-mile park buffer. This editing was difficult due to the lack of field data for 

areas outside the park (other than incidental observations and image annotations collected during 

the 1999 field season). 

Further edits were completed based on comments from NPS and AKNHP during an onsite review 

of the draft landcover classification map on August 21, 2000. This review focused on the 

evaluation of both the digital and hardcopy versions (1:100,000 scale plots of the NE, NW, SE and 

SW sections of the study area). NPS staff familiar with the Park or with particular areas within the

Park reviewed and annotated recommendations on the draft plots. An overflight was conducted to 

visit areas of concern that were accessible. All requested edits, including refinements to existing 

models were implemented. 

Following implementation of all requested edits, the unfiltered pixel map was filtered to two 

different levels of aggregation. The first level was referred to as the ‘single pixel eliminated’ 

version. All single “detached” pixels were identified and recoded to the mapping class of the 

majority occurring pixels surrounding the detached pixel. Single pixels were essentially regarded 

as noise, and there removal greatly increased the readability / interpretability of the unfiltered 

product. The second level of aggregation was approximately a 2 acre minimum mapping unit 

(MMU). This was accomplished by first running the unfiltered map through a clumping 

algorithm, which grouped connected pixels into millions of individual clusters of like mapping 

class pixels. Next, an elimination algorithm was applied to the clumped file so that all groups of 

less than approximately 2 acres (9 pixels) were automatically recoded to the majority occurring 

class surrounding the clump. Initially, NPS was given the draft map at a variety of aggregation 

levels, including an unfiltered level, a single pixel eliminated level, a .80 acre mmu level, a 2 acre 

mmu level and a 5 acre mmu level. Ultimately however, NPS decided that the single pixel 

eliminated and the ~2 ac. mmu aggregation levels best retained the detail for their level of interest. 

57
 

 

 

The NPS-AKSO office was supplied with the digital version of the unfiltered LC database so 

additional levels of aggregation can be performed as required by users in the future. 

2.10 QUALITY CONTROL AND EVALUATION 

Throughout the project, from fieldwork to final product, the image analyst, botanists, and ecologist 

team members remained consistent. This ensured a high level of consistency in the landcover 

map, and provided the image analysts with much needed ecological and botanical local expertise. 

Several teleconference calls related to spectral classification output, classification development, 

and modeling and editing output resulted in discussions and involvement of the project team

throughout map development. Additionally, the NPS-AKSO office was given versions of all key 

interim products for review and evaluation. This included preliminary and final spectral 

classification outputs of all image processing blocks, final model output from all image processing 

blocks and multiple mosaic wide draft versions. Draft landcover maps at 1:250,000 and 1:100,000 

scale (along with prints of the associated imagery) were presented to Denali NP&P staff August 

23-25, 2000 for review. Comments and feedback received from either NPS or AKNHP on any of 

the interim products was integrated into the final versions of those datasets. Flexibility and 

customization by the contractor with regard to certain project specifications such as mapping 

classes, scale and layout of the hardcopy products, and multiple versions of the Landcover 

database (i.e., multiple mmu’s, versions with and without burn areas, detailed and general mapping 

class levels) resulted in a more useable suite of products. 

58
 

 

 

CHAPTER THREE 

RESULTS AND DISCUSSION 

3.1 Project Results (final maps, databases and files) 

The following landcover databases were produced from this project: 

• Digital, unfiltered, 25 class (detailed) landcover database of the study area in ERDAS 

Imagine Format. 

• Digital, single pixels eliminated, 25 class (detailed) landcover database of the study area in 

ERDAS Imagine Format 

• Digital, 2 acre mmu, 25 class (detailed) landcover database of the study area in ERDAS 

Imagine Format (Figure 18).

• Digital, unfiltered, 16 class (general) landcover database of the study area in ERDAS 

Imagine Format 

• Digital, single pixels eliminated, 16 class (general) landcover database of the study area in 

ERDAS Imagine Format 

• Digital, 2 acre mmu, 16 class (general) landcover database of the study area in ERDAS 

Imagine Format (Figure 19). 

• Digital, clumped file for the unfiltered, 25 class (detailed) landcover database of the study 

area in ERDAS Imagine Format. (This file allows users to produce the detailed landcover 

database at aggregation levels(mmu) other than those provided). 

• Two hardcopy, 36” x 40” photographic quality maps of the 2 acre mmu, 25 class (detailed) 

landcover database of the study area. Includes Park Boundary, legend, and Project 

description. 

• An enhanced Arcview 3.2 project file that includes Project related datalayers: the FirePro 

Vegetation field database, the 1999 fieldwork database, the final signature file, the final 

landcover database and the Park boundary vector layer. 

• Two hardcopies of the Final Report (Volumes I and II) as well as digital versions (MS 

Word file for text files and MS Excel files for tables, if separate from the text file). 

59
Figure 18. The detailed (25) mapping class Denali NP&P landcover map (2 ac mmu). 

60
Figure 19. The general (16) mapping class Denali NP&P landcover map (2 ac mmu). 

61
 

3.2 DISTRIBUTION OF LANDCOVER WITHIN DENALI NP&P 

The final mapping classes for both the 25 class (detailed) and the 16 class (general) landcover 

databases, as well as their distribution in the Denali NP&P Project Area are provided in Tables 11, 

12a and 12b below. 

The names of the final landcover classes used in the Detailed Denali Landcover Mapping Project 

are listed below. A class key and description (species, photos, physical site variables) for each of 

these classes can be found in Appendices 5.1 & 5.2. 

Table 11. Area (ac) and percent distribution of the detailed (25) landcover mapping class for Denali NP&P. 

PARK Park and 

Buffer 

Landcover Classes Area (acres) Percent 

Distribution

 Area (Acres) Percent 

Distribution 

Dense-Open Spruce Forest 50,359.02 0.84 157,156.52 1.65 

Open - Woodland Spruce 551,427.78 9.15 931,270.54 9.75 

Stunted Spruce 853,984.85 14.16 1,283,661.22 13.44 

Broadleaf 97,062.91 1.61 253,607.63 2.65 

Spruce-Broadleaf 149,929.39 2.49 509,643.88 5.34 

Alder 260,197.98 4.32 533,799.04 5.59 

Willow 95,590.17 1.59 195,666.18 2.05 

Closed Low Shrub Birch 54,033.90 0.90 87,201.42 0.91 

Low Shrub Birch-Ericaceous-Willow 570,700.34 9.47 898,321.93 9.40 

Low Shrub-Sedge 277,322.25 4.60 450,123.90 4.71 

Peatland 2,303.66 0.04 34,475.01 0.36 

Herbaceous-Shrub 68,871.56 1.14 132,299.29 1.38 

Dwarf Shrub 346,846.88 5.75 517,563.60 5.42 

Dwarf Shrub-Rock 73,894.04 1.23 107,685.76 1.13 

Dry-Mesic Herbaceous 45,684.09 0.76 95,915.64 1.00 

Wet Herbaceous 38,426.74 0.64 74,171.66 0.78 

Aquatic Herbaceous 563.95 0.01 590.10 0.01 

Sparse Vegetation 289,308.38 4.80 389,988.64 4.08 

Bare Ground 592,298.24 9.82 710,194.02 7.43 

Snow -Ice 932,789.58 15.47 1,049,705.66 10.99 

Shadow-Indeterminate 413,259.80 6.85 657,181.47 6.88 

Silty Water 30,765.06 0.51 67,957.35 0.71 

Clear Water 60,764.31 1.01 100,879.80 1.06 

Burn 172,528.50 2.86 296,213.54 3.10 

Cloud 357.05 0.01 17,299.60 0.18 

Total 6,029,270 100.0 9,552,573 100.0 

Based on spectral and ecological similarities, the detailed landcover mapping class types were 

combined to create a general landcover map. This was to provide users with a more accurate and 

aggregated database if needed. 

62
 

 

 

 

 

 

 

Table 12a. General mapping class list. 

General Landcover Classes Combined Detailed Landcover Classes 

Needleleaf Dense-Open Spruce 

Open-Woodland Spruce 

Stunted Spruce Stunted Spruce 

Broadleaf Broadleaf Forest 

Spruce-Broadleaf Spruce-Broadleaf 

Tall Shrub Alder 

Willow 

Low Shrub (drier sites) Closed Low Shrub Birch 

Low Shrub Birch-Ericaceous-Willow 

Low Shrub (lowland / moister sites) Low Shrub-Sedge 

Peatland (Southside only) 

Dwarf Shrub Dwarf Shrub 

Dwarf Shrub-Rock 

Herbaceous-shrub Dry-Mesic Herbaceous 

Herbaceous-Shrub (Southside only) 

Wet herbaceous Wet Herbaceous 

Sparse Vegetation-Bare Ground Sparse Vegetation 

Bare Ground 

Snow-Ice Snow-Ice 

Water Silty Water 

Clear Water 

Aquatic Herbaceous 

Burn Burn 

Indeterminate Cloud 

Shadow (terrain and cloud) 

63
 

 

 

 

Table 12b. Area (ac) and percent distribution of the general (16) landcover 

mapping class for Denali NP&P. 

General Landcover Classes

PARK Park and Buffer 

 Area (acres)

Percent 

Distribution Area (Acres)

Percent 

Distribution

Needleaf 601,787 10.0 1,088,427 11.4 

Stunted Spruce 853,985 14.2 1,283,661 13.4 

Broadleaf 97,063 1.6 253,608 2.7 

Spruce-Broadleaf 149,929 2.5 509,644 5.3 

Tall Shrub 355,788 5.9 729,465 7.6 

Low Shrub (drier sites) 624,734 10.4 985,523 10.3 

Low Shrub (moister sites) 279,626 4.6 484,599 5.1 

Dwarf Shrub 420,741 7.0 625,249 6.5 

Herbaceous-shrub 114,556 1.9 228,215 2.4 

Wet herbaceous 38,427 0.6 74,172 0.8 

Sparse Vegetation- Bare 881,607 14.6 1,100,183 11.5 

Snow-Ice 932,790 15.5 1,049,706 11.0 

Water 92,093 1.5 169,427 1.8 

Burn 172,528 2.9 296,214 3.1 

Cloud 357 0.0 17,300 0.2 

Indeterminate (Shadow) 413,260 6.9 657,181 6.9 

Totals 6,029,270 100.0 9,552,573 100.0 

3.3 ACCURACY ASSESSMENT 

Confusion matrices (also called error matrices) and associated statistics have been adopted by the 

remote sensing community as the standard technique for accuracy assessment (Campbell 1987, 

Congalton 1991, Jensen 1996). 

A confusion matrix is a square array of numbers set out in rows and columns that represents the 

number of information classes or classification categories, used to compare (on a category-bycategory basis) the relationship between known reference data and the corresponding results of a 

classification. The columns and rows of an error matrix show the number of sample units assigned 

to a particular landcover class (classification data) relative to the actual number of sample units 

that belong to the landcover class (reference data). The error of commission is computed as 100% 

minus the producer’s accuracy and error of omission is 100% minus user’s accuracy. From an 

error matrix the overall accuracy of the classification of a landcover map can be quantified along 

with both the producer’s accuracy (or errors of omission or exclusion) and user’s accuracy (or 

errors of commission or inclusion). 

The overall accuracy is computed as the sum of all the main diagonal cells in the error matrix 

divided by the total number of sample units used in the error matrix. The overall accuracy shows 

the proportion of the classification that is correct but it does not indicate how the accuracy is 

distributed across the individual landcover classes. (Story and Congalton 1986). 

64
 

 

The producer’s accuracy is the sum of all correctly classified sample units in a landcover class 

divided by the total number of reference data in that landcover class. The producer’s accuracy 

indicates how well members of a particular landcover were classified, or shows the ease or 

difficulty with which members of a particular landcover class were correctly identified, classified 

or mapped by the mapping methodology. 

The user’s accuracy indicates the probability that a particular landcover class (classification data) 

would represent that landcover class in the reference or ground truth data. The user’s accuracy, 

computed as the sum of all correctly classified sample units in a landcover class divided by the 

total number of classification data in that landcover class, is termed a measure of reliability in 

specific map categories. 

Both qualitative and quantitative evaluations of errors were made at various stages of developing 

the Denali NP&P landcover map for the purpose of improving or refining the mapping process. 

Accuracy assessments were performed on the endpoint (25 class) and general (16 class) final 

Denali NP&P landcover in order to quantitatively determine what proportion of the overall 

landcover map was correct, the probability that individual landcover classes were correctly 

mapped, which landcover classes were overestimated or underestimated, and the distribution of 

errors between mapping classes. 

Ideally, the best way to assess the accuracy of the map would be to use an independent database, 

collected and processed similar to the manner in which the the spectral training database was 

derived from the FirePro and 1999 Field data. In lieu of an additional field collection effort for 

accuracy assessment purposes, it was hoped that some of the ground reference data (i.e., the data 

collected for the supervised training database) could be reserved for accuracy assessment purposes. 

Unfortunately, all 650+ signatures in the supervised / signature training database were needed for 

spectral processing; precluding the reservation of a portion of them for later use. Therefore, the 

estimates of accuracy throughout this document are primarily based on the ~2 acre minimum 

mapping unit landcover database (prior to adding the burn mask) compared with the supervised 

training data set. 

The results of this comparison were used to generate the confusion matrices used to quantitatively

assess the landcover map. Admittedly, using the training database introduces some bias that would 

be avoided by using an independent database. Collecting an accuracy database would have 

required another field collection season, which was beyond the scope and resources of the project. 

Evaluating the landcover map with the training database is a measure of how well the training 

database was able to map itself. It is also an indication of the homogeneity (“spectral purity”) of 

the training signatures. As discussed in Section 2.7, confusion matrices were generated during the 

iterative ML classification / signature evaluation process for each processing block. The confusion 

matrix generated for the final ML classification run of each block is available in Appendix 5.5. 

Again, these were for the individual processing blocks, prior to modeling and mosaicking. The 

per class accuracies for some of the mapping classes (dwarf shrub-rock, for example) were actually 

higher immediately following final spectral classification then they were after significant post 

processing (i.e. modeling and editing). This is primarily because the same post spectral processing 

procedures (mosaicking, modeling and editing) that were performed on the LC map were not 

65
 

 

performed of the training database. Applying the same processing procedures to the training 

database would have introduced even greater bias to the dataset. 

Although using the training database doesn’t give an absolutely objective or reliable assessment of 

the map’s accuracy, it does provide useful information regarding which mapping classes are 

generally confused, and what mapping classes caused the confusion. This information facilitates 

greater interpretability, and therefore understanding of the landcover database. Confusion matrices 

were generated for the 25 endpoint mapping classes and the 16 general mapping classes (at ~ 2 

acre MMU). An estimate of the overall accuracy (with the approximately 650 training areas) for 

the detailed landcover class map was 81.6% (Table 13). Accuracies per mapping class are 

discussed in Section 3.31 below. The detailed landcovermap was then further grouped, based on 

ecological similarities to form the general landcover class map. The overall accuracy for the 

general landcover map was 84.8% (Table 14). 

Since some of the classes (mixed spruce–broadleaf, willow, wet herbaceous, etc) exhibited a fairly 

significant amount of confusion, and since the Northside (which includes the Broad Pass area) and 

Southside areas of the Park are ecologically distinct, confusion matrices were also generated for 

the north and southsides of the Alaska Mountain Range (Figure 20, Tables 15 and 16). 

66
Table 13. Confusion matrix for the Final Denali LC Mosaic (2 ac., no burn version) x training signature data (12/17/00):

 Reference / Training Data

Dense Open Spruce Forest (white-like)

Open - Woodland Spruce Forest

Open - Woodland Stunted Spruce

Broadleaf Forest

Mixed Spruce-Broadleaf Forest

Alder Shrub

Willow Shrub

Closed Low Birch Shrub

Low Shrub Birch-Ericaceous-Willow

Low Shrub-Sedge

Peatland

Herbaceous / Shrub

Mixed Dwarf Shrub

Mixed Dwarf Shrub-Rock

Dry / Mesic Herbaceous

Wet Herbaceous

Aquatic Herbaceous

Sparse Vegetation

Bare Ground

Snow / Ice

Shadow

Silty Water

Clear Water

Burn

Cloud

Theme 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Row Tota

1 1092 475 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 157

2 119 4998 1131 9 15 2 0 0 3 5 0 0 4 0 4 9 0 0 0 0 0 0 0 0 0 6293 0 327 5833 7 118 0 9 168 60 104 0 0 1 0 0 29 0 0 0 0 0 0 0 0 0 6654 0 28 0 3798 2235 12 26 0 0 0 6 0 0 0 11 4 0 0 0 0 0 0 0 0 0 612

5 8 154 70 153 3634 27 47 0 0 1 9 0 2 0 3 4 0 0 0 0 0 0 0 0 0 411

6 0 16 11 44 57 6854 252 0 175 2 1 0 4 0 19 7 0 0 0 0 0 0 0 0 0 744

7 0 0 20 305 0 203 681 0 67 25 0 0 21 0 15 19 0 7 0 0 0 0 0 0 0 136

8 0 0 52 0 0 0 0 600 43 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 72

9 0 28 124 222 7 112 86 91 3877 425 0 0 75 1 56 41 0 1 0 0 0 0 0 0 0 514

10 0 0 19 71 6 48 41 32 382 3308 28 0 71 1 55 58 0 0 0 0 0 0 0 0 0 41211 6 2 5 0 5 0 0 0 0 27 233 0 0 0 0 10 0 0 0 0 0 0 0 0 0 28

12 0 0 0 0 0 0 11 0 0 0 0 0 5 0 135 0 0 0 0 0 0 0 0 0 0 15

13 0 0 0 60 0 44 8 0 39 18 0 0 1039 32 44 0 0 0 12 0 0 0 0 0 0 129

14 0 0 0 12 2 10 0 0 1 0 0 0 27 89 4 0 0 26 6 0 0 0 0 0 0 17

15 0 0 0 0 0 0 20 0 0 30 0 0 43 0 811 0 0 0 0 0 0 0 0 0 0 90

16 17 1 5 1 0 4 17 0 2 24 6 0 1 0 2 475 0 0 0 0 0 0 0 0 0 55

17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 43 0 0 0 0 0 0 0 0 4

18 0 0 0 29 0 4 19 0 0 0 0 0 7 39 0 0 0 395 369 0 0 2 0 0 0 86

19 0 0 0 15 0 1 0 0 0 0 0 0 5 0 0 1 0 22 1060 7 0 114 0 0 0 122

20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 2376 0 0 0 0 0 23821 0 0 5 0 1 0 19 0 0 0 0 0 21 0 0 0 0 0 15 0 0 0 0 0 0 622 0 0 0 5 0 0 1 0 0 0 0 0 0 0 0 0 0 0 26 0 0 1388 0 0 0 14223 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 34 0 0 0 0 0 8 3599 0 0 364

24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Col. Tota 1242 6029 7278 4731 6087 7321 1237 891 4649 3995 283 0 1326 162 1159 691 43 451 1499 2383 0 1512 3599 0 0 4618

Prod. % 87.9 82.9 80.1 80.3 59.7 93.6 55.1 67.3 83.4 82.8 82.3 NA 78.4 54.9 70.0 68.7 100.0 87.6 70.7 99.7 NA 91.8 100.0 NA NA 5656

Theme 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 

Image Classification
68
 Table 14. Confusion matrix for the Final Denali LC Mosaic (General Classes) (2 ac., no burn version) x training signature data (12/18/00):

 Reference / Training Data

Dense Open / Open Spruce

Stunted Spruce

Broadleaf Forest

Mixed Spruce-Broadleaf Forest

Alder / Willow

Closed Low Shrub Birch / Low

Shrub Birch-Eric-Willow

Low Shrub Sedge (and Southside

Peatland)

Mixed Dwarf Shrub / Dwarf Shrub

Rock

Dry Mesic Herb (and Southside

Herbaceous/Shrub)

Wet Herbaceous

Sparse / Bare

Snow / Ice

Silty Water/Clear Water/Aquatic

Herb

Burn

Cloud

Shadow

Theme 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Row ToUser % Th

1 6689 1138 9 25 3 3 9 4 6 18 0 0 0 0 0 0 7904 84.6 

2 327 5827 7 118 9 200 103 1 0 24 0 0 0 0 0 0 6616 88.1 

3 28 0 3777 2235 34 0 6 0 10 4 0 0 0 0 0 0 6094 62.0 

4 158 70 150 3613 68 0 6 0 2 3 0 0 0 0 0 0 4070 88.8 

5 18 33 370 74 8027 263 31 16 34 26 6 0 0 0 0 0 8898 90.2 

6 26 174 220 7 193 4624 487 80 56 40 1 0 0 0 0 0 5908 78.3 

7 7 24 71 12 84 408 3559 69 55 72 0 0 0 0 0 0 4361 81.6 

8 0 1 77 2 54 40 18 1179 47 0 48 0 0 0 0 0 1466 80.4 

9 0000 35 0 30 65 947 0 0 0 0 0 0 0 1077 87.9 

10 18 410 11 2 29 1 2 470 0 0 0 0 0 0 538 87.4 

11 0 0 44 0 21 0 0 52 0 1 1845 7 125 0 0 0 2095 88.1 

12 0 0 0 0 0 0 0 0 0 0 10 2376 0 0 0 0 2386 99.6 13 0 3 5 0 0 0 0 0 0 33 25 0 5029 0 0 0 5095 98.7 

14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 N

A 

15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 

16 0 4 0 1 19 0 0 21 0 0 15 0 0 0 0 0 60 0.0 

Col. Total 7271 7278 4731 6087 8558 5540 4278 1488 1159 691 1950 2383 5154 0 0 0 47962 To

Prod. % 92.0 80.1 79.8 59.4 93.8 83.5 83.2 79.2 81.7 68.0 94.6 99.7 97.6 NA NA NA 56568 To

Theme 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 84.79 % 

Image Classification
70
Figure 20. The detailed (25) mapping class Denali NP&P landcover map (2 ac mmu), shown 

with the Northside – Southside divide. 

71
72
Table 15. Confusion matrix for the Final NORTHSIDE Denali LC Mosaic (2 ac., no burn version) x training signature data (12/18/00):

 Reference / Training Data

Dense Op ne Spruce Fo er st w( h ti el ki e)

Open W- ood al nd Sp ur ce Fo er st

Open W- ood al nd S ut n et d Spruce

Bro da

el af Fo er st

M xi ed Spruce B- ro da

el af Fo er st

Alder Shrub

Wil ol w Sh ur b

Clos de L wo

Bi cr h Sh ur b

L wo Shrub B ri hc E- ricaceous W- il ol w

L wo Shrub S- edge

Peat al nd

He br ac oe us S/ hrub

M xi ed

wD

arf Sh ur b

M xi ed

wD

arf Sh ur b R- ock

Dry M/

es ci Herbaceous

We He t

br ac oe us

Aquat ci He br ac oe us

Sparse V ge e at t oi n

Ba er Ground

Sn wo / cI e

Sh da

wo

Si tl

Wy

a et r

Clea Wr 

a et r

Bu nr

Cloud

Theme 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Row Total User % Theme

1 842 468 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1310 64.3 1

2 117 4732 1131 9 15 20005004049000000000 6028 78.5 2

3 0 327 5792 7 118 0 9 168 60 104 0 0 1 0 0 29 000000000 6615 87.6 3

4 0 28 0 3206 113 12 26 0 0 0 0 0 0 0 11 4 000000000 3400 94.3 4

5 1 146 70 126 992 0 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1357 73.1 5

6 0 16 11 10 28 1235 15 0 175 0 0 0 0 0 0 0 000000000 1490 82.9 6

7 0 0 20 282 0 27 474 0 67 7 0 0 21 0 9 19 0 0 0 0 0 0 0 0 0 926 51.2 78 0 0 52 0 0 0 0 600 43 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 721 83.2 89 0 28 124 222 7 96 86 91 3853 425 0 0 75 1 56 17 010000000 5082 75.8 9

10 0 0 17 71 0 19 41 32 382 3269 0 0 67 0 3 51 000000000 3952 82.7 10

11 0000000000000000000000000 0 NA 11

12 0000000000000000000000000 0 NA 1213 0 0 0 58 0 44 8 0 39 18 0 0 654 32 29 0 0 0 9 0 0 0 0 0 0 891 73.4 1314 0 0 0 12 0 10 0 0 1 0 0 0 27 52 4 0 0 26 6 0 0 0 0 0 0 138 37.7 1415 0 0 0 0 0 0 4 0 0 29 0 0 28 0 595 0 0 0 0 0 0 0 0 0 0 656 90.7 1516 17 1 5 1 0 4 17 0 2 21 0 0 1 0 2 438 0 0 0 0 0 0 0 0 0 509 86.1 16

17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 43 0 0 0 0 0 0 0 0 43 100.0 17

18 0 0 0 29 0 0 19 0 0 0 0 0 6 32 0 0 0 180 161 0 0 2 0 0 0 429 42.0 18

19 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 18 964 0 0 114 0 0 0 1106 87.2 19

20 0000000000000000000 2090 00000 2090 100.0 20

21 0 0 5 0 1 0 19 0 0 0 0 0 21 0 0 0 0 0 4 0 0 0 0 0 0 50 0.0 21

22 0 0 0 5 0 0 1 0 0 0 0 0 0 0 0 0 0 0 26 0 0 1093 0 0 0 1125 97.2 22

23 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 34 0 0 0 0 0 0 3072 0 0 3109 98.8 23

24 0000000000000000000000000 0 NA 24

25 0000000000000000000000000 0 NA 25

Col. Tota 977 5746 7230 4048 1274 1449 741 891 4622 3904 0 0 905 117 713 601 43 225 1170 2090 0 1209 3072 0 0 34176 Total correct

Prod. % 86.2 82.4 80.1 79.2 77.9 85.2 64.0 67.3 83.4 83.7 NA NA 72.3 44.4 83.5 72.9 100.0 80.0 82.4 100.0 NA 90.4 100.0 NA NA 41027 Total pixels

Theme 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 83.30 % Correct

Image Classification
74
Table 16. Confusion matrix for the Final Southside Denali LC Mosaic (2 ac., no burn version) x training signature data (12/18/00):

 Reference / Training Data

D ne se Open Sp ur ce Fo er st w( h ti el ki e)

Open W- ood al nd Sp ur ce Fo er st

Open W- ood al nd Stunted Sp ur ce

B or

da

el af Fo er st

M xi

de Sp ur ce-B or

da

el af Fo er st

A dl er Sh ur b

Wil ol w Sh ur b

C ol s de L wo

B ri hc Sh ur b

L wo Sh ur b B ri hc -Er ci aceousWil ol w

L wo Sh ur b-Sedge

Peatl na d

He br ac oe us S/ h ur b

M xi

de Dwarf Sh ur b

M xi

de Dwarf Sh ur b R- ock

Dry M/

es ci He br aceous

We Ht

e br ac oe us

Aquat ci He br ac oe us

Spa sr e V ge etation

Ba er G or und

Sn wo / cI e

Sh da

wo

Si tl

Wy

a et r

C el a Wr

a et r

Bu nr

C ol ud

Theme 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Row ToUser % Theme

1 250 7 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 264 94.7 1

2 2 266 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 271 98.2 23 0 0 41 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 41 100.0 34 0 0 0 592 2122 00000600000000000000 2720 21.8 4

5 7 8 0 27 2642 27 25 001902034000000000 2755 95.9 5

6 0 0 0 34 29 5619 237 0 0 2 1 0 4 0 19 7 0 0 0 0 0 0 0 0 0 5952 94.4 6

7 0 0 0 23 0 176 207 0 0 18 0 0 0 0 6 0 0 7 0 0 0 0 0 0 0 437 47.4 78 0000000000000000000000000 0 #DIV/0! 8

9 0 0 0 0 0 16 0 0 24 0 0 0 0 0 0 24 0 0 0 0 0 0 0 0 0 64 37.5 9

10 0 0 2 0 6 29 0 0 0 39 28 0 4 1 52 7 0 0 0 0 0 0 0 0 0 168 23.2 1011 6 2 5 0 5 0 0 0 0 27 233 0 0 0 0 10 0 0 0 0 0 0 0 0 0 288 80.9 1112 0 0 0 0 0 0 11 0 0 0 0 0 5 0 135 0 0 0 0 0 0 0 0 0 0 151 0.0 1213 0 0 0 2 0 0 0 0 0 0 0 0 385 0 15 0 0 0 3 0 0 0 0 0 0 405 95.1 1314 0 0 0 0 2 0 0 0 0 0 0 0 0 37 0 0 0 0 0 0 0 0 0 0 0 39 94.9 1415 0 0 0 0 0 0 16 0 0 1 0 0 15 0 216 0 0 0 0 0 0 0 0 0 0 248 87.1 1516 0 0 0 0 0 0 0 0 0 3 6 0 0 0 0 37 0 0 0 0 0 0 0 0 0 46 80.4 1617 0000000000000000000000000 0 NA 17

18 0 0 0 0 0 4 0 0 0 0 0 0 1 7 0 0 0 215 208 0 0 0 0 0 0 435 49.4 1819 0 0 0 5 0 1 0 0 0 0 0 0 5 0 0 1 0 4 96 7 0 0 0 0 0 119 80.7 1920 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 286 0 0 0 0 0 297 96.3 2021 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 11 0.0 2122 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 295 0 0 0 295 100.0 2223 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 527 0 0 535 98.5 2324 0000000000000000000000000 0 NA 24

25 0000000000000000000000000 0 NA 25

Col. Tota 265 283 48 683 4813 5872 496 0 27 91 283 0 421 45 446 90 0 226 329 293 0 303 527 0 0 12007 Total correct

Prod. % 94.3 94.0 85.4 86.7 54.9 95.7 41.7 NA 88.9 42.9 82.3 NA 91.4 82.2 48.4 41.1 NA 95.1 29.2 97.6 NA 97.4 100.0 NA NA 15541 Total pixels

Theme 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 77.26 % Correct

Image Classification
 

76
 

 

 

 

 

3.31 DISCUSSION OF ACCURACY OF LANDCOVER CLASSES 

The estimated overall accuracy for the detailed class landcover data at 2 ac MMU when compared 

to the training database was 81.6%. 

The estimated overall accuracy for the general class landcover data at 2 acre MMU when 

compared to the training data was 84.8%. 

Classes showing primary confusion are discussed below. All accuracies refer to the detailed class 

landcover data (prior to the digital integration of the burn mask) at a 2 acre mapping unit. Refer to 

Section 2.83 for other examples of confusion encountered during the map development process 

(and some of the modeling and editing steps employed to remedy them). 

Mapping classes showing a particularly high degree of confusion (less than 65% producer’s or 

user’s accuracy) included Spruce-Broadleaf, Broadleaf, Willow, Dwarf Shrub-Rock and Sparse 

Vegetation. Mapping classes with a producer’s accuracy between 65 and 70% included Closed 

Low Shrub Birch and Wet Herbaceous. Mapping classes with a User’s accuracy between 65 and 

70% included Dense-Open Spruce. All other mapping classes accuracies (both producer’s and 

user’s) were 70% or higher. 

The producer’s accuracy for the Dense-Open Spruce class was 87.9%, with a user’s accuracy of 

69.4%. Most of the errors of commission and omission were due to confusion with the Open – 

Woodland Spruce class. Error can generally be expected at the upper and lower canopy percent 

limits for similar types.

The producer’s accuracy for the Open–Woodland Spruce class was 82.9%, with a user’s accuracy 

of 79.3%. Most of the errors of commission were with the Stunted Spruce class while most of the 

errors of omission were with the Dense-Open Spruce class. There were also omission errors with

the Stunted Spruce class and the Spruce-Broadleaf class. Woodland classes can be easily confused 

depending on understory species. 

The producer’s accuracy for the Stunted Spruce class was 80.1%, with a user’s accuracy of 87.6%. 

Most of the errors of commission and omission were due to confusion with the Open–Woodland 

Spruce class. 

The producer’s accuracy for the Broadleaf class was 80.3%, with a user’s accuracy of 62.1%. 

Most of the errors of commission were with the Spruce-Broadleaf class, while most of the 

omission errors were with Willow and Low Shrub Birch-Ericaceous-Willow. In general Broadleaf 

was confused with Spruce-Broadleaf South of the Alaska range and with Willow North of the 

Alaska Range. 

The producer’s accuracy for the Spruce-Broadleaf class was 59.7%, with a user’s accuracy of 

88.4%. Most of the errors of commission were with the Open–Woodland Spruce class and the

Broadleaf class. Most of the error of omission was with the Broadleaf class. The SpruceBroadleaf class by definition is spectrally mixed, and was very difficult to separate spectrally from

open spruce forests with tall shrub understory (northside) or from mixed areas with a high 

77
 

 

proportion of broadleaf (southside). 

The producer’s accuracy for the Alder class was 93.6%, with a user’s accuracy of 92.1%. Most of 

the errors of commission and omission were with the Willow class. 

The producer’s accuracy for the Willow class was 55.1%, with a user’s accuracy of 50.0%. Most 

of the errors of commission were with the Broadleaf and Alder classes. Most of the omission error 

was with Alder. Despite the significant spectral confusion with other classes, Willow was 

maintained as a class due to its ecological significance. 

The producer’s accuracy for the Closed Low Shrub Birch class was 67.3%, with a user’s accuracy 

of 83.2%. Most of the errors of commission and omission were with Stunted Spruce and Low 

Shrub Birch-Ericaceous-Willow. Shrub birch vegetation types are highly variable and are 

frequently a component of woodland spruce communities. 

The producer’s accuracy for the and Low Shrub Birch-Ericaceous-Willow class was 83.4%, with a 

user’s accuracy of 75.3%. Most of the errors of commission and omission were with Low ShrubSedge. In general the and Low Shrub Birch-Ericaceous-Willow contained more herbaceous 

vegetation than the Closed Shrub Birch class and would be expected to be similar in reflectance to 

low shrub sedge. 

The producer’s accuracy for the Low Shrub-Sedge class was 82.8%, with a user’s accuracy of 

80.3%. Most of the errors of commission and omission were with Stunted Spruce and and Low 

Shrub Birch-Ericaceous-Willow. The low shrub classes are broadly variable and some confusion 

among classes is expected. 

The producer’s accuracy for the Peatland class was 82.3%, with a user’s accuracy of 80.9%. Most 

of the errors of commission and omission were with Low Shrub-Sedge. 

Herbaceous-Shrub – There were no Herbaceous-Shrub signatures in the spectral database, and 

therefore no ready means to quantitatively access the accuracy of this class (refer to section 2.84 

for a discussion on how this class was mapped). 

The producer’s accuracy for the Dwarf Shrub class was 78.4%, with a user’s accuracy of 80.2%. 

Most of the errors of omission were with Low Shrub Birch-Ericaceous-Willow and Low ShrubSedge. Distribution of this class was enhanced through modeling in many of the processing 

blocks. Since the same modeling procedures were not applied to the training/accuracy dataset, 

accuracies for many of the significantly modeled classes tended to be lower than they were 

immediately following the spectral classification. Since reviews of the modeled out for this class 

were more favorable than the reviews of the spectral classification output for this class, the 

modeled output was used despite the higher accuracies associated with the spectal classification 

output. 

The producer’s accuracy for the Dwarf Shrub-Rock class was 54.9%, with a user’s accuracy of 

50.3%. Most of the errors of omission and commission were with Dwarf Shrub and the Sparse 

Vegetation class. The number of Dwarf Shrub-Rock signatures available for the classification 

78
and/or the accuracy assessment was significantly less than preferred. Phenological development 

based on image date will influence classification of these types. Distribution of this class was 

enhanced through modeling in many of the processing blocks. Since the same modeling 

procedures were not also applied to the training/accuracy dataset, accuracies for many of the 

significantly modeled classes tended to be lower than they were immediately following the spectral 

classification. Since reviews of the modeled out for this class were more favorable than the 

reviews of the spectral classification output for this class, the modeled output was used despite the 

higher accuracies associated with the spectal classification output results. 

The producer’s accuracy for the Dry-Mesic Herbaceous class was 70.0%, with a user’s accuracy of

85.6%. Most of the errors of omission and commission were with the low shrub, herbaceous shrub 

and dwarf shrub classes. 

The producer’s accuracy for the Wet Herbaceous class was 68.7%, with a user’s accuracy of 9.7%. 

Errors of omission and commission were relatively small, but scattered with with a number of 

different mapping classes (Stunted Spruce, Willow, Low Shrub and Water). 

The producer’s accuracy for the Aquatic Herbaceous class was 100%, with a user’s accuracy of 

100%. The number of Aquatic Herbaceous signatures available for the classification and/or the 

accuracy assessment was significantly less than preferred. 

The producer’s accuracy for the Sparse Vegetation class was 87.6%, with a user’s accuracy of 

45.7%. Most of the errors of omission and commission were with the Bare Ground and Dwarf 

Shrub-Rock classes. Distribution of this class was enhanced through modeling in many of the 

processing blocks. Since the same modeling procedures were not also applied to the 

training/accuracy dataset, accuracies for many of the significantly modeled classes tended to be 

lower than they were immediately following the spectral classification. Since reviews of the 

modeled out for this class were more favorable than the reviews of the spectral classification 

output for this class, the modeled output was used despite the higher accuracies associated with 

the spectal classification output results. 

The producer’s accuracy for the Bare Ground class was 70.7%, with a user’s accuracy of 86.5%. 

Most of the errors of omission and commission were with the Sparse Vegetation class. 

Distribution of this class was enhanced through modeling in many of the processing blocks. Since 

the same modeling procedures were not also applied to the training/accuracy dataset, accuracies 

for many of the significantly modeled classes tended to be lower than they were immediately 

following the spectral classification. Since reviews of the modeled out for this class were more 

favorable than the reviews of the spectral classification output for this class, the modeled output 

was used despite the higher accuracies associated with the spectal classification output results. 

The producer’s accuracy for the Snow/Ice class was 99.7%, with a user’s accuracy of 99.5%. Most 

of the minor errors of omission and commission were with the bare class.

The producer’s accuracy for the Silty Water class was 91.8%, with a user’s accuracy of 97.7%. 

Most of the minor errors of omission and commission were with the Bare Ground class. 

79
 

 

 

 

The producer’s accuracy for the Clear Water class was 100%, with a user’s accuracy of 98.8%. 

The minor error of commission was with the Silty Water class. 

Accuracies were not reported for herbaceous shrub, shadow, burn or cloud mapping classes. This 

was either because there were no signatures available for the particular class (i.e, herbaceous shrub 

and burn), or because the class was so heavily modeled/edited (clouds, shadow) that the signatures 

were obsolete. 

3.4 DISCUSSION OF STRENGTHS AND LIMITATIONS 

To properly interpret and apply the landcover classes developed in the Denali NP&P mapping 

project, users must bear in mind the characteristics of the data used to derive the map, as well as 

the characteristics of the vegetation and other landcover types of interest. It is important to note 

that the Denali NP&P landcover project had the objectives to 1) map landcover characteristics that 

could be recognized by Landsat TM and SPOT XS imagery, and 2) delimit mapping classes that 

are flexible enough to describe potential new classes derived from Landsat imagery. Hence, 

although the mapping classes were derived from the Alaska Vegetation Classification (Viereck et 

al. 1992), they did not strictly adhere to this classification hierarchy. In the Denali NP&P project, 

the landcover classification system was customized so as to take advantage of the characteristics of 

the satellite data, while balancing the mapping detail needs of the end user. This classification 

system and the determination of final mapping classes remained open for discussion well into the 

final image processing phases of the project. Review of the Landcover Class Descriptions 

(Appendix 5.2) provides additional insight into the species composition of the mapping classes 

characterized by the Landsat TM and SPOT XS data. 

In general, given the pixel size of the Landsat TM and SPOT XS satellite data (28.5 and 20 

meters, respectively), the scale of derived maps should be no larger than 1:63,360. But at this 

large scale, very few landcover types can be consistently mapped without generating a great deal 

of “noise” or scattered pixels. A more appropriate mapping unit for Landsat TM derived 

landcover products is a 5 acre mmu (25 pixels). However, at this mapping unit size, many of the 

small linear features (streams, riparian shrub areas, ridge lines, pipelines) can be lost in the 

filtering process. In order to preserve more of the spatial detail found in the unfiltered landcover 

data, the final landcover database was filtered to approximately 2 ac (9 pixels). While this 

mapping unit resolution provides a good compromise for legibility (not pixilated), 5 ac may be a 

more appropriate minimum mapping unit for this landcover data. 

In general, these landcover map products can be used to understand the distribution of the mapped 

units within the study area. Ground data were used to define plant associations in order to provide 

a hierarchical link between the relatively coarse scale landcover classes and the species 

information (Volume 2). Knowledge of plant associations occurring within the mapped units 

significantly adds to the database’s usefulness. Other uses for these mapping products include 

broad scale wildlife habitat modeling and coarse scale analyses such as regional ecological 

planning and monitoring.

80
LITERATURE CITED 

Cambell, J. 1987. Introduction to Remote Sensing, New York: Gulided Press 551 p. 

Clark, M. 1998. Denali Park - Ecoregions and the National Hierarchical Framework of Ecological 

Units. Department of Agriculture, Natural Resources Conservation Service. 

Congalton, R.G. 1991. A Review of Assessing the Accuracy of Classifications of Remotely 

sensed data, Remote Sensing of Environment, Vol. 37, pp.35-46. 

Csejtey et al., 1992. Geology and Geochronology of the Healy Quadrangle, 

South-central Alaska. 

Densmore, R. 1992. Vegetation monitoring protocol development. Report for Denali National 

Park and Preserve. 

ECOMAP. 1993. National hierarchical framework of ecological units. ECOMAP, USDA Forest 

Service, Washington, DC. October 29, 1993. National hierarchical framework of ecological units. 

Grossman, D.H., D. Faber-Langendoen, A.S. Weakley, M. Anderson, P. Bourgeron, R. Crawford, 

K. Goodin, S. Landaal, K. Metzler, K. Patterson, M. Pyne, M. Reid, and L. Sneddon. 1998. 

International classification of ecological communities: terrestrial vegetation of the United States. 

Volume I. The National Vegetation Classification System: development, status, and applications. 

The Nature Conservancy, Arlington, Virginia, USA. 

Hanson, Herbert C. 1950. Vegetation and soil profiles in some solifluction and mound areas in 

Alaska. Ecology. 31(4):606-630. 

Hanson, Herbert C. 1951. Charateristics of some grassland, marsh, and other plant communities in 

western Alaska. Ecological Monographs. 21(4):317-378. 

Heebner, Deborah K. 1982. The numerical analysis of vegetation plots in Denali National Park and 

Preserve. Fairbanks, AK: University of Alaska. 243 p. M.S. thesis. 

Jensen, J.R. 1996. Introductory Digital Image Processing, Prentice-Hall, Inc. 

Markon, Flemming, and Binnian Map derived from Hughes STX Corporation, EROS Alaska Field 

Office arcticle-Characteristics of vegetation phenology over the Alaskan landscape using AVHRR 

time-series data by. In Polar Record 31 (177): 179-190 (1995). 

McNab, H.W. and Avers, P.E. 1994. Ecological Subregions of the United States: Section 

Descriptions. Publication WO-WSA-5. Department of Agriculture, Forest Service. Washington, 

D.C. 

Nowacki, G. and T. Brock. February 1995. ALASKA-ECOMAP version 2.0. United States 

Department of Agriculture-Forest Service. 

81
Pratt, V.E. and F.G. Pratt. 1993. Wildflowers of Denali National Park. Alaskakrafts, Inc. 

Anchorage, AK. 166p. 

Story, M. and R.G. Congalton 1986. Accuracy Assessment: A User’s Perspective. 

Photogrammetric Engineering and Remote Sensing, Vol. 52, No. 3, pp. 397-399. 

USDA-National Forest Service - National Hierarchical Framework of Ecological Units, 1993; and 

Ecoregions and Subregions of Alaska, ECOMAP version 2.0. 

USDA-SCS Exploratory Soil Survey of Alaska, 1979. 

USGS - Geologic Map of Alaska, compiled by Helen Beikman, 1980. 

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

Pacific Northwest Research Station, U. S. Forest Service, Portland, OR. Gen. Tech. Rep. PNW-GTR286. 

278 p. 

Viereck L.A., and E.L. Little, Jr. 1972. Alaska trees and shrubs. Agriculture Handbook No. 410 Forest 

Service, reprinted by the University of Alaska Press, Fairbanks, Alaska.

Wahrhaftig, C., and R. F. Black, 1958. Quaternary and Engineering Geology in 

the Central Part of the Alaska Range. USGS-Professional Paper 293A&B. 

82
 

 

 Appendix 5.1 Key to Landcover Classes 

The herbaceous/shrub and peatland landcover classes are not included in the key.

GROWTH FORM GROUPS 

1. Tree canopy cover is greater than or equal to 10% for needleleaf trees, deciduous trees, or deciduousneedleleaf mix ........................................................................................................................................2 

1. Tree canopy cover is less than 10% or absent..........................................................................................3 

2. Needleleaf tree cover is greater than or equal to 75% of the total tree cover......Needleleaf Forest Types 

2. Needleleaf tree cover is less than 75% of the total tree cover............ Broadleaf and Mixed Forest Types 

3. Shrub cover is greater than or equal to 25% ..........................................................................Shrub Types 

3. Shrub cover is less than 25%....................................................................................................................4 

4. Herbaceous cover is greater than or equal to 25% ....................................................... Herbaceous Types 

4. Herbaceous cover is less than 25% ..........................................................................................................5 

5. Total vegetation cover is 15-24%..................................................................................Sparse Vegetation 

5. Total vegetation cover is less than 15% ................................................................................ Bare Ground 

NEEDLELEAF FOREST TYPES 

1. Tree canopy cover is 45-70%.....................................................................................Dense-Open Spruce 

1. Tree canopy cover is 10-44%...................................................................................................................2 

2. Average tree height is greater than or equal to 7.5 meters ................................. Open-Woodland Spruce 

2. Average tree height is less than 7.5 meters........................................................................Stunted Spruce 

BROADLEAF AND MIXED FOREST TYPES 

1. Greater than or equal to 75% of the total tree cover is deciduous.............................................. Broadleaf 

1. Deciduous and needleleaf trees each contribute 25-75% of the total tree cover........................................

 .............................................................................................................................. ........Spruce-Broadleaf 

SHRUB TYPES 

1. Willow greater than 20 centimeters tall contributes 75% or more of the total shrub cover.......................

......................................................................................................................................................Willow 

1. Willow greater than 20 centimeters tall contributes less than 75% of the total shrub cover....................2 

2. Alder contributes 75% or more of total shrub cover .........................................................................Alder 

2. Alder contributes less than 75% of total shrub cover...............................................................................3 

3. Shrub birch is greater than 75% of total shrub cover and total shrub cover is 75-100% ...........................

 .......................................................................................................................... Closed Low Shrub Birch 

3. Shrub birch is less than 75% of total shrub cover ....................................................................................4 

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4. Willow, shrub birch or ericaceous shrubs—all greater than 20 centimeters tall—together contribute 25-

75% of the total shrub cover and total shrub cover is 25-100% .............................................................5 

4. Willow, shrub birch or ericaceous shrubs—all greater than 20 centimeters tall—together do not 

contribute 25-75% of the total shrub cover ............................................................................................6 

5. Graminoids contribute 25-75% of the total cover; tussock formation is common.....................................

 .............................................................................................................................. .........Low Shrub-Sedge 

5. Less than 25% sedges present ........................................................ Low Shrub Birch-Ericaceous-Willow 

6. Shrub height is less than 20 centimeters, and greater than 25% of the site contains rock .........................

 .............................................................................................................................. ..... Dwarf Shrub-Rock 

6. Shrub height is less than 20 centimeters, and less than 25% of the site contains rock ......... Dwarf Shrub 

HERBACEOUS TYPES 

1. Vegetation is terrestrial; not submerged, floating, or growing in permanent water.................................2 

1. Vegetation is submerged, floating, or growing in permanent water ......................... Aquatic Herbaceous 

2. Site is dry to mesic; little to no standing water .....................................................Dry-Mesic Herbaceous 

2. Dominant vegetation is emergent; semi-permanent or standing water is present ........... Wet Herbaceous 

84
 

 

 

Appendix 5.2. Landcover Mapping Class Descriptions 

The names and Viereck codes of the landcover classes in the Denali NP&P project area are listed below. 

Landcover class descriptions were derived from several sources including literature descriptions, 

Fireprodata, Natural Resources Conservation Service data , 1999 FVA fly-over data, and 1999 ground site 

descriptions. Plant association information can be found in Volume 2: Landcover Classes and Plant 

Associations.

DENSE-OPEN SPRUCE LANDCOVER CLASS 1 

Classification—Tree canopy cover is between 45% to 70% with needleleaf tree cover greater than or equal 

to 75% of the total tree cover. The class is comparable to the Viereck et al. (1992) hierarchical levels 

closed needleleaf forest (IA1) and open needleleaf forest (IA2). 

Distribution and Site Characteristics—The Dense-Open Spruce class occurs on low elevation (107 to 731 

meters based on field data; Table 3) floodplains, toeslopes, and valley bottoms primarily north of the Alaska 

Range within Denali National Park and Preserve. It generally occurs as small to medium size patches on 

well-drained, loamy or sandy soils. These sites have little (5 to 10 degrees) to no slope, and generally occur 

on South and West aspects. Disturbance is typically from alluvial and fire processes. They are commonly

found in the Toklat drainage, west of the Kantishna Hills, and the toeslopes of the Alaska Range in the 

west-central part of the park. Although it occurs in all ecoregions, it is more prevalent in the Kuskokwimalluvial fans and flood plains ecoregion than other ecoregions (Tables 4a and 4b; Clark 1998). The denseopen spruce class covers 0.8% (50,359 acres) of Denali National Park and Preserve (Table 5). 

Vegetation—Picea glauca or Picea mariana cover ranges from 45% to 70%. Tree height can reach greater 

than 20 meters. Larix laricina, alders (such as Alnus crispa) and willows (such as Salix planifolia) occur in 

canopy breaks along with ericaceous shrubs (such as Arctostaphylos rubra, Vaccinium vitis-idaea, and V. 

uliginosum). There frequently is a high graminoid component associated with these sites. Mosses are 

85
common as well as small amounts of lichen. The Dense-Open Spruce class is typically interspersed with 

other spruce types such as the Open-Woodland Spruce and Stunted Spruce landcover classes. 

Landcover Class Accuracy—The producer’s accuracy for the Dense-Open Spruce class is 87.9%, with a

user’s accuracy of 69.4%. Most of the errors of commission and omission are due to confusion with the 

Open-Woodland Spruce class. All accuracies are based on the 25-class landcover map at a 2-acre mapping 

unit prior to the digital integration of the burn mask. 

86
 

OPEN-WOODLAND SPRUCE LANDCOVER CLASS 2 

Classification—Tree canopy cover is 10% to 44% with needleleaf cover greater than or equal to 75% of the 

total tree cover. Needleleaf tree height is greater than or equal to 7.5 meters. The class is comparable to the 

Viereck et al. (1992) hierarchical levels open needleleaf forest (IA2) and needleleaf woodland (IA3). 

Distribution and Site Characteristics—The Open-Woodland Spruce class occurs primarily on lower 

elevation (80 to 912 meters based on field data; Table 3) landscapes north of the Alaska Range within 

Denali National Park and Preserve. The dominant landscapes are alluvial fans and outwash-floodplains. 

Site conditions range from strong sunlight and well-drained soils on gentle slopes and terraces to sites with 

poorly drained peat and silt soils. The latter is typically dominated by Picea mariana. Most sites have little 

to no slope although a few sites reached a slope of 40 degrees. When sloping sites occur they generally

occupy east aspects. This class occurs in a region of high fire frequency. This landcover class is most 

common on the Kuskokwim-alluvial fans and floodplains ecoregion and is also common in the 

Kuskokwim-Minchumina Basin ecoregion (Tables 4a and 4b; Clark 1998); in addition it is the most 

common type in the Kuskokwim-low mountains and pediments ecoregion. The Open-Woodland Spruce 

landcover class covers 9.1% (551,428 acres) of Denali National Park and Preserve (Table 5). 

Vegetation—The Open-Woodland Spruce class is dominated by Picea mariana, however Picea glauca also 

occurs. Average needleleaf cover is 40%, and tree height is typically greater than 7.5 meters. Ericaceous 

shrubs are commonly found in the understory including Ledum decumbens, Vaccinium uliginosum, 

Cassiope tetragona, and Chamaedaphne calyculata. Other shrubs include Betula nana, Empetrum nigrum, 

Alnus species, and Salix species. Mosses and lichens also occur. Some sites contain tussocks ranging up to 

90% tussock cover, and average tussock cover in sites with high graminoid content is 35%. The class is 

typically interspersed with other spruce classes such as the Dense-Open Spruce and Stunted Spruce 

landcover classes. 

Landcover Class Accuracy—The producer’s accuracy for the Open-Woodland Spruce class is 82.9%, with 

a user’s accuracy of 79.3%. Most of the errors of commission are with the Stunted Spruce class while most 

of the errors of omission are with the dense-open spruce class. There are also significant omission errors 

with the Stunted Spruce class and the Spruce-Broadleaf class.

87
STUNTED SPRUCE LANDCOVER CLASS 3 

Classification—Tree canopy cover is 10% to 44% with needleleaf tree cover greater than or equal to 75% 

of the total tree cover. Needleleaf tree height is less than 7.5 meters. The class is comparable to the 

Viereck et al. (1992) hierarchical levels open needleleaf forest (IA2), needleleaf woodland (IA3), open 

dwarf tree scrub (IIA2), and dwarf tree scrub woodland (IIA3). The cutoff for stunted spruce in the Denali 

National Park and Preserve landcover classification is 7.5 meters whereas the cutoff for dwarf tree scrub is 

3 meters in Viereck et al. (1992). 

Distribution and Site Characteristics—The Stunted Spruce class is the most common vegetated class in

Denali National Park and Preserve, covering 14.2% (853,985 acres) of its total area (Table 5). The class is 

primarily found on low to mid elevation (166 to 841 meters based on field data; Table 3) sites north of the 

Alaska Range. It occupies sites with poorly drained, cold soils with some peat formation, permafrost is 

common, and with little to no slope. This class occurs in a region of high fire frequency, although the sites 

have likely not recently been burned. It dominates the Kuskokwim-alluvial fans and floodplains ecoregion, 

the Kuskokwim-glaciated hills and pitted plains ecoregion, and the Kuskokwim-Minchumina basin 

ecoregion and is common in the Kuskokwim-low mountains and pediments ecoregion (Tables 4a and 4b; 

Clark 1998). 

Vegetation—The forest canopy of the Stunted Spruce class is dominated by Picea mariana although Picea 

glauca may also occur. Average needleleaf cover is 30% and tree height averages 4 meters. These sites 

commonly have a high percent of ericaceous shrubs and a thick graminoid layer in the understory. Tussock

formation is common and its cover reaches greater than 90% in some sites and averages 40% cover. Many

sites are dominated by ericaceous shrubs, whereas others are a mix of low shrubs (such as Salix species and 

Betula species) and ericaceous shrubs. Shrub species include Ledum decumbens, Vaccinium uliginosum, 

Chamaedaphne calyculata, Betula nana, Salix species and occasionally Alnus species. Larix laricina has 

been found in trace amounts on many sites, and lichens and mosses are common. 

88
 

Stunted Spruce stands are commonly found adjacent to the Open-Woodland Spruce and occasionally near 

the dense-open spruce map classes. Periodically, small patches of the Dry-Mesic Herbaceous class are 

found within large patches of Stunted Spruce often on lacustrine deposits. 

Landcover Class Accuracy—The producer’s accuracy for the Stunted Spruce class is 80.1%, with a user’s

accuracy of 87.6%. Most of the errors of commission and omission are due to confusion with the OpenWoodland Spruce class.

89
 

BROADLEAF LANDCOVER CLASS 4 

Classification—Betula papyrifera, Populus tremuloides and Populus balsamifera, individually or 

combined, with a canopy cover of 10% to 100%, with needleleaf less than 25% of the total tree cover. The 

class is comparable to the Viereck et al. (1992) hierarchical levels closed broadleaf forest (IB1), open 

broadleaf forest (IB2), and broadleaf woodland (IB3).

Distribution and Site Characteristics—This class occurs at low elevations (93 to 774 meters based on 

field data; Table 3) primarily in the Lake Minchumina Basin, toeslopes of the Kantishna Hills, lower slope 

positions and rolling hills on the south side of the Alaska Range, and in localized patches on outwash plains 

and floodplains throughout Denali National Park and Preserve. The slope ranges from relatively level on 

floodplains, to moderate to steep on hillsides. The class is concentrated in the Kuskokwim-low mountains 

and pediments ecoregion and the Alaska Range-Kantishna Hills ecoregion (Tables 4a and 4b; Clark 1998).

This class does not, however, dominate any one ecoregion. These deciduous forests cover 1.6% (97,063 

acres) of Denali National Park and Preserve (Table 5). 

Vegetation—Forest canopy closure for the Broadleaf class ranges from 10% to 100%, with an average 

cover of 75%. Betula papyrifera is the most common species dominating sites on both the north and south 

sides of the Alaska Range. Populus tremuloides often occurs as a sub-dominant to Betula papyrifera, but 

also occurs as the dominant deciduous tree species in the northeast portion of the Park. Populus 

balsamifera is typically restricted to floodplains occurring as small to mid size patches, but is also found in 

small patches on hillsides throughout the park. From an aerial perspective, understory shrubs are typically

Alnus and Salix species. Small coverage of grasses and sedges also occur. Mosses and lichens are present

in varying amounts. Adjacent landcover classes include Open-Woodland Spruce and, occasionally, DenseOpen Spruce. Small patches of Dry-Mesic Herbaceous or Wet Herbaceous often grow adjacent to this type

on riparian areas. 

Landcover Class Accuracy—The producer’s accuracy for the Broadleaf class is 80.3%, with a user’s

accuracy of 62.1%. Most of the errors of commission are with the Spruce-Broadleaf class, while most of 

the omission errors are with the Willow and Low Shrub Birch-Ericaceous-Willow map class. 

90
 

SPRUCE-BROADLEAF LANDCOVER CLASS 5 

Classification—Tree canopy cover is 10% to 100%, with needleleaf and deciduous trees each contributing 

25% to 75% of the total tree cover. The class is comparable to the Viereck et al. (1992) hierarchical levels 

closed mixed forest (IC1), open mixed forest (IC2), and mixed woodland (IC3).

Distribution and Site Characteristics—The Spruce-Broadleaf class is common on low elevation (91 to 

721 meters based on field data; Table 3) foothills and floodplains south of the Alaska Range, and as 

scattered patches throughout the forested region—especially adjacent to floodplains—north of the Alaska 

Range. It is a dominant map class—along with Low Shrub Birch-Ericaceous-Willow—in the Cook inletglaciated hills and plains ecoregion (Tables 4a and 4b; Clark 1998). Slopes range from relatively level on 

floodplains, to moderate or steep on hillsides, and the soils are generally mesic with no peat development. 

This landcover class comprises 2.5% (149,929 acres) of Denali National Park and Preserve (Table 5). 

Vegetation—The dominant tree species are typically either Picea glauca mixed with Betula papyrifera or

Picea glauca mixed with Populus balsamifera. The white spruce-paper birch mix is generally found on

sideslopes and toeslopes. Populus tremuloides and Picea mariana may also occur, sometimes as a strong 

codominant. Total tree cover ranges from 30% to 75%. When the white spruce-paper birch canopy is open, 

the trees tend to grow in a clumped distribution. Therefore, open patches in the canopy are common and 

allow for other species to take advantage of the available sunlight. Low and tall shrubs, such as Salix 

species, Alnus species, and Vaccinium uliginosum dominate these patches. Mosses or lichens also occur in 

minor amounts. 

The white spruce-balsam poplar mix occurs on disturbed, sandy soils along riparian corridors with little to 

no slope. It is dominated by Picea glauca and Populus balsamifera, although Betula papyrifera is 

frequently, though not always, present. Canopy cover is often patchy and the open patches are typically 

dominated by Alnus species often growing over 3.6 meters tall. 

91
The Spruce-Broadleaf landcover class occurs adjacent to needleleaf and deciduous classes, as well as small 

patches of tall shrub along riparian corridors. 

Landcover Class Accuracy—The producer’s accuracy for the Spruce-Broadleaf class is 59.7%, with a 

user’s accuracy of 88.4%. Most of the errors of commission are with the Open-Woodland Spruce class and 

the Broadleaf class. Most of the error of omission is with the Broadleaf class. The Spruce-Broadleaf class 

by definition is spectrally mixed. It is very difficult to separate from stands of open-spruce forest with a tall

shrub understory that typically occur on the north side of Denali, or from mixed areas with a high 

proportion of broadleaf that typically occur on the south side of the park. All accuracies are based on the 

25-class landcover map at a 2-acre mapping unit prior to the digital integration of the burn mask.

92
 

ALDER LANDCOVER CLASS 6 

Classification—Tree canopy cover is less than 10% (or absent). Total shrub cover is 25% to 100% and 

alder constitutes greater than 75% of the total shrub cover. The class is comparable to the Viereck et al. 

(1992) hierarchical levels closed tall alder (IIB1b), and open tall scrub alder (IIB2b). 

Distribution and Site Characteristics—The Alder class dominates most steep slopes at low to mid 

elevations (191 to 1,124 meters based on field data; Table 3) on the south side of the Alaska Range and, to a 

lesser extent, the Kantishna Hills. It also occurs consistently in small patches on new alluvial deposits 

along floodplains. The steep slopes are typically colluvium and glacial drift, and slope values range from 0 

to over 50% on all aspects. This class dominates the Alaska Range-south-central mountains and valleys

ecoregion, and also is concentrated in the Alaska Range-interior mountains and valleys ecoregion and 

Alaska Range-Kantishna Hills ecoregion within Denali (Tables 4a and 4b; Clark 1998). The Alder 

landcover class covers 4.3% (260,198 acres) of Denali National Park and Preserve (Table 5). 

Vegetation—Alnus species or a combination of alder and tall Salix species (>1.5 meters in height) 

dominate the landcover type. Alnus crispa is the most common species, however, Alnus sinuata and Alnus 

tenuifolia dominate some sites. Alder height ranges from less than 1.5 meters at higher elevations to 4.5 

meters tall downslope. Alder cover or a combination of alder and willow is also variable, ranging from

25% to 95%. Sites with a more closed canopy are more common throughout Denali National Park and 

Preserve than the open sites. Large homogenous patches of open alder, however, do occur and typically

grade into the closed alder patches. 

Canopy cover of understory vegetation ranges from sparse to dense. Associated understory shrubs include 

Salix species, Echinopanax horridus, Sorbus scopulina, and Spirea beauverdiana. A significant herbaceous 

layer includes Veratrum viride, Epilobium angustifolium, Heracleum lanatum, and small amounts of Iris 

setosa, Petasites species, and Boykinia richardsonii. High graminoid content may be present typically 

dominated by Calamagrostis canadensis. Ferns are also common including Dryopteris dilatata. 

93
The Alder class grows adjacent to Broadleaf, Spruce, Willow, Low Shrub and Dwarf Shrub classes. The 

pure alder sites often blend into sites with willow as a co-dominant species. Alder also commonly grades 

into the spruce classes at times forming extensive areas of high alder cover and low spruce cover. Sites 

dominated by both alder and willow occur as small discontinuous patches in narrow steep draws, the wetter 

depressions of sideslopes, and disturbed riparian corridors. Alder-willow dominated sites generally grade 

into the Willow class in the drainages and pure alder sites on the steep sideslopes. 

Landcover Class Accuracy—The producer’s accuracy for the Alder class is 93.6%, with a user’s accuracy

of 92.1%. Most of the errors of commission and omission are with the Willow class.

94
 

WILLOW LANDCOVER CLASS 7 

Classification—Tree canopy cover is less than 10% (or absent). Total shrub cover is 25% to 100%, and 

willow greater than 20 centimeters tall comprises greater than 75% of the total shrub cover. The class is 

comparable to the Viereck et al. (1992) hierarchical levels closed tall willow (IIB1a), open tall willow 

(IIB2a), closed low willow (IIC1b), and open low scrub (11C2). 

Distribution and Site Characteristics—The Willow class occurs on a variety of sites throughout the park. 

Willow dominated by low stature (20 centimeters to 1.5 meters in height) willow is common at mid 

elevations (range of 191 to 1,116 meters based on field data; Table 3) mixed with other low shrub classes 

on flat river drainages and the rolling foothills bordering the north side of the Alaska Range. When tall 

willow (>1.5 meters) dominates the class, it occurs primarily in small patches on new alluvial stream/river 

deposits, alluvial fans, colluvial sideslopes and bordering small mountain streams. Both tall and low 

willows occur on upper slope positions mixed in with or above the Alder class in the Alaska Range and 

Kantishna Hills. 

The class does not dominate any ecoregion, but is concentrated in the Alaska Range-Kantishna Hills 

ecoregion and the Alaska Range-south-central mountains and valleys ecoregion of the park (Tables 4a and 

4b; Clark 1998). It covers 1.6% (95,590 acres) of Denali National Park and Preserve (Table 5). On the 

landcover map, this class is underrepresented on the south side of the Alaska Range due to its partial 

inclusion in the Herbaceous-Shrub class. 

Vegetation—This class includes all sites dominated by willows greater than 20 centimeters tall. Willow 

cover ranges from 25% to 95%, and its height ranges from 20 centimeters to 4.5 meters. Sites dominated 

by open tall willow and closed low willow are common in the park, whereas closed tall willow is 

uncommon. The dominant overstory species include Salix alaxensis, S. barclayi, S. bebbiana, S. glauca, S. 

lanata, and S. planifolia. Other overstory and understory shrubs include Shepherdia canadensis, Vaccinium 

uliginosum, Betula nana, and Spirea beauverdiana. Tall alder (Alnus species) may also be found in isolated 

locations within the landcover class. Grasses, sedges, and mosses are an important component in some sites 

95
and include Heracleum lanatum, Calamagrostis canadensis, C. inexpansa, Epilobium angustifolium, 

Streptopus species and Veratrum viride. 

The Willow class is most commonly found next to low shrub classes on gentle toeslopes, fans, floodplains

and wide valleys. It is found near needleleaf classes in narrow drainages and also in wide valleys. The 

Dry-Mesic Herbaceous class occurs adjacent to the Willow class on sideslopes and saturated toeslopes. 

Landcover Class Accuracy—The producer’s accuracy for the Willow class is 55.1%, with a user’s

accuracy of 50.0%. Most of the errors of commission are with the Broadleaf and Alder classes. Most of the 

omission error is with Alder.

96
CLOSED LOW SHRUB BIRCH LANDCOVER CLASS 8 

Classification—Tree canopy cover is less than 10% (or absent). Greater than 75% of the total shrub cover 

is shrub birch, and total shrub cover is 75% to 100%. The class is comparable to the Viereck et al. (1992) 

hierarchical level closed low shrub birch IIC1a. 

Distribution and Site Characteristics— The Closed Low Shrub Birch landcover class is uncommon 

within Denali National Park and Preserve and is concentrated in the northeast portion of the park. It 

typically occurs at mid elevations (439 to 1,097 meters based on field data; Table 3) on gentle to moderate

slopes within the foothills and broad valleys bordering the north side of the Alaska Range and the Kantishna 

Hills. It also occurs on floodplains. The class does not dominate any one ecoregion, but is concentrated in 

the Alaska Range-interior mountains and valleys ecoregion (Tables 4a and 4b; Clark 1998). It covers 0.9%

(54,034 acres) of Denali National Park and Preserve (Table 5). The landcover class typically occurs 

adjacent to various needleleaf forest and low shrub classes. 

Vegetation—Betula glandulosa or B. nana dominates this class. Average shrub cover is 75%. Tussocks 

are uncommon, but may be present. Associated species include low willow (Salix species), Vaccinium 

uliginosum, and various graminoid species. Small amounts of Picea glauca, Ledum decumbens, and moss 

also occur. 

Landcover Class Accuracy—The producer’s accuracy for the Closed Low Shrub Birch class is 67.3%, 

with a user’s accuracy of 83.2%. Most of the errors of commission and omission are with the Stunted 

Spruce class and the Low Shrub Birch-Ericaceous-Willow class.

97
LOW SHRUB BIRCH-ERICACEOUS-WILLOW LANDCOVER CLASS 9 

Classification—Tree canopy cover is less than 10% (or absent). Shrub birch, ericaceous shrubs and low 

willow (20 centimeters to 1.5 meters tall) individually contribute 25% to 75% of the total shrub cover; and 

total shrub cover is 25% to 100%. The class is comparable to the Viereck et al. (1992) hierarchical levels 

closed low scrub (IIC1), and open low scrub (IIC2). 

Distribution and Site Characteristics—The Low Shrub Birch-Ericaceous-Willow landcover class is an 

ecologically broad class ranging from sites dominated by open willow to Betula nana and ericaceous 

shrubs. The class is a dominant type above the boreal forest region and below alpine on gentle to moderate

slopes within the foothills and broad valleys bordering the Kantishna Hills, the north side of the Alaska 

Range, and within Broad Pass. The elevation ranges from 204 to 1,068 meters based on field data (Table 

3). It dominates several ecoregions including the Cook Inlet-glaciated hills and plains, Alaska Range-front 

range, Alaska Range-Kantishna Hills and Alaska Range-Teklanika mountains and plateaus ecoregions 

(Tables 4a and 4b; Clark 1998). It covers 9.5% (570,700 acres) of Denali National Park and Preserve 

(Table 5). Because this class covers a wide range of site conditions across the park, it is found adjacent to 

numerous landcover classes. However, it is most commonly found elevationaly above needleleaf classes 

and mixed in with other shrub classes. 

Vegetation—Species composition and cover often fluctuate within and between sites. The dominant 

species are Betula nana, Chamaedaphne calyculata, Salix barrattiana, S. glauca, S. planifolia, Ledum 

decumbens, Vaccinium uliginosum, and Empetrum nigrum. Herbaceous species include Achillea species, 

Calamagrostis lapponica, Epilobium angustifolium, and Rumex species. Mosses are common on some sites

and lichens are rare. 

Landcover Class Accuracy—The producer’s accuracy for the Low Shrub Birch-Ericaceous-Willow class

is 83.4%, with a user’s accuracy of 75.3%. Most of the errors of commission and omission are with the 

Low Shrub-Sedge class. 

98
 

LOW SHRUB-SEDGE LANDCOVER CLASS 10 

Classification—Tree canopy cover is less than 10% (or absent). Low (20 centimeters to 1.5 meters tall) 

mixed shrubs and graminoids each contribute 25% to 75% of the total cover, and tussock formation is 

common. The class is comparable to the Viereck et al. (1992) hierarchical level open low scrub (IIC2). 

Distribution and Site Characteristics—The Low Shrub-Sedge class is a common class occurring 

elevationaly above the boreal forest and below alpine (168 to 1,052 meters based on field data; Table 3). It 

occurs on gentle to moderate slopes of foothills and broad valleys bordering the Kantishna Hills and the 

north side of the Alaska Range, and also on the hills north of Lake Minchumina. The soils are mesic with 

organic tussocks over top of mineral soil. Small seasonal pools of water often occur between tussocks. The 

class dominates the Alaska Range-Toklat Basin ecoregion and is common in other ecoregions including the 

Alaska Range-Teklanika mountains and plateaus and Kuskokwim-alluvial fans and flood plains ecoregions 

(Tables 4a and 4b; Clark 1998). It covers 4.6% (277,322 acres) of Denali National Park and Preserve 

(Table 5). 

The LowShrub-Sedge class commonly forms a mosaic with the Stunted Spruce and Low Shrub BirchEricaceous-Willow landcover classes. 

Vegetation—Low shrubs over a lush sedge/graminoid layer that typically form tussocks characterize this 

class. The dominant species are Salix species, Betula nana, Chamaedaphne calyculata, Ledum decumbens, 

Carex aquatilis, Eriophorum brachyantherum, E. vaginatum and other graminoids. The combined shrub

cover ranges from 25% to 74%. Picea mariana occurs sporadically, and Sphagnum species are a frequent 

associate along with other mosses. 

Landcover Class Accuracy—The producer’s accuracy for the Low Shrub-Sedge class is 82.8%, with a 

user’s accuracy of 80.3%. Most of the errors of commission and omission are with Stunted Spruce and Low 

Shrub Birch-Ericaceous-Willow map classes. 

99
PEATLAND LANDCOVER CLASS 11 

Classification—The Peatland landcover class occurs on peatlands (bogs and fens) south of the Alaska 

Range. The peatlands north of the Alaska Range are not included in this class. The Peatland landcover 

class is comprised of a variety of already defined landcover classes that occur on peat deposits including 

Stunted Spruce, Willow, Dry-Mesic Herbaceous and Wet Herbaceous. In addition, the Myrica gale plant 

association occurs in this landcover class. This combination class is defined because it is spectrally distinct 

on the south side of the Alaska Range, and the individual classes could not be spectrally separated reliably

on peat deposits. The class is comparable to the Viereck et al. (1992) hierarchical levels open needleleaf 

forest (IA2), needleleaf woodland (IA3), open dwarf tree scrub (IIA2), dwarf tree scrub woodland (IIA3), 

closed tall willow (IIB1a), open tall willow (IIB2a), closed low willow (IIC1b), open low scrub (11C2), dry 

graminoid herbaceous (IIIA1), mesic graminoid herbaceous (IIIA2), dry forb herbaceous (IIIB1), mesic 

forb herbaceous (IIIB2), wet graminoid herbaceous (IIIA3) and wet forb herbaceous (IIIB3). 

Distribution and Site Characteristics—The Peatland landcover class is found only in the lowlands (74 to 

315 meters based on field data; Table 3) south of the Alaska Range. Peat deposits (i.e., peatlands, muskeg, 

bogs, fens) are composed of >0.5 meters of accumulated peat in areas of restricted drainage with water 

tables at or close to the peat surface. They occupy former lake basins and other closed depressions, 

alluvium along some valley floors, and are abundant on older glacial drift deposits within the boreal forest. 

The class covers 0.04% (2,304 acres) of Denali National Park and Preserve (Table 5). Peatlands are 

generally found adjacent to forested areas in Denali. 

The gradient of nutrient availability and water sources in peatlands is typically divided into nutrient rich 

(fen) and nutrient poor (bog) peatlands. Fens are wetlands with wet organic soils, dominated by aquatic, 

emergent, and dwarf shrubs, or raised peat dominated by shrubs and trees. Ground water, the primary water 

source in a fen, is nutrient rich because of its contact with mineral soils. Waters may be acidic or basic, but 

100
 

 

typically with a pH above 4.7. Water is lost through evapotranspiration, seepage (infiltration through the 

soil), and surface outflow. 

Bogs are wetlands with organic soils, typically dominated by Sphagnum (peat moss) species, sedges, 

grasses, or reeds. Bogs require depressions (ponds) in level areas where precipitation exceeds 

evapotranspiration. Precipitation is the primary water source in a bog, with little or no ground-water flow. 

Consequently the sites are nutrient-poor and acidic, commonly with a pH less than 4.7. The water table is 

at or close to the surface most of the year. Because of the continuum of site and vegetation change within a 

peatland, it is often difficult to clearly separate a fen from a bog in the field. 

Vegetation—In level areas, peatlands generally exhibit recognizable and consistent vegetation zonation 

patterns that are directly associated with different water depths. The vegetation, on a wet to dry moisture 

gradient, changes from aquatic (pond), to emergent and mesic herbaceous (Calamagrostis canadensis, 

Carex rostrata, C. aquatilis and Eleocharis quinqueflora), to shrub-dominated wetland (Myrica gale and 

Betula nana), to dwarf tree (Picea mariana). Sphagnum species occur in most dominance types. Not all 

the vegetation zones are always expressed, and the zonation may change abruptly, such as from a pond to a

shrubland. Fens on ground with a slight gradient and with heavy subsurface and surface waterflow often 

develop a patterned ground of vegetated ridges, and vegetated or unvegetated hollows filled with water. 

Landcover Class Accuracy—The producer’s accuracy for the Peatland class is 82.3%, with a user’s

accuracy of 80.9%. Most of the errors of commission and omission are with the Low Shrub-Sedge class. 

101
 

HERBACEOUS-SHRUB LANDCOVER CLASS 12 

Classification—The Herbaceous-Shrub landcover class is a mixture of three already defined landcover 

classes: Willow, Dwarf Shrub and Dry-Mesic Herbaceous. This combination class was created because of

extensive over-classification of the Low Shrub-Sedge class on the upper slopes on the south side of the 

Alaska Range. The Low Shrub-Sedge class at this slope position was actually a combination of the Willow, 

Dwarf Shrub and Dry-Mesic Herbaceous classes. We were unable, however, to split the Low Shrub-Sedge 

class into its proper components—Willow, Dwarf Shrub and Dry-Mesic Herbaceous. The Low Shrub-Sedge 

class, consequently, was reclassified to a new class—Herbaceous-Shrub—at or above alder line (generally

elevations of 600 m or greater) on the park’s south side. For a complete description of the methods used to 

define this class see Stevens et al. (2001). The class is comparable to the Viereck et al. (1992) hierarchical 

levels closed tall willow (IIB1a), open tall willow (IIB2a), closed low willow (IIC1b), open low scrub 

(11C2), dwarf scrub (IID), dry graminoid herbaceous (IIIA1), mesic graminoid herbaceous (IIIA2), dry forb

herbaceous (IIIB1) and mesic forb herbaceous (IIIB2). 

Distribution and Site Characteristics—The Herbaceous-Shrub class is found on mountain ridges and 

slopes on the south side of the Alaska Range. It occurs both above the alder zone and mixed in with the 

alder class moving down slope. The Willow and Dry-Mesic Herbaceous classes occur adjacent to this class 

both up and down slope. The Dwarf Shrub class typically borders the Herbaceous-Shrub class upslope. 

The sites are typically moist, and occur below 1,500 meters elevation (Table 3). It is a major component of 

the Alaska Range-south-central mountains and valleys ecoregion (Tables 4a and 4b; Clark 1998), and 

covers 1.1% (68,872 acres) of Denali National Park and Preserve (Table 5). 

Vegetation—For species and structural information please review the individual landcover classes that 

make up the Herbaceous-Shrub class: Willow, Dwarf Shrub and Dry-Mesic Herbaceous. Note that sites 

dominated by Dryas octopetala are uncommon in the Herbaceous-Shrub class. Instead, dwarf shrub 

species are mixed, including Empetrum nigrum and Vaccinium uliginosum, and have a strong graminoid 

component. The Dry-Mesic Herbaceous component is typically lush and includes Calamagrostis 

canadensis, Athyrium filix-femina, Epilobium angustifolium, Geranium erianthum, Heracleum lanatum and

Sanguisorba stipulata. 

Landcover Class Accuracy—The accuracy of the Herbaceous-Shrub class was not assessed because the 

class is a combination of other map classes. 

102
DWARF SHRUB LANDCOVER CLASS 13 

Classification—Tree canopy cover is less than 10% (or absent). Shrub cover is greater than 25%, shrub 

height is less than 20 centimeters, and less than 25% of the site contains rock. The class is comparable to 

the Viereck et al. (1992) hierarchical level dwarf scrub (IID). 

Distribution and Site Characteristics— The Dwarf Shrub landcover class dominates alpine areas in the 

Alaska Range and the Kantishna Hills. It is found on unstable to stable surfaces such as gentle to steep 

mountain slopes, high mountain valley floors, and rounded mountain ridges (often exposed bedrock). 

Slopes range from 0 to >50%. The soils are a thin layer of dry to mesic sand, litter or organic matter. Its 

elevation ranges up to 1,335 meters based on field data (Table 3). This class dominates the Alaska Rangeinterior mountains and valleys ecoregion, and is common in the Alaska Range-front range and Alaska

Range-south–central mountains and valleys ecoregions (Tables 4a and 4b; Clark 1998). It covers 5.8%

(346,847 acres) of Denali National Park and Preserve (Table 5). On the landcover map, this class is 

underrepresented on the south side of the Alaska Range due to its partial inclusion in the Herbaceous-Shrub 

class. 

The Dwarf Shrub landcover class often grades into the bare ground, sparse vegetation or Dwarf ShrubRock landcover classes and, on more stable sites, the Alder and Willow landcover classes. 

Vegetation— The Dwarf Shrub class is comprised of vegetation that generally reaches a height of 20 

centimeters or less. Most sites are a blend of dwarf shrubs with some sedge and lichen species often 

interspersed with exposed rock. The dominant dwarf shrubs include Dryas octopetala, D. integrifolia, Salix 

reticulata, S. arctica, S. phlebophylla, Cassiope tetragona, Vaccinium uliginosum, Arctostaphylos rubra 

and A. alpina. Forbs include Epilobium angustifolium, Aster species, Heracleum lanatum, Veratrum viride, 

Anemone narcissiflora, and Campanula species. Some sites, however, have a strong graminoid presence, or 

are dominated by Dryas species in the dwarf shrub layer. 

Landcover Class Accuracy—The producer’s accuracy for the Dwarf Shrub class is 78.4%, with a user’s

accuracy of 80.2%. Most of the errors of omission are with the Low Shrub Birch-Ericaceous-Willow and

Low Shrub-Sedge map classes. 

103
DWARF SHRUB-ROCK LANDCOVER CLASS 14 

Classification—Tree canopy cover is less than 10% (or absent). Shrub cover is greater than 25%, shrub 

height is less than 20 centimeters, and rock cover is greater than 25%. The class is comparable to the 

Viereck et al. (1992) hierarchical level dwarf scrub (IID). 

Distribution and Site Characteristics—The physical site characteristics of the Dwarf Shrub-Rock 

landcover class are similar to the Dwarf Shrub landcover class, however it has a much higher surface rock 

component. It is a minor class of alpine areas in the Alaska Range and the Kantishna Hills, and is found on 

unstable to stable surfaces such as gentle to steep mountain slopes, high mountain valley floors, and 

rounded mountain ridges. In addition, it occurs on sandbars and alluvial fans. The slope ranges up to 

>50%, and the elevation ranges up to 1,335 meters based on field data (Table 3). The soils are a thin layer 

of dry to mesic sand, litter or organic matter. Vegetation typically grows on the thin organic mat. This 

class is common in the Alaska Range-high mountains ecoregion and the Alaska Range-interior mountains

and valleys ecoregion (Tables 4a and 4b; Clark 1998). It covers 1.2% (73,894 acres) of Denali National 

Park and Preserve (Table 5). 

The Dwarf Shrub-Rock class often grades into the Bare Ground, Sparse Vegetation or Dwarf Shrub 

landcover classes. On more stable surfaces, it grades into the Alder and Willow classes. 

Vegetation—This class is dominated by dwarf shrubs, primarily Dryas octopetala and D. integrifolia, 

interspersed with exposed rock. Few additional species are present. Other dwarf shrubs include Salix 

reticulata, S. arctica, S. phlebophylla, Cassiope tetragona, Vaccinium uliginosum, Arctostaphylos rubra, 

and A. alpina. Forbs include Epilobium angustifolium, Aster species, Heracleum lanatum, Veratrum viride, 

Anemone narcissiflora, and Campanula species. Fruticose lichen species are common. 

Landcover Class Accuracy—The producer’s accuracy for the Dwarf Shrub-Rock class is 54.9%, with a 

user’s accuracy of 50.3%. Most of the errors of omission and commission are with the Dwarf Shrub and 

Sparse Vegetation classes. The number of Dwarf Shrub-Rock signatures available for the accuracy

assessment is significantly less than preferred.

104
 

DRY-MESIC HERBACEOUS LANDCOVER CLASS 15 

Classification—Tree canopy is less than 10% (or absent), shrub cover is less than 25%, and herbaceous 

cover is greater than 25%. Vegetation is terrestrial (not submerged, floating, or growing in permanent 

water). Site is dry to mesic with little to no standing water. The class is comparable to the Viereck et al. 

(1992) hierarchical levels dry graminoid herbaceous (IIIA1), mesic graminoid herbaceous (IIIA2), dry forb

herbaceous (IIIB1), and mesic forb herbaceous (IIIB2). The photo on the left is from the south side of the 

Alaska Range, and the photo on the right is a moist depression in the northwest region of Denali National 

Park and Preserve. 

Distribution and Site Characteristics— The Dry-Mesic Herbaceous class is a minor class that occurs 

throughout the park under a variety of site conditions. It primarily occurs as rich herbaceous meadows on 

mesic sideslopes ranging up to >50% slope and river terraces on the south side of the Alaska Range. The 

class occurs both above the alder zone up to 1,200 meters, and mixed in with alder moving down slope 

(Table 3). On the landcover map, it is underrepresented on the south side of the Alaska Range due to its 

partial inclusion in the Herbaceous-Shrub class. 

It also occurs as grassy meadows, dominated by Calamagrostis canadensis, found on low elevation moist—

and periodically wet—depressions within the boreal forest in the northwestern portion of the park. These 

sites are moist, however, standing water may be present in small amounts seasonally. Tussock tundra, 

dominated by Eriophorum species, also forms a portion of this class occurring as small patches on gentle to 

moderate slopes of foothills and broad valleys bordering the Kantishna Hills and the north side of the 

Alaska Range, and also on hills near Lake Minchumina. Tussock tundra soils are mesic with tussocks of 

organic matter and live vegetation over mineral soil. Small seasonal pools of water are often found between 

the tussocks. The Dry-Mesic Herbaceous class nearly always occurs as small patches within a mosaic of 

other classes. Most landcover classes occur adjacent to the class due to its wide geographic distribution and 

elevation range. It is uncommon in most ecoregions except the Alaska Range-south-central mountains and 

valleys ecoregion (Tables 4a and 4b; Clark 1998), and covers 0.8% (45,684 acres) of Denali National Park 

and Preserve (Table 5). 

Vegetation—The Dry-MesicHerbaceous class is a grouping of plant associations that encompasses dry or 

mesic sites as well as lush herbaceous meadows. Herbaceous meadows contain such forbs as Veratrum 

viride, Ligusticum scoticum, Geranium erianthum, Cicuta douglasii, Iris setosa, Athyrium filix-femina, 

Calamagrostis canadensis, Epilobium angustifolium, Heracleum lanatum and Sanguisorba stipulata. Total

cover often exceeds 90%. Low shrubs may also occur including Spiraea beauverdiana and Salix planifolia. 

The grassy meadows in the boreal forest are dominated by Calamagrostis canadensis with cover values up 

to 95%. Other species present but uncommon are Betula glandulosa, Vaccinium uliginosum, Athyrium filixfemina and Myrica gale. 

105
 

Sites that are dominated by tussocks have high levels of graminoids, predominantly Eriophorum species, 

and mosses. Surface water, Betula nana, and Rubus chamaemorus also occur. Tussock cover ranges up to 

90% on sites sampled. 

Landcover Class Accuracy—The producer’s accuracy for the Dry-Mesic Herbaceous class is 70.0%, with 

a user’s accuracy of 85.6%. Most of the errors of omission and commission are with the Low Shrub BirchEricaceous-Willow, Herbaceous-Shrub and Dwarf Shrub classes. 

106
WET HERBACEOUS LANDCOVER CLASS 16 

Classification—Tree canopy is less than 10% (or absent), shrub cover is less than 25%, and herbaceous 

cover is greater than 25%. The dominant vegetation is emergent; semi-permanent or standing water is 

present. The class is comparable to the Viereck et al. (1992) hierarchical levels wet graminoid herbaceous 

(IIIA3), and wet forb herbaceous (IIIB3). 

Distribution and Site Characteristics—The Wet Herbaceous class occurs in small patches throughout the

park’s boreal forest zone and at higher elevations dominated by low shrubs. The sites have standing or 

semi-permanent water dominated by hydrophilic vegetation. It most frequently occurs on lake edges, but 

also on saturated river terraces, the edge of ponds, sloughs, fens, other patterned ground, and occasionally in

pools of hillside streams. The sites have little to no slope. Relative to other classes, the Wet Herbaceous 

class is generally small in size and patchy in distribution throughout Denali National Park and Preserve. 

The highest elevation, based on field data, is 824 meters (Table 3). It is uncommon in all ecoregions except 

the Kuskokwim-alluvial fans and flood plains ecoregion and the Kuskokwim-Minchumina Basin ecoregion 

(Tables 4a and 4b; Clark 1998). The class covers 0.6% (38,427 acres) of Denali National Park and Preserve 

(Table 5). 

Vegetation—Herbaceous composition is highly variable and includes Menyanthes trifoliata, Potentilla 

palustris, Cnidium cnidiifolium, Eriophorum brachyantherum, E. vaginatum, Equisetum fluviatile, Carex 

rostrata, C. aquatilis and C. lasiocarpa. Each species may dominate a site. Low shrubs associated with the 

wet herbaceous class include Betula nana, Salix species, Potentilla fruticosa, Myrica gale, and Spirea 

beauverdiana. Sphagnum species sometimes form a floating mat interspersed with the emergent vascular 

species. 

Landcover Class Accuracy—The producer’s accuracy for the Wet Herbaceous class is 68.7%, with a 

user’s accuracy of 89.7%. Errors of omission and commission are relatively small, but scattered with a 

number of different mapping classes (Stunted Spruce, Willow, Low Shrub-Sedge and Water). 

107
AQUATIC HERBACEOUS LANDCOVER CLASS 17 

Classification—Tree canopy is less than 10% (or absent), shrub cover is less than 25%, and herbaceous 

cover is greater than 25%. Vegetation is submerged, floating, or growing in permanent water. The class is 

comparable to the Viereck et al. (1992) hierarchical level freshwater aquatic herbaceous (IIID1). 

Distribution and Site Characteristics—The Aquatic Herbaceous class occurs in small patches of open 

water throughout the park’s boreal forest zone and at higher elevations dominated by low shrubs. The sites 

are in open water of lakes, ponds, sloughs and fens. It is generally small in size and patchy in distribution 

throughout Denali. The Aquatic Herbaceous class is uncommon in all ecoregions (Tables 4a and 4b; Clark

1998), and covers less than 0.01% (564 acres) of Denali National Park and Preserve (Table 5). 

Vegetation—A high cover of water is found in this class. Associated vegetation includes Nymphaea 

tetragona, Nuphar polysepalum, and a few graminoids. Other aquatic species occur but were not sampled 

during the field season. 

Landcover Class Accuracy—The producer’s accuracy for the Aquatic Herbaceous class is 100%, with a

user’s accuracy of 100%. The number of Aquatic Herbaceous signatures available for the accuracy

assessment is significantly less than preferred. All accuracies are based on the 25-class landcover map at a

2-acre mapping unit prior to the digital integration of the burn mask. 

108
SPARSE VEGETATION LANDCOVER CLASS 18 

Classification—The Sparse Vegetation landcover class refers to sites with 15% to 24% total vegetation 

cover. 

Distribution and Site Characteristics— The Sparse Vegetation class is common in alpine areas of the 

Alaska Range. It is generally found on unstable to stable upslope bedrock and unconsolidated rock in the 

mountains and high mountain valley floors. This class also occurs on sandbars, alluvial fans and the debris

covered surfaces of glaciers. The physical site characteristics are similar to the Dwarf Shrub-Rock class 

however it has a much higher surface rock and mineral soil component. The slope ranges up to >50%, and

the elevation ranges up to 1,142 meters based on field data, however, the spectral map places the maximum

elevation at 4,527 meters (Table 3). The soils are a thin layer of dry to mesic sand, litter or organic matter. 

The sparse vegetation typically occurs as patches on the organic matter interspersed with exposed rock. 

This class is common in the Alaska Range-high mountains, Alaska Range-interior mountains and valleys, 

and Alaska Range-south-central mountains and valleys ecoregions (Tables 4a and 4b; Clark 1998). The 

Sparse Vegetation landcover class covers 4.8% (289,308 acres) of Denali National Park and Preserve 

(Table 5). 

The class frequently occurs next to the Bare Ground, Dwarf Shrub-Rock and Dwarf Shrub classes. 

Vegetation—The Sparse Vegetation class is dominated by dwarf shrubs—primarily Dryas octopetala—

surface rock, and exposed mineral soil. Other dwarf shrubs include Dryas integrifolia, Salix reticulata, S. 

arctica, S. phlebophylla, Cassiope tetragona, Vaccinium uliginosum, Arctostaphylos rubra, and A. alpina. 

Forbs include Epilobium angustifolium, Aster species, Heracleum lanatum, Veratrum viride, Anemone 

narcissiflora, and Campanula species. 

Landcover Class Accuracy—The producer’s accuracy for the Sparse Vegetation class is 87.6%, with a 

user’s accuracy of 45.7%. Most of the errors of omission and commission are with the Bare Ground and 

Dwarf Shrub-Rock map classes. 

109
BARE GROUND LANDCOVER CLASS 19 

Classification—Bare Ground represents areas with less than 15% vegetation cover. 

Distribution and Site Characteristics— The Bare Ground class primarily occurs on steep upslope 

bedrock, unconsolidated rock, mountain peaks, jutting rock outcrops, and talus slopes. It also represents 

exposed alluvial deposits along rivers, especially those with wide outwash plains. In addition, it represents 

the debris-covered surfaces of glaciers, typically increasing in cover toward a glacier’s terminus. Bare 

Ground is often found adjacent to the Sparse Vegetation, Dwarf Shrub-Rock and Dwarf Shrub landcover 

classes. Elevation for the class ranges up to 5,536 meters based on the spectral map (Table 3). It dominates 

the Alaska Range-high mountains ecoregion (Tables 4a and 4b; Clark 1998) and covers 9.8% (592,298 

acres) of Denali National Park and Preserve (Table 5). 

Landcover Class Accuracy—The producer’s accuracy for the Bare Ground class is 70.7%, with a user’s

accuracy of 86.5%. Most of the errors of omission and commission are with the Sparse Vegetation class. 

110
 

SNOW-ICE LANDCOVER CLASS 20 

The Snow-Ice class refers to areas covered with snowfields, glaciers or late lying snow. Glacier surfaces 

have less exposed ice as one moves towards the terminus. The elevation ranges up to 6,188 meters based 

on the spectral map (Table 3). The Snow-Ice class dominates the Alaska Range-high mountains ecoregion, 

and is the most common class in Denali National Park and Preserve covering 15.5% (932,790 acres) of its 

total area (Table 5). 

Landcover Class Accuracy—The producer’s accuracy for the Snow-Ice class is 99.7%, with a user’s

accuracy of 99.5%. Most of the minor errors of omission and commission are with the Bare Ground class. 

SHADOW-INDETERMINATE LANDCOVER CLASS 21 

The Shadow-Indeterminate landcover class includes areas of severe terrain shadow where landcover could 

not be reliably identified. This class covers 6.9% (657,887 acres) of Denali National Park and Preserve. 

Landcover Class Accuracy—Accuracies were not reported for the Shadow-Indeterminate mapping class

because the class was so heavily edited that the signatures were obsolete. 

111
SILTY WATER LANDCOVER CLASS 22 

Silty Water refers to water containing moderate to high levels of glacial silt and debris. The major rivers 

and portions of some lakes—such as Lake Minchumina—are mapped as silty water. The Silty Water class 

is common in the Cook Inlet-braided flood plains and stream terrace ecoregion. The Silty Water class 

covers 0.5% (30,765 acres) of Denali National Park and Preserve (Table 5). 

Landcover Class Accuracy—The producer’s accuracy for the Silty Water class was 91.8%, with a user’s

accuracy of 97.7%. Most of the minor errors of omission and commission were with the Bare Ground 

class. 

CLEAR WATER LANDCOVER CLASS 23 

Clear water indicates water with little to no sediment or glacial silt. Most lakes and ponds are mapped as 

Clear Water in the park. The class covers 1.0% (60,764 acres) of Denali National Park and Preserve (Table 

5). 

Landcover Class Accuracy—The producer’s accuracy for the Clear Water class is 100%, with a user’s 

accuracy of 98.8%. The minor error of commission is with the Silty Water class. 

112
 

BURN LANDCOVER CLASS 24 

Description—The Burn landcover class represents large areas that have recently been scarred by wildfires.

The class occurs predominantly in the northwest region of the park on level landscapes and rolling hills. 

The burns were dominated, pre-fire, by the various spruce dominated landcover classes. 

Because limited air and ground information was collected within burn scars, it was not possible to 

accurately map the class. However, many of the more recent or very intense burns (as indicated by the 

scarring on the raw imagery) on the park’s west side were spectrally classified as Wet Herbaceous. Many

of the older, or less intense burns were spectrally mapped to the Willow or Low Shrub-Sedge classes. See 

the description for the plant association Larix laricina, Betula papyrifera, Picea mariana, Ledum palustre, 

B. nana, Rubus chamaemorus, Eriophorum vaginatum, Oxycoccus microcarpos for a description of a postfire community. The Burn class is common in the Kuskokwim-alluvial fans and floodplains ecoregion 

(Tables 4a and 4b), and covers 2.9% (172,528 acres) of Denali National Park and Preserve (Table 5). 

Burn areas were identified and mapped by establishing thresholds within the NDVI layers created from the 

1994 and 1996 SPOT imagery and from the TM imagery. The thresholds for the upper and lower NDVI 

values for each image block were defined through an interactive review with the satellite imagery and the 

associated thematic NDVI layers. Once the range of NDVI values corresponding to burn areas (i.e., no or 

very low 'greenness') were established, a burn mask was developed that was constrained by the fire polygon 

coverage provided by the Park. However, users should take note that in addition to legitimate burn areas, 

some areas of legitimate wet herbaceous (i.e., wet herbaceous that was not burned) as well as some areas of 

water and aquatic herbaceous areas fell into the threshold range of NDVI values and were included in the 

burn mask. To help alleviate the misclassification of legitimate water (both silty and clear) and Aquatic 

Herbaceous to Burn, an additional water and aquatic herbaceous mask was also provided. See Volume 1 

(Stevens et al. 2001) for a complete desription of the methods used to develop the Burn landcover class. 

Landcover Class Accuracy—Accuracies were not reported for the Burn landcover class. 

113
 

CLOUD LANDCOVER CLASS 25 

The Cloud landcover class designates areas masked by cloud cover on the satellite imagery. Due to the lack 

of spectral data, vegetation classes could not be assigned to these sites. The Cloud landcover class covers 

0.01% (357 acres) of Denali National Park and Preserve (Table 5). 

Landcover Class Accuracy—Accuracies were not reported for the Cloud landcover class because the class 

was so heavily edited that the signatures were obsolete.

114
 

 

 

 

 

 

 

 

 

 

 

Appendix 5.3. Fly-Over (Polygon) and Ground Site (Point) Field Forms 

1999 DENALI LANDCOVER M APPING PRO JECT ; FVA "FLY -O VER " DATA FO RM 

A ir Photo N um ber: Poly g on code: Lat/Long:

Landcover class nam e: Landcover class code:

D ate: Survey ors: A . G aribaldi, J. Stevens

Photos: R oll num ber: Fram e N um ber: A zim uth:

Video:tape #: Footage: D ig photo: ses. # : photo #: azim uth:

Slope (0 -100 degrees): A sp ect (0-360 deg rees):

M odifer: R iparian A lpine Subalpine Boreal Tussock B og M eadow

L an d fo rm : S p r u c e L o w s h r u b 

H y d rolog ic Regim e (circle o n e): D ry - M esic - Wet - Aq u a tic C lo se d W h ite Sp ru ce C lo sed L o w W illo w

% Tu sso ck s: C lose d B la c k Spru c e C lose d Lo w A ld e r-W illow

D om in ant Species: % Can o p y H eig ht (ft) % To tal Co v e r O p en W h ite Sp ru ce C lo sed Lo w S h ru b Birc h

C o v e r O p en B lack Sp ru ce O p e n Lo w A ld er

W o o d la n d Sp ru c e (E ric ) O p e n Lo w Ald e r-W illo w

W o o d la n d Sp ru c e (L ic h e n ) O p e n Lo w W illow

W o o dlan d Sp ru c e (S e dge ) O p e n Lo w M ix Sh rub-S e dge Tu sso ck

D e cid u o u s f o r e s t O p e n Lo w S h ru b Birch -E ricaceo u s

C lo se d Birc h O p e n Lo w S hru b Birc h -W illo w

C lo se d Asp e n D w a r f s h r u b 

C lo se d Ba lsam Popla r D ry a s D w a rf S h ru b

C lo se d M ix e d De c iduo u s W illo w Dw a rf Shru b

O p en Birc h E ric . Be arb e rry /V accin ium D w . S h ru b

O p en A sp en E ric . Ca ssio p e Dwa rf S h ru b

O p en Balsam Po p la r H e r b a c e o u s 

O p en M ixed De c id uous D ry/M e sic G ra ss-F o rb

W o o d la n d Birc h D ry /M e sic Sed g e -F o rb

W o o d lan d Balsam Po p lar T u sso ck Tu n d ra

W o o d la n d De c iduou s W e t g ram inoid/fo rb

M ix e d f o r e s t O t h e r 

C lo se d M ixe d L ic h e n

O p en M ixed S p a rs e V e g e ta tion

W o o d la n d M ix e d A q u a tic

T a ll s h r u b B a re Ground

C lo se d Ta ll A lde r S now /Ic e

C lo se d T a ll W illo w

M o ss C lo se d Ta ll A lde r-W illo w

L ich en C lose d T a ll Sh rub Birc h

B a re grou n d O p en Ta ll Ald e r O p e n T a ll Shrub Birc h

W a te r O p e n T a ll W illow O p e n T a ll A ld e r-W illow

T o ta l co v e r: H e ig h t (feet) C ommen ts:

Tree

N eed leaf

B ro ad leaf

S h ru b

T a ll (> 5 ft.)

L ow (8 in ch e s to 5 ft.)

D w arf (< 8 in ch es)

H erb aceo u s

L ich en /M oss In c lu sio n : Clum p ed v s. ran d o m distribu tion (c irc le one)

B a regrou n d

W a te r

115
 

 

 

1999 DENALI LANDCOVER MAPPING PROJECT; SITE DESCRIPTION FORM 

Air Photo Number: Polygon code: Landcover class name:

Date: Surveyors: Photos: Roll number: Frame Number:

Lat: Long: W P: Error:+ Elevation:

Slope (0-100 degrees): Aspect (0-360 degrees): Landform:

Hydrologic Regime: Dry Mesic Wet Aquatic Biome: Alpine Subalpine Boreal Bog Herb meadow Tussock Rip

Unvegetated Surface: ___% Water ___% Bare soil ___% Bedrock ___% Litter, duff

___% Wood ( > 1 cm) ___% Large rocks (cobbles, boulders > 10 cm) ___% Small rocks (gravel, 0.2-10 cm)

___% Sand (0.1-2 mm) ___% Other (describe):

 Total cover Soils: 

Air Ground Height (ft)pH

De

Needleaf Texture

Broaleaf Depth (ft)

Tall shrub (>5 ft) Water table

Low (8 in. to 5 ft) pth to permafrost

Lichen Berries 

B d uld

Wate stop

Tuss s d stop

Spec d nc

Dwarf (<8 inches)

Forb

Grass

Sedge

Moss

aregroun Wo not stop

r Could

ock ul

ies

Sho

% Cover Height (feet) bh (i hes) Comments (bottom branches)

116
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix 5.4 The number of Mapping Class Signatures per Image Processing Block 

The number and spatial extents of a particular landcover type’s spectral signatures directly effects how well 

that type will be mapped by the ML classifier. For this reason, the number of signatures (as well as location 

and size) of each potential landcover type was tracked. The number of original signatures derived from

either the FirePro or 1999 field data for each of the target mapping classes is shown to the right. The final 

number of signatures used for generating the base spectral classification map (i.e., the 25 endpoint classes) 

is shown to the left. Although the table shows how the Target classes were generally combined to form the 

enpoint class, the number of signatures for each endpoint class may not necessarily equal the total number 

of combined Target Class signatures. This is because signatures were keyed, deleted or added as 

appropriate. 

(Note: Balsam Poplar signatures were later added to the Birch / Aspen /Mixed Deciduous signatures to 

create the Broadleaf Forest Class. The Herbaceous-Shrub and Burn Classes did not have any signatures and 

are not listed in these tables). 

Denali LC Endpoint Mapping Classes - October 12, 2000 

TM Processing Block 1 70/15&16

Endpoint

Class Proposed Endpoint Class Name

# of

sigs

Target

Class Target Class Name 

# of

sigs 

1 Dense Open Spruce Forest (white like) 8 1 Open White Spruce 25

2 Open Spruce Forest (black or white) 30 2 Open Black Spruce 18

3 Stunted Spruce

39

3 Open Stunted Spruce (~black spruce) 15

5 Stunted Spruce Ericaceous 13

6 Stunted Spruce Sedge 6

4 Birch / Aspen / Mixed Deciduous 

19

7 Closed Birch 12

11 Open Birch 2

8 Closed Aspen 0

12 Open Aspen 0

10 Closed Mixed Deciduous 5

14 Open Mixed Deciduous 0

5 Balsam Poplar

11

9 Closed Balsam Poplar 0

13 Open Balsam Poplar 11

6 Spruce-Broadleaf 

8

16 Closed Mixed 3

17 Open Mixed 10

7 Alder 

22

19 Closed Tall Alder -Willow 4

20 Closed Tall Alder 6

22 Open Tall Alder 10

30 Open Low Alder 1

24 Closed Low Alder-Willow 1

29 Open Low Alder-Willow 1

8 Willow 

13

21 Closed Tall Willow 4

23 Open Tall Willow 4

26 Closed Low Willow 4

9 Closed Low Shrub Birch 11 28 Closed Low Shrub Birch 9

10 Low Shrub Birch-Ericaceous-Willow 25 Closed Low Shrub Birch-Willow 6

27 

Closed Low Shrub Birch-Ericaceous 

Shrub 6

117
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Denali LC Endpoint Mapping Classes - October 12, 2000 

TM Processing Block 1 70/15&16 

Endpoint

Class Proposed Endpoint Class Name

# of

sigs 

Target

Class Target Class Name 

# of

sigs 

36

31 Open Low Shrub Birch-Eric-Willow 5

53

Open Low Shrub Birch-Ericaceous 

Shrub 18

32 Open Low Willow 2

11 Open Low Shrub-Sedge 

33

33 Open Low Mixed Shrub-Tussock 30

44 Tussock * 5

12 Open Low Shrub Bog-Like 0 43 Open Low Shrub Bog 0

13 Mixed Dwarf Shrub

12

37 Mixed Dwarf Shrub 8

38 Mixed Dwarf Shrub-Sedge 1

36 Dryas Dwarf Shrub 2

39 Mixed Dwarf Shrub-Lichen 1

14 Mixed Dwarf Shrub-Rock 2 40 Mixed Dwarf Shrub-Rock 2

15 Dry-Mesic Herb

6

41 Dry/Mesic Grass 3

42 Dry/Mesic Grass-Forb 2

43 Dry/Mesic Sedge-Forb 0

44 Tussock * 5

16 Wet Herb 13 45 Wet Herbaceous 14

17 Aq uatic 0 46 Aquatic 0

18 Spa rse 2 47 Sparse Vegetation 2

19 Bare 12 49 Bare Ground 12

20 Sn ow-Ice 4 50 Snow/Ice 4

21 Sha dow 4 51 Shadow/Indeterminate 4

22 Silty 5 52 Silty Water 5

23 Clear 3 53 Clear Water 3

24 Burn 0 55 Burn site 0

25 Cloud 4 56 Cloud 4

118
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Denali LC Endpoint Mapping Classes - October 12, 2000

TM Processing Block 2 71/15 

Endpoint

Class Proposed Endpoint Class Name

# of

sigs

Target

Class Target Class Name

# of 

sigs 

1 Dense-Open Spruce 3 1 Open White Spruce 3

2 Open Spruce 9 2 Open Black Spruce 9

3 Stunted Spruce

18

3 Open Stunted Spruce (~black spruce) 5

5 Stunted Spruce Ericaceous 7

6 Stunted Spruce Sedge 6

4 Birch / Aspen / Mixed Deciduous 

25

7 Closed Birch 15

11 Open Birch 5

8 Closed Aspen 0

12 Open Aspen 0

10 Closed Mixed Deciduous 5

14 Open Mixed Deciduous 0

5 Balsam Poplar

2

9 Closed Balsam Poplar 0

13 Open Balsam Poplar 2

6 Spruce-Broadleaf 

4

16 Closed Mixed 2

17 Open Mixed 3

7 Alder 

3

19 Closed Tall Alder -Willow 2

20 Closed Tall Alder 0

22 Open Tall Alder 1

30 Open Low Alder 0

24 Closed Low Alder-Willow 0

29 Open Low Alder-Willow 0

8 Willow 

2

21 Closed Tall Willow 1

23 Open Tall Willow 1

26 Closed Low Willow 0

9 Closed Low Shrub Birch 0 28 Closed Low Shrub Birch 0

10 Low Shrub Birch-Ericaceous-Willow 

6

25 Closed Low Shrub Birch-Willow 0

27 

Closed Low Shrub Birch-Ericaceous 

Shrub 3

31 Open Low Shrub Birch/Eric/Willow 1

35 

Open Low Shrub Birch-Ericaceous 

Shrub 2

32 Open Low Willow 1

11 Open Low Shrub-Sedge 

13

33 Open Low Mixed Shrub-Tussock 9

44 Tussock * 1

12 Open Low Shrub Bog-Like 0 34 Open Low Shrub Bog 0

13 Mixed Dwarf Shrub 

1

37 Mixed Dwarf Shrub 1

38 Mixed Dwarf Shrub-Sedge 0

36 Dryas Dwarf Shrub 0

39 Mixed Dwarf Shrub-Lichen 0

14 Mixed Dwarf Shrub-Rock 0 40 Mixed Dwarf Shrub-Rock 0

15 Dry / Mesic Herb 41 Dry/Mesic Grass 1

42 Dry/Mesic Grass-Forb 1

43 Dry/Mesic Sedge-Forb 1

119
 

 

 

 

 

 

Denali LC Endpoint Mapping Classes - October 12, 2000

TM Processing Block 2 71/15 

Endpoint

Class Proposed Endpoint Class Name

# of

sigs

Target

Class Target Class Name

# of 

sigs 

3 44 Tussock * 1

16 Wet Herb 7 45 Wet Herbaceous 7

17 Aquatic 0 46 Aquatic 0

18 Sparse 1 47 Sparse Vegetation 1

19 Bare 1 49 Bare Ground 1

20 Sn ow-Ice 0 50 Snow-Ice 0

21 Sha dow 5 51 Shadow-Indeterminate 5

22 Silty 7 52 Silty Water 7

23 Clear 6 53 Clear Water 6

24 Hum an 0 54 Human development 0

25 Burn 0 55 Burn site 1

26 Cloud 5 56 Cloud 5

120
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Denali LC Endpoint Mapping Classes - October 12, 2000 

TM Processing Block 3 70/16 

Endpoint

Class Proposed Endpoint Class Name

# of

sigs

Target

Class Target Class Name

# of

sigs 

1 Dense-Open Spruce 3 1 Open White Spruce 2

2 Open Spruce 2 2 Open Black Spruce 1

3 Stunted Spruce

2

3 Open Stunted Spruce (~black spruce) 1

5 Stunted Spruce Ericaceous 1

6 Stunted Spruce Sedge 1

4 Birch / Aspen / Mixed Deciduous 

0

7 Closed Birch 0

11 Open Birch 0

8 Closed Aspen 0

12 Open Aspen 0

10 Closed Mixed Deciduous 0

14 Open Mixed Deciduous 0

5 Balsam Poplar

14

9 Closed Balsam Poplar 6

13 Open Balsam Poplar 8

6 Spruce-Broadleaf 

18

16 Closed Mixed 1

17 Open Mixed 16

7 Alder 

35

19 Closed Tall Alder -Willow 6

20 Closed Tall Alder 19

22 Open Tall Alder 7

30 Open Low Alder 2

24 Closed Low Alder-Willow 1

29 Open Low Alder-Willow 1

8 Willow 

11

21 Closed Tall Willow 1

23 Open Tall Willow 4

26 Closed Low Willow 2

9 Closed Low Shrub Birch 0 28 Closed Low Shrub Birch 0

10 Low Shrub Birch-Ericaceous-Willow 

8

25 Closed Low Shrub Birch-Willow 0

27 

Closed Low Shrub Birch-Ericaceous 

Shrub 1

31 

Open Low Shrub Birch/Eric ShrubWillow 4

35 Open Low Shrub Birch-Ericaceous Shrub 9

32 Open Low Willow 3

11 Open Low Shrub-Sedge 

12

33 Open Low Mixed Shrub-Tussock 4

44 Tussock * 0

12 Open Low Shrub Bog-Like 10 34 Open Low Shrub Bog 12

13 Mixed Dwarf Shrub 

19

37 Mixed Dwarf Shrub 4

38 Mixed Dwarf Shrub-Sedge 1

36 Dryas Dwarf Shrub 8

39 Mixed Dwarf Shrub-Lichen 4

14 Mixed Dwarf Shrub-Rock 1 40 Mixed Dwarf Shrub-Rock 5

15 Dry-Mesic Herb 41 Dry/Mesic Grass 1

42 Dry/Mesic Grass-Forb 5

43 Dry/Mesic Sedge-Forb 5

121
 

 

 

 

 

 

 

Denali LC Endpoint Mapping Classes - October 12, 2000 

TM Processing Block 3 70/16 

Endpoint

Class Proposed Endpoint Class Name

# of

sigs

Target

Class Target Class Name 

# of

sigs 

11 44 Tussock * 0

16 Wet Herb 7 45 Wet Herbaceous 8

17 Aquatic 0 46 Aquatic 0

18 Sparse 7 47 Sparse Vegetation 2

19 Bare 7 49 Bare Ground 7

20 Sn ow-Ice 6 50 Snow-Ice 6

21 Sha dow 4 51 Shadow-Indeterminate 0

22 Silty 4 52 Silty Water 0

23 Clear 3 53 Clear Water 0

24 Hum an 0 54 Human development 0

25 Burn 0 55 Burn site 0

26 Cloud 3 56 Cloud 0

122
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Denali LC Endpoint Mapping Classes - October 12, 2000 

TM Processing Block 4 71/16 

Endpoint

Class Proposed Endpoint Class Name

# of

sigs

Target

Class Target Class Name 

# of

sigs 

1 Dense-Open Spruce 6 1 Open White Spruce 14

2 Open Spruce 14 2 Open Black Spruce 22

3 Stunted Spruce

14

3 Open Stunted Spruce (~black spruce) 21

5 Stunted Spruce Ericaceous 18

6 Stunted Spruce Sedge 5

4 Birch / Aspen / Mixed Deciduous 

4

7 Closed Birch 2

11 Open Birch 0

8 Closed Aspen 0

12 Open Aspen 0

10 Closed Mixed Deciduous 0

14 Open Mixed Deciduous 0

5 Balsam Poplar

8

9 Closed Balsam Poplar 5

13 Open Balsam Poplar 26

6 Spruce-Broadleaf 

10

16 Closed Mixed 7

17 Open Mixed 18

7 Alder 

11

19 Closed Tall Alder -Willow 10

20 Closed Tall Alder 24

22 Open Tall Alder 17

30 Open Low Alder 3

24 Closed Low Alder-Willow 0

29 Open Low Alder-Willow 2

8 Willow 

11

21 Closed Tall Willow 1

23 Open Tall Willow 6

26 Closed Low Willow 9

9 Closed Low Shrub Birch 2 28 Closed Low Shrub Birch 2

10 Low Shrub Birch-Ericaceous-Willow 

13

25 Closed Low Shrub Birch-Willow 7

27 

Closed Low Shrub Birch-Ericaceous 

Shrub 8

31 

Open Low Shrub Birch/Eric ShrubWillow 5

35 

Open Low Shrub Birch-Ericaceous 

Shrub 20

32 Open Low Willow 6

11 Open Low Shrub-Sedge 

12

33 Open Low Mixed Shrub-Tussock 16

44 Tussock * 3

12 Open Low Shrub Bog-Like 7 34 Open Low Shrub Bog 7

13 Mixed Dwarf Shrub 

14

37 Mixed Dwarf Shrub 16

38 Mixed Dwarf Shrub-Sedge 2

36 Dryas Dwarf Shrub 9

39 Mixed Dwarf Shrub-Lichen 3

14 Mixed Dwarf Shrub-Rock 3 40 Mixed Dwarf Shrub-Rock 5

15 Dry-Mesic Herbaceous 41 Dry/Mesic Grass 6

42 Dry/Mesic Grass-Forb 7

123
 

 

 

 

 

 

Denali LC Endpoint Mapping Classes - October 12, 2000 

TM Processing Block 4 71/16 

Endpoint

Class Proposed Endpoint Class Name

# of

sigs

Target

Class Target Class Name 

# of

sigs

9

43 Dry-Mesic Sedge-Forb 3

44 Tussock * 3

16 Wet Herb 5 45 Wet Herbaceous 15

17 Aquatic 1 46 Aquatic 2

18 Sparse 3 47 Sparse Vegetation 4

19 Bare 7 49 Bare Ground 20

20 Snow-Ice 5 50 Snow/Ice 7

21 Sha dow 2 51 Shadow-Indeterminate 1

22 Silty 3 52 Silty Water 0

23 C lear 7 53 Clear Water 1

24 Human 0 54 Human development 0

25 Burn 0 55 Burn site 2

26 Cloud 3 56 Cloud 1

124
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Denali LC Endpoint Mapping Classes 

TM Processing Block 5 - 69/16 

Endpoint

Class Proposed Endpoint Class Name

#of 

sigs

Target

Class Target Class Name 

#of 

sigs 

1 Dense-Open Spruce 4 1 Open White Spruce 11

2 Open Spruce 3 2 Open Black Spruce 1

3 Stunted Spruce

2

3 Open Stunted Spruce (~black spruce) 0

5 Stunted Spruce Ericaceous 1

6 Stunted Spruce Sedge 1

4 Birch / Aspen / Mixed Deciduous 

0

7 Closed Birch 0

11 Open Birch 0

8 Closed Aspen 0

12 Open Aspen 0

10 Closed Mixed Deciduous 0

14 Open Mixed Deciduous 0

5 Balsam Poplar

4

9 Closed Balsam Poplar 4

13 Open Balsam Poplar 4

6 Spruce-Broadleaf 

4

16 Closed Mixed 0

17 Open Mixed 9

7 Alder 

6

19 Closed Tall Alder -Willow 4

20 Closed Tall Alder 4

22 Open Tall Alder 7

30 Open Low Alder 2

24 Closed Low Alder-Willow 2

29 Open Low Alder-Willow 1

8 Willow 

5

21 Closed Tall Willow 0

23 Open Tall Willow 8

26 Closed Low Willow 1

9 Closed Low Shrub Birch 2 28 Closed Low Shrub Birch 5

10 Low Shrub Birch-Ericaceous-Willow 

13

25 Closed Low Shrub Birch-Willow 5

27 

Closed Low Shrub Birch-Ericaceous 

Shrub 3

31 

Open Low Shrub Birch/Eric ShrubWillow 10

35 

Open Low Shrub Birch-Ericaceous 

Shrub 16

32 Open Low Willow 10

11 Open Low Shrub-Sedge 

3

33 Open Low Mixed Shrub-Tussock 10

44 Tussock * 2

12 Open Low Shrub Bog-Like 8 34 Open Low Shrub Bog 11

13 Mixed Dwarf Shrub 

7

37 Mixed Dwarf Shrub 10

38 Mixed Dwarf Shrub-Sedge 5

36 Dryas Dwarf Shrub 6

39 Mixed Dwarf Shrub-Lichen 4

14 Mixed Dwarf Shrub-Rock 1 40 Mixed Dwarf Shrub-Rock 3

15 Dry / Mesic Herb 41 Dry/Mesic Grass 0

42 Dry/Mesic Grass-Forb 7

43 Dry/Mesic Sedge-Forb 4

125
 

 

 

 

 

 

 

Denali LC Endpoint Mapping Classes 

TM Processing Block 5 - 69/16 

Endpoint

Class Proposed Endpoint Class Name

#of

sigs

Target

Class Target Class Name 

#of 

sigs 

5 44 Tussock * 2

16 Wet Herb 2 45 Wet Herbaceous 8

17 Aquatic 0 46 Aquatic 0

18 Sparse 2 47 Sparse Vegetation 3

19 Bare 1 49 Bare Ground 16

20 Snow-Ice 3 50 Snow-Ice 4

21 Shadow 3 51 Shadow-Indeterminate 1

22 Silty 1 52 Silty Water 3

23 C lear 2 53 Clear Water 0

24 Hum an 0 54 Human development 0

25 B urn 0 55 Burn site 0

26 C loud 3 56 Cloud 1

126
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Denali LC Endpoint Mapping Classes 

1996 SPOT XS Processing Block Northside 

Endpoint

Class Proposed Endpoint Class Name

#of 

sigs

Target

Class Target Class Name 

#of

sigs

1 Dense-Open Spruce 5 1 Open White Spruce 5

2 Open Spruce 24 2 Open Black Spruce 24

3 Stunted Spruce

26

3 Open Stunted Spruce (~black spruce) 6

5 Stunted Spruce Ericaceous 13

6 Stunted Spruce Sedge 7

4 Birch / Aspen / Mixed Deciduous 

9

7 Closed Birch 4

11 Open Birch 0

8 Closed Aspen 0

12 Open Aspen 0

10 Closed Mixed Deciduous 5

14 Open Mixed Deciduous 0

5 Balsam Poplar

5

9 Closed Balsam Poplar 0

13 Open Balsam Poplar 5

6 Spruce-Broadleaf 

13

16 Closed Mixed 1

17 Open Mixed 12

7 Alder 

22

19 Closed Tall Alder -Willow 5

20 Closed Tall Alder 7

22 Open Tall Alder 9

30 Open Low Alder 1

24 Closed Low Alder-Willow 5

29 Open Low Alder-Willow 3

8 Willow 

6

21 Closed Tall Willow 1

23 Open Tall Willow 5

26 Closed Low Willow 6

9 Closed Low Shrub Birch 6 28 Closed Low Shrub Birch 5

10 Low Shrub Birch-Ericaceous-Willow 

5

25 Closed Low Shrub Birch-Willow 8

27 

Closed Low Shrub Birch-Ericaceous 

Shrub 9

31 

Open Low Shrub Birch/Eric ShrubWillow 5

35 

Open Low Shrub Birch-Ericaceous 

Shrub 10

32 Open Low Willow 4

11 Open Low Shrub-Sedge 

8

33 Open Low Mixed Shrub-Tussock 19

44 Tussock * 7

12 Open Low Shrub Bog-Like 14 34 Open Low Shrub Bog 0

13 Mixed Dwarf Shrub 

7

37 Mixed Dwarf Shrub 10

38 Mixed Dwarf Shrub-Sedge 3

36 Dryas Dwarf Shrub 5

39 Mixed Dwarf Shrub-Lichen 2

14 Mixed Dwarf Shrub-Rock 15 40 Mixed Dwarf Shrub-Rock 3

15 Dry-Mesic Herb 41 Dry/Mesic Grass 3

42 Dry/Mesic Grass-Forb 3

43 Dry/Mesic Sedge-Forb 0

127
 

 

 

 

 

 

 

Denali LC Endpoint Mapping Classes 

1996 SPOT XS Processing Block Northside 

Endpoint

Class Proposed Endpoint Class Name

#of 

sigs

Target

Class Target Class Name 

#of

sigs

19 44 Tussock * 7

16 Wet Herb 0 45 Wet Herbaceous 6

17 Aquatic 5 46 Aquatic 1

18 Sparse 13 47 Sparse Vegetation 2

19 Bare 2 49 Bare Ground 7

20 Snow-Ice 3 50 Snow-Ice 4

21 Shadow 13 51 Shadow-Indeterminate 4

22 Silty 6 52 Silty Water 6

23 Clear 1 53 Clear Water 5

24 Hum an 2 54 Human development 0

25 Burn 7 55 Burn site 0

26 Cloud 4 56 Cloud 0

128
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Denali LC Endpoint Mapping Classes 

1996 SPOT XS Processing Block - Southside 

Endpoint

Class Proposed Endpoint Class Name

#of 

sigs

Target

Class Target Class Name 

#of 

sigs

1 Dense-Open Spruce 0 1 Open White Spruce 0

2 Open Spruce 4 2 Open Black Spruce 4

3 Stunted Spruce

3

3 Open Stunted Spruce (~black spruce) 2

5 Stunted Spruce Ericaceous 1

6 Stunted Spruce Sedge 3

4 Birch / Aspen / Mixed Deciduous 

0

7 Closed Birch 0

11 Open Birch 0

8 Closed Aspen 0

12 Open Aspen 0

10 Closed Mixed Deciduous 0

14 Open Mixed Deciduous 0

5 Balsam Poplar

11

9 Closed Balsam Poplar 4

13 Open Balsam Poplar 7

6 Spruce-Broadleaf 

8

16 Closed Mixed 0

17 Open Mixed 10

7 Alder 

17

19 Closed Tall Alder -Willow 3

20 Closed Tall Alder 4

22 Open Tall Alder 8

30 Open Low Alder 2

24 Closed Low Alder-Willow 0

29 Open Low Alder-Willow 0

8 Willow 

3

21 Closed Tall Willow 0

23 Open Tall Willow 3

26 Closed Low Willow 3

9 Closed Low Shrub Birch 3 28 Closed Low Shrub Birch 0

10 Low Shrub Birch-Ericaceous-Willow 

12

25 Closed Low Shrub Birch-Willow 0

27 

Closed Low Shrub Birch-Ericaceous 

Shrub 0

31 

Open Low Shrub Birch/Eric ShrubWillow 5

35 

Open Low Shrub Birch-Ericaceous 

Shrub 3

32 Open Low Willow 1

11 Open Low Shrub-Sedge 

10

33 Open Low Mixed Shrub-Tussock 1

44 Tussock * 0

12 Open Low Shrub Bog-Like 7 34 Open Low Shrub Bog 0

13 Mixed Dwarf Shrub 

10

37 Mixed Dwarf Shrub 6

38 Mixed Dwarf Shrub-Sedge 1

36 Dryas Dwarf Shrub 4

39 Mixed Dwarf Shrub-Lichen 2

14 Mixed Dwarf Shrub-Rock 1 40 Mixed Dwarf Shrub-Rock 1

15 Dry-Mesic Herb 41 Dry/Mesic Grass 0

42 Dry/Mesic Grass-Forb 9

43 Dry/Mesic Sedge-Forb 2

129
 

 

 

 

 

 

 

Denali LC Endpoint Mapping Classes 

1996 SPOT XS Processing Block - Southside 

Endpoint

Class Proposed Endpoint Class Name

#of 

sigs

Target

Class Target Class Name 

#of 

sigs

10 44 Tussock * 0

16 Wet Herb 5 45 Wet Herbaceous 0

17 Aq uatic 2 46 Aquatic 0

18 Spa rse 5 47 Sparse Vegetation 0

19 Bare 8 49 Bare Ground 0

20 Sn ow-Ice 5 50 Snow-Ice 0

21 Sha dow 0 51 Shadow-Indeterminate 0

22 Silty 1 52 Silty Water 1

23 C lear 3 53 Clear Water 3

24 Hum an 0 54 Human development 0

25 B urn 0 55 Burn site 0

26 C loud 0 56 Cloud 0

130
Appendix 5.5 

Following the implementation of the spectral classification for each Image Processing Block, a confusion 

matrix was generated and submitted to NPS for review. Results from the spectral classification were used 

to refine the spectral signatures, which were then used to generate a new and better spectral classification. 

The final confusion matrices from this iterative process are presented on the following pages. 

131
132
 Confusion matrix for TM Block 2 Spectral Classification (ver. 4) x training signature data (5/19/00):

Reference / Training Data

Dense Open Spruce Forest (white-like)

Open (Tall) Spruce Forest (black or

white)

Stunted Spruce

Aspen / Birch / Mixed Deciduous

Balsam Poplar

Mixed Spruce-Broadleaf

Alder Shrub

Willow Shrub

Closed Low Birch Shrub

Low Shrub Bir-Eric-Willow

Open Low Shrub-Sedge

Open Low Shrub (bog-like)

Mixed Dwarf Shrub

Mixed Dwarf Shrub-Rock

Dry / Mesic Herbaceous

Wet Herbaceous

Aquatic

Sparse Vegetation

Bare

Snow / Ice

Shadow

Silty Water

Clear Water

Human Development

Burn

Theme 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 2

1 145 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

2 3 776 5 0 0 4 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 

3 0 2 1973 0 0 3 0 0 0 13 30 0 0 0 1 1 0 0 0 0 0 0 0 0 0 

4 0 0 0 1917 6 15 5 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

5 0 0 0 22 28 0 3 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

6 0 0 2 9 0 233 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

7 0 0 0 30 1 0 87 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

8 0 0 0 29 2 0 4 24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

10 0 0 5 2 0 0 0 0 0 878 29 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

11 0 0 54 0 0 0 0 0 0 142 637 0 0 0 1 0 0 0 0 0 0 0 0 0 0 

12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

13 0 0 0 0 0 0 0 0 0 1 2 0 12 0 0 0 0 0 0 0 0 0 0 0 0 

14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 35 0 0 0 0 0 0 0 0 0 0 

16 0 7 13 0 0 1 0 0 0 0 0 0 0 0 0 209 0 0 0 0 0 0 0 0 0 

17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 23 0 0 0 0 0 0 0 

19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 34 0 0 0 0 0 0 

20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 352 0 0 0 0 

22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 562 0 0 0 

23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2592 0 0 24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8

Col. Tota 148 791 2052 2009 37 256 99 35 0 1035 698 0 12 0 38 211 0 23 34 0 352 562 2592 0 0 8

Prod. % 98.0 98.1 96.2 95.4 75.7 91.0 87.9 68.6 NA 84.8 91.3 NA 100.0 NA 92.1 99.1 NA 100.0 100.0 NA 100.0 100.0 100.0 NA NA 9

Theme 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 2

 Note: There were 26 proposed endpoint mapping classes at the completion of Block 2. These were later refined to the 25 mapping classes detailed in this report.

Image Classification
134
 

 

 Confusion matrix for TM Block 3 Spectral Classification (ver. 3) x training signature data (6/12/00):

Dense Open Spruce Forest (whitelike) 

Open (Tall) Spruce Forest (black or 

white) 

Stunted Spruce 

Aspen / Birch / Mixed Deciduous 

Balsam Poplar 

Mixed Spruce-Broadleaf 

Alder Shrub

Willow Shrub

Closed Low Birch Shrub 

Low Shrub Bir-Eric-Willow 

Open Low Shrub-Sedge 

Open Low Shrub (bog-like) 

Mixed Dwarf Shrub 

Mixed Dwarf Shrub-Rock 

Dry / Mesic Herbaceous 

Wet Herbaceous 

Aquatic 

Sparse Vegetation 

Bare 

Snow / Ice 

Shadow 

Silty Water

Clear Water

Human Development 

Burn 

Cloud 

Theme 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 Row 

1 170 39 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

2 6 240 0 0 0 6 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

3 0 0 42 0 2 0 0 0 0 0 2 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 

4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

5 0 0 0 0 581 2271 33 21 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 

6 5 1 0 0 66 2266 21 3 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

7 0 0 0 0 19 60 5496 64 0 0 4 0 3 0 42 0 0 0 0 0 0 0 0 0 0 0 

8 0 0 0 0 2 19 100 492 0 0 14 0 4 0 49 1 0 0 0 0 0 0 0 0 0 0 

9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

10 0 0 0 0 0 0 1 0 0 352 7 0 7 0 7 6 0 0 0 0 0 0 0 0 0 0 

11 0 0 3 0 0 0 6 12 0 5 292 25 13 0 16 11 0 0 0 0 0 0 0 0 0 0 

12 1 3 0 0 5 38 0 1 0 0 25 245 0 0 6 4 0 0 0 0 0 0 0 0 0 0 

13 0 0 0 0 0 0 15 1 0 1 1 0 608 0 38 0 0 2 1 0 0 0 0 0 0 0 

14 0 0 0 0 0 0 0 0 0 0 0 0 2 42 0 0 0 3 0 0 0 0 0 0 0 0 

15 0 0 0 0 4 2 44 5 0 3 5 3 17 0 427 0 0 0 0 0 0 0 0 0 0 0 

16 0 0 3 0 1 0 0 0 0 7 5 2 0 0 0 105 0 0 0 0 0 0 0 0 0 0 

17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

18 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 1 0 218 0 0 0 0 0 0 0 0 

19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 374 0 0 0 0 0 0 2 

20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 433 0 0 0 0 0 0 

21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 785 0 0 0 0 0 

22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 314 0 0 0 0 

23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1199 0 0 0 

24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5021 

Col. Tota 182 283 48 0 680 4666 5716 599 0 368 355 283 654 45 587 136 0 223 375 433 785 314 1199 0 0 5023 

Prod. % 93.4 84.8 87.5 na 85.4 48.6 96.2 82.1 na 95.7 82.3 86.6 93.0 93.3 72.7 77.2 na 97.8 99.7 100.0 100.0 100.0 100.0 na na 100.0 

Theme 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 

 Note: 

There were 26 mapping classes proposed at the completion of TM Block3, this was later refined to the 25 mapping classes defined in this report. 

Image Classification 
136
 Confusion matrix for TM Block 4 Spectral Classification (ver. 4)x training signature data (6/07/00):

ens p

pr or wh ke

e all uc res

ac it

tunt e

p ir ix ec ous 

Balsam Poplar

ixed Spruce-Broadleaf 

lder Shrub 

Willow Shrub 

losed Low Birch Shrub 

Low Shrub Bir-Eric-Willow 

Open Low Shrub-Sedge 

Open Low Shrub (bog-like) 

Mixed Dwarf Shrub 

Mixed Dwarf Shrub-Rock 

ry / Mesic Herbaceous 

Wet Herbaceous

Aquatic 

Sparse Vegetation 

Bare 

Snow / Ice 

Shadow 

Silty Water 

Clear Water 

Human Development 

Burn 

Cloud 

Theme 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 Ro

1 594 90 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 

2 42 2307 166 0 0 0 0 0 0 5 0 5 0 0 2 1 0 0 0 0 0 0 0 0 0 0 

3 0 244 1955 0 0 3 0 4 0 18 2 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 

4 0 0 0 69 31 6 5 6 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 

5 0 0 0 9 466 30 7 12 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 

6 10 6 1 2 29 447 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 

7 0 0 0 6 8 4 1973 10 0 32 2 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 

8 0 0 2 5 8 0 26 445 4 48 8 0 49 0 20 2 0 0 0 0 0 0 0 0 0 0 

9 0 0 0 0 0 0 0 3 194 44 4 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 

10 0 10 34 0 1 0 20 9 11 1240 85 0 13 0 21 3 0 0 0 0 0 0 0 0 0 0 

11 0 0 0 0 7 0 1 1 10 118 796 0 6 0 20 0 0 0 0 0 0 0 0 0 0 0 

12 0 12 15 0 0 0 0 0 0 1 0 387 0 1 1 1 0 0 0 0 0 0 0 0 0 0 

13 0 0 0 0 1 0 0 28 10 20 18 0 673 0 29 0 0 0 0 0 0 0 0 0 0 0 

14 0 0 0 0 0 0 0 0 0 0 0 0 2 107 0 0 0 1 0 0 0 0 0 0 0 0 

15 0 9 3 1 0 0 14 23 0 59 28 6 16 0 689 0 0 0 0 0 0 0 0 0 0 0 

16 1 4 16 0 4 7 0 4 0 8 0 1 0 0 0 195 0 0 0 0 0 0 0 0 4 0 

17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 43 0 0 0 10 0 0 0 0 0 

18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 85 1 0 0 0 0 0 0 0 

19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 458 0 0 23 0 0 0 0 

20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 584 0 0 0 0 0 0 

21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1997 0 0 0 0 0 

22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 770 0 0 0 0 

23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5192 0 0 0 

24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 79 0 

26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17417 

Col. Tota 647 2682 2192 92 555 499 2046 545 229 1593 943 399 765 108 791 215 43 86 463 584 2017 793 5192 0 83 17417 

Prod. % 91.8 86.0 89.2 75.0 84.0 89.6 96.4 81.7 84.7 77.8 84.4 97.0 88.0 99.1 87.1 90.7 100.0 98.8 98.9 100.0 99.0 97.1 100.0 NA 95.2 100.0 40

Theme 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 

D

S

M

A

C

D

ed Spruc

As en / B ch / M ed D idu

e O en S uce F

est ( ite-li ) 

Op n (T ) Spr e Fo t (bl k or wh e) 

Note: There were 26 proposed endpoint mapping classes at the completion of Block 4. 

These were later refined to the 25 mapping classes detailed in this report. 

Image Classification 
138
 Confusion matrix for TM Block 5 Spectral Classification (ver. 1) x training signature data (6/08/00):

Dense Open

Spruce

Forest (whitelike) 

Open (Tall) Spruce

Forest (black or 

white) 

Stunted Spruce Aspen /B irch / Mixed Deciduous Balsam Poplar Mixed Spruce-Broadleaf Alder Shrub Willow Shrub Closed Low Birch Shrub Low Shrub Bir-Eric-Willow Open Low Shrub-Sedge Open Low Shrub (bog-like) Mixed Dwarf Shrub Mixed Dwarf Shrub-Rock Dry / Mesic Herbaceous Wet Herbaceous Aquatic Sparse Vegetation Bare Snow / Ice Shadow Silty Water Clear Water Human Development Burn Cloud 

Theme 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 

1 246 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

2 3 261 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

3 0 2 68 0 1 0 0 0 0 0 0 13 0 0 0 1 0 0 0 0 0 0 0 0 0 0 

4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

5 0 0 1 0 211 54 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

6 1 2 0 0 14 237 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

7 0 0 0 0 0 15 269 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

8 0 0 0 0 0 0 0 147 0 4 0 0 1 0 13 0 0 0 0 0 0 0 0 0 0 0 

9 0 0 0 0 0 0 0 0 119 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

10 0 1 0 0 0 0 0 1 6 582 12 0 7 0 1 0 0 0 0 0 0 0 0 0 0 0 

11 0 0 0 0 0 0 0 0 0 19 133 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 

12 0 1 12 0 0 0 0 0 0 1 0 254 0 0 0 2 0 0 0 0 0 0 0 0 0 0 

13 0 0 0 0 0 0 0 2 0 19 7 0 194 0 20 0 0 0 0 0 0 0 0 0 0 0 

14 0 0 0 0 0 0 0 0 0 0 0 0 0 37 0 0 0 0 0 0 0 0 0 0 0 0 

15 0 0 0 0 0 0 0 5 0 3 0 0 4 0 236 0 0 0 0 0 0 0 0 0 0 0 

16 0 0 2 0 0 0 0 0 0 0 0 5 0 0 0 39 0 0 0 0 0 0 0 0 0 0 

17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0 0 0 0 

19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0 

20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1558 0 0 0 0 0 0 

21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 964 0 361 0 0 0 

22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 513 0 0 0 0 

23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 632 0 1011 0 0 0 

24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 35413 

Col. Tota 250 267 84 0 226 307 272 155 125 633 152 273 210 37 270 42 0 113 23 1558 1596 513 1372 0 0 35413 

Prod. % 98.4 97.8 81.0 NA 93.4 77.2 98.9 94.8 95.2 91.9 87.5 93.0 92.4 100.0 87.4 92.9 NA 100.0 100.0 100.0 60.4 100.0 73.7 NA NA 100.0 

Theme 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 

 Note: There were 26 proposed endpoint mapping classes at the completion of Block 5. 

These were later refined to the 25 mapping classes detailed in this report. 

Image Classification 
140
 

 Confusion matrix for Southside SPOT Spectral Classification (ver. 3) x training signature data (6/08/00):

Dense Open

Spruce

Forest (whitelike) 

Open (Tall) Spruce

Forest (black or 

white) 

Stunted Spruce Aspen /B irch / Mixed Deciduous Balsam Poplar Mixed Spruce-Broadleaf Alder Shrub Willow Shrub Closed Low Birch Shrub Low Shrub Bir-Eric-Willow Open Low Shrub-Sedge Open Low Shrub (bog-like) Mixed Dwarf Shrub Mixed Dwarf Shrub-Rock Dry / Mesic Herbaceous Wet Herbaceous Aquatic Sparse Vegetation Bare Snow / Ice Shadow Silty Water Clear Water Human Development Burn Cloud 

Theme 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 R

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 574 0 0 1 4 0 0 0 2 0 3 0 0 0 10 0 0 0 0 0 0 0 0 0 0 3 0 0 176 0 37 0 0 0 0 0 3 9 0 0 0 21 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 1 9 0 3717 95 35 0 13 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 6 0 10 0 0 55 803 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 93 0 2722 0 0 0 5 0 1 0 3 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 203 0 0 3 0 9 0 22 0 0 2 0 0 0 0 0 0 0 0 9 0 0 0 0 46 0 7 5 346 0 28 0 1 0 28 0 0 0 0 0 0 0 0 0 0 0 

10 0 0 0 0 0 0 0 0 0 952 12 0 10 0 1 1 0 0 0 0 0 0 0 0 0 0 11 0 0 7 0 7 0 37 1 13 0 688 0 14 0 25 2 0 0 0 0 0 0 0 0 0 0 12 0 7 18 0 0 2 1 0 0 8 0 317 0 0 0 31 0 0 0 0 0 0 0 0 0 0 13 0 0 0 0 0 0 3 34 6 6 64 0 1767 4 52 0 0 14 0 0 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 0 0 48 90 0 0 0 0 0 0 0 0 0 0 0 0 15 0 0 0 0 0 0 4 55 6 2 29 0 79 0 612 0 0 24 0 0 0 0 0 0 0 0 16 0 42 14 0 22 0 0 0 0 3 1 8 0 0 0 189 0 0 0 0 0 0 0 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 18 0 1 0 0 0 0 0 0 0 0 0 0 9 0 9 0 0 711 27 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 20 2676 0 0 0 0 0 0 0 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1783 0 0 0 0 0 0 21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7843 0 0 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2100 0 0 0 24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 35413 

Col. Tota 0 635 224 0 3978 904 2809 298 384 973 833 337 1941 94 752 259 0 771 2703 1783 0 7843 2100 0 0 35413 

Prod. % NA 90.4 78.6 NA 93.4 88.8 96.9 68.1 90.1 97.8 82.6 94.1 91.0 95.7 81.4 73.0 NA 92.2 99.0 100.0 NA 100.0 100.0 NA NA 100.0 

Theme 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 

 Note: There were 26 proposed endpoint mapping classes at the completion of the Southside SPOT block. 

These were later refined to the 25 mapping classes detailed in this report. 

Image Classification 
142
e

s

e s r 

ar re t a a l le

 Confusion matrix for proposed endpoint (32 class) SPOT96 (North)Spectral Classification x training signature data (3/2/00):

Dense Open

Spruce

Forest (white-like) 

Open (Tall) Spruce

Forest (black or white) 

St

nted Spru

e (bl

ck) 

C C C C C C C C C O Open

Low Shrub Birch-Eric-Willow 

Open

Low Shrub-Tussock 

Open

Low Shrub Bog 

Dr

as

Dwarf Shrub 

M M M D W A SpB Snow / Ice Shadow Si yC 

ix

-

ry / Me

ic

H

rb 

o

a

l

sed / Open

Mixed Spr

ce-B oadl

af 

o

h

-

pen

Low Willow Shrub 

o l o

l

l

sed Low S

rub Birch

Eric-Willow 

o l sed / Open Tall Willow ix / bg ed Dwarf Shrub Lichen o u r

e

l

sed / Open Tall Alder o e

/

ir

l

sed / Open

Bals

m

Poplar 

osed Low Willow l sed Low A der-Willow y ed Dwarf Shrub Shru

Sed

e 

o

h

-

l

sed Low S

rub Birch

Eric 

ix

-

ed Dwarf Shrub Rock u c a l sed / Open Asp n and or

B

ch 

et Herb quatic 

Theme 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 

1 309 177 3 0 0 104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 

2 44 4031 895 0 0 1565 1 3 1 0 0 7 0 0 0 0 0 2 1 0 0 7 0 0 0 0 1 0 0 

3 0 447 6051 4 18 229 15 7 3 5 0 340 19 221 137 0 0 0 1 0 0 1 0 0 0 0 0 0 0 

4 0 0 25 313 1 8 44 0 0 2 0 18 0 0 2 0 3 39 7 0 2 0 0 0 0 0 0 0 0 

5 0 0 49 4 1144 42 4 0 2 6 0 34 12 0 0 0 8 29 2 2 0 2 0 0 0 0 0 0 0 

6 32 376 138 21 25 2741 27 0 0 0 0 14 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 

7 0 3 13 15 13 54 1605 0 0 173 0 0 60 0 3 0 0 0 0 0 6 0 0 0 0 0 0 0 0 

8 0 10 23 0 1 0 7 263 15 0 0 599 0 57 34 0 4 33 6 0 58 3 0 0 0 0 0 0 0 

9 0 1 26 0 0 0 0 0 279 0 23 103 0 67 25 0 1 23 0 2 84 0 0 0 0 0 0 0 0 

10 0 0 3 0 1 3 55 0 0 369 0 2 4 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

11 0 0 0 0 2 0 0 0 43 0 988 68 3 455 4 0 42 3 0 0 1 93 0 0 0 0 0 0 0 

12 0 0 18 0 0 2 0 14 2 6 95 2054 3 878 17 0 5 5 7 0 44 28 0 0 0 0 0 0 0 

13 0 0 13 0 13 2 98 0 0 16 2 7 405 502 1 0 26 11 0 0 0 54 0 0 0 0 0 0 0 

14 0 0 0 0 0 0 0 6 2 0 77 216 2 2227 6 0 0 1 0 0 31 47 0 0 0 0 0 0 0 

15 0 0 102 0 0 0 16 6 66 5 2 2174 13 239 2258 0 0 7 0 0 60 0 0 0 0 0 0 0 0 

16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

17 0 0 0 1 24 1 0 4 3 0 9 0 13 0 0 0 272 21 4 0 1 0 0 0 0 0 0 0 0 

18 0 3 0 31 79 5 1 37 68 0 1 46 10 6 20 0 75 596 9 45 60 4 0 5 0 0 0 0 0 

19 0 0 0 1 2 0 0 5 4 0 0 111 0 0 0 0 3 11 75 0 26 0 0 0 0 0 0 0 0 

20 0 0 0 0 0 0 0 2 0 0 20 0 0 0 0 0 21 40 0 163 0 0 0 2 0 0 0 0 0 

21 0 0 0 5 0 2 36 10 41 6 20 181 5 384 71 0 1 24 8 0 686 1 0 0 0 0 0 0 0 

22 0 7 3 0 24 3 0 5 0 0 7 19 34 593 0 0 1 8 0 0 17 114 0 3 0 0 0 0 0 

23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 97 0 0 0 0 0 0 

24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 4 0 6 0 0 0 354 3 0 0 0 0 

25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1253 0 1 2 209 

26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3078 0 0 0 

27 2 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 647 0 151 

28 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1629 0 

29 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 763 

30 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

31 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

32 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

Col. Tota 387 5058 7362 395 1347 4761 1909 362 529 588 1244 5993 583 5632 2578 0 469 857 125 218 1076 354 97 364 1257 3078 650 1631 1123 

Prod. % 79.8 79.7 82.2 79.2 84.9 57.6 84.1 72.7 52.7 62.8 79.4 34.3 69.5 39.5 87.6 NA 58.0 69.5 60.0 74.8 63.8 32.2 N/A 97.3 99.7 100.0 99.5 99.9 67.9 N

Theme 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 

Image Classification 

Note: There were 32 proposed endpoint mapping classes at the completion of the Northside SPOT block. These were later refined to the 25 mapping classes detailed in this report

 This dataset was not used for the base classification, and thus did not undergo multiple signature review evaluations and r

efinements. 
144
 

Appendix 5.6 Modeling Reports for Landsat TM Blocks 1-5 

Following the implementation of the appropriate models for each Image Processing Block, a report 

documenting the results and any remaining classification (confusion) issues was generated and submitted 

(along with the resulting digital model files) to NPS for review. 

Modeling Report for Processing Block 1 - May 19, 2000 

Upon finalizing the supervised Maximum Likelihood (ML) spectral classification for Landsat TM 

Processing Block 1, ten sequential modeling steps were implemented to improve landcover 

characterization. Modeling specifics are described in each of the steps below. Initial modeling parameters

were established based on interactive review of the spectral classification with each of the modeling data 

layers and the Landsat TM and SPOT image data. Field data and other available vegetation data were also 

referenced along with the image and modeling data in establishing and refining these parameters. After 

each model run, the results were reviewed and compared with the TM and SPOT imagery, field data, and 

other available data for the project area. Based on these reviews, parameters were refined and the models 

re-run until the model outputs were determined to best represent the particular landcover class(es) being 

modeled. Model parameters can be found in the ERDAS Imagine model files (*.gmd), as well as the 

discussion of each model presented below. The order in which the models are applied (as detailed in the 

steps below) is significant as several of the models operate on the output of a previous model. 

Phase I. Development of the Spectral/Environmental Data Layers 

Shaded Relief (p7088_shd_them.img) – This layer was generated from the ITM DEM (Albers), and the 

sensor-to-surface orientation parameters sun elevation and solar azimuth for the Landsat TM image 

acquisition date and path/row. The file was recoded to an unsigned 8-bit file for modeling purposes. 

TNDVI (p7088_tndvi_them.img) – This layer is essentially an NDVI layer that has been rescaled to an 8-

bit unsigned file ranging from 0-255 for modeling purposes (as opposed to a more cumbersome floating

point –1 to +1 range). 

Slope (p7088_slp_5low.img) – “Raw” slope as calculated from the ITM DEM included too many contour

line relicts to be effectively integrated in modeling. Therefore, a smoothed slope layer was generated by a 

5x5 low pass filtering of the DEM prior to creation of the slope layer. These slope values should therefore 

be considered as relative values, rather than absolute percentages. 

Isodata (cluster) 5-band TM data (p7088_iso_cls.img) – This layer was developed through a strict 

parameter ISODATA clustering process to generate a set of approximately 240 spectral signatures, which 

were then used as input into a supervised ML classification of the 5-band Landsat TM data (Bands 

2,3,4,5,7). The resulting single band, 8-bit thematic image is a compact and interpretable representation of 

the spectral variability found in the 5-band TM image.

ML classification (p7088_mlcls_26_sub.img) – This is the 8-bit unsigned thematic image file of the final 

ML classification iteration to be used as the starting point for modeling and future processing. 

Band 5 TM data (p7088_bnd5_them.img) – This is the 8-bit unsigned thematic image file for Landsat 

TM Band 5 (middle IR). This layer is to be used for limited modeling during the manual editing process to 

improve definition of the water and open low shrub (bog-like) classes. 

145
Phase II. Develop models to correct problems in the supervised ML classification 

1. Objective: Correct striping patterns in the supervised ML spectral classifications. 

Rationale/Assumptions: TM Bands 4, 5, and 7 were determined to be free of striping in the source 

data. The supervised ML classification process was therefore repeated using only these three bands 

of data. The output from this ML spectral classification (effectively “destriped”) was then 

interactively reviewed with the field data, SPOT and TM image data, the output from the 5-band 

supervised ML classification (“striped”), and NPS comments from review of the 5-band ML 

classification, to identify the ideal combination of classes from each of the two ML classification 

outputs. Although the striping was eliminated in the Bands 4,5,7 ML classificaition, the mixed 

forest and dry/mesic herbaceous mapping classes were determined to be better represented in the 5-

band (Band 2,3,4,5,7) ML classification. Therefore, these two classes from the 5-band 

classification were burned into the “destriped” ML classification. 

Model: None (supervised ML classification and manual recode) 

Output: p70r16_mlcls_26_destripe_sub.img

 p7016_mlcls_5bnd_class6_15.img

2. Identify snow/ice mapping class clusters in ISODATA data layer, and recode to create ice mask (all 

other mapping classes zeroed out). Although the snow/ice class is well represented in the ML 

classification, the ISODATA derived snow/ice picks up some of the more lightly or partially

covered snow/ice areas. 

Model: None (manual recode) 

Output: p7088_ice_sub.img 

3. Objective: Identify terrain shadow/indeterminate mapping class by modeling with shaded relief 

(illumination). 

Rationale/Assumptions: Interactive review of the shaded relief, and imagery revealed that the 

shadow/indeterminate class was typically found in those areas where shaded relief was less than or

equal to 70. Because of the lower resolution and different source of the shaded relief layer (60m

pixels, DEM) when compared with the Landsat TM source data (28.5m pixels, multispectral 

scanner), some degree of over classification of shadow occurred as the result of this model. 

However, we believe that this parameter (70) represents a good overall estimate of the upper end of 

the complete shadow class for Processing Block 1, and is therefore the most feasible approach to 

consistently characterizing the mapping class throughout Processing Block 1. 

Results: Improved characterization of shadow class. Because of the resolution and source

differences between the illumination and Landsat TM data, some manual edits will still be required. 

Model: p7088_shadow.gmd

Output: p7088_shadow.img 

4. Objective: Correct over classification of the water mapping classes on partially shadowed upslopes 

by modeling with shaded relief and slope. 

Rationale/Assumptions: Because of the “casting” of partial terrain shadow across large areas where 

the water class is likely to occur (such as east-west oriented valleys), shaded relief is not as itself a 

146
 

 

good layer for reducing over classification of water. Water typically occurs only on areas with little 

or no slope. Interactive review of the shaded relief, slope, and imagery revealed that the water class

was not typically found in areas where shaded relief was less than 135 and slope greater than 16. 

Erroneous water areas that fell within these parameters were determined to more appropriately 

represent the bare mapping class. 

Results: Corrected most of the water found on partially shadowed upslopes. There is still some 

speckling of erroneous upslope water that will have to be dealt with during manual editing. 

Model: p7088_upslopeh20_to_bare.gmd 

Output: p7088_upslope_h2o_to_bare.img

5. Objective: Correct over classification of spruce forest, mixed forest, woodland, and wet herbaceous 

areas in partial terrain shadow areas based on shaded relief (illumination). 

Rationale/Assumptions: Very significant misclassification of these classes was found on partially

shadowed N-NW aspects, most obviously in the Kantishna Hills region. Fortunately, a vegetation 

map derived from photointerpretation was available for this area in the NPS GIS Browser. This

“minveg” polygon coverage was reviewed along with the Landsat and SPOT imagery and field data 

to develop a modeling approach to resolving the problem. Other areas such Jenny Creek were also 

reviewed in establishing the modeling approach. From this review, there was found to be a 

consistent relationship between the misclassification of certain mapping classes in partial terrain

shadow, and the correct corresponding general vegetation type as indicated by the “minveg” 

database. “Partial terrain shadow” in this context was found to be those areas with illumination 

values between 70 and 134 (less than 70 was found to be legitimate shadow, while greater than 134 

was generally found to be well characterized by the ML classification). Unfortunately, the 

“minveg” classification is quite general (8 mapping classes), which required that some assumptions

be made based on field experience and limited field data available from the region. For example, 

those partially shadowed areas mapped as open spruce forest in the ML classification were 

consistently mapped as “tall shrub” in the “minveg” classification. Under the Denali landcover 

classification system, these areas could correspond to the alder shrub or willow shrub mapping 

classes. Based on our field data and limited experience in the area (and review of the SPOT and 

Landsat TM imagery), such “tall shrub” areas were modeled into our alder shrub mapping class. 

The shadow effected mixed spruce-broadleaf forest ML class was also found to correspond to a 

“tall shrub” type, and thought to be best represented as the alder shrub mapping class. The shadow 

effected dense open spruce forest ML class was found to correspond to the shadow/indeterminate 

mapping class. Shadow effected stunted spruce/woodland ML class was found to correspond to a 

“short shrub” type, and thought to be best represented by the low shrub birch/ericaceous/willow 

mapping class. Shadow effected wet herbaceous ML areas were found to correspond to the “alpine 

tundra” type, and thought to best represented by the mixed dwarf shrub mapping class. All of the 

above relationships are consistent with the priniciple of reduced surface reflectance of the 

respective landcover type based on reduced incoming solar radiation (illumination). The practical 

effect when reviewing the TM imagery or spectral signatures using a 4,5,3 (RGB) band 

combination is, of course, a “darker” signature. TNDVI was found to be inconclusive in 

improving this model beyond what could be done with just the ML classification and shaded relief. 

TNDVI was found to provide better “vision” through partial terrain shadow; However, the various 

combinations of slope, aspect, vegetation, and percent composition (various ratios of bare 

rock/talus, rock types/reflectane, snow/ice, dwarf shrubs, and other pseudo-veg cover (crustose 

lichen or moss) make the NDVI indicator very difficult to interpret beyond application as a general 

indicator of biomass in relation to exposed barren areas. This application of TNDVI is exploited in 

Step 8. 

147
 

 

6.

7.

8.

9.

Results: Corrected most of the major problems found in partially shadow areas. Spot checking of 

field data throughout the processing block did not reveal any major problems or inconsistencies. 

The assumption as to which mapping class the model assigns the “tall shrub” or “low shrub” types 

may benefit from some further review by NPS staff based on local knowledge of areas within 

Processing Block 1. This local knowledge will need to be incorporated in the manual editing 

process. 

Model: p7088_partial_terrain_shadow_correction.gmd 

Output: p7088_spmixed_wdlnd_wetherb_to_shadow_alder_lowsh.img

Mosaic the output from Steps 1-4 into the ML classification output (p7088_mlcls_26_sub.img). 

This output becomes the input file for Step 6. 

Model: p7088_mosaic_step1_4.mos

Output: p7088_mosaic_step1_4.img

Objective: Additional correction of overclassification of water and wet herbaceous classes on

upslopes. 

Rationale/Assumptions: Additional interactive review of the output of Steps 1-5, slope, imagery,

and field data revealed that the mapping classes clear water and silty water on areas with slope 

values greater than or equal to 12 were most appropriately mapped as the bare class. Wet 

herbaceous areas on these fairly steep slopes appeared to be more appropriately mapped into one of 

the dwarf shrub classes. The decision was made to model these erroneous upslope wet herbaceous 

areas into the mixed dwarf shrub class after review of the TNDVI revealed that this layer could be 

used if necessary to improve separation of the dwarf shrub rock class from the mixed dwarf shrub 

class (the dwarf shrub rock class has a lower TNDVI than the mixed dwarf shrub class). However, 

based on reviewing the final model results (Step 9) with the imagery and field data, it appears that 

the dwarf shrub rock and mixed dwarf shrub classes are represented fairly well, and should require 

no further TNDVI modeling. 

Results: Corrected most of the erroneous upslope wet herbaceous, and the remaining erroneous

upslope water class. Some wet herbaceous can still be found in upper elevation ridgeline and flatter 

areas. These areas will require further evaluation and correction (if determined to be in error) 

during the manual editing process. 

Model: p7088_upslopeh20_wetherb_to_bare_dwfsh.gmd 

Output: p7088_step3_upslopeh20_wetherb_to_bare_dwfsh.img

Mosaic the output from Steps 5-6 into the output from Step 4. This mosaic becomes the input file 

for Step 8. 

Model: p7088_mosaic_step5_6.mos (Mosaic file; Note that paths will need to be edited to your 

local drive space if you want to run the mosaic process) 

Output: p7088_mosaic_step5_6.img

Objective: Correct overclassification of floodplain dwarf shrub rock and upslope bare mapping 

class using TNDVI data layer. 

148
Rationale/Assumptions: Additional interactive review of the output of Step 7, imagery, and field 

data revealed that the dwarf shrub rock class on areas with slope values less than or equal to 12 

(flatter areas predominantly in valleys and floodpalins) and TNDVI values between 124 and 173 

(fairly low “greenness”) was more appropriately mapped as the sparse mapping class. This 

improves separation of the gravel bar sparsely vegetated sites (sparse class) from the upslope/alpine 

dwarf shrub rock class. The TNDVI range of 124-173 appears to be a good range for the sparse 

class in general. In upslope areas with slope values greater than 12, and within this “target” 

TNDVI range for the sparse class, the bare class was believed to more consistently mapped as the 

sparse class as well. 

Model: p7088_lowlanddwfshrock_uplandbare_to_sparse.gmd 

Output: p7088_step4_lowlanddwfshrock_uplandrock_to_sparse.img 

10. Objective: Mosaic the output from Step 8 into the mosaic from Step 7. This mosaic is the final 

output from the modeling process. 

Model: p7088_mosaic_step8_9.mos 

Output: p7088_mosaic_step8_9.img This is the final model output which is identical to the 

p7088_mdl_26.img file previously delivered.. (Mosaic file; Note that paths will need to be edited 

to your local drive space if you want to run the mosaic process) 

Summary of known problems/limitations following modeling for Block 1: 

• Some wet herb still on ridge tops (to be manually edited) 

• Some water still on glacial outwash/moraines where rock or sparse is a more appropriate mapping 

class (to be manual edited) 

• Still some overclassification of wet herbaceous on floodplains/along rivers (to be manual edited). 

• Some striping/banding, which occurs in the source data is still problematic in some areas of this 

processing block. Striping/banding effects are most pronounced in Bands 2 and 3. ESC will 

investigate developing a new model data layer of a spectral classification of Bands 4, 5 and 7 (since 

these are stripe-free). Areas of striping in the original 5-band (bands 2-5, 7) ML spectral 

classification will be compared to the results of this 3-band (bands 4,5,7) ML classification. If 

ESC and NPS are still unable to consistently reconcile (model) misclassifications in the striped 

areas, then these areas will be manually edited prior to delivery of the Draft 1 Map. 

• Willow shrub (perhaps overclassified where mapped outside of drainages, streams, floodplains. 

Can be manual edited, but will need guidance on how to handle/recode on a consistent basis). 

• Band 5 modeling will also be incorporated into the manual editing process to improve spatial detail 

of water classes and the open low shrub (bog-like) class. 

• Based on feedback from NPS, additional spectral processing and ML classification iterations were 

implemented, resulting in improved characterization of the closed low shrub birch class. Modeling

efforts were therefore not focused on addressing this issue. 

• The dense open spruce 

• SPOT data does not appear to add significantly to the quality of the spectral classification or 

modeling, but is an excellent source of reference data during modeling and manual editing for those

areas where the SPOT coverage is available. 

• Burn areas will be handled during manual editing using the SPOT and TM imagery to capture the 

most current burn perimeters. 

149
Modeling was not conducted on Processing Block 2 

Modeling Report for Processing Block 3, Denali Landcover Mapping Project 

June 19, 2000 

Upon finalizing the supervised Maximum Likelihood (ML) spectral classification for Landsat TM 

Processing Block 3, ten sequential modeling steps were implemented to improve landcover 

characterization. Modeling specifics are described in each of the steps below. Initial modeling 

parameters were established based on interactive review of the spectral classification with each of the 

modeling data layers and the Landsat TM and SPOT image data. Field data and other available vegetation 

data were also referenced along with the image and modeling data in establishing and refining these 

parameters. After each model run, the results were reviewed and compared with the TM and SPOT 

imagery, field data, and other available data for the project area. Based on these reviews, parameters were 

refined and the models re-run until the model outputs were determined to best represent the particular 

landcover class(es) being modeled. Model parameters can be found in the ERDAS Imagine model files 

(*.gmd), as well as the discussion of each model presented below. The order in which the models are 

applied (as detailed in the steps below) is significant as several of the models operate on the output of a 

previous model. 

Phase I. Development of the Spectral/Environmental Data Layers 

ShadedRelief (p70r16_shd_ sub_them.img) – This layer was generated from the ITM DEM (Albers), and 

the sensor-to-surface orientation parameters sun elevation and solar azimuth for the Landsat TM image 

acquisition date and path/row. The file was recoded to an unsigned 8-bit file for modeling purposes. 

TNDVI (p70r16_sub_tndvi_them.img) – This layer is essentially an NDVI layer that has been rescaled to 

an 8-bit unsigned file ranging from 0-255 for modeling purposes (as opposed to a more cumbersome

floating point –1 to +1 range). 

Slope (p70r16_slp_5low .img) – “Raw” slope as calculated from the ITM DEM included too many contour

line relicts to be effectively integrated in modeling. Therefore, a smoothed slope layer was generated by a 

5x5 low pass filtering of the DEM prior to creation of the slope layer. These slope values should therefore 

be considered as relative values, rather than absolute percentages. 

Isodata(cluster) 5-band TM data (p70r16_sub_iso_cls.img) – This layer was developed through a strict 

parameter ISODATA clustering process to generate a set of approximately 240 spectral signatures, which 

were then used as input into a supervised ML classification of the 5-band Landsat TM data (Bands 

2,3,4,5,7). The resulting single band, 8-bit thematic image is a compact and interpretable representation of 

the spectral variability found in the 5-band TM image.

ML classification (p7016_mlcls_26_sub.img) – This is the 8-bit unsigned thematic image file of the final 

ML classification iteration to be used as the starting point for modeling and future processing. 

Band 5TM data (p70r16_sub_bnd5_them.img) – This is the 8-bit unsigned thematic image file for 

Landsat TM Band 5 (middle IR). This layer is to be used for limited modeling during the manual editing 

process to improve definition of the water and open low shrub (bog-like) classes. 

150
 

Supervised ML classification of TM Bands 4,5,7 (p7016_mlcls_26_destripe_sub.img) – The supervised 

ML classification was reprocessed using only bands 4, 5, and 7 as these were found to be relatively 

“stripeless” (no banding in the data). 

Phase II. Develop models to correct pro blems in the supervised ML classification 

1. Objective: Correct striping patterns in the supervised ML spectral classifications. 

Rationale/Assumptions: TM Bands 4, 5, and 7 were determined to be free of striping in the source 

data. The supervised ML classification process was therefore repeated using only these three bands 

of data. The output from this ML spectral classification (effectively “destriped”) was then 

interactively reviewed with the field data, SPOT and TM image data, the output from the 5-band 

supervised ML classification (“striped”), and NPS comments from review of the 5-band ML 

classification, to identify the ideal combination of classes from each of the two ML classification 

outputs. Although the striping was eliminated in the Bands 4,5,7 ML classificaition, the mixed 

forest and dry/mesic herbaceous mapping classes were determined to be better represented in the 5-

band (Band 2,3,4,5,7) ML classification. Therefore, these two classes from the 5-band 

classification were burned into the “destriped” ML classification. 

Model: None (supervised ML classification and manual recode) 

Output: p70r16_mlcls_26_destripe_sub.img

 p7016_mlcls_5bnd_class6_15.img

2. Identify snow/ice mapping class clusters in ISODATA data layer, and recode to create ice mask (all 

other mapping classes zeroed out). Although the snow/ice class is well represented in the ML 

classification, the ISODATA derived snow/ice picks up some of the more lightly or partially

covered snow/ice areas. 

Model: None (manual recode) 

Output: p7016_ice_sub.img 

3. Objective: Identify terrain shadow/indeterminate mapping class by modeling with shaded relief 

(illumination). 

Rationale/Assumptions: Interactive review of the shaded relief, and imagery revealed that the 

shadow/indeterminate class was typically found in those areas where shaded relief was less than or 

equal to 80. Because of the lower resolution and different source of the shaded relief layer (60m

pixels, DEM) when compared with the Landsat TM source data (28.5m pixels, multispectral 

scanner), some degree of over classification of shadow occurred as the result of this model. 

However, we believe that this parameter (80) represents a good overall estimate of the upper end of 

the complete shadow class for Processing Block 3, and is therefore the most feasible approach to 

consistently characterizing the mapping class throughout Processing Block 3. Because of the 

“casting” of partial terrain shadow across large areas where the water class is likely to occur (such 

as east-west oriented valleys), shaded relief is not as itself a good layer for reducing over 

classification of water. Water typically occurs only on areas with little or no slope. Interactive 

review of the shaded relief, slope, and imagery revealed that the water class was not typically found 

in areas where shaded relief ranges between 80 and 136, and slope greater than 16. Erroneous 

water areas that fell within these parameters were determined to more appropriately represent the 

shadow mapping class. 

151
4.

5.

6.

7.

8.

9.

Results: Improved characterization of shadow class, and reduced overclassification of water 

classes. Because of the resolution and source differences between the illumination and Landsat TM

data, some manual edits will still be required. Corrected most of the water found on partially

shadowed upslopes. There is still some speckling of erroneous upslope water that will have to be 

dealt with during manual editing. 

Model: p7016_shadow.gmd

Output: p7016_shadow.img 

Mosaic the output from Steps 1-3 into the “destriped” ML classification output 

(p7016_mlcls_26_destripe_sub.img). This output becomes the input file for Step 5. 

Model: p7016_mosaic_step1_3.mos (mosaic file) 

Output: p7016_mosaic_step1_3.img

Objective: Correct additional overclassification of the water mapping classes on upslopes by 

modeling with the slope layer. 

Rationale/Assumptions : Additional upslope areas misclassified as water classes on upslopes, but 

not in terrain shadow effected areas, were reviewed with the TM and SPOT imagery to determine 

the most realistic class reassignment. These areas were found to be best represented by the bare 

class. 

Results: Corrected most of the water found on partially shadowed upslopes. There is still some 

speckling of erroneous upslope water that will have to be dealt with during manual editing. 

Model: p7016_upslope_h20_to_bare.gmd

Output: p7016_upslope_h2o_to_bare.img

Objective: Correct overclassification of dry/mesic herbaceous class on upslopes by modeling with 

TNDVI layer. 

Results: 

Model: p7016_herb_to_lowshsedge.gmd 

Output: p7016_herb_to_lowshsedge.img 

Objective: Correct overclassification of wet herbaceous on upslope areas 

Results: 

Model: p7016_wetherb_to_dwfsh.gmd 

Output: p7016_wetherb_to_dwfsh.img 

Mosaic the output from Steps 5-7 into the output from Step 4 (p7016_mosaic_step1_3.img) This 

output becomes the input file for Step 9.

Model: p7016_mosaic_step5_7.mos (mosaic file) 

Output: p7016_mosaic_step5_7.img

Objective: Correct over classification of mixed forest, spruce forest, woodland, and in partial 

terrain shadow areas based on shaded relief (illumination). 

152
Rationale/Assumptions: Significant overclassification of these classes was found on partially 

shadowed N-NW aspects. A similar phenomemon was observed in Block 1. Image interpretation 

of the Landsat and SPOT data, and review of the field data and samples of aerial photos were used 

to identify what mapping classes these partially shadowed areas actually represent. From this 

review, there appeared to be a fairly consistent relationship between the misclassification of certain 

mapping classes in partial terrain shadow, and the correct corresponding general vegetation type as 

indicated through image interpretation. “Partial terrain shadow” in this context was found to be

those areas with illumination values between 80 and 157 (80 or less was found to be legitimate 

shadow, while greater than 157 was generally found to be well characterized by the ML 

classification). Assumptions similar to those applied in Block 1 were required, supplemented based 

on field experience and limited field data available from these N-NW aspects. However, unlike 

Block 1 where a photo-interpreted database (minveg) was available for verifying assumptions, in 

Block 3 there was no similar database. Mixed forest in partial terrain was found to correspond to 

the alder shrub class, while balsam poplar in these same shadow effected areas appears to be best 

represented by the willow shrub class. Open spruce and dense open spruce in partial shadow was

believed to be best represented by the shadow/indeterminate mapping class. Shadowed stunted 

spruce (woodland) class was determined to correspond best with the low shrub 

birch/ericaceous/willow class. In addition, open low shrub (bog) mapped in partial terrain shadow 

was determined to be more appropriately mapped as the shadow/indeterminate class. All of the 

above relationships are consistent with the principle of reduced surface reflectance of the respective 

landcover type based on reduced incoming solar radiation (illumination). The practical effect when 

reviewing the TM imagery or spectral signatures using a 4,5,3 (RGB) band combination is, of

course, a “darker” signature. TNDVI was found to be inconclusive in improving this model 

beyond what could be done with just the ML classification and shaded relief. TNDVI was found to 

provide better “vision” through partial terrain shadow; However, the various combinations of slope, 

aspect, vegetation, and percent composition (various ratios of bare rock/talus, rock 

types/reflectance, snow/ice, dwarf shrubs, and other pseudo-veg cover (crustose lichen or moss) 

make the NDVI indicator very difficult to interpret beyond application as a general indicator of 

biomass in relation to exposed barren areas. Because of the early date of the TM acquistion for this 

block (late-June), and the fact that most of the alpine areas where this type of TNDVI modeling is 

most effective are still covered with melting snow/ice, TNDVI modeling for the upper 

elevation/alpine areas is not practical. 

Results: Corrected most of the problems found in partially shadow areas. Spot checking of field 

data throughout the processing block did not reveal any major problems or inconsistencies. 

However, particularly since there was not a “minveg-like” database available for model calibration

(as was the case in Block 1), site specific observations and recommendations based on NPS field 

review of these N-NW aspect sites would be very helpful and could be incorporated during manual 

editing. 

Model: p7016_partial_terrain_shadow_correction.gmd 

Output: p7016_sp_mixed_poba_wdlnd_to_ shadow_alder_willow_lowsh.img 

10. Mosaic the output from Step 9 into the output from Step 8 (p7016_mosaic_step8_9.img) This 

output becomes the final output for the Block 3 modeling. 

Model: p7016_mosaic_step8_9.mos (mosaic file) 

Output: p7016_mosaic_step8_9.img

153
Summary of known problems/limitations following modeling for Block 3: 

• Although much of the striping from the Band 2,3,4,5,7 spectral classification was significantly 

minimized by using the Band 4,5, 7 ML classification, some striping was re-introduced by burning 

the mixed spruce-broadleaf class from the Band 2-5, 7 classification into the Band 4,5,7 

classification. Areas of open shrub-sedge occurring in the remaining striped areas will be manually 

edited to mixed spruce, balsam poplar or some other appropriate class as determined by imagery

and field data interpretation. 

• Some misclassified wet herb still occurs on ridge tops and in gently sloping areas (to be manual

edited). These sites are probably more accurately described as mesic herbaceous or mixed dwarf 

shrub. 

• Some water still on glacial outwash/moraines where rock or sparse is a more appropriate mapping 

class (to be manual edited) 

• Still some overclassification of wet herbaceous on floodplains/along rivers (to be manual edited). 

• Willow shrub (perhaps overclassified where mapped outside of drainages, streams, floodplains. 

Can be manual edited, but will need guidance on how to handle/recode on a consistent basis). 

• Band 5 modeling will also be incorporated into the manual editing process to improve spatial detail

of water, open low shrub (bog-like) class, wet herbaceous, and possibly open-low shrub-sedge 

classes. There are a few areas where water is inappropriately mapped as shadow, however, this 

corrections will be most efficiently implemented during this manual editing process. 

• Balsam poplar appears to be overclassified in mixed forest areas. However, after reviewing the 

imagery and numerous field points, we found that these sites represent fairly well the dominant 

broadleaf forest and tall shrub composition, with relatively lower needleaf components (one notable 

site is SIG_ID 829, which is only 10% spruce). We suggest modifying the balsam poplar forest 

class description as appropriate to describe this phenomena, and perhaps changing the name to

allow for this mixture. We can use band 5 and/or NDVI to model more of these broadleaf dominant 

areas into the mixed forest class, but believe that the current balsam poplar class realistically

represents broadleaf (forest and tall shrub) sites, as opposed to a more even mix or spruce 

dominated sites (which are mapped as the mixed forest class). 

• The spectral classification from the SPOT data (September) will be investigated to see if 

improvement can be made in the alpine areas (hilltops/ridges) where the mid-June TM data 

typically is still covered or partially covered by seasonal snow/ice. The SPOT data is an excellent 

source of reference data during modeling and manual editing for those areas where the SPOT 

coverage is available. 

• Burn areas will be handled during manual editing using the SPOT and TM imagery to capture the 

most current burn perimeters. 

Summary of Modeling for Processing Blocks 3 – 5 (8/18/00) 

Addendum to Modeling Report for Processing Block 3, Denali Landcover Mapping Project of 6/19/00 

Integration of SPOT imagery

Due to the early acquisition dates of the landcover base-data Landsat TM imagery for Processing Block 3 

(late-June), lingering snow cover and various stages of snowmelt and vegetation green-up were observed in 

the upper elevation (alpine/ridgelines) in this Landsat TM image. This created confusion in the supervised 

ML spectral classification of the TM data, particularly in those areas of partial snowmelt. As approximately

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50% of Block 3 can be considered upper elevations (>750m), the scale of the problem was fairly significant.

As such, this issue was identified as a known problem in the Block 3 Modeling Report of 06/19/00 (pg 6), 

and potential possible solution proposed was to investigate the SPOT data (acquired early Sept.) for 

improving the classification in the upper elevation TM data areas. 

Interactive review of the field data, SPOT data, TM data, and their respective supervised ML classifications 

confirmed that the SPOT data could be integrated in the upper elevations. First, specific areas in the TM 

imagery, DEM, and slope data were reviewed to determine what values of these layers, if any, correlated to 

the TM image “problem” areas. Elevations above approximately 775 m were found to correspond fairly

well with the snowmelt/snowcover areas of the TM imagery. Slope was typically a redundant factor at 

these elevations, and was therefore not used in the modeling process. Specific areas were then reviewed in 

the SPOT imagery, ML classification, and DEM data to determine what elevations were appropriate for

usage of the SPOT ML classification for improving the Landsat TM-derived primary landcover 

classification. Elevations above 784 m were determined to be an improvement in the SPOT data over the 

TM-derived classification. Elevations above 784 m in the Landsat TM-derived landcover classification 

were then “zeroed-out,” and the SPOT ML classification substituted for these areas. The spectral ML 

classification for the prospective SPOT “fill-in” areas and field data were then reviewed to identify any

major errors that could be handled through modeling. Spruce forest classes and water were found to occur 

in known shadow areas, and were corrected using models based on shaded relief and slope parameters. 

Errors were also found in the SPOT spectral classification in areas mapped as wet herbaceous. These areas

were determined to correspond well with the tall alder shrub class in these upper elevations, and recoded as 

such. After reviewing the SPOT/TM boundary, and making appropriate boundary edits to ensure seamless 

integration of the two data sets, the upper elevation (>784 m) SPOT-derived landcover was burned into the 

primary TM-derived landcover data. 

Known Problems with Block 3 related to SPOT data integration 

The SPOT spectral classification used in the upper elevations introduces some erroneous “sparse 

vegetation” mapping class areas. These areas require manual recoding during final edits, and are actually 

either snow/ice or the shadow mapping class. Some upper elevation water also requires recoding during 

final edits to barren or shadow classes. The SPOT classification also introduces significant 

overclassification of low shrub sedge and dry/mesic herbaceous to the upper elevation areas on the 

Southside. These should be discussed during the draft review. 

Summary of Processing Block 4 Modeling 

The modeling process for Block 4 was similar to that of Block 3 and Block 1. Refer to the detailed Block 3 

Modeling Report (6/19/00) and Block 1 Modeling Report (5/19/00) for specific order of processing and 

steps. 

Prior to Block 3-like processing, an additional series of modeling steps were implemented to improve the 

classification for this early date imagery. First, all the training signatures were re-reviewed and reevaluated to develop “tighter,” more precisely defined signatures, in the event that this may improve the 

separability of several of the classes (in particular the spruce classes and mixed forest classes). The 

supervised ML classification was then re-run, and the results reviewed in detail to refine the cut-lines along 

Processing Block boundaries so as to minimize the landcover differences between the Processing Blocks. 

Illumination was used to improve identification of terrain shadow areas. Erroneous upslope water was 

corrected to shadow or barren using slope and shaded relief models. TNDVI thresholds were used to 

breakout erroneous barren as either mixed dwarf shrub, dwarf shrub rock, or sparse. 

Known Problems with Block 4 

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Although the vast majority of clouds have been removed from the landcover data, some cloud shadows in 

this processing block still require manual edits (currently not all the clouds have their shadows!). Also, the 

early acquisition of this scene (late June) resulted in a similar snowmelt problem in the upper elevations as 

occurred in Block 3. However, much of this processing block was not covered by the 1996 SPOT data, so a

similar approach to integrating SPOT data to improve these upper elevation areas was not feasible (because

of poor radiometric quality of the 1994 SPOT for this area, ’94 SPOT spectral processing was eliminated 

early on in the project, and is beyond the scope of the current Task Order). This problem is fairly easy to 

find in the SE portion of Processing Block 4 along the Block 3 boundary. However, it does accurately

represent the landcover during the late-June Landsat TM snapshot in time. The mapping class descriptions 

for snow/ice, barren and sparse mapping classes could be modified to include partial snowmelt as an 

included landcover type, or in some way this problem should be conveyed to the landcover map user. 

Summary of Processing Block 5 Modeling 

The modeling process for Block 5 was similar to that of Block 3 and Block 1. Refer to the detailed Block 3 

Modeling Report (6/19/00) and Block 1 Modeling Report (5/19/00) for specific order of processing and 

steps. The primary difficulty with the Landsat TM spectral classification for Block 5 is relatively low solar 

angle during the early September acquisition, which results in a significantly larger shadow mapping class 

proportion. Barren and snow/ice classes 1996 SPOT spectral classification were integrated in a similar 

fashion as in Block 3 to improve the Landsat TM-derived classification. 

Known problems with Block 5 

The above-mentioned abundance of the shadow class is a difficult problem to solve. The shadow could be 

modeled to some extent with TNDVI, but modeled to what? We don’t have the field data or detailed PIderived vegetation dataset to test and establish realistic modeling parameters. In GAAR, Sara Wesser 

preferred that in such a situation, we not guess but instead simply maintain these areas as the 

shadow/indeterminate mapping class. Other known problems appear to be an abundance of tall willow 

shrub. These areas should be investigated by NPS and examined during the on-site review to determine 

what the appropriate class(es). 

Summary of Cloud-Fill Processing Blocks 

The general could-fill processing block from the Mosaic Plan (5/1/00) were fine-tuned by detailed 

delineation of all the cloud and cloud-shadow areas within the Cloud-fill Block. These areas were “zeroedout” of the Landsat TM derived draft landcover classification, and the Cloud-fill Block spectral ML 

classifications were used to fill in. Block 1 and Block 3 modeling steps were generally followed based on 

the context (surrounding processing block) of the particular cloud area. Some manual edits of the Cloud-fill 

Block spectral ML classification were required to ensure a realistic and seamless “fit” into the larger draft 

landcover classification. 

Some additional known problems throughout the draft landcover classification and general 

comments: 

The following list of “final edits” could be made to the map following the draft review provided that 

detailed and location specific feedback in the form of annotations on the imagery, AOI’s, or lat/lon’s plus 

editing instructions are obtained from NPS during the review period. 

Lakes/water bodies often include erroneous shadow or spruce, especially along the edges. These areas 

should be corrected in final edits through manual recoding and/or TM Band 5 modeling. 

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Wet herbaceous is overclassified throughout the project area. These areas should be corrected during final 

edits through through manual recoding and/or TM Band 5 modeling. 

Mesic herbaceous appears to be overclassified in the Block 3 and Block 5 areas. If this is determined to be 

the case during the draft review, we will need specific comments/annotations to make implementing these 

changes a possibility.

Tall willow shrub appears to be misclassified in many areas throughout the draft landcover. Recommend 

considering collapsing the tall alder and tall willow shrub, or the tall willow and low shrub birchericaceous-willow into a single class. Which collapsing scheme depends on NPS’s feedback. These types 

are not spectrally separable on a consistent basis, and also do not appear to lend themselves to realistic 

modeling. A more time consuming alternative (for both NPS and ESC) is to have NPS provide annotations 

and detailed instructions on how to handle the tall willow shrub areas on a case-by-case basis. It is fairly 

easy to say leave those tall willow areas that occur in drainages alone (makes sense because much of the 

shrub in drainages is willow). However, how to handle those tall willow areas mapped outside the 

drainages is the real issue. (Note: The issue of whether the tall willow was either over or misclassified was 

raised by ESC at the Draft Review. AKNHP and NPS reviewed its extent and distribution, and felt it 

appeared reasonable). 

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Appendix 5.7 GLOSSARY 

Most of the ecological terms below are from Hamilton (1999c), Gallant et al. (1995), Anderson et al. 

(1998), Viereck et al. (1992) and Gabriel and Talbot (1984).

Albedo—Same as reflectivity. The percentage of incoming radiation that is reflected by a natural surface 

such as a ground, ice, snow, water, clouds, or particulates in the atmosphere. 

Alliance—A physiognomically uniform group of plant associations sharing one or more dominant or 

diagnostic species, which as a rule are found in the uppermost stratum of the vegetation (Grossman et al. 

1998).

Alluvial fans—Erosional-depositional system in which rock and sediment are transported down-valley and 

deposited where it emerges from the confines of the valley into a larger valley or plain. They tend to be fanshaped in plan view; a segment of a cone radiating away from a single point source. 

Alluvial—Characterized by the deposition of sediment by a stream or other running water at any point 

along its course. 

Alpine—The zone on mountain tops between permanent snow and the cold limits of trees. 

Annual—Plant species that complete their life-cycle within a single growing season. 

Aquatic—Refers to sites with vegetation that is submerged, floating, or growing in permanent water. 

Bare-ground—Refers to (1) a landcover class with less than 15 percent vegetation cover, and (2) a soil 

surface devoid of vegetation. 

Bedrock—Refers to exposed rock typically at higher elevations and includes all the jagged rocky ridges, 

peaks, cirque headwalls and cirque basins. It has little or no soil development. 

Biennial—Plant species that complete their life-cycles within two growing seasons. 

Boreal—Northern biogeographical region typically referring to subpolar and cold temperate areas. 

Brackish—Tidal water with a salinity of 0.5-30 parts per thousand.

Broad-leaved—Describes a plant with leaves that have well-defined leaf blades and are relatively wide in 

outline (shape) as opposed to needle-like or linear; leaf area is typically greater than 500 square millimeters 

or 1 square inch. 

Bryophyte—Nonvascular, terrestrial green plant, including mosses, hornworts, and liverworts. 

Bunch grass—Multi-stemmed (Caespitose) life form of grasses characterized by clumps of erect shoots 

that slowly spread horizontally by tillers, generally creating distinct individual plants spaced across the 

ground; often applied to sedges and other graminoids with similar life forms. 

Caespitose (cespitose)—Describes a low branching pattern from near the base that forms a multi-stemmed 

or a bunched appearance. 

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Canopy cover—The percent of the ground in the polygon covered by the gross outline of an individual 

plant's foliage (canopy), or the outline collectively covered by all individuals of a species or life-form

within the polygon (Daubenmire 1959). 

Classification—Process of assigning individual pixels of an image to categories, generally on the basis of 

spectral reflectance characteristics. 

Cliff—Any high, very steep to perpendicular, or overhanging face of a rock outcrop. 

Cold-deciduous—Describes a plant that sheds its leaves as a strategy to avoid seasonal periods of low

temperature, often initiated by photoperiod; applied to vegetation adapted to seasonal cold season 

influences (temperate). 

Colluvium—Unconsolidated surface materials that have been transported downslope and deposited on the 

lower slopes. Colluvium is moved by landslides, flow slides, talus rubble, rock-glaciers, solifluction and 

unconsolidated runoff. 

Creeping—Describes the pattern of stems growing at or just beneath the surface of the ground and usually 

producing roots at nodes. 

Crustose lichen—Lichen life form that grows in intimate contact with its substrate, lacks a lower cortex 

and rhizoids (root-like structures), and is impossible to separate from the substrate without destroying the 

thallus; lichen with an unlobed, flattened thallus, growing adnate to the substrate. 

Cushion plant—A low, woody, plant life form so densely branched that it forms a compact canopy that is 

pad- or bolster-like in appearance; usually with microphyllous foliage; characteristic of alpine and tundra 

plants. 

Deciduous—Describes a woody plant that seasonally loses all of its leaves and becomes temporarily barestemmed. 

Digital image processing—computer manipulation of the digital-number values of an image. 

Digital image—an image where the property being measured has been converted from a continuous range 

of analogue values to a range expressed by a finite number of integers, usually recorded as binary codes 

from 0 to 255, or as one byte. 

Dominant—An organism, group of organisms, or taxon that by its size, abundance, or coverage exerts 

considerable influence upon an association's biotic (such as structure and function) and abiotic (such as 

shade and relative humidity) conditions. 

Ecoregions—Landscape units defined based on similar patterns in potential natural communities, soils, 

hydrologic function, landform, topography, lithology, climate, and natural processes such as nutrient 

cycling, productivity, succession, and natural disturbance regimes associated with flooding, wind, or fire. 

Climate, as modified by topography, is the dominant criterion at the scale mapped for Denali. 

Eolian—Wind blown sand and silt deposits. 

Ephemeral forb vegetation—Annual vegetation that, during favorable periods, dominate areas that are 

usually sparsely vegetated or unvegetated for most of the year. 

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Epiphyte—Vascular plant that grows by germinating and rooting on other plants or other perched 

structures; sometimes called "air plants." 

Evergreen—Describes a plant that has green leaves all year round.

Field verification area—An area sampled during the field season composed of multiple polygons. 

Floodplain—A fluvial plain formed by non-glacial fed rivers. Along a meandering river—meandering 

rivers have one or two main channels—alluvium is deposited on convex curves in the river channel. The 

opposing concave bank is cut, providing sediment for deposition on convex curves downstream and 

creating a series of similar bands of alluvial deposits. The channel thus meanders laterally across the 

floodplain. 

Foliose lichen—Lichen life form that is leafy in appearance and loosely attached to its substrate; lichen 

with a lobed, flattened thallus growing loosely attached to the substrate, the lobes flattened or inflated with 

distinctly differentiated upper and lower surfaces; umbilicate lichens are included. 

Forb—A broad-leaved herbaceous plant. 

Fresh water—Water with a salinity of less than 0.5 parts per thousand. 

Frost boils—These are areas of bare soil which are sufficiently disturbed by frost action to prevent plant 

colonization. On slopes, fine material in unsorted circles moves slowly downslope producing banked or 

"stepped" frost boils (from Gabriel and Talbot. 1984). 

Frost Scar—These are exposed bare mineral soil formed by frost action in the soil. 

Fruticose lichen—Lichen life form that is bunched, shrubby or "hairy" in appearance and loosely attached 

to its substrate; lichen with the thallus branched, the branches solid, or hollow and round, or flattened 

without distinctly differentiated upper and lower surfaces; squamulose lichens are included. 

Geographic information system (GIS)—A data-handling and analysis system based on sets of data 

distributed spatially in two dimensions. The data sets may be map oriented, when they comprise qualitative 

attributes of an area recorded as lines, points, and areas often in vector format, or image oriented, when the 

data are quantitative attributes referring to cells in a rectangular grid usually in raster format. It is also 

known as a geobased or geocoded information system. 

Geomorphic process—Refers to the mechanical transport of organic and inorganic material such as mass 

movement, surface erosion, the transport of material (silt) by water, and biogenic soil movement by root 

throw and animals. 

Glacial drift—the material transported by a glacier and then deposited either directly from the ice or from

the melt water. Numerous types of glacial drift occur including moraines, kettle-kame topography, eskers, 

drumlins, glacial till and outwash. 

Glacial outwash—Fluvial plains formed when glacially fed rivers deposit their sediment in stream

channels and the associated plain. Due to continual channel shifting the sediment is spread across an area

called an outwash plain. Outwash plains typically have braided rivers consisting of multiple, wide, shallow

channels characterized by rapid erosion, deposition and channel shifts. 

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Glacial till—A surface formed by sediment originating directly from glacial ice and typically has no 

discernible sediment stratification. 

GPS–Global Positioning System—The GPS is a worldwide satellite navigation system that is funded and 

supervised by the U.S. Department of Defense. GPS satellites transmit specially coded signals. These 

signals are processed by a GPS receiver that computes extremely accurate measurements, including 3-

dimensional position, velocity, and time on a continuous basis. 

Graminoid—Grasses and grass-like plants, including sedges and rushes. 

Grassland—Vegetation dominated by perennial graminoid plants. 

Ground layer—Applies to the herbaceous layer. 

Growth form—The shape or appearance of a plant; it primarily reflects the influence of growing 

conditions. 

Herbaceous—A vascular plant without significant woody tissue above or at the ground; an annual, 

biennial, or perennial plant lacking significant thickening by secondary woody growth, with perennating 

buds borne at or below the ground surface (hemicryophytes, geophytes, helophytes, and therophytes of 

Raunkier). 

Ice wedge polygons—Refers to patterned ground with polygons typically connected in a polygonal pattern 

that is similar to the pattern produced by mud cracks. They are formed by large masses of ice—called ice 

wedges—that grow in thermal contraction cracks in permafrost. 

Image—pictorial representation of a scene recorded by a remote sensing system. Although image is a 

general term, it is commonly restricted to representations acquired by non-photographic methods. 

Interpretation—The process in which a person extracts information from an image. 

IR color photograph—Color photograph in which the red-imaging layer is sensitive to photographic IR 

wavelengths, the green-imaging layer is sensitive to red light, and the blue-imaging layer is sensitive to 

green light. Also known as camouflage detection photographs and false-color photographs. 

Krummholz—Growth form assumed by tree species at the upper treeline or in the alpine zone; 

characterized by a creeping and multi-stemmed growth pattern due to desiccation and physical damage 

caused by wind and blowing ice crystals near the upper treeline; the same species grows as an erect, singlestemmed tree at lower elevation. 

Lacustrine deposits—derived from materials deposited in lake water and exposed when the water level is 

lowered or the land is raised. 

Landsat (formerly ERTS)—The Landsat program, first known as the Earth Resources Technology 

Satellite (ERTS) Program, is a development of the National Aeronautics and Space Administration (NASA) 

in association with NOAA, USGS, and the Space Imaging. The activities of these combined groups led to 

the concept of dedicated Earth-orbiting satellites, the defining of spectral and spatial requirements for their 

instruments, and the fostering of research to determine the best means of extracting and using information 

from the data. The first satellite, ERTS 1, was launched on July 23, 1972. The second satellite was launched

on January 22, 1975. Concurrently, the name of the satellites and program was changed to emphasize its 

prime area of interest (land resources). The first two satellites were designated as Landsats 1 and 2. Landsat 

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3 was launched on March 5, 1978. Landsat 4 was launched on July 16, 1982. Landsat 5 (launched March 1, 

1984) is currently in service providing selected data to worldwide researchers. 

Lichen—An organism generally recognized as a single plant that consists of a fungus and an alga or 

cyanobacterium living in symbiotic association. 

Life form—The shape or appearance of a plant that mostly reflects inherited or genetic influences. 

Low shrub—Low-growing shrub life form between 0.1 and 1 meter tall. 

Matted—Describes a creeping plant that by reiterative growth has overlapping stems and forms a low, 

dense ground cover. 

Mesic—Sites are moist and tussocks may dominate. Permanent standing water is not present. 

Mixed forest—Describes vegetation in which evergreen and deciduous species each generally contribute 

25-75% to the total canopy cover. 

Montane—Describes the zone in mountainous regions where the influence of altitude (vertical relief) 

results in local climatic regimes that are sufficiently different from those in the adjacent lowlands as to 

cause a complex vertical climate-vegetation-soil zonation; includes vegetation at the base of a mountain 

when it is different from lowland vegetation. 

Mosaic—Composite image or photograph made by piecing together individual images or photographs 

covering adjacent areas. 

Nonvascular plant—A plant without specialized water or fluid conductive tissue (xylem and phloem); 

includes bryophytes, lichens, and algae. 

Patterned ground—Refers to regions with perennially frozen ground that often have their surface 

materials arranged into distinct geometric shapes. The features, collectively known as patterned ground,

include polygons, nets, circles and stripes. 

Pavement—A relatively flat surface of consolidated material, generally exposed bedrock. 

Perennial—Plant species with a life-cycle that characteristically lasts more than two growing seasons and 

persists for several years. 

Pingo—These are ice-filled conical hills or mounds, 20 to 400 m in diameter and 10 to 70 m high. 

Pixel—Contraction of picture element. 

Plant association—The finest level of both the Viereck et al. (1992) and National Vegetation Classification 

System (Grossman et al. 1998) classifications. It is defined as "a plant community type of definite floristic 

composition, uniform habitat conditions, and uniform physiognomy" (Flahault and Schroter 1910). 

Prostrate shrub—Shrub life form growing less than 0.1 meters tall. 

Pulvinate mosses—Mosses growing in cushion-like mats or clumps. 

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Remote sensing—Collection and interpretation of information about an object without being in physical 

contact with the object. 

Resolution—Ability to separate closely spaced objects on an image or photograph. Resolution is 

commonly expressed as the most closely spaced line-pairs per unit distance that can be distinguished. Also 

called spatial resolution. 

Rock Glacier—Tongue-shaped or lobate masses of unsorted, angular frost-rived material with interstitial 

ice. They typically originate in cirques or in high, steep-walled recesses. 

Saltwater—Water with a salinity of greater than 30 parts per thousand. 

Satellite—An object in orbit around a celestial body.

Scene—Area on the ground that is covered by an image or photograph. 

Scree—A sheet of coarse rock debris covering a mountain slope without an adjacent cliff. 

Scrub—Vegetation dominated by shrubs, including thickets. 

Seasonal—Showing periodicity related to the seasons; applied to vegetation exhibiting pronounced 

seasonal periodicity marked by conspicuous physiognomic changes. 

Shrub—A perennial woody species with a life form that is usually less than 4 to 5 meters or 13 to 4.8 

meters in height; typically, plants have several stems arising from or near the ground, but this term includes 

short tuft-tree, bamboo, and woody vine species; length of vine may exceed 5 meters; shrub species growth 

form may be taller than 5 meters or single-stemmed under certain environmental conditions. 

Solifluction (or gelifluction)—The flow of soil in association with frozen ground. During the spring and 

summer thaw, water in the active layer cannot penetrate below the permafrost table. Soils are often 

saturated, and the loss of friction and cohesion causes them to behave like viscous fluids. The soil thus 

slowly ‘flows. ’ The downslope fronts of the solifluction lobes are marked by near-vertical scarps as high as 

2 m.

Sorted circles—Similar to sorted polygons but circular in outline. 

Sorted polygons—Refers to patterned ground where sorting separates the coarse from the fine soil 

particles, and typically produces a surface feature of fine materials bordered by stones in a variety of 

geometric shapes; polygons, circles, nets and stripes. 

Sorted stripes— Refers to patterned ground that consists of linear alignments of soil, vegetation and stones 

on slopes. They are strips of stone separated by broad zones of finer sediment and vegetation. The strips are 

up to several meters in width and 100 m in length. 

Sparsely vegetated—Describes vegetation with low total plant cover (between 15 and 25%) that is 

scattered; areas with high cover of crustose lichen and no other vegetation are included here. 

Spectral reflectance—Reflectance of electromagnetic energy at specified wavelength intervals.

Stomata—Pores or openings for gas exchange that are generally concentrated on leaf surfaces. 

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Subalpine—Upper mountain vegetation immediately below the cold limits of tree and tall shrub growth. 

Supervised classification—Digital-information extraction technique in which the operator provides 

training-site information that the computer uses to assign pixels to categories. 

Tall shrub—Tall-growing shrub life form greater than 1 meter tall.

Talus—A sloping accumulation of coarse rock fragments at the base of a cliff. 

Temperate—Geographically, the region between the polar and tropical regions; climatically, the region is 

moderate with distinct seasons of alternating long, warm summers and short, cold winters. 

Terraces—Floodplains and outwash plains removed from frequent flooding due to down-cutting of the 

river. 

Texture—Frequency of change and arrangement of tones on an image. 

Thematic Data—Thematic data layers in a data set are layers of information that deal with a particular 

theme. These layers are typically related information that logically go together. Examples of thematic data 

would include a data layer whose contents are roads, railways, and river navigation routes. 

Thematic Mapper (TM)—A cross-track scanner deployed on Landsat that records seven bands of data 

from the visible through the thermal IR regions. 

Thermokarst—Refers to the collapse of the soil surface due to the thawing of ice rich permafrost, resulting 

in ponds and hills. 

TM—Thematic mapper. 

Topographic Map—Map that presents the horizontal and vertical positions of the features represented; 

distinguished from a plainimetric map by the addition of relief in measurable form. 

Topography—Configuration (relief) of the land surface; the graphic delineation or portrayal of that 

configuration in map form, as by contour lines; in oceanography the term is applied to a surface such as the 

sea bottom or a surface of given characteristics within the water mass. 

Training area—A sample of the Earth’s surface with known properties; the statistics of the imaged data 

within the area are used to determine decision boundaries in classification. 

Training site—Area of terrain with known properties or characteristics that is used in supervised 

classification. 

Treeline—A zone where the normal growth of trees is limited; cold temperatures often combined with 

drought form the upper or arctic treeline, and drought combined with hot temperatures form lower or arid 

treeline. 

Tundra—The treeless region north of the Arctic Circle (arctic tundra) or above the treeline of high 

mountains (alpine tundra) and on some sub-Antarctic islands; characterized by very low winter 

temperatures, short cool summers, permafrost below a surface layer subject to summer melt, short growing 

season, and low precipitation. 

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Tussock—Graminoid life form consisting of bunch-like tufts, sometimes more than 1 meter or 3 feet tall, in 

which the hard, old, withered leaves are intermingled with the fresh, young, green leaves. 

Type—A general term used in place of "vegetation type" or "alliance". Plant associations are often referred 

to as "types", but the term generally is not applied to alliances, and needs to be corrected in descriptions 

where it’s used. 

Understory—General term that in these descriptions applies to the shrub and herbaceous layers of a 

vegetation type, as well as the tree regeneration layer. We have tried to use "shrub layer" or "herbaceous 

layer" in most cases, but understory is used in some places. "Undergrowth" is the more specific term used 

for shrub and herbaceous layers in forests or woodland vegetation types, and has been little used in these 

descriptions. 

Unsupervised classification—Digital information extraction technique in which the computer assigns 

pixels to categories with no instructions from the operator. 

Vascular plant—Plant with water and fluid conductive tissue (xylem and phloem); includes seed plants, 

ferns, and fern allies. 

Wet—Refers to sites where the dominant vegetation is emergent—not submerged or floating—and semipermanent or standing water is present. 

Woody plant—Plant species life form with woody tissue and buds on that woody tissue near or at the 

ground surface or above; plants with limited to extensive thickening by secondary woody growth and with 

perennating buds; includes phanerophytes and chamaephytes of Raunkier. 

Woody—Containing lignified plant tissue. 

Xeromorphic—Describes plants with morphological and physiological characters that tolerate persistently 

low water availability, such as succulence, specialized leaf surfaces for light reflectance or water retention, 

opportunistic leaf growth, leaf-size reduction with increased thickness and sunken stomata, revolute 

margins, or stem and leaf modification to form thorns or spines.

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The Department of the Interior protects and manages the nation’s natural resources and cultural heritage; provides scientific and

other information about those resources; and honors its special responsibilities to American Indians, Alaska Natives, and

affiliated Island Communities. 

NPS D-387, March 2001
National Park Service 

U.S. Department of the Interior 

Natural Resource Program Center 

1201 Oakridge Drive, Suite 150 

Fort Collins, CO 80525 

www.nature.nps.gov 

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