National Park Service 

U.S. Department of the Interior 

Natural Resource Program Center 

Katmai National Park and Preserve 

Landcover Mapping Project 

Final Report

Natural Resource Technical Report NPS/KATM/NRTR—2003/002 
ON THE COVER 

Subeset of Katmai classification. 

Graphic by Geographic Resource Solutions. 
Katmai National Park and Preserve 

Landcover Mapping Project Final Report

Natural Resource Technical Report NPS/KATM/NRTR—2003/002 

Ken Stumpf 

Geographic Resource Solutions 

1125 16th Street, Suite 213 

Arcata, CA 95521 

June 2003 

U.S. Department of the Interior 

National Park Service 

Natural Resource Program Center 

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

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

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

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

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

and is designed and published in a professional manner. 

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

of scientific studies in the physical, biological, and social sciences for both the advancement of 

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contributors with a forum for displaying comprehensive data that are often deleted from journals 

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addresses natural resource management issues; natural resource inventory and monitoring 

activities; resource assessment reports; scientific literature reviews; and peer reviewed 

proceedings of technical workshops, conferences, or symposia.

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

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endorsement or recommendation for use by the National Park Service. 

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

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

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

sending a request to the address on the back cover. 

Please cite this publication as: 

Stumpf, K. 2003. Katmai National Park and Preserve landcover mapping project final report. 

Natural Resource Technical Report NPS/KATM/NRTR—2003/002. National Park Service, 

Fort Collins, Colorado. 

NPS D-118, June 2003 
Table of Contents

INTRODUCTION...........................................................................................................................................3

PROJECT AREA..........................................................................................................................................4

PROJECT METHODOLOGY........................................................................................................................5

DATA ACQUISITION AND PREPARATION .........................................................................................................5

Image Acquisition and Normalization....................................................................................................5

Development of Ecoregion Areas .......................................................................................................10

Identification of Candidate Image Classification Training Sites..........................................................10

FIELD TRAINING SITE DATA COLLECTION ....................................................................................................19

Field Data Collection Goals.................................................................................................................19

Field Data Collection Operations ........................................................................................................21

Aerial Survey Logistics........................................................................................................................21

Aerial Survey Results..........................................................................................................................23

Supplemental Data Sources ...............................................................................................................24

IMAGE CLASSIFICATION TRAINING SET DEVELOPMENT.................................................................................25

Supervised Training Set Development Strategy.................................................................................25

Supervised Training Site Development Techniques...........................................................................25

Supervised Training Set Development ...............................................................................................26

Supervised Training Site Review and Evaluation ...............................................................................27

Supervised Training Set Development Results ..................................................................................33

Unsupervised Training Set Development ...........................................................................................36

IMAGE CLASSIFICATION..............................................................................................................................37

Supervised Classification....................................................................................................................37

Unsupervised Classification................................................................................................................38

LAND COVER DATABASE DEVELOPMENT.....................................................................................................38

Final Pixel Map Development .............................................................................................................38

Attribute Development.........................................................................................................................40

RESULTS....................................................................................................................................................43

DATA MODELING .......................................................................................................................................44

DATA DISPLAY AND REPRESENTATION........................................................................................................45

LAND COVER TYPE AREA DISTRIBUTION AND SOURCE ................................................................................46

DELIVERABLES ..........................................................................................................................................49

CONCLUSIONS AND DISCUSSION .........................................................................................................50

SCENE REPLACEMENT...............................................................................................................................50

Class Representation and Training Site Availability ...........................................................................50

Positional Accuracy of Results............................................................................................................51

TRAINING DATA COLLECTION EFFORTS ......................................................................................................51

Uncertainty of Training Site Locations ................................................................................................51

Land Cover Data Estimates ................................................................................................................52

Aerial Survey Data Collection .............................................................................................................54

IMAGE PROCESSING ISSUES.......................................................................................................................54

Water and Non-Water Confusion in Mountainous Regions ................................................................54

Water and Isolated Herbaceous Type Pixels......................................................................................55

Spruce/Wet Herbaceous Confusion....................................................................................................55

BIBLIOGRAPHY.........................................................................................................................................56

APPENDICES.............................................................................................................................................57

APPENDIX A: KATMAI MAPPING PROJECT AERIAL SURVEY FORM ................................................................57

APPENDIX B: KATMAI MAPPING PROJECT LAND COVER TYPE CLASSIFICATION ............................................58

APPENDIX C: KATMAI MAPPING PROJECT LAND COVER DENSITY CLASSIFICATION.......................................63

APPENDIX D: TABLE ITEM LISTINGS AND DEFINITIONS.................................................................................64

 2
Introduction 

The Katmai Land Cover Mapping Project was a National Park Service project conducted under Task 

Order #99003 under Contract #1443CX991097005. This Task Order’s project background and 

objectives included: 

“The Inventory and Monitoring (I&M) Program of the National Park Service (NPS) seeks ... to provide reliable and 

consistent scientific information that may be used to assess the status and trends of national park ecosystems. 

Land cover mapping is conducted under the inventory portion of I&M Program with the goal of providing basic 

vegetation information that is useful for making resource management decisions on a Park-wide basis. I&M 

Program mapping is intended to provide data that will conform to NPS standards, be compatible with other I&M 

Program mapping efforts, and provide the information required for the design of monitoring programs within the 

National Park System. Resulting map data and descriptions will provide valuable baseline information about 

vegetation/land cover status and are also expected to provide input to resource assessment efforts, such as 

habitat evaluation and fire fuels modeling. The Alaska Support Field Office (AKSO) recognizes that the size and 

remote nature of Alaska’s National Parks require that land cover mapping efforts are conducted at a smaller scale

with coarser resolution than other non-Alaskan I&M Program land cover mapping efforts within the NPS system.” 

The Katmai National Park and Preserve (KATM) Land Cover Mapping Project was initiated in July, 

2000 in an effort to upgrade and improve vegetation/land cover information for this region. Projects

of this nature covering millions of acres are typically based on satellite imagery and provide 

generalized categorical values and class information. The AKSO also recognizes that the KATM 

Land Cover Mapping Project has to be compatible with similar efforts, but also wishes to determine if 

the KATM mapping effort could possibly form the basis of more detailed mapping efforts of the 

future. Land cover mapping efforts involving the use of traditional remote sensing methodologies 

generally produce static categorical data products that lose all detailed information at the pixel level. 

This loss of information has seriously limited the usefulness and accuracy of previous image 

classification mapping efforts, making it difficult to truly verify existing classification results or to 

derive new map data using reclassification logic or rule-based aggregation processes. As a direct

result, such map products have limited adaptability to diverse resource management needs, which 

may require different, more detailed information than that delivered in a single categorical land cover 

map. 

Seeking a more powerful and versatile product, the AKSO issued a Work Order to Geographic 

Resource Solutions (GRS) to develop a land cover classification map of Katmai National Park and 

Preserve. The project was to benefit from GRS’s land cover mapping techniques, which enable the 

development and maintenance of detailed original land cover data throughout map development 

processes and delivery of a final map data set that retains this highly detailed “bird’s-eye view” level 

of information in the form of a land cover database. Generalized land cover maps may then be 

derived from this detailed land cover database. In addition, alternative more directed detailed maps 

may be derived from the land cover database as needed. 

The KATM Land Cover Mapping Project was undertaken by GRS, in association with the Alaska 

Natural Heritage Program (AKNHP), and with support from the AKSO. GRS directed training site

selection, navigation, and verification components of field data collection efforts, as well as all image 

data acquisition, preparation, training, evaluation, and classification efforts that led to the creation of 

the delivered land cover data sets. The AKNHP provided expertise regarding vegetation 

classification, land cover type description, and plant species identification during field data collection 

efforts, as well as input and feedback regarding post-field class descriptions and classificationrelated issues. AKSO personnel carried out ground vegetation transects concurrent with aerial 

surveys, managed field data collection logistics, and provided project coordination and review.

 3
Project Area 

Katmai National Park and Preserve is approximately 4-million acres in size, spanning the Alaska

Range on the Alaskan Peninsula. KATM encompasses a diversity of vegetation and landforms 

in a landscape that is largely unchanged by human development. The southern and eastern 

portions of the Park border the coastline of Cook Inlet and Shelikof Strait with a climate 

moderated by the ocean. The Alaska Range parallels the coast in this region and includes a 

number of glaciers and peaks above 7000’ elevation, as well as dramatic volcanoes. The 

northern and western portions of KATM are of much more moderate relief, contain many large 

lakes, and experience the colder climate of the Bristol Bay region. 

The project area included all portions of Katmai National Park and Preserve, including a 10-mile 

buffer adjacent to KATM, where possible (Figure 1). Field data collection and verification was 

conducted within the actual KATM boundaries; however, the mapping effort was performed on 

the entire project area. 

Figure 1: Katmai Land Cover Mapping Project Area 

 4
Project Methodology

The methodology GRS used during this mapping project employed many of the basic 

components of typical image processing projects. Satellite imagery of the subject area was 

acquired and prepared for classification; “ground truth” information describing the variety of land 

cover types present in the Katmai National Park & Preserve were acquired; image classification 

training sets representative of the different land cover components were developed and tested; 

supervised and unsupervised classification techniques were applied; and resulting classification 

pixel data sets were merged to form a detailed and accurate pixel map of the project area. 

However, these workflow processes were not applied in what might be considered a traditional 

or textbook sense during this project; the development of highly detailed and accurate pixel 

data, as undertaken in this project, requires a different approach to image processing. In order 

to provide a thorough understanding of the processes used during this project, each component 

of GRS’s land cover mapping methodology will be described. Major phases of this methodology 

include Data Acquisition and Preparation, Field Training Site Data Collection, Image 

Classification Training Set Development, Image Classification, and Land Cover Pixel Map 

Development. 

Data Acquisition and Preparation

The initial phase of this project started in July, 2000 and involved acquiring and preprocessing 

of the imagery prior to the collection of any field data or “ground truth.” This setup phase of the 

project included three significant tasks, which are a) acquisition and normalization of the satellite 

imagery, b) development of ecoregion areas, and c) identification of candidate image 

classification field training sites. 

Completion of these three tasks was integral to the successful development of comprehensive 

and accurate training data sets and subsequent image classification efforts. Completion of 

these tasks greatly enhances field data collection efforts by reducing the total number of sites 

required for the classification effort, as well as the number of sites subsequently rejected as 

unsuitable due to confusion or suspect data. Reduction of field data collection needs is a 

significant concern in a project such as the Katmai Land Cover Mapping Project, due to the 

KATM project area’s limited access, large size, high cost of data acquisition, and short ten-day 

window of field data collection activities. Data preparation is a key factor in eliminating 

potentially invalid, confused, and redundant training sites and developing the comprehensive 

and representative project training data set necessary to develop an accurate and detailed land 

cover map. 

Image Acquisition and Normalization

A total of five Landsat scenes were processed during this project – three LANDSAT TM-5 

scenes and two LANDSAT TM-7 scenes. At the start of the project, three LANDSAT TM-5 

scenes were selected by AKSO and provided to GRS on July 12th, 2000. These three scenes 

were acquired by the same instrument, but at different times. Table 1 lists specific information 

about these three scenes. 

 5
Table 1: LANDSAT imagery available for KATM Land Cover Mapping Project. 

SCENE PATH ROW DATE GMT SUN SUN PIXEL

ELEVATION AZIMUTH SIZE(m)

TM7219 72 19 08/30/91 2029 38.09 152.48 30.0 

TM7119 71 19 09/03/95 2029 35.31 146.83 30.0 

TM7018 70 18 08/21/87 2047 41.04 151.81 30.0 

TM7019 70 19 08/16/00 2112 43.21 157.58 30.0

TM7020 70 20 08/16/00 2112 44.33 156.10 30.0

These three initial scenes were delivered to GRS in ERDAS IMAGINE format, and contained all 

seven spectral bands (1, 2, 3, 4, 5, 6, and 7). Each original image was translated into an 

Intergraph/MGE-compatible TIFF format bands for review and evaluation of actual coverage 

and quality. No digital elevation data were delivered with these three initial scenes. 

Evaluation of the supplied imagery confirmed the need to utilize all three images in order to 

cover the entire KATM project area. In addition, review indicated the need to generate a cloud 

cover mask for scene TM7119, as significant portions of the image were effected by the 

presence of cloud cover. Fortunately, TM7219 had no such problem and full coverage was 

provided by the combination of these three scenes. Evaluation also confirmed the presence of 

striping in portions of scenes TM7219 and TM7018. Striping was particularly conspicuous over 

open water surfaces, but also appeared to extend somewhat into small land areas. De-striping 

of the scenes was not attempted, since it required scenes in their raw uncorrected format. As 

these were the only scenes that provided full coverage of KATM project area, the apparent 

striping was recognized as an artifact of the imagery that might cause classification problems. 

 

An additional concern with respect to these three scenes was the differences caused by

senescence of vegetation, particularly in scenes 7018 and 7119. Due to these concerns, GRS

understood that these scenes might be replaced by the AKSO with more current imagery (in the 

latter stages of the project), should the AKSO and GRS determine acquisition of this imagery 

would be beneficial to the Project. Replacement of some of the original imagery did in fact 

occur, as scene TM7018 and most of TM7119 were replaced by two scenes of LANDSAT TM-7 

imagery acquired during August, 2000. The cloud-free coverage of nearly half the KATM 

project area, more recent acquisition date, higher radiometric quality, and replacement of the 

earliest and latest acquired scenes were strong arguments for replacing the TM7018 and 

TM7119 scenes in this project. While initial efforts were applied to the three original scenes, the 

additional 2000 scenes were also processed using the same methodology to consistently 

preprocess the imagery prior to developing training data and classification information from this 

new imagery. Information describing the acquisition specifics of these two “replacement” 

scenes is also shown in Table 1. All project images and data layers/sets developed throughout 

the project were created in or re-projected to match the original imagery projection parameters 

shown in Table 2. 

After reviewing all imagery and extracting image-specific information regarding sun elevation 

and azimuth angle, GRS proceeded to process the imagery in preparation for training site 

development and classification applications. This preparation involves the normalization of 

the images for differential illumination due to topographic influences of slope and aspect. 

 6
Table 2: Projection Parameters of the Katmai Land Cover Mapping Project. 

Parameter Value 

Projection Albers 

Units Meters 

Spheroid Clarke1866 

X shift 0.0000000 

Y shift 0.0000000 

First Standard Parallel 55 00 0.000 

Second Standard Parallel 65 00 0.000 

Central Meridian -154 0 0.000

Central Parallel 50 00 0.000 

False Easting 0.00000 

False Northing 0.00000 

Integral to the GRS workflow is the use of imagery that has been normalized with respect to 

differential illumination resulting from the influences of slope and aspect. During past projects 

GRS has evaluated several methods of image normalization and presently uses the Backwards 

Radiance Transformation Correction (BRTC) based on a non-Lambertian assumption and a 

Minnaert constant (Civco 1989) (Colby 1991) to normalize the imagery. This technique uses 

estimates of slope and aspect from a co-registered digital elevation model (DEM), and image 

acquisition sun-angle and azimuth parameters to normalize the images for differential 

illumination caused by terrain relative to the sensor. This correction method minimizes the 

effect of aspect and slope, but maintains the signatures of the different land cover types, 

thereby resulting in more accurate classification information (Geographic Resource Solutions 

1993). In addition to improving the consistency of the classification, GRS has determined that 

normalization often results in the reduction of the number of training sets required to classify a 

given land cover condition. This is a significant factor when undertaking data collection efforts 

in very remote, rugged, and inaccessible areas, such as Katmai National Park & Preserve. 

DEMs used during the image geo-correction process were acquired after delivery of the original 

imagery. The original DEMs used were a series of USGS 15 minute Alaska DEMs that covered 

the entire project area. These were re-projected and re-sampled to 30 m pixel size using 

bilinear convolution to match the KATM Project (AKSO) projection parameters (Table 2) and 

image specifications (Table 1). Individual DEMs were subsequently consolidated into a single 

project area DEM, which was used to generate slope, aspect, and incidence angle data sets 

that corresponded to pixels in the raw imagery (Note: DEM, slope, and aspect map data used in 

this project for this purpose are included on the delivery CD-ROM as ArcInfo grid data sets). 

GRS then used a proprietary process called GRS_illumcor to normalize each band of the 

imagery. Normalization coefficients were based on processing each scene, exclusive of large 

bodies of water, as these have been shown to negatively effect normalization results. 

Normalized imagery were checked relative to the raw imagery to determine overall correctness 

of the process. Cross-tabulation matrices were generated and checked to determine the 

presence/absence of ‘data shadows’ in the normalized bands. These would have indicated 

extreme and inappropriate adjustments during the normalization process that typically result for 

differences in the registration of the DEM data and the satellite imagery. In this project, these 

type data might also result from the resampling (bilinear convolution) of the original DEM data 

that was performed to reproject the imagery to the proper Albers projection – the resampled 

data might appear smoothed and less rugged than DEM data acquired at the same 30 meter 

resolution. These possibly smoothed data might also cause some alignment issues with the 

original satellite imagery. No such artifacts were found in the data during the examination of the 

cross tabulation reports. The normalized imagery were also examined for “hot spots”, which are 

 7
also indicative of areas of misalignment between the DEMs relative to the imagery. “Hot-spots”

are small areas of pixels that have approached the maximum digital value (255) of the 8-bit 

data. They occur most frequently in areas of extreme slope facing away from the sun where 

lighter digital values are present. They result from misalignment of the imagery and the DEMs 

(it is really an illuminated area in the imagery overlaying a dark slope in the DEM data). In some 

cases, the DEM data is simply wrong, leading to the over-correction (brightening) of the imagery 

thereby forming the “hot-spot” (see Figure 2). Imagery and DEMs were processed and results 

were reviewed until the most suitable normalization results were obtained. While some minor 

“hot-spots” were found, no major misalignment problems between the DEM data and imagery 

were identified that would have seriously impacted the use of these data in this project. 

Figure 2: “Hot-spot” in the imagery Figure 3: DEM cliff (edge mismatch) in a lake 

Some elevation data mismatches were observed along the edges of DEM quadrangles after 

merging the individual DEMs into a project-wide DEM. This edge mismatch tended to happen 

along a few DEMs that lacked proper edge matching of elevations (see Figure 3). This 

mismatch was particularly evident in some lakes where the elevations of the lakes appeared to 

have been ‘burned’ into the DEMs, but with different elevations, thereby causing the mismatch 

(an elevation “cliff” in a lake). Misalignments resulted in areas of high slope/aspect and resulted 

in “hot-streaks” or narrow areas of abnormally bright pixels along seams between a very few 

DEM quadrangles. “Hot-streaks” varied between one and two pixels in width, and sometimes 

ran the entire height or width of the quadrangles involved. In some areas the terrain was so 

steep that even normalization could not bring any data out of these areas (terrain shadows). 

These types of DEM artifacts affected extremely small extents of the project area and did not 

affect the development of signatures for valid vegetation cover classes, since no training areas 

were developed in areas with these “hot” artifacts or terrain shadows. Some of these very dark 

pixel areas were subsequently classed as “water” due to the similarity of these areas of dark 

data and “water” signatures. These pixels were subsequently modeled to “terrain shadow” in 

the final pixel classification map. 

Examples of image normalization are shown in Figures 4 and 5. These figures represent two 

subsets of raw and normalized imagery, along with the associated DEMs used while processing

Katmai National Park. Both figures demonstrate how the normalization process removes the 

effects of differential illumination from the imagery. In Figure 4 the effects of normalization can 

be seen along the opposite aspects along both the east/west and north/south oriented ridges. 

The normalized image shows data of similar spectral content on both sides of the opposite 

slopes. 

 8
Figure 4: Illumination Normalization - Raw Image / DEM / Normalized Image

In Figure 5 this can be seen in the left portion of the image along the eastern shore of the lake. 

A significant dark area that appears to be a part of the lake is actually a shadow that has been 

normalized to show that it has spectral data. The overall impact of this process is that there is 

less confusion amongst different land cover types and greater agreement of spectral signatures 

between similar land cover types resident on different slopes and aspects. The end result is 

that fewer training sites are needed to develop the image training sets to classify the spectrum 

of land cover types.

 Figure 5: Illumination Normalization - Raw image / DEM / Normalized image 

 9
Development of Ecoregion Areas

A land cover mapping project of this nature requires the development of training data sets that 

are representative of the entire range of land cover types that are present in the project area. 

Ideally, all of the training sites in a project area would have distinct spectral signatures

corresponding to different land cover characteristics. GRS stratifies the project area prior to 

field data collection efforts in an effort to recognize the diversity and relative magnitude of land 

cover types present in the project area. Unfortunately, stratification of the entire project area 

using an isodata classifier does not often provide for suitable representation of the many land 

cover types of interest. Some types, such as Coniferous Forest, may be clustered into a very 

few isodata classes while other classes such as Water or Barren may be represented in too 

many different isodata classes (size of a cluster is important in this process and small, less 

frequently occurring types may be clustered into a single larger type during the process). The 

resulting stratification may be more generalized for some, often important, land cover types and 

excessive for other types – and therefore may not be a representative grouping of the range of 

potential land cover types of interest that might be used for field data sampling. In order to 

develop additional strata for the limited types, an initial higher-level stratification by major 

ecotype regions is applied to each scene. Isodata classes are developed for each ecotype 

region to increase the number of land cover type strata that may subsequently be sampled and 

used in the land cover classification.

Ecotype boundaries for the KATM project area were provided by the AKSO and made available 

to GRS in digital format for use in this project. These ecotype boundaries represented over 

twenty sub-regions of the KATM project area. After consulting AKSO and AKNHP vegetation 

experts to determine the most appropriate use of these ecotype areas, some geographically 

distinct areas of similar land cover characteristics were generalized into larger ecotypes, 

resulting in eleven consolidated ecotype regions (see Figure 6). These generalized ecoregions 

were used for the initial stratification of the image data set that would be used in subsequent 

training data development efforts. 

Identification of Candidate Image Classification Training Sites

The principal objective of this task was to develop a set of candidate training areas that would 

maximize field data collection efforts during the ten-day period allotted for aerial survey of field 

sites. This would be accomplished by eliminating the collection of data from invalid or 

heterogeneous spectral sites, redundant sites, and sub-minimum size sites while at the same 

time developing a training data set representative of the area being mapped, both in terms of 

the diversity of land cover types present and the geographic distribution of those types. Areas 

suitable for aerial survey would be identified prior to field data collection efforts to maximize 

efforts during the limited time available for field data collection. 

Image Stratification 

The creation of a pool of potential/candidate training sites relies on stratification of the project 

area. The project area is stratified into many different classes, where each class represents a 

grouping of spectrally similar pixels; through stratification, areas of spectral homogeneity that 

represent different land cover characteristics are identified. 

 10
Figure 6: Katmai Eco-Region Boundaries 

The goal of image stratification is to group the project area data into a large number of different 

classes, each representative of a somewhat unique set of land cover features that need to be 

sampled and represented in the final data set. Stratification yields two significant results. One 

result is that the diversity of the sample area is represented in the stratified image. A second 

result is that the frequency or magnitude of each class is estimated by the number of pixels that 

are assigned to each individual class. After stratification the potential diversity of the area and 

the relative significance, in terms of size, of each class may be determined. This information 

enables the development of training data representative of all the diverse land cover data 

present in the area, while not under sampling some classes and over sampling others. 

During stratification, much of the same data is processed that will also be processed during the 

subsequent classification efforts. The data foundation for this effort is the imagery and 

ecoregion area data used to differentiate different portions of the project area. Ecotype regions 

are introduced to form an initial level of stratification, while the classification within the ecotype 

region is a second level of stratification that assures that many strata representative of the 

diversity of land cover types throughout the project area will be developed. 

A large number of classes are developed during the isodata classification processing within 

each area of interest (ecotype region). Between 30-40 training classes per isodata 

classification are typically developed, and sometimes as many as training 50-60 training classes 

 11
are developed in each scene/ecoregion data set. If initial classification efforts result in too few 

or too many classes, the isodata classification statistical parameters are reset and the imagery 

is reclassified to develop the desired number of isodata classes. Too few classes will yield 

sample areas with too much variability, whereas too many classes may result in a very

heterogeneous data set with areas too small to realistically identify and sample in the field. 

Development of unsupervised classification data was completed for each of the three original 

scenes using ZI Imaging’s Image Analyst software in a Bentley Systems’ MicroStation 

environment. GRS used an ISODATA clustering algorithm based on a minimum JeffriesMatusita (J-M) distance of 1.4 and an initial random seeding of 230 classes to generate 

unsupervised classes within the 11 ecotype regions present within each LANDSAT image. For 

each image, original spectral bands 1,2,3,4,5, 7, plus a NDVI band were classified. Each NDVI 

band was the result of the (B4 – B3)/(B4 + B3) ratio and was utilized in lieu of normalized 

imagery. (Note: Normal GRS procedures call for processing normalized imagery. Stratification

works best when processing normalized imagery as normalization will typically result in fewer, 

more distinct classes, rather than the myriad of ‘shades of gray’ present when processing the 

non-normalized imagery. During this project, this was not possible, as there was insufficient 

time to finish the normalization processing prior to the initiation of the field data collection efforts. 

The NDVI band was included in an attempt to enable further differentiation of the imagery that 

might normally be accomplished through use of the normalized imagery. Normalization 

processes were completed and applied during the field data collection efforts in King Salmon, 

Alaska). 

The application of ecotype region masks limited the spectral variability found within any 

particular region and enhanced the identification of separable classes throughout the project 

area. Clustering parameters used in unsupervised classifications ensured high homogeneity of 

resulting classes. Careful review of the J-M distance reports generated for each scene 

confirmed sufficient separability of the unsupervised classes. 

Candidate Site Selection

The end result of the stratification process is a set of isodata class maps that contain pixels that 

have been stratified into the many different spectrally homogeneous classes. This map was 

then processed and pertinent database information was developed to guide the selection of 

potential candidate training site areas. Processing in grid format yielded a data set of isodata 

type polygons, each having a unique identifier and pixel frequency. These data were loaded 

into a table (isodata) for processing. A total of 8.6 million unique areas were identified. A small 

selection of data from the isodata table is shown in Table 3. Pixel frequency values may be 

used to filter areas too small to sample, as well as describe the distribution of the project area, 

or ecoregion area, by isodata class. The distribution of pixels by isodata class was generated 

by simply summing the pixel frequency by isodata value. Pixel frequency by isodata class for

one of the ecoregions is shown in Table 4. This information was useful in the identification of 

the relative abundance of the different classes and the identification of both common and scarce 

isodata classes. Frequently occurring isodata classes are readily distinguished from scarce 

isodata classes. Scarce isodata classes were identified and targeted for sampling. 

 12
The next step in the sample area selection 

process was to filter out the areas thought to be 

too small to sample. The minimum size (number 

of pixels) sample site will obviously be related to 

the resolution of the imagery being processed. 

The most important aspects of this minimum 

size limit are that the sample site is large 

enough to use as a supervised image 

classification training site, the site can be easily

located in the field by the field crew, and the site

can be distinguished from the surrounding land 

cover types. There is no point in selecting a 

sample site that is too small to use as part of the 

supervised classification training set, or that 

cannot be found or identified in the field when 

performing an aerial survey from a helicopter. 

For this project, a minimum size training site 

area of 60 pixels, or approximately 13.0 acres 

was selected. Areas meeting this minimum size 

were readily identified by performing a simple 

query of the isodata class database based on 

the size attribute of each contiguous area. 

This query had the following construct: 

select id, iso_class from isodata where 

 pix_count >= 60 

Several records that met these criteria are 

shown as bold type in Table 3. 

Identification of areas larger in size than this 

minimum size reduced the number of isodata 

class areas from over 8.6 million areas to only 

36,833 potential sample areas that were all at 

least 60 pixels in size. These database table 

rows were inserted into a new database table 

called candidate_trsite in which candidate 

training data sample site data were stored. This 

reduced set of candidate areas was checked to 

determine that it was still representative of all the isodata classes present in the stratified 

isodata data and to assure that all isodata classes were represented in the candidate_trsite 

table. The database check that was performed to determine that all isodata classes were 

represented in the candidate sample data set was performed using a database query of the 

following construct: 

Table 3: Isodata Table Data 

id iso_class #pixels 

_____ ________ ______ 

... 

24971 16024 14 

24972 16003 1 

24973 16020 1 

24974 16021 1 

24975 16003 3 

24976 16021 78 

24977 16024 3 

24978 16003 9 

24979 16007 2 

24980 16010 1 

24981 16010 12 

24982 16024 1 

24983 16019 5 

24984 16010 3 

24985 16010 1 

24986 16024 70

24987 16027 1 

24988 16011 1 

24989 16027 3 

24990 16024 1 

24991 16011 2 

24992 16021 1 

24993 16010 1 

24994 16021 2 

24995 16011 1 

24996 16007 1 

24997 16027 1 

24998 16011 3 

24999 16021 1 

25000 16024 2 

... 

select distinct iso_class from isodata 

where iso_class not in (select distinct iso_class from candidate_trsite) 

This query did not identify any missing isodata classes. All iso_classes had at least one 

sample area that met the minimum size limit. 

 13
Table 4: Pixel Frequency by Isodata Class 

 Iso Cumul. 

Class Frequency Pct(%) Pct(%)

_____ ________ _____ ______

.... 

16001 458483 14.0% 14.0%

16002 72529 2.2% 16.2%

16003 239658 7.3% 23.5%

16004 127876 3.9% 27.4%

16005 38189 1.2% 28.6%

16006 97988 3.0% 31.6%

16007 205247 6.3% 37.8%

16008 201082 6.1% 44.0%

16009 16901 0.5% 44.5%

16010 253098 7.7% 52.2%

16011 79024 2.4% 54.6%

16012 219726 6.7% 61.3%

16013 21269 0.6% 62.0%

16014 119732 3.7% 65.6%

16015 11098 0.3% 66.0%

16016 117028 3.6% 69.6%

16017 86893 2.7% 72.2%

16018 15197 0.5% 72.7%

16019 31445 1.0% 73.6%

16020 143917 4.4% 78.0%

16021 128875 3.9% 82.0%

16022 8862 0.3% 82.2%

16023 29193 0.9% 83.1%

16024 245038 7.5% 90.6%

16025 11469 0.4% 91.0%

16026 50330 1.5% 92.5%

16027 68518 2.1% 94.6%

16028 21670 0.7% 95.2%

16029 155979 4.8% 100.0% 

... 

Total 3276314 100.0% 

The next step was to determine if any 

isodata classes were extremely rare or too 

few in number, such that sampling these 

areas might be difficult. It was important to 

identify and include scarce classes in the 

candidate database so there would be 

sufficient coverage of the many land cover 

types present in the project area. Scarce 

classes were identified using the following 

query: 

select iso_class, count(*) from 

candidate_trsite 

 group by iso_class 

 order by iso_class having count(*) < 5 

This query identified 42 iso_classes that 

were considered scarce iso_classes - 

those having less than 5 candidate sample 

sites of the minimum 60 pixel size 

throughout the entire project area. 

Additional candidate areas were generated 

for these isodata classes by decreasing the 

minimum size limit from 60 pixels to 45 

pixels, or approximately 10.0 acres. 305 of 

these additional sample areas for scarce 

isodata classes were added to the 

candidate sample site database, bringing 

the total number of candidate sample area 

sites to 37,138. Sites in scarce 

iso_classes were flagged in the database, 

to identify their presence in subsequent 

database query operations. Scarce sites 

were flagged by setting a database table 

column value to reflect the scarcity of the 

class using the following SQL statement: 

update candidate_trsite set tr_status = 1 where iso_class in (select distinct iso_class from

candidate_trsite group by iso_class having count(*) < 5)

Information regarding scarcity of iso_class sites was very useful in prioritizing sample sites 

when actually selecting the specific training data sample sites that would comprise the sampling 

plan during the field data collection sampling efforts. 

 14
The next step in this process was to move from the grid world into the vector world, in order to 

integrate and manipulate the candidate sample unit data in a graphic context. To accomplish 

this conversion process, two steps were necessary. First, the isodata class map was reclassed

to form a candidate area grid map - all pixels in areas that were not candidate sample units 

were reset to a value of 0 (NODATA), while all other pixel values remained the same. The 

resulting grid map represented only those areas that had been determined to be candidate 

training data collection sites. A portion of the resulting grid is shown in the left side of Figure 7. 

This candidate area grid was then vectorized to form a vector database representing the 

candidate sample areas. Other data, such as isodata class labels, were developed to enhance 

the training site information. These area boundaries and corresponding labels could then be 

overlaid on the imagery, as shown in the right side of Figure 7. The resulting database 

represented the initial set of candidate training sites. 

Figure 7: Candidate Training Sites 

 15
Candidate Site Field Maps and Data 

A series of 1:47,500 scale (20” X 15”) color maps were developed to enable training area 

location and navigation in the field. Each map showed candidate training area polygons, their 

unique isoclass numbers, and 400’ elevation contour lines, over a 4,5,3 RGB composite built

from LANDSAT imagery. A total of forty field maps were generated and labeled using a rowcolumn code with origin on the upper left corner of the project area grid. Two copies of each 

map were produced, one for the aerial survey crew and the other for the ground survey crew. 

All maps were laminated for protection and to enable flight planning and notations directly on 

the map with erasable color pens. A portion of one of these field maps is shown in Figure 8. 

Figure 8: Field Map with Candidate Training Sites 

Efforts were made to pre-determine potential training site locations that covered the spectrum of 

significant land cover types within the project area. However, all unsupervised areas meeting 

homogeneity and size requirements were kept as part of the pool of candidate training sites, in 

order to enable changes in the field (i.e., opportunistic sampling) due to weather or field 

conditions. Additional training site characteristics were also generated; overlaying the candidate 

training sites with the slope, aspect, and elevation themes enabled the development of site 

specific estimates of these characteristics for each candidate site. X,Y coordinate values 

representing each training site were also loaded into the database table. These training area 

coordinates were used for navigation and positional confirmation using GPS receivers in the 

 16
field. Location of training areas in this manner allowed for careful flight planning and more 

efficient and safer access to field plots. The pool of candidate sites was completed and 

mapped. 

Field Sampling Restrictions 

Aerial sampling from a Bell-Jet Ranger IV helicopter was used to maximize the number and 

diversity of sampled training sites. This approach would yield the land cover components as 

viewed from above (“bird’s-eye view”) and enabled rapid (and safer) on-demand access to 

target training areas. However, constraints to sampling efforts existed due to fuel supply and d 

availability, aircraft range, crew-ferrying time, no-flight zones, and weather conditions. No 

training areas were targeted in the AKSO-defined no-flight zones shown in Figure 9. In addition, 

training sites where identified and targeted in areas of image overlap, as these sites could be 

used for training in all overlapping scenes. Large groups of candidate training site areas 

incorporating as many ecoregions and isodata classes as possible were identified for sampling. 

Figure 9: AKSO-designated no-flight zones over Katmai project area 

 17
Aware of these constraints, and at the request of the AKSO, efforts were made to maximize field 

data collection efficiency by reducing travel time between training collection sites and the 

number of collection sites required to develop comprehensive supervised classification training 

data. Thus, potential training sites were organized as groups of sites, as much as possible, to

minimize distance traveled between sample areas, while at the same time facilitate access by 

both ground and helicopter crews and adequately sample the geographic diversity of the Katmai 

Project Area. Sample areas that fulfilled the sampling constraints, as well as the apparent 

project training data needs were identified. These proposed sample areas are shown in 

Figure 10. 

Figure 10: Sampling Areas-of-Interest in the Katmai Project Area 

 18
Field Training Site Data Collection 

Field data or “ground truth” information is one of the critical components of a land cover 

mapping effort like this project. The field data provide the land cover or “bird’s-eye view” 

descriptions that will both guide the classification efforts as well as describe the resulting land 

cover features in the final classification map. Accurate and detailed field data are essential for 

the successful completion of such an effort. Any misinformation collected during this stage and 

later applied during classification efforts will be embedded in the final map. 

Field Data Collection Goals 

Image classification training data sets must encompass the complete range of significant land 

cover characteristics present in the project area. To this end, GRS worked with AKSO staff and 

AKNHP botanical and ecological experts to identify a matrix of the land cover characteristics 

and conditions thought to exist in the project area. This matrix represented the variety of land 

cover characteristics, including species/cover components and vegetation density classes. The 

land cover class matrix was composed of a combination of the target (known) land cover 

classification system, as well as other recognized (potential) land cover types that might also be 

found within the project area. This land cover type matrix formed the basis of the land cover 

classification system that would be applied during the mapping project. This matrix also formed 

the basis for field data collection efforts, as classes that would ultimately be recognized and 

mapped would require training site locations associated with the many different land cover 

attribute descriptions (“ground truth”). The range of possible land cover types must be 

adequately sampled within the subject imagery to develop suitable classification training sets 

that will yield accurate and reliable image classification pixel maps. The basic matrix/listing of

land cover types used to guide field data collection efforts is shown in Table 5. 

At each field data collection site information would be developed that would describe the cover 

characteristics of the site. This information amounted to a “bird’s-eye view” from above of the 

land cover components that summed to a total of 100 percent cover. A field form was 

developed by AKSO, GRS, and AKNHP staff to record this information. A copy of one of these 

field forms is shown in Appendix A. The cover characteristics recorded at each site amount to 

a “bird’s-eye view” of the site, such that the site would be describes by cover characteristics that 

would sum to 100% cover. Additional cover (understory or overtopped) would also be noted as 

part of the information. In additional any specific characteristics regarding moisture regime and 

environment would also be recorded. Lastly, a land cover type would be assigned based on the 

cover composition of the site relative to the land cover classification rules being used in this 

project. 

The land cover classification system used for KATM was a modification of the Alaska vegetation 

classification developed by Viereck (Viereck et al 1992), and was designed by AKNHP 

vegetation experts in conjunction with AKSO. The system defined cover classes in terms of 

vegetation communities named after dominant species existing in principal layers. Schematic 

descriptions of included cover classes can be reviewed in the diagram in Appendix B. A snippet 

of the C code used to estimate the cover class is also included in Appendix B. 

 19
Table 5: Data Source Summary: Frequency by Type and Source 

Type Aerial Aerial TSmith AKSO Suppl. Total 

Sampled Used Used Used Used Used 

Spruce:Woodland 12 12 0 0 1 13

Spruce:Open 10 9 3 0 2 14

Spruce:Closed 1 1 0 0 1 2

Spruce Stunted:Woodland 0 0 0 0 1 1

Spruce Stunted:Open 0 0 1 0 0 1

Spruce/Deciduous:Woodland 2 2 0 0 0 2

Spruce/Deciduous:Open 7 7 0 1 1 9

Spruce/Deciduous:Closed 2 2 0 0 0 2

Balsam Poplar:Woodland 1 1 3 0 0 4

Balsam Poplar:Open 9 7 3 2 0 12

Balsam Poplar:Closed 1 1 1 0 0 2

Birch:Woodland 5 4 2 0 0 6

Birch:Open 5 5 0 0 0 5

Birch:Closed 6 6 2 0 0 8

Deciduous:Woodland 0 0 1 0 0 1

Deciduous:Open 2 2 1 0 0 3

Deciduous:Closed 3 3 1 0 0 4

Alder-Willow:Tall:Open 2 1 0 1 1 3

Alder:Tall:Open 9 9 0 1 1 11

Birch-Willow:Tall:Open 1 1 0 0 0 1

Willow:Tall:Open 5 5 3 3 0 11

Alder-Willow:Tall:Closed 4 4 0 0 2 6

Alder:Tall:Closed 18 18 5 2 5 30

Mixed Shrub:Tall:Closed 0 0 0 0 1 1

Willow:Tall:Closed 2 2 4 2 0 8

Alder-Willow:Low:Open 0 0 0 0 0 0

Birch-Willow:Low:Open 1 1 0 0 0 1

Mixed Shrub:Low:Open 7 7 10 1 1 19

Willow:Low:Open 18 16 0 0 5 21

Mixed Shrub:Low:Closed 1 1 2 0 1 4

Dwarf Shrub:Mixed 54 50 9 3 6 68

Forb 0 0 0 1 1 2

Graminoid 0 0 0 1 0 1

Graminoid:Dry/Mesic 4 4 7 1 3 15

Herbaceous:Marsh 13 12 11 1 4 28

Lichen 2 2 0 0 0 2

Moss/Lichen 1 1 1 0 0 2

Sparse Vegetation 5 5 0 0 13 18

Barren 9 9 0 0 5 14

Snow/Ice 0 0 0 0 2 2

Terrain Shadow 0 0 0 0 0 0

Water 0 0 0 0 3 3

Totals 222 210 70 20 60 360

 20
Field Data Collection Operations

Field data collection efforts were scheduled to occur during the first two weeks of August, 2000. 

Data collection efforts were initiated from King Salmon, AK, located approximately 5 miles west 

of the northwesterly boundary of the Park. The collection of field data was a joint effort by 

personnel from AKSO, GRS, and the AKNHP. All field data efforts were coordinated with AKSO 

personnel and guided by the project data collection needs, as identified by GRS, AKNHP, and 

AKSO. GRS was responsible for the development of daily aerial field survey data collection 

plans that included the selection of potential candidate field training sites and the information 

necessary to locate these locations. The AKNHP was responsible for the ecological 

characterization of the different land cover types present in the KATM project area. This 

included the development of detailed land cover descriptions for the field training sites actually 

visited during aerial surveys. The AKSO was responsible for planning and coordinating all field 

data collection logistics over the two-week field data collection effort. 

GRS staff flew in and set up a computer system in the project’s headquarters in the Katmai 

National Park & Preserve offices at King Salmon. This system contained all project data, as 

well as GIS and Image Processing software. GRS personnel used this system to develop daily 

field survey plans and schedules, query and review candidate training site locations for 

alternative plans, and monitor data collection progress relative to project land cover information 

needs. 

Field crews arrived at King Salmon on August 1st, 2000 to setup the project headquarters. 

Ground reconnaissance, as well as the development of proposed flight plans, started on August

2nd, in preparation for the start of data collection efforts on August 3rd. Bad weather forced 

cancellation of fieldwork on the 3rd and this time was directed towards additional planning of field 

data collection and discussion of data collection goals and objectives. After the weather 

improved, daily aerial surveys based from King Salmon were accomplished from August 4th

through 8th. On August 9th data collection operations were moved from the project’s 

headquarters in King Salmon to Kakhonak, 88 miles to the northeast along the south shore of 

Lake Illiamna. Daily aerial surveys of the northern and northeastern portions of the KATM 

project area were initiated from Kakhonak on August 10th through 12th. Field sampling 

concluded on August 12th and field crews returned to the Anchorage area the evening of the 

12th. In all, field data was collected on eight days during the nearly two week field data

collection efforts. During those days, approximately 32 hours of flight time or approximately 4 

hours/day of actual aerial survey time were logged surveying aerial sites. Much of the 

remaining time was spent transporting field data collection crews to and from the field and 

refueling the helicopter. 

Aerial Survey Logistics

GRS staff generated a daily flight plan consisting of a list of scheduled training sites, reviewed 

and selected the day before, to fulfill data collection needs. This flight plan guided the aerial 

survey data collection efforts using the Bell helicopter. This tabular report formed the basis of 

the field sampling form that included the sampling area number (a regional number assigned to

different areas of the project area), candidate training site iso_class number, positional 

coordinates (lat/longs), aspect, slope, and elevation of the sample site locations. A unique 

training site identification number was assigned to each aerial survey site as it was visited. This 

trsite_id was composed of concatenating the date with a sequential visit numbers for each 

location. For example, training area code “081022” would designate the 22nd area visited on 

August 10th. Training area IDs were generated by GRS personnel for each training area visited. 

This report was ultimately generated using the GIS by performing a formatted query of the 

 21
candidate sites sequenced for any particular day. Field maps, imagery, and aerial photos were 

reviewed and sampling sequences were developed to minimize travel time. 

Field maps were annotated with additional information as an aid in navigation to the selected 

areas. A tentative sampling sequence was delineated on appropriate field maps indicating 

selected flight paths, and used as a flight plan for the aerial survey efforts. Figure 11 shows a 

portion of one of these annotated field maps. 

Figure 11: Annotated Field Map with Selected Training Sites

Actual field locations, based on GPS receivers, were planned for collection as part of this effort. 

The original work plan called for the use of a Precision Lightweight GPS Receiver (PLGR), to 

locate and “mark” surveyed training area locations from the air. However, aerial survey crews 

were quickly forced to rely on the helicopter’s installed Trimble GPS navigation unit instead, 

when the handheld instrument failed to function properly while in flight. This equipment failure 

necessitated a daily transformation of coordinate values from the project’s projection

parameters, to the aircraft’s GPS navigation parameters, which differed in projection units and 

datum. GRS automated this process in order to generate reliable navigational coordinate 

positions for daily sampling schedule reports. The reverse process was also automated in order 

to facilitate the input of training site field coordinates in the project’s database and maps. 

Unfortunately, this unexpected development created uncertainty regarding the true location of 

some surveyed sites, in particular those sampled the first two days of aerial survey efforts. 

Ground crews were able to successfully use the PLGR units to locate their field plots. 

 22
GRS made efforts to adhere and exceed daily aerial survey schedules. However, weather 

conditions and other restrictions did not always allow for complete sampling of the scheduled 

areas or sites. Opportunistic sampling was implemented whenever feasible in order to

supplement scheduled sampling efforts and prevent shortage of sampled areas due to changing 

weather conditions. Opportunistic sampling was implemented in two ways. The first way 

entailed locating alternative iso_class training areas printed on field maps that were spectrally 

equivalent to and nearby scheduled areas (i.e., same iso_class code) that could not be 

sampled. The second type of opportunistic sampling involved spotting and sampling of 

promising areas while en route to other locations. These areas were not necessarily plotted on 

the field maps and may not have had assigned iso_class codes. However, they were visually 

estimated to be large and appeared homogeneous enough to qualify as valid sampling sites. 

These sites also tended to be land cover types known to be ‘rare’ or missing in the land cover 

matrix. 

The aerial survey crew consisted of the helicopter pilot, one GRS image processing specialist 

and one AKNHP botanical specialist. The GRS specialist’s responsibilities included navigating 

to and locating scheduled training sites using coordinates listed in the daily sample schedule 

form and on the field maps. Target coordinates were read to the pilot, who would enter them 

into the aircraft’s navigation system and navigate to the desired training site location. As the 

helicopter approached a target location, the GRS specialist used field maps to confirm arrival to 

the target site. Once arrival to a target sample site was visually confirmed and its extent 

described to the entire field crew, the pilot began to slowly circle the target site and the AKNHP 

botanist proceeded with the ecological characterization of the land cover present within the 

training site. Meanwhile, the GRS specialist photographed and videotaped the site, recorded 

coordinate readouts from the aircraft’s navigation console, and recorded pertinent comments. 

Once the AKNHP botanist and GRS specialist declared completion of their tasks, the GRS

specialist provided navigational information and the pilot began flying to the next training site. 

Each day after aerial survey site data were collected, the data were processed and the number 

of sites that had been sampled were tallied in the land cover matrix thereby indicating the 

frequency of samples by land cover type. This information was used to identify ‘scarce’ types 

still lacking samples as opposed to common types with many samples. Subsequent data 

collection efforts were adjusted daily in an effort to completely cover/fill the land cover type 

matrix. In addition, remaining unsampled types were identified so that they could be sampled 

as opportunistic training sites, if the survey crew found suitable sites of these types. 

Aerial Survey Results

An average sampling day consisted of approximately four hours for actual training area aerial 

surveying. The total of 222 training sites were described by the aerial survey crew, while 86 

ground sites were described by the AKSO ground crew. An average of 27 sites were surveyed 

from the air per sampling day. At the end of each sampling day, each GRS image-processing 

specialist imported coordinate values for sampled training sites into the project database and 

maps. Each sampled site field-recorded position was converted into a point feature attributed 

with its unique training site ID. This ID was later used to relate each point to its respective land 

cover description data. All recorded digital imagery was also saved to disk. These efforts 

resulted in plotted flight paths, where each aerial site was a waypoint on the flight path. 

Locations were reviewed relative to the candidate site locations and the imagery, and adjusted 

for any inaccuracies in the GPS locations collected from the helicopter’s GPS unit. 

Unfortunately, this device was no better than ± 100-200 meters, often indicating significant 

differences from sample areas and indicated ground position. Being able to review the data as 

it was collected was a significant advantage as positions were adjusted, when possible, to 

conform to known positions relative to indicated positions. 

 23
As data collection efforts progressed, field data were reviewed on a daily basis to assess the 

variability and number of land cover classes visited to-date. These reviews enabled GRS

personnel to guide data collection efforts to include classes that appeared under-represented in 

the current sample set. A summary of the number of sites surveyed from the air by land cover 

type during the August, 2000 aerial survey is shown in Table 5. These data collection sites and 

classes were distributed over the entire project extent visited, spanning all three LANDSAT 

scenes. Those types shaded in light green were not sampled during the aerial survey and data 

was estimated from one of the other available sources. 

Cover data for surveyed sites were imported into the project database tables and made 

available for database query and report tools used in the subsequent resolution of class 

confusion, assessment of training class performance, and characterization of each site’s land

cover attributes. SQL queries were performed to check for invalid data, such as sites for which 

the total “bird’s-eye” view cover did not equal 100 percent. SQL statements were also used to 

generate the modified Viereck land cover types, to check for incorrect or inconsistent field calls. 

Data inconsistencies were identified and corrected. Data were summarized using 

GRS_transum_tr to develop estimates of cover by individual specie, as well as groups of 

species, and were loaded into the relational database tables as attributes of each site. In 

addition, the predominant cover component and percent cover were also loaded into the 

database tables. 

Supplemental Data Sources

In addition to the aerial survey data, additional data sources were also used during this project. 

These sources included AKSO ground survey site data, Dr. Thomas Smith plot data, and photointerpreted data. Land cover data for all three of these sources were processed to develop the 

100% “bird’s-eye” view land cover descriptions. These data were also loaded into the relational 

database tables and processed to develop information comparable to the information developed 

from the aerial survey sites. Data inconsistencies were identified by GRS and corrected by 

AKSO and AKNHP as best as possible to develop usable training data. Summary data were 

generated and loaded into the database. Locations were input into the GIS and all sites were 

spatially referenced to the project database, in the event that they might be used as 

supplemental training sites. Areas of questionable location, as identified by AKSO, were 

identified and withheld from use as potential supplemental training sites. 

In addition, supplemental data were provided from an AKSO fly-over of portions of the Katmai 

project area during August, 2001. This fly-over was designed to provide additional information 

for supplemental sites identified by GRS. Digital photography and verbal descriptions were 

acquired in an effort to gather additional information about these few questionable areas. These 

data did not have “bird’s-eye” view cover descriptions, but rather general type descriptions. 

Cover descriptions were developed for a few of these sites (those that were used as training 

areas), but only to a very general level based on photo-interpretative efforts performed in the 

office. 

 24
Image Classification Training Set Development 

GRS uses both supervised and unsupervised classification to develop land cover pixel 

classification maps. Both these methods are dependent on training sets. Training set 

development is a critical component of a classification effort. “Ground truth” or land cover 

attribute data must be properly correlated to spectral data to develop accurate and detailed land 

cover map data. If the attribute descriptions are not properly associated with the spectral data, 

then the resulting land cover map will be an inaccurate representation of the project area. Care 

must be exercised to properly train the spectral data with the “ground truth.” 

The goal of this phase of the project is to build image training data sets that most accurately 

reflect the land cover types present in the project area, while at the same time minimizing 

confusion and uncertainty within the training data. Training areas should reflect the areas 

described during data collection efforts and have small statistical variation within the spectral 

data of each training site. Training areas of this sort tend to minimize overlap of spectral 

statistics and confusion between training data. Proper spatial registration, as well as the 

appropriate size, are requirements of accurate training data. As training sites are developed 

they are reviewed to determine their validity. Reviews include evaluations of confusion and 

fidelity/self-classification. Valid sites are used in classification efforts and invalid sites are 

withdrawn and discarded. A key component in GRS’ supervised methodology is that an 

individual training class is developed for each survey site, rather than for groups of sites that are 

thought to represent the range of a training class. This different approach tends to keep 

spectral statistical ranges small relative to the statistics for classes formed from groups of sites 

and results in reduced statistical overlap and confusion of data for data of different land cover 

characteristics. 

Supervised Training Set Development Strategy

The first sites used to develop image training data were the aerial sites that were surveyed 

during the August, 2000 field data collection effort. These were the sites that were selected for 

sampling based on their spectral homogeneity and size. Several obvious land cover classes 

such as water, snow/ice, and barren, were developed from training areas selected with the aid 

of aerial photography and satellite imagery. Where possible, the same obvious land cover 

training sites were used in each scene in the areas of scene overlap, to minimize the number of 

training sites needed to classify the imagery. 

After review of the resulting frequency of sites by class (Table 5), certain land cover types still 

were lacking representative training sites. For these missing or under-represented land cover 

types, additional sites were added using data from the alternative sources of training site 

information. Training sites were first added from those areas visited by AKSO ground crews, as 

these sites were comprised of data most compatible with the aerial sites in terms of the data 

collection time period and descriptive information. Additional sites, if still needed, were then 

added from the Dr. Thomas Smith survey sites. Lastly, after review of all these additional sites, 

supplemental sites were added from photo-interpreted areas to finalize the classification training 

data sets. 

Supervised Training Site Development Techniques

Training areas were developed in sets that would be applied to the specific images (scene) 

during classification efforts. Three sets were initially developed, one for each original scene – 

7219, 7119, and 7018. As scenes were added during the course of the project, additional 

 25
training sets were developed for these newly acquired data (TM7). Sets of training areas were 

initially developed scene by scene attempting to sample the same areas within overlapping 

scenes. Each scene specific training set was later copied and modified (deletions and 

additions) to represent specific classification efforts (e.g. “spruce” versus “no spruce”, “water” 

versus “no water”, and so forth) for that specific scene. Training areas within each training set 

were created individually using region growing techniques, a neighborhood seeding method that 

used a 3x3 pixel initial seed window surrounding a point interactively selected on the image to 

grow a homogeneous spectral area around the seed window. Location of the initial point in 

each scene was determined by the approximate coordinate location of the surveyed site, as 

indicated by a labeled point in the GIS survey site coverage overlaid on the imagery. Training 

area pixel inclusion was determined specific to each scene using a statistical threshold of 2 

standard deviations from the seed area mean spectral data. The maximum search area extent 

was limited to a 41x41 pixel area. 

These parameters were the basic parameters applied while developing training sites at all 

training site locations. However, if a sufficiently large enough area could not be grown using 

these exact parameters, then the standards were relaxed as follows: 

Option 1: The search area was expanded to 81x81 pixels. Initial seeding points were moved 

slightly, but were still quite close to the location of the training site point, so that the sample area 

represented the surveyed area. If this approach did not produce a sufficiently large enough 

training area, then option 2 was applied. Option 1 was the most desirable, since it would still 

keep training area spectral statistics as tight as possible, thereby reducing potential confusion 

during classification. 

Option 2: The seed area would be enlarged to an initial 5x5-pixel window. If this approach did 

not produce a satisfactory training area, then option 3 was applied. 

Option 3: The seed area would be set back to an initial 3x3-pixel window and the pixel inclusion

parameters would be relaxed to a threshold of 2.5 standard deviations. If this approach did not 

produce a satisfactory training area, then option 4 was applied. 

Option 4: The seed area would be set back to an initial 3x3-pixel window and the pixel inclusion

parameters would be relaxed to a threshold of 3.0 standard deviations. This last choice usually 

generated areas of sufficient size if the prior options did not.

Although the ideal training area size was thought to be greater than 60 pixels, a minimum of 15 

pixels was used in an effort to develop very tight spectral statistics. Sites this small most 

commonly occurred when training sites were developed using sites that were not pre-qualified 

as part of the candidate training site selection process. These smaller training areas were 

included in the initial classification in order to discard as few training sites as possible. 

Supervised Training Set Development

Each of the aerial survey sites visited during the 2000 field season was included in the initial 

image classification training sets developed for this project. As land cover characteristics for 

these sites were evaluated and tabulated, missing or underrepresented types were then added 

to the training data sets. The following sources of additional training site data were reviewed 

and used when possible. 

 

 26
The second source of training sites were those sites visited by the AKSO ground crew during 

the same 2000 field season. Although the cover for these sites was verified on the ground, 

there was no assurance that every site would exhibit the necessary spectral homogeneity and 

extent required for a training site. In some cases, it was possible to verify suitability for inclusion 

of these sites as training classes from digital photographs taken by the AKSO ground crew. 

The third source for training sites were those visited by Dr. Thomas Smith’s ground crew in past

years. Dr. Smith’s database was comprised of over 400 sites, of which over 170 were 

considered as potential training sites after being verified for positional accuracy by AKSO and 

ANHP personnel. Land cover data often did not total to 100% cover, but these site values were 

reviewed and adjusted by AKSO and AKNHP staff to develop corrected “bird’s-eye” view land 

cover summaries for each site to be used in the training data sets. 

 

Finally, a fourth source for training site locations were those sites developed by GRS personnel, 

to represent obvious types and remaining void areas in the classification. In some cases these 

areas represented the obvious types like water, barren, and glacier/snow types that were easily 

identified from aerial photography. However, some of these additional sites were added in 

stubborn gaps that remained in the classification maps after initial classification efforts. After 

growing training polygons in these remaining unclassified areas, a combination of aerial photointerpretation and comparison of statistical similarity with existing classes were used to define 

preliminary cover type descriptions for these supplemental classes. Many of these training 

sites, and/or spectrally similar areas, were visited by AKSO personnel during a supplemental 

aerial survey undertaken during September of 2001. Although this fixed-wing survey did not 

produce detailed “bird’s-eye” view cover component data for the visited sites, it provided GRS

with general categorical descriptions of existent land cover types and digital photographs of 

these supplemental sites. 

Supervised Training Site Review and Evaluation 

The appropriateness of the training data was determined in two ways. One way involved a 

review of classification confusion reports that described confusion between training signatures 

within each training data set. The second involved a review of classification fidelity to determine 

if classified pixels within training sites yield the same land cover data descriptions (attributes) as 

the field survey data descriptions. If these evaluations yielded results indicating problem 

situations or data inconsistencies regarding a site, then the specific site was reevaluated and 

the problem identified. If the problem could not be resolved, the site was discarded from the 

training data set. 

Unfortunately, both of these evaluations are only useful in the determination of whether or not 

the training data that have been developed provide results that are consistent with other training 

data. Neither of these measures necessarily indicates whether or not the training data are an 

accurate representation of what the true land cover conditions are. For instance, if the 

vegetation in a specific site is incorrectly identified, or assigned incorrect cover estimates, these 

data will not necessarily be identified as incorrect or inconsistent unless there are other similar, 

slightly overlapping training sites that indicate an inconsistency amongst the land cover data. 

Therefore, sites that are totally non-overlapping will tend to have high pixel fidelity, but could still 

be wrong, if the underlying “ground truth” are incorrect. Therefore, data lacking confusion and 

exhibiting high self-classification may still be wrong. Large training data sets with multiple 

sample sites are desirable as they may help confirm the ‘goodness’ of the data. 

 27
 28

Class Confusion Evaluation 

Training classes were evaluated for confusion in terms of their Jeffries-Matusita (J-M) distance,

which is a reliable indicator of class separability (Swain and Davis 1978). A maximum J-M 

distance of 2.0 between two classes means that they are perfectly distinguishable from each 

other. A minimum J-M distance of 0.0 indicates that two classes are, as far as the classifier is 

concerned, spectrally undistinguishable from each other. In general, the probability of error in 

distinguishing two classes drops as their J-M distance increases. At a J-M distance of 1.4, this 

probability is approximately 1%. Figure 12 shows the relationship of the J-M distance to the

upper and lower limits of the probability of error.

Figure 12: Probability of Error and J-M Distance 

The Confusion Report used to evaluate class confusion is generated for all training sites used in

all of the training sets. The target minimum J-M distance value for inclusion in the Confusion 

report was set at a 1.4 threshold. Training sites related by J-M distance values below this

threshold were reviewed and evaluated.

Sample portions of the Confusion Report are shown in Table 6. The data in this report are

organized by training site and the training sites are listed in order of the Calculated Class. For 

each site, all potentially confused training sites are listed in order of the magnitude of the J-M 

distance, along with the land cover characteristics of these sites. The training set in which the 

confusion exists is also listed for each confused site. The training set name is a combination of
::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::

Bad Confusion: :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: Trsite_id: 80816 33 Willow:Low:Open 2 0 68 30 0  Training Cover Calculated Tree Pct Shrub Forb Barren Ot Set Confused_id J-M Dist Type Class Cover Conifer Cover Cover Cover Co -------- ----------- -------- ----- ----------------------- ----- ------- ----- ----- ----- -- 7119_sup 10158 0.669 11 Balsam Poplar:Open 30 0 5 60 5  7119_sup 1062 1.001 11 Balsam Poplar:Open 46 0 0 75 0  7119_sup 80501 1.058 13 Birch:Open 75 20 20 5 0  7119_sup 80820 1.104 13 Birch:Open 35 0 43 17 5  7119_sup 80802 1.137 13 Birch:Open 35 0 50 15 0  7018_sup 80820 1.150 13 Birch:Open 35 0 43 17 5  7119_sup 81008 1.178 21 Alder:Tall:Closed 2 0 93 5 0  7018_sup 80821 1.341 12 Deciduous:Mixed:Closed 70 0 15 15 0  7119_sup 80706 1.359 68 Wet:Sedge 0 0 0 28 0  7018_sup 81008 1.392 21 Alder:Tall:Closed 2 0 93 5 0 ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::Good/Acceptable Confusion: ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::Trsite_id: 80713 Hwd Balsam Poplar:Open 50 0 25 25 0  Training Cover Calculated Tree Pct Shrub Forb Barren Ot Set Confused_id J-M Dist Type Class Cover Conifer Cover Cover Cover Co -------- ----------- -------- ----- ----------------------- ----- ------- ----- ----- ----- -- 7119_npgW 80707 1.013 Hwd Balsam Poplar:Open 41 0 9 50 0  7119_ypgW 80707 1.013 Hwd Balsam Poplar:Open 41 0 9 50 0  7119_ypgW 80819 1.309 Hwd Balsam Poplar:Open 55 0 40 5 0  7119_npgW 80819 1.309 Hwd Balsam Poplar:Open 55 0 40 5 0  7119_ypgW 1076 1.336 Hwd Balsam Poplar:Open 35 0 45 20 0  7119_npgW 1076 1.336 Hwd Balsam Poplar:Open 35 0 45 20 0 -------------------------------------------------------------------------------------------------------------------- Trsite_id: 80815 Hwd Birch:Closed 70 0 5 25 0  Training Cover Calculated Tree Pct Shrub Forb Barren Ot Set Confused_id J-M Dist Type Class Cover Conifer Cover Cover Cover Co -------- ----------- -------- ----- ----------------------- ----- ------- ----- ----- ----- -- 7119_npgW 80821 0.557 Hwd Birch:Closed 70 0 15 15 0  7119_ypgW 80821 0.557 Hwd Birch:Closed 70 0 15 15 0  TM7_ypgW 80821 1.075 Hwd Birch:Closed 70 0 15 15 0  TM7_npgW 80821 1.075 Hwd Birch:Closed 70 0 15 15 0  TM7_ypgW 80825 1.145 Hwd Balsam Poplar:Open 50 0 45 5 0  TM7_npgW 80825 1.145 Hwd Balsam Poplar:Open 50 0 45 5 0  7119_npgW 80814 1.288 Hwd Birch:Closed 75 0 5 20 0  7119_ypgW 80814 1.288 Hwd Birch:Closed 75 0 5 20 0  7219_ypg 80501 1.364 Hwd Birch:Closed 75 20 20 5 0  7219_ypgW 80501 1.364 Hwd Birch:Closed 75 20 20 5 0  7219_ypg 80503 1.375 PHw Mixed Deciduous-Conifer:Closed 75 32 10 15 0  7219_ypgW 80503 1.375 PHw Mixed Deciduous-Conifer:Closed 75 32 10 15 0  TM7_ypgW 80814 1.394 Hwd Birch:Closed 75 0 5 20 0  TM7_npgW 80814 1.394 Hwd Birch:Closed 75 0 5 20 0 

 29

Table 6: Confusion Report (portion)
the characteristics of the training set that distinguish it from other training sets. These

characteristics include the scene, set/mask (all supervised [sup], spruce [ypg], and no spruce 

[npg]), and subset (water [W] versus no water). 7219_ypgW represents the training set with Water 

sites for the Spruce masked area of scene 7219.

Confusion data were reviewed to identify potential sources of confusion. Review involved 

identification of nonconforming sites and was not based on any quantitative data limits, as there is 

tremendous potential overlap of land cover characteristics that have different land cover types. In 

general, those sites that were nonconforming are those that were not expected to be confused 

when one considered their descriptive data. Confusion between sites of similar or equal land cover 

types or cover characteristics was considered in agreement and acceptable. This type confusion 

was “good confusion,” as this sort of comparison confirms that the overlapping signatures are 

classifying pixels of similar land cover characteristics. On the other hand, confusion between sites 

of dissimilar or completely different land cover types or cover characteristics was considered 

unacceptable. This confusion was “bad confusion,” as it indicates similar or overlapping spectral

data for dissimilar land cover type characteristics. The magnitude of the confusion, as indicated by

the J-M distance, was also considered when evaluating training classes. J-M distance values near 

or above 1.3 were considered still viable for class separation, given a less than 10% chance of 

misclassification. In reality, class separation is still possible at lower J-M distances, but the 

probability of classification error is greater. In these cases, the nature of what classes were 

confused with each other became most important in evaluating the confusion. For the Katmai 

Land Cover Mapping Project, if the confused classes were of fairly similar land cover 

characteristics, GRS accepted the overlap of the spectral training data, since the resulting class 

would likely still fall within the similar land cover type. However, if land cover values were 

sufficiently confused and the types were dissimilar, then the confusion had to be resolved. 

The first step in resolving the confusion was to recheck the location, shape, and size of the training 

areas involved to be certain that the training data had been collected in the appropriate locations. 

If the training polygons were correct, then resolution required either removal of the badly confused

class or segregation of the confused classes into training data sets that would be applied 

separately to the imagery. The segregation of training data into sets that had “spruce” data and 

“no spruce” data is an example of how confusion between some of the low-density spruce types 

and non-spruce types could be resolved. 

After resolving as much class confusion as was possible, a certain degree of confusion remained. 

The confusion that occurred between Spruce and Wet Herbaceous classes could not be resolved 

by means of training area editing. In order to resolve these inconsistencies, a spruce mask was 

developed based on knowledge of spruce distribution within the project area. The extent of this 

mask was delineated by the AKSO and applied by GRS. The spruce mask was implemented by 

creating a second training set (for each scene) in which all classes containing more than 2.5% 

spruce cover were eliminated. The “no spruce” training set was then applied to those portions of

the imagery that were thought to be void of spruce (as indicated by the mask), while the training 

sets that included spruce were applied to those portions of the imagery where spruce presence 

was acceptable. The development and application of different training sets was instrumental in 

minimizing the confusion between a few Wet Herbaceous and White Spruce training classes, that

otherwise were virtually inseparable. In another instance of confusion, Water classes were being 

misapplied to pixels in barren or sparsely vegetated mountainous areas. Development of training

sets with “water” and with “no water” classes resolved this classification problem. After review and 

evaluation of the training data within the specific training data sets, all invalid or confused classes 

that could not be resolved were removed. The complete Confusion Report generated after review 

and resolution of all confusion is included as a project deliverable along with all the other delivered 

data. 

 30
Classification Fidelity Evaluation 

A test of how the confusion affects the classification output may be determined by review of the 

classification fidelity data. All training classes were evaluated for classification fidelity. Each 

training site was evaluated to determine how well the individual training area was classified by 

comparing the characteristics of the pixels that formed each training class with the observed field 

characteristics of the training area. Fidelity is viewed in two ways. The first compares the number 

of pixels of the training area that were actually classed as that training class value. The second 

compares the land cover characteristics developed by summarizing the classified pixel attributes of

the training site with the observed land cover characteristics. 

The Fidelity Report (see Table 7) is used to evaluate classification fidelity. Formatted as a 

spreadsheet, this report shows fidelity data for different versions of the participating training sets, 

as applied to each scene (the training set names follow the same conventions as in the Confusion 

Report). Report data were developed by computing for each training area the stratified averages 

of the different classified pixels’ land cover values based on a Maximum Likelihood, 90% threshold, 

supervised classification of these areas. Fidelity data included the scene ID, training site ID, 

percent pure (self-classification), area pixel count, calculated cover-type code, predominant cover 

component, and predominant component’s cover, as well as a break-down of training area cover 

composition based on the classification of the training area pixels. These data were then 

compared to the “ground truth”, a more generalized calculated field call based on the field data, 

and the field described cover components of each site (also included in the table) to evaluate the 

fidelity of each training class. 

The following criteria were used in evaluating pixel fidelity for each training class:

• Self-Match -- the level of self-classification (percent pure) of an area – how many pixels of the 

training area were classified as that same class. The following percent self-classification limits 

were used to describe the degree of self-matching: 

Degree of Match Self-Classification Limits

 

Strong Match (M) >= 75% 

Slight Match (m) >= 50% and < 75% 

Slight Mismatch (n) >= 25% and < 50% 

Strong Mismatch (N) < 25% 

• Type-Match -- the degree of matching land cover attributes. 

• Sites that matched land cover classes of type, predominant species, and density for the 

land cover components. 

• Sites that showed land cover class component values that differed from calculated ones by 

no more than approximately ten percent – these areas might not exactly match class 

values, but still matched land cover attributes. These cases typically concerned 

comparison of values that are similar, but which may in fact span type or class thresholds or 

limits. 

 31
 Table 7: Detail of Pixel Fidelity Report for Katmai training classes. 

 32
• Sites that showed land cover class component values that differed from calculated ones by 

no more than approximately twenty-five percent – these areas typically did not match class 

values in one category of land cover, but still matched the general land cover attributes. 

Type match comparison typically concerned comparison of values that are similar in most cases,

but not all cases, sometimes resulting in one different categorical value because the field attributes 

and weighted averages spanned the threshold of a class decision rule value. 

Sites that met the first two type-match criteria for similarity ( <= 10 percent difference ) of the major 

type characteristics (classes and /or values) were considered strong type matches (M). Sites 

showing class and attribute values that differed by more than 10 percent but less than 25 percent

were not considered as strong a match, but still considered a slight match (m). Sites that differed 

by more than 25 percent were considered mismatches (m) and were deemed unacceptable if 

causes of the mismatch could not be resolved and eliminated. 

Matches were determined for each training set in which a training site participated. A separate line 

in the report describes the fidelity results of a training class within a training set. The degree of 

self-match was developed for each site by training set based on the percent self-classification. In

addition, a degree of self-match was determined for the site as a whole, based on the average of 

the percent self-classification values by training set. The report also includes a description of the 

training site field description (TrainCall), as well as the calculated summary and type data 

(TrainCalc’ed[date]) to verify that the calculated data based the project classification logic and 

lookup tables agree with the field description (different dates reflect values based on corrections 

and modifications made to the field data descriptions to correct missing and erroneous data). 

Table 8 shows results of the evaluation of the Fidelity report data, for both self matches and type 

matches for all the training sites involved in the project. 

It is apparent in these data that a low “percent pure” self-match value did not necessarily imply 

poor fidelity of the class. While only 65 percent of the sites were classified as strong matches 

based on self-classification, 99.7 percent of the sites were considered matches by type 

characteristics. Nearly all of the sites that did not self-match (“purity”) did match by type 

characteristics. Sites confused with other sites of very similar land cover attributes show these low 

“percent pure” values, but still yield very similar land cover characteristics resulting in a match. 

This situation indicates that the “good confusion” of training site data helps develop similar land 

cover characteristics, as opposed to different values. In earlier reports, truly confused sites 

showed mismatches by self-match and type-match. These mismatched sites typically confirmed 

confusion of the subject site with another site. This confusion was addressed by reviewing the 

aforementioned J-M Distance Confusion Report, assessing which sites participated in the 

confusion, and resolving or removing the truly confused (“bad”) training sites from the same 

training set, thereby avoiding the inclusion of confused training data in the classification effort. 

While no accuracy assessment was performed on this data set, the resolution of class confusion,

as determined from fidelity and J-M distance reports, builds confidence in these classification data. 

Supervised Training Set Development Results

The complete training data set used for this project was comprised of 360 training sites. Of the 

original 222 aerial survey sites, 210 were found suitable for inclusion in the final training data sets. 

These sites are identifiable in the final pixel map, as they have trsite_id values that range between 

80401 and 91226. 

 33
Table 8: Fidelity Report Match by Purity and Type 

All Scenes ------- Self-Classification -------- ------------ Same Type -------------

Type N n m M N n m M 

< 25% 25-49% 50-74% >= 75% slight same 

Spruce:Woodland 0 1 1 11 0 0 0 13

Spruce:Open 0 1 5 8 0 0 0 14

Spruce:Closed 0 0 0 2 0 0 0 2

Spruce Stunted:Woodland 0 0 0 1 0 0 0 1

Spruce Stunted:Open 0 0 1 0 0 0 0 1

Spruce/Deciduous:Woodland 0 1 0 1 0 0 0 2

Spruce/Deciduous:Open 0 2 5 2 0 0 0 9

Spruce/Deciduous:Closed 0 0 1 1 0 0 0 2

Balsam Poplar:Woodland 0 1 1 2 0 0 0 4

Balsam Poplar:Open 1 2 6 3 0 0 0 12

Balsam Poplar:Closed 0 1 1 0 0 0 0 2

Birch:Woodland 1 0 1 4 0 0 0 6

Birch:Open 0 1 2 2 0 0 0 5

Birch:Closed 0 0 1 7 0 0 0 8

Deciduous:Woodland 0 0 0 1 0 0 0 1

Deciduous:Open 0 0 3 0 0 0 0 3

Deciduous:Closed 0 0 1 3 0 0 0 4

Alder-Willow:Tall:Open 0 0 2 1 0 0 0 3

Alder:Tall:Open 0 0 4 7 0 0 0 11

Birch-Willow:Tall:Open 0 0 0 1 0 0 0 1

Willow:Tall:Open 0 0 5 6 0 0 0 11

Alder-Willow:Tall:Closed 0 1 3 2 0 0 0 6

Alder:Tall:Closed 0 5 11 14 0 0 1 29

Mixed Shrub:Tall:Closed 0 0 0 1 0 0 0 1

Willow:Tall:Closed 0 1 4 3 0 0 0 8

Birch-Willow:Low:Open 0 0 0 1 0 0 0 1

Mixed Shrub:Low:Open 0 2 8 9 0 0 0 19

Willow:Low:Open 0 1 5 15 0 0 0 21

Mixed Shrub:Low:Closed 0 1 0 3 0 0 0 4

Dwarf Shrub:Mixed 1 4 14 49 0 0 0 68

Forb 0 0 1 1 0 0 0 2

Graminoid 0 0 0 1 0 0 0 1

Graminoid:Dry/Mesic 0 0 4 11 0 0 0 15

Herbaceous:Marsh 0 0 5 23 0 0 0 28

Lichen 0 0 0 2 0 0 0 2

Moss/Lichen 0 0 0 2 0 0 0 2

Sparse Vegetation 0 0 0 18 0 0 0 18

Barren 0 0 3 11 0 0 0 14

Snow/Ice 0 0 0 2 0 0 0 2

Water 0 0 0 3 0 0 0 3

Totals 3 25 98 234 0 0 1 359

Percent Match 65.0% 99.7%

 34
A total of 20 of the AKSO sites participated in final training sets. Classes of this type are identified 

by their trsite_id value, which is between 1000 and 1900. These trsite_id values were developed 

by adding a constant value of 1000 to the AKSO site number. 

A total of 70 of the Dr. Thomas Smith ground sites were added to the composite training data set. 

These sites are identified by trsite_id values that ranged from 10,000 to 10999. These values were 

developed by adding a constant of 10,000 to the Dr. Smith site id values.

GRS added 60 additional sites to the final training data sets. 10 of these sites were in obvious 

classes of water, barren, and snow/glacier. These sites are identified by trsite_id values between 1 

and 10. The other 50 were in land cover classes representing vegetation types not included in any 

of the field data training sets. 13 of these sites represented Sparse Vegetation types, 8 sites 

represented Forb types, 6 represented Dwarf Shrub types, 17 represented shrub types and 6 

represented tree types. The sites have trsite_id values that range from 1901- 4999. 

Of all the 360 training sites developed, some were used in the classification of more than one 

scene, as they were located in areas covered by overlapping imagery, while others were used in 

only one scene. Of the 360 training sites, 264 were used for more than one scene. However, 84

sites were used only for scene 7219, 11 sites were used only for scene 7119, and one site was 

used for only the TM7 replacement imagery. A breakdown of the number of training sites by scene 

and source is shown in Table 9. This indicates that the training sets for the TM7 imagery were an

improvement, in terms of number of sites, over the original 7018 scene, there were still nearly 100 

fewer sites in the TM7 training sets than were present in the 7219 or 7119 training data sets. 

 Table 9: Supervised Training Sites by Scene and Source 

Source Aerial 

Survey

AKSO TSmith Supplemental Total 

Scene

7219 168 7 34 36 245

7119 157 11 39 43 250

7018 41 5 33 12 91

TM7 68 12 37 32 149

Total 210 20 70 60 360

All of the 360 training sites comprised the different training sets used to classify the imagery. As

there were issues involving the misapplication of Water classes, four training sets were developed 

for each scene to guide the application of Spruce and Water training data. A total of 9 different 

training sets were developed. The following training sets were developed for each scene that had 

Water and/or Spruce misclassification issues – Spruce without Water, Spruce with Water, No 

Spruce without Water, and No Spruce with Water. The number of training sites by scene and 

training set is shown below in Table 10. 

 35
 Table 10: Supervised Training Sites by Training Set 

Scene 7219 7219 7119 7119 TM7 TM7 

Training set No 

Water 

Water No 

Water 

Water No 

Water 

Water 

Spruce 241 245 N/A 250 N/A 149

No Spruce 185 189 N/A 229 144 147

Unsupervised Training Set Development

Unsupervised training data sets were also developed to perform an ISODATA classification of the

imagery. These unsupervised data sets were created using the same statistical parameters and 

techniques that were applied during the initial image pre-processing classification efforts during the 

development of the candidate training site locations with a few exceptions. While the initial 

unsupervised classification efforts were used to develop classes within different strata of the 

project area, these unsupervised efforts were designed to develop classification maps that could 

be used to supplement supervised classification data and fill in remaining gaps and voids in the 

supervised pixel maps. 

 

GRS generated unsupervised training sets for each normalized LANDSAT image set. The major 

ecoregions that formed the basis for stratification and unsupervised classification during the 

training site selection process were not involved in this mapping effort. Instead, unsupervised 

training data were developed for the cloud free portions of each scene. As many as 200 training 

classes were targeted for inclusion in each training set. If too few classes were developed, then 

the statistical parameters were relaxed and initial seed values altered to generate more classes. 

Resulting signature files were reviewed for valid (non-singular) covariance matrices prior to 

acceptance of an unsupervised training set. 

As the project progressed and initial results were reviewed, it became apparent that there would be 

a need to segregate the imagery into two major areas – those areas where spruce was present 

and those areas were it was not present. After scene replacement with the newer imagery, the 

spruce mask used during supervised classification efforts was applied during the development of

unsupervised class maps, resulting in both “spruce” and “no spruce” unsupervised class maps for 

the two major scenes that now covered the project area – scenes 7219 and TM7. Scene 7119 was 

not reprocessed in this manner, as it contributed so little area to the final map within the park 

boundaries.

 

A total of 110 classes were developed in the ‘spruce’ unsupervised training set for scene 7219 and 

were numbered from 51001-51117. 206 classes were developed in the “no spruce” unsupervised 

training set for 7219 and were numbered from 55101 to 55212. 95 classes were developed in the 

‘spruce’ unsupervised training set for scene TM7 and were numbered from 53101-53097. 182 

classes were developed in the “no spruce” unsupervised training set for scene TM7 and were 

numbered from 57101 to 57186. 

 36
Image Classification 

GRS applied a hybrid classification methodology that uses both supervised and unsupervised 

techniques. Multiple classification maps are developed using supervised and unsupervised 

classification techniques with different statistical thresholds and training sets. The resulting 

different pixel class maps are then systematically merged to form a composite or final land cover 

pixel classification map. Classification data of the highest level of confidence are used first to form

the initial classification map. Classification data from progressively lower levels of statistical 

confidence are then added to this initial map, filling in voids and gaps in the prior version of the 

map, until the final pixel map has been developed. Supervised classification techniques typically 

result in the classification of approximately 90-95% of the mapped area, while unsupervised 

techniques typically classify the remaining 5-10% of the imagery, depending on how thoroughly the 

training data represent the variety of land cover characteristics found in the project area. 

Supervised Classification

Supervised classifications efforts were carried out for all participating scenes using bands 1, 2, 3, 

4, 5, and 7 and the Maximum Likelihood classifier. Initial efforts involved the three initial scenes 

7219, 7119, and 7018. Classification efforts were performed once again after replacement of 

scene 7018 and the majority of scene 7119 with the new TM7 data. In this second effort, a (B5 – 

B4)/(B5 + B4) ratio band was added to the classification. This pseudo-NDVI band was introduced 

in an attempt to increase separability between Spruce and Wet Herbaceous classes. 

Two masks were developed to limit the application of the training data. A cloud mask was 

developed to limit the application of all training data to only those portions of the scene that were 

cloud-free and remove clouded portions of the project areas from the final pixel classification map. 

A spruce mask was also used in this classification effort in an effort to reduce confusion and 

misapplication of spruce in areas/types in which it did not occur. Some confusion existed between 

some Spruce and Wet Herbaceous types, while spruce was also occurring in low densities in types 

dominated by other land cover attributes, such as Dwarf Shrub. The spruce mask was developed 

based on knowledge of spruce distribution within the project area by the AKSO and applied by 

GRS in the individual scene classifications. The spruce mask was used to limit the assignment of

spruce characteristics to those areas were spruce presence was acceptable. 

Each scene was classified using the Maximum Likelihood classifier with different combinations of 

mask values, training sets, and statistical (confidence) thresholds. Supervised classifications were

performed on each scene using increasingly relaxed confidence thresholds of 90%, 97.5%, and

100%. With a maximum of four training sets and three statistical thresholds, it was possible to

develop a total of 12 (4X3) supervised classification maps for each scene. However, not all 

combinations of mask/area and training set were run, if the resulting classification map would make 

an insignificant contribution to the final map. For example, the water misclassification issue 

primarily involved scene 7219. Therefore, it was not necessary to split the training sites into 

separate ”water” and “no water” training sets for scenes TM7 or 7119. In all, a total of 10 

supervised classification maps were developed for scene 7219, 7 classification maps were 

developed for scene TM7, and 4 classification maps were developed for scene 7119. 

 37
Unsupervised Classification

Unsupervised classification maps were generated using bands 1, 2, 3, 4, 5, and 7. 

The iso-clustering method was used to develop unsupervised class maps for all participating 

scenes. Parameters for this process were selected to ensure the maximum number of separable 

classes within each data set. The resulting unsupervised signature files were applied using the 

Maximum Likelihood classifier and 97.5% threshold to classify each scene. Two unsupervised 

classifications were performed for scenes 7219 and 7119, one for the “spruce” region and one for

the “no spruce” region of each scene. Scene 7119 was not reclassified using masked or split 

(water/non-water) training classes due to the very small area this scene contributed to the project 

area. 

In all, a total of 12 classification maps were developed for scene 7219, 9 classification maps were 

developed for scene TM7, and 4 classification maps were developed for scene 7119. 

Land Cover Database Development 

The resulting classification maps of each scene were then used to create one complete 

classification map. This map is a composite of portions of all the supervised and unsupervised 

classification maps. Land cover attribute descriptions were developed for each pixel class in the

final map, based on the “ground truth” data used to estimate the attributes of each training class. 

Unsupervised class attributes were developed by relating each unsupervised class to supervised 

classes that occupied the same locations, reviewing these data for satisfactory data content, and 

then calculating land cover attributes based on the specific related supervised pixel classes. Land 

cover components of the “ground truth” information were included in the final database tables. 

Final Pixel Map Development

The final pixel classification map was a composite pixel map built from the supervised and 

unsupervised classification maps developed for each participating scene and training set. All raw 

classification grids were standardized to common training class values by means of class lookup

tables that matched each raw-grid value to its particular class id through each class’ unique gridval

value. This was achieved using custom GRS grid reclassification processing software. The final 

map’s grid values were composed of the standardized gridval values each representing a specific 

supervised or unsupervised class with unique land cover attributes. 

 

Pixels for this final composite map were assembled or included from the supervised and 

unsupervised classifications by overlaying them in a hierarchical order based on the specific 

scene, the training set, and the statistical threshold of the classification. As each subsequent pixel 

classification map was overlaid on the composite map, pixel values were transferred into the 

composite map when the pixel value filled a VOID location in the composite map. In this way, 

subsequent map data never replaced data in the composite map, but instead contributed new data 

in this map. All map data were mosaiced by training set for a given threshold level and scene 

before data from another threshold level or scene were added to the final map. The source of 

each pixel location is represented in the source grid as a numeric value that represents the scene,

threshold, and training set. The mosaicing hierarchy was based on the following priorities in Table

11. 

 

 38
 Table 11: Mosaicing Priorities for Pixel Map Development

• Scene Priority – the scene value is the 3rd (leftmost) character of the source 

value. the order in which the three scenes were mosaiced was: 

 Order Scene Source value (X)

1 7219 2

2 TM7 

3 7119 1

• Statistical Threshold – the threshold level is represented by the 2nd digit (X) 

of the source value. The order in which classifications of different thresholds were 

mosaiced was: 

Order Threshold Source value (X)

1 supervised 90% threshold 1

2 supervised 97.5% threshold 2

3 unsupervised 97.5% threshold 3

4 supervised 100% threshold 4

• Training Set – the training set is represented by the 1st (rightmost) digit (X) of

the source value. The order by scene in which the different training set 

classifications were mosaiced was:

 

Scene Order Training Set Source value (X)

7219 1 “spruce” and “no water” 4 

7219 2 “spruce” and “water” 3 

7219 3 “no spruce” and “no water” 2 

7219 4 “no spruce” and “water” 1 

TM7 1 “spruce” and “water” 3 

TM7 2 “no spruce” and “water” 1 

TM7 3 “no spruce” and “no water” 2 

7119 1 “spruce” and “water” 3 

7119 2 “no spruce” and “water” 1 

The scene order affected which pixels would be used in the final map in areas of overlap where 

coverage was provided by multiple scenes. Scene 7219 took precedence over scene TM7, which 

took precedence over 7119. The final supervised 100% threshold classification was used to force 

every pixel location to have a class value in the final pixel map. These pixels can be distinguished

from other pixels as they have values between 26001 and 50001. These values were determined 

by taking the original pixel values (1001-25001) and adding an offset of 25000 to each value. 

 39
Attribute Development

The Katmai land cover type database consists of two database tables. These two tables contain

all the information that describes each class of the final pixel classification map. The main table is 

the value attribute table or .VAT table. The VALUE item (the column that contains the actual 

numeric pixel values) represents the grid values (gridval) of the grid data. This table contains all 

the land cover class summary information such as the trsite_id and calc_class, as well as 

percentage cover of trees, conifers and hardwood cover composition, and percent cover of all 

other general land cover components including, tall and low shrubs, dwarf shrub, and herbaceous 

forbs. These cover components comprise the “bird’s-eye view” description of each land cover type. 

Each table record also contains information regarding scene participation, hydric regime, and 

modifier. The second table (cover_info.dat) contains the individual species records (species,

species code, cover, strata, and so forth) for each supervised training site used in the 

classification. This table, when joined to the .VAT table using the trsite_id columns of the .VAT 

table and cover_info.dat tables. A sample listing of a database record is shown in Table 12. 

Listings and definitions of the attributes of the katmai_lcover.vat and the cover_info.dat tables are

included in Appendix D. 

 

VALUE(gridval) = 4011 

COUNT = 68972 

SOURCE = AIR_00 

SPEC_CLASS = Spruce:Open 

GRIDCLVAL = 4 

MAJOR_CLASS = Spruce:Open 

REGIME = mesic 

MODIFIER = boreal 

REMAP_VAR = 0 

TYPE = PGl 

PREDOMINANT_COMP = White Spruce 

PR_COMP_COVER = 0.0 

COVER_CLASS = Open 

TREE_COVER = 25.0 

OTHER_VEG_COVER = 70.0 

CONF_COVER = 25.0 

HDWD_COVER = 0.0 

SHR_COVER = 15.0 

TSH_COVER = 0.0 

LSH_COVER = 15.0 

DSH_COVER = 45.0 

HRB_COVER = 10.0 

BAR_COVER = 5.0 

OTH_COVER = 0.0 

TRSITE_ID = 80401 

CALC_CLASS = White Spruce:Open 

ALNUS_COVER = 0.00 

SALIX_COVER = 13.00 

Table 12: Sample Land Cover Type Attribute Listing (katmai_lcover.vat) 

 40
The land cover characteristics or attributes in the final pixel classification map database, as 

represented in the katmai_lcover.vat table, are derived from two sources – supervised 

classification data and unsupervised classification data. 

The first source of information is the entire set of land cover information (field observations) 

associated with each training site used to form a training class in the supervised training sets. 

Throughout this entire classification effort a link has been retained that associates the original 

training site description with the gridval of the final pixel map. This link is used to relate or join the

land cover attributes of the description with the mapped pixel elements. Using this link aerial 

survey data, Thomas Smith data, AKSO ground data, or supplemental data were associated with

the proper pixel values in the final map. 

The second source of data describes the unsupervised 

pixel classes used to supplement the supervised 

classification data. All the unsupervised data were 

developed and applied without knowledge of any land 

cover characteristics. In order to estimate the land cover 

characteristics of unsupervised classes, the isodata

classes developed in unsupervised classifications were 

related to supervised classes of the merged 90% 

threshold Maximum Likelihood classification using cross 

tabulation reports of the appropriate grids (the cross 

tabulation report describes a pixel-by-pixel overlay of the 

supervised and unsupervised pixel maps). Unsupervised 

map data were related to supervised map data that 

reflected similar masking of the spectral data (e.g. 

unsupervised “no spruce” classification map data were related to supervised “no spruce” map 

data). Each cross tabulation report described the frequency of each supervised class pixel (grid)

value that comprised each unsupervised class of that map, as shown in the sample data in Table 

13. In this example, unsupervised class 53057 corresponds to three supervised classes with grid 

values 10003, 10034, and 10036 – all different Open Broadleaf classes - with differing numbers of 

pixels (6, 5533, and 2635). Land cover attributes were then estimated by processing these data

with GRS_polysum. This process estimates land cover attributes of each unsupervised class by 

calculating the average attributes of all the different class attributes of the supervised pixels that

are related to that unsupervised class weighting each contributing class by the number of pixels of

that class. Using this approach, an unsupervised class’ characteristics are based solely on the 

attributes of all the supervised class attributes that occupy the same pixel locations in the 

supervised pixel map. An unsupervised class comprised solely of one supervised class (see 

53054) would be assigned the attributes of that class, as the weighted average would be the same 

as the supervised class values. GRS_polysum rejected unsupervised classes lacking sufficient 

correlation to supervised classes during processing of these data. Sufficient correlation was set at 

a minimum of 50 percent coverage of the unsupervised class with supervised pixel values. Cross 

tabulation data were also reviewed to remove any water pixels from classes that did not appear to

be “water” types. Land cover attributes were estimated in this manner for the classes in each 

unsupervised classification map that was used in the development of the final pixel classification 

map. 

Table 13: CrossTab Results 

 ... 

53054,10034,388 

53057,10003,6 

53057,10034,5533 

53057,10036,2635 

53058,10003,245 

53058,10035,3920 

53058,10080,169 

53058,10081,1628 

53058,10082,931 

...

The land cover components estimated at each supervised training site were also included in a 

database table. These table records may be joined to the land cover attributes table using the 

trsite_id columns as the key field. These data were not generated for unsupervised classes. A 

sample listing of a record from this table is shown in Table 14. 

 41
Record 1: 

 TRSITE_ID = 80404 

 SOURCE = AIR_00 

 SPECIES = Picea glauca 

 SPCODENUM = 45 

 SPCODE = PIGL 

 COVER = 15.0 

 STRATA = TR_CNFR 

 COMMENT = 

 GRIDVAL = 6001 

Record 2: 

 TRSITE_ID = 80404 

 SOURCE = AIR_00 

 SPECIES = Salix L 

 SPCODENUM = 138 

 SPCODE = SALIX 

 COVER = 5.0 

 STRATA = TS 

 COMMENT = 

 GRIDVAL = 31001 

 Table 14: Sample Cover Data Attribute Listing (cover_info.dat) 

 42
Results 

The results of this land cover mapping project are contained in the composite classification pixel 

map and the associated data tables that describe the land cover characteristics of each pixel type. 

A total of 1,302 pixel classes are represented in the final map. Final map frequency of pixels by 

source, listed in the order in which the pixels were added to the final pixel classification map is 

shown in Table 15. A summary of pixels by scene and classification effort is shown in Table 16. 

Table 15: Final Pixel Map Frequency by Source 

Classification

Source 

Value Frequency

Percent of 

Total

7219/"spruce-no water"/90% ML 214 4,094,263 13.6%

7219/"spruce-water"/90% ML 213 1,136,223 3.8%

7219/"no spruce-no water"/90% ML 212 4,368,705 14.5%

7219/"no spruce-water"/90% ML 211 367,358 1.2%

TM7/"spruce-water"/90% ML 13 1,904,169 6.3%

TM7/"no spruce-no water"/90% ML 12 1,109 0.0%

TM7/"no spruce-water"/90% ML 11 9,111,461 30.3%

7219/"spruce-no water"/97.5% ML 224 994,093 3.3%

7219/"spruce-water"/97.5% ML 223 16,572 0.1%

7219/"no spruce-no water"/97.5% ML 222 1,295,317 4.3%

7219/"no spruce-water"/97.5% ML 221 14,706 0.0%

TM7/"spruce-water"/97.5% ML 23 57,249 0.2%

TM7/"no spruce-no water"/97.5% ML 22 4,565 0.0%

TM7/"no spruce-water"/97.5% ML 21 1,937,149 6.4%

7119/"spruce-water"/97.5% ML 123 197,380 0.7%

7119/"no spruce-water"/97.5% ML 121 64,093 0.2%

7219/"spruce-water"/97.5% UnSup 233 771,317 2.6%

7219/"no spruce-water"/97.5% UnSup 231 1,104,106 3.7%

TM7/"spruce-water"/97.5% UnSup 33 29,449 0.1%

TM7/"no spruce-water"/97.5% UnSup 31 2,248,643 7.5%

7219/"spruce-water"/100% ML 243 102,190 0.3%

7219/"no spruce-water"/100% ML 241 55,294 0.2%

TM7/"no spruce-water"/100% ML 41 90,326 0.3%

7119/"spruce-water"/100% ML 143 131,305 0.4%

7119/"no spruce-water"/100% ML 141 18,810 0.1%

Total 30,115,852 100.0%

7219 14,320,144 47.6%

TM7 15,384,120 51.1%

7119 411,588 1.4%

 

 43
Table 16: Number of Pixels by Classification Effort

SceneTraining Set 

90%

Threshold

97.5%

Threshold

Isodata 

Classification

100%

Threshold Total 

Percent 

of 

Project

Percent 

by

Scene

7219 spruce – water 1,136,223 16,572 771,317 102,190 2,026,302 6.7% 

7219 spruce - no water 4,094,263 994,093 0 0 5,088,356 16.9% 

7219 No spruce – water 367,358 14,706 1,104,106 55,294 1,541,464 5.1% 

7219 No spruce - no water 4,368,705 1,295,317 0 0 5,664,022 18.8% 47.6%

TM7 spruce – water 1,904,169 57,249 29,449 0 1,990,867 6.6% 

TM7 spruce - no water 0 0 0 0 0 0.0% 

TM7 No spruce – water 9,111,461 1,937,149 2,248,643 90,326 13,387,579 44.5% 

TM7 No spruce - no water 1,109 4,565 0 0 5,674 0.0% 51.1%

7119 spruce – water 0 197,380 0 131,305 328,685 1.1% 

7119 No spruce – water 0 64,093 0 18,810 82,903 0.3% 1.4%

 

Total 20,983,288 4,581,124 4,153,515 397,925 30,115,852 100.0% 100.0%

 

Percent of Total 69.7% 15.2% 13.8% 1.3% 100.0% 

Data Modeling 

All of the pixel classes in the final pixel map were derived from the image classification processes, 

with one exception. This one exception in the delivered data set involves one class developed 

using post-classification modeling of the data. After review of these classification data, there was 

an indication that there were some “water” pixels in locations or situations where they should not 

exist. These situations were typically areas of north or northwesterly aspect on steeper aspects. 

These water pixels were very likely the result of very dark (low digital value) pixels in areas of very 

low illumination that are most appropriately described as “terrain shadows.” After all the bands 

were normalized for differential illumination, these very dark pixels were still very dark. 

Unfortunately, very dark pixels tend to be confused with “water” pixels, as water reflects virtually no 

energy. These dark pixels were subsequently classified in error as “water” pixels, when they 

should be labeled as “terrain shadow” pixels. Post processing or modeling was applied to the final 

pixel classification map, in an effort to identify, and reclass as “terrain shadow” as many of these 

misclassified “water” pixels as possible. 

This modeling effort entailed identifying suspect “water” locations in the final map and altering their 

values to indicate “terrain shadow” as opposed to “water.” Reclassification to “terrain shadow” was 

accomplished using the following rules: 

1. The pixel was classified as a “water” type 

2. The slope of the pixel was greater than 4 degrees or 9% slope 

3. The aspect of the pixel was between an azimuth of 258 and 23 degrees (WSW to NNE 

aspects) 

A total of 38,532 pixels that meet these criteria were reclassified as “terrain shadow.” 

Unfortunately, further investigation revealed that some valid “water” pixels were now misclassified

as “terrain shadow.” This misclassification resulted along the edges of lakes where the slope map 

may have been slightly misaligned with the satellite imagery, resulting in areas of high slope along 

the edges of large bodies of water that overlap water pixels. This misalignment of the slope and 

“water” data caused some valid pixels to be changed to “terrain shadow” pixels. As further 

modeling refinements and pixel aggregation were not in the scope of this mapping effort, these 

data remained in the “terrain shadow” class in the final map. 

 44
Data Display and Representation 

The resulting pixel map represents the 1,302 pixel classes used in the classification of the spectral 

data. These data values alone are not of much use, as they simply represent a value from 0 to 

59,999. However, when these values are joined to the land cover information data associated with 

each training site, the resulting database includes many different levels of information. These 

levels range from major land cover types to percent composition of individual specie groups, such 

as alder and willow. All of these data may be used to develop different maps and information. As 

the terrain shadowing modeling effort indicates, these data may be used to develop any number of

different maps based on the detailed information present in this data base, as well as ancillary data 

(spatially co-registered) that may enhance modeling efforts. Figure 13 demonstrates this 

capability, as it represents the field estimated types (“calculated”) on the left in an area dominated 

by shrub types, but shows on the right a map of the herbaceous cover for the same area. While 

the herbaceous cover (or any other cover component) is not a part of the type map, each may be 

mapped separately using the database attributes. Many of the different attributes in Table 13 may 

be represented in similar maps, thereby resulting in many maps of the Katmai project area, rather 

than one land cover type map. Similarly, these same attributes can be used to model the data and 

enhance or modify the pixel classification map into other maps representing related data, such as 

wildlife habitat suitability or fire fuel class. 

Figure 13: Type Map and Cover Component Map 

 45
Land Cover Type Area Distribution and Source 

Table 17 represents the distribution of pixels and area by the forty (40) “calculated” land cover 

classes used in this project. These are the classes that were used when the land cover 

distributions were processed using the various land cover component thresholds used to make the 

field calls for each training site. Pixels values reflect only those pixels within the Katmai National 

Park and Preserve and do not include pixels within the ten-mile buffer around the park. 

 Table 17: Area and Frequency by ‘Calculated’ Land Cover Class

“Calculated” Class Pixel 

Frequency

Acreage Percent 

of Total 

Type 

Total 

Stunted Spruce:Wdlnd 3,016 670.6 0.0% 

White Spruce:Wdlnd 280,766 62,426.9 1.5% 

Stunted Spruce:Open 13,633 3,031.2 0.1% 

White Spruce:Open 282,833 62,886.5 1.5% 

White Spruce:Closed 9,882 2,197.2 0.1% 3.2%

Mixed Deciduous-Conifer:Wdlnd 81,161 18,045.7 0.4% 

Mixed Deciduous-Conifer:Open 294,354 65,448.1 1.6% 

Mixed Deciduous-Conifer:Closed 39,813 8,852.2 0.2% 2.3%

Balsam Poplar:Wdlnd 141,496 31,460.9 0.8% 

Birch:Wdlnd 205,832 45,765.7 1.1% 

Mixed Deciduous:Wdlnd 109,769 24,406.6 0.6% 

Balsam Poplar:Open 327,959 72,920.0 1.8% 

Birch:Open 524,826 116,692.4 2.9% 

Mixed Deciduous:Open 247,475 55,024.8 1.3% 

Balsam Poplar:Closed 92,468 20,559.8 0.5% 

Birch:Closed 221,558 49,262.3 1.2% 

Mixed Deciduous:Closed 52,978 11,779.4 0.3% 10.5%

Tall shrub:Open:Alder 547,386 121,708.5 3.0% 

Tall shrub:Open:Alder-Willow 253,202 56,298.2 1.4% 

Tall shrub:Open:Mix 37,914 8,430.0 0.2% 

Tall shrub:Open:Willow 780,693 173,583.2 4.3% 

Tall shrub:Closed:Alder 1,102,781 245,197.8 6.0% 

Tall shrub:Closed:Alder-Willow 461,069 102,516.4 2.5% 

Tall shrub:Closed:Mix 140,543 31,249.0 0.8% 

Tall shrub:Closed:Willow 297,044 66,046.2 1.6% 19.7%

Low shrub:Open:Mix 532,783 118,461.6 2.9% 

Low shrub:Open:Alder-Willow 18,206 4,048.0 0.1% 

Low shrub:Open:Willow 822,019 182,771.8 4.5% 

Low shrub:Closed:Mix 89,657 19,934.8 0.5% 8.0%

Dwarf Shrub 2,541,267 565,038.0 13.8% 13.8%

Herbaceous:Forb 826,521 183,772.8 4.5% 

Herbaceous:Graminoid 197,483 43,909.4 1.1% 

Herbaceous:Moss 199,309 44,315.4 1.1% 

Herbaceous:Lichen 24,789 5,511.7 0.1% 6.8%

Sparse Vegetation 966,119 214,811.7 5.3% 5.3%

Barren 3,026,054 672,828.0 16.5% 16.5%

Snow/Glacier 1,139,822 253,433.7 6.2% 6.2%

Water 1,384,385 307,811.1 7.5% 7.5%

Terrain Shadow/Unclassified 38,532 8567.4 0.2% 

Unclassified 11,636 2,587.2 0.1% 0.3%

Total 18,369,033 4,084,262.6 100.0% 100.0%

 46
 47

Table 18 represents the distribution of pixels and area by the major land cover classes that were 

derived by collapsing the forty “calculated” land cover classes in Table 17 into twenty-five (25) land 

cover classes that tend to reflect more generalized vegetation and density classes. 

 Table 18: Area and Frequency by Major Land Cover Class 

Major Class Pixel 

Frequency 

Acreage Percent 

of Total 

Spruce Stunted:Woodland 3,016 670.6 0.0% 

Spruce:Woodland 280,766 62,426.9 1.5% 

Spruce Stunted:Open 13,633 3,031.2 0.1% 

Spruce:Open 282,833 62,886.5 1.5% 

Spruce:Closed 9,882 2,197.2 0.1% 

Spruce/Deciduous:Woodland 81,161 18,045.7 0.4% 

Spruce/Deciduous:Open 294,354 65,448.1 1.6% 

Spruce/Deciduous:Closed 39,813 8,852.2 0.2% 

Deciduous:Woodland 457,097 101,633.2 2.5% 

Deciduous:Open 1,100,260 244,637.3 6.0% 

Deciduous:Closed 367,004 81,601.5 2.0% 

Tall Shrub:Open 1,619,195 360,019.9 8.8% 

Tall Shrub:Closed 2,001,437 445,009.5 10.9% 

Low Shrub:Open 1,373,008 305,281.5 7.5% 

Low Shrub:Closed 89,657 19,934.8 0.5% 

Dwarf Shrub 2,541,267 565,038.0 13.8% 

Herbaceous - Forb 1,024,004 227,682.2 5.6% 

Herbaceous - Moss 199,309 44,315.4 1.1% 

Herbaceous - Lichen 24,789 5,511.7 0.1% 

Sparse Vegetation 966,119 214,811.7 5.3% 

Barren 3,026,054 672,828.0 16.5% 

Snow/Ice 1,139,822 253,433.7 6.2% 

Water 1,384,385 307,811.1 7.5% 

Terrain Shadow 38,532 8,567.4 0.2% 

Unknown 11,636 2,587.2 0.1% 

Total 18,369,033 4,084,262.6 100.0% 

Table 19 represents the “calculated” land cover type by their source, in terms of the classification 

threshold. The four sources represent the three thresholds of supervised classifications (90%, 

97.5%, and 100%) and the unsupervised classification. These values indicate the source of the 

different land cover type data, which can be interpreted to indicate different levels of confidence in 

the pixel data. For instance, pixels derived from the 90% threshold classification efforts were 

classified with tighter statistics than pixels derived from the 97.5% threshold classification efforts 

and would be expected to be more a more accurate assignment of pixel classes than those pixels 

derived from classification efforts with looser statistics. Note that some “mixed” types were derived 

solely from the unsupervised classification efforts. These “mixed” types arise from the computation 

of the weighted average characteristics of each unsupervised class based on the overlay of the 

supervised pixel data with the unsupervised data. The primary source(s) for each land cover type 

is indicated in boldface type. In a few cases, two sources contributed nearly equally and both are 

highlighted. 
Table 19: Area by Cover Type and Source 

Class 

90%

Threshold 

97.5%

Threshold Iso Classif. 

100%

Threshold Total 

90%

Threshold 

97.5%

Threshold 

Stunted Spruce:Wdlnd 1,919 1,091 0 6 3,016 63.6% 36.2% White Spruce:Wdlnd 191,940 52,060 36,586 180 280,766 68.4% 18.5% Stunted Spruce:Open 10,494 3,135 0 4 13,633 77.0% 23.0% White Spruce:Open 214,339 55,059 13,386 49 282,833 75.8% 19.5% White Spruce:Closed 7,425 2,443 0 14 9,882 75.1% 24.7% Mixed Deciduous-Conifer:Wdlnd 35,792 7,286 38,083 0 81,161 44.1% 9.0% Mixed Deciduous-Conifer:Open 188,628 48,258 57,392 76 294,354 64.1% 16.4% Mixed Deciduous-Conifer:Closed 32,141 7,670 0 2 39,813 80.7% 19.3% Balsam Poplar:Wdlnd 71,002 17,123 53,266 105 141,496 50.2% 12.1% Birch:Wdlnd 111,919 35,343 58,356 214 205,832 54.4% 17.2% Mixed Deciduous:Wdlnd 14,460 3,794 91,495 20 109,769 13.2% 3.5% Balsam Poplar:Open 264,475 63,272 16 196 327,959 80.6% 19.3% Birch:Open 385,298 99,223 39,925 380 524,826 73.4% 18.9% Mixed Deciduous:Open 158,885 36,941 50,797 852 247,475 64.2% 14.9% Balsam Poplar:Closed 75,477 16,793 0 198 92,468 81.6% 18.2% Birch:Closed 173,138 48,132 0 288 221,558 78.1% 21.7% Mixed Deciduous:Closed 41,345 11,356 0 277 52,978 78.0% 21.4% Tall shrub:Open:Alder 277,774 62,792 204,173 2,647 547,386 50.7% 11.5% Tall shrub:Open:Alder-Willow 114,432 18,307 120,380 83 253,202 45.2% 7.2% Tall shrub:Open:Mix 0 0 37,914 0 37,914 0.0% 0.0% Tall shrub:Open:Willow 591,153 158,351 26,864 4,325 780,693 75.7% 20.3% Tall shrub:Closed:Alder 827,863 174,139 76,584 24,195 1,102,781 75.1% 15.8% Tall shrub:Closed:Alder-Willow 370,168 80,983 8,689 1,229 461,069 80.3% 17.6% Tall shrub:Closed:Mix 94,046 41,371 0 5,126 140,543 66.9% 29.4% Tall shrub:Closed:Willow 249,085 47,534 0 425 297,044 83.9% 16.0% Low shrub:Open:Mix 401,713 104,609 16,643 9,818 532,783 75.4% 19.6% Low shrub:Open:Alder-Willow 0 0 18,206 0 18,206 0.0% 0.0% Low shrub:Open:Willow 585,361 163,003 68,802 4,853 822,019 71.2% 19.8% Low shrub:Closed:Mix 69,413 19,946 0 298 89,657 77.4% 22.2% Dwarf Shrub 1,456,350 426,870 638,704 19,343 2,541,267 57.3% 16.8% Herbaceous:Forb 417,052 220,282 181,357 7,830 826,521 50.5% 26.7% Herbaceous:Graminoid 139,309 55,261 0 2,913 197,483 70.5% 28.0% Herbaceous:Moss 121,516 48,694 28,641 458 199,309 61.0% 24.4% Herbaceous:Lichen 19,877 4,889 0 23 24,789 80.2% 19.7% Sparse Vegetation 389,843 242,490 325,481 8,305 966,119 40.4% 25.1% Barren 1,780,561 513,993 665,303 66,197 3,026,054 58.8% 17.0% Snow/Glacier 931,450 77,337 114,694 16,341 1,139,822 81.7% 6.8% Water 1,350,176 25,755 6,343 2,111 1,384,385 97.5% 1.9% UnClassified/Terrain Shadow 20,580 7,848 21,237 503 50,168 41.0% 15.6% Total 12,186,399 3,003,433 2,999,317 179,884 18,369,033 66.3% 16.4%  48
Deliverables 

Deliverables for this mapping project included the following data themes and tables: 

1. Katmai_lcover – an ArcInfo grid coverage and associated attribute data that represents the 

final composite pixel map representing the results of the supervised and unsupervised 

classification efforts implemented during this project. 

2. Cover_info.dat – an ArcInfo table that contains the species-specific cover descriptions for 

each supervised training site associated with a pixel class in the Katmai_landcover grid. 

3. Source – an ArcInfo grid coverage that corresponds to the Katmai_landcover grid and 

represents the source, in terms of the scene and classification, of each pixel’s classification 

information. 

4. Slope - an ArcInfo grid coverage that corresponds to the Katmai_landcover grid and represents 

the slope, in terms of degrees, as developed from the DEMs. 

5. Aspect - an ArcInfo grid coverage that corresponds to the Katmai_landcover grid and 

represents the aspect, in terms of degrees counter clockwise from North, as developed from 

the DEMs. 

6. BTMGain - an ArcInfo grid coverage that corresponds to the Katmai_landcover grid and 

represents the change in pixel value in Band 4 resulting from implementation of the illumination 

normalization process. 

7. Trsites – an ArcInfo point coverage that represents the location and associated information 

describing each supervised training site used during the supervised classification efforts. 

8. Ecoregions – an ArcInfo area coverage representing the original set of ecoregions used during 

the initial candidate site selection processing. Stratification was based on the value in the 

ecoregion column of the ecoregions.pat table. 

9. Katmai_confusion.rpt – a text file that contains the confusion data summary for the

supervised classification training data sets. 

10. Katmai_fidelity_review.xls – an Excel spreadsheet that contains the fidelity data summary by

site, as well as summaries in total and by scene. 

11. Sample ArcView v3.2a legend files (*.avl) to assist in viewing these data.

All of these deliverables were referenced to the Project Alber’s coordinate system, as defined in 

Table 2. All data were delivered on CD-ROM in both an ArcInfo grid/cover format as well as an 

ArcInfo interchange format. All deliverables are located under the ‘Katmai_Mapping_2000’ 

directory on the CD-ROM. Coverages were delivered under the ‘Katmai_Maps’ directory, 

interchange files were delivered in zipped format under the ‘exports’ directory, and reports were 

delivered under the ‘reports’ directory. This information is contained in a readme.txt file found 

under the main Katmai_Mapping_2000 directory. 

 49
Conclusions and Discussion 

Scene Replacement 

The two 2000 TM7 scenes used to replace the original Landsat TM5 data were initially acquired 

from the EROS Data Center in a geo-corrected format. This imagery was rejected due to 

unacceptable preprocessing of the imagery – Band 4 digital data had been adjusted differently in

each scene using a cubic convolution filter, thereby preventing the mosaicing and subsequent 

classification of these new scenes. As this was the only delivery format available from the EROS 

Data Center, alternative means of orthorectification were acquired through Resource GIS and 

Imaging (RGI) of Vancouver, Canada. The scenes provided by RGI were then mosaiced and used 

for classification efforts in this project. 

Class Representation and Training Site Availability

Replacement of scenes 7119 and 7018 with newer TM7 scenes 7019 and 7020 generated a 

change in the distribution and availability of training areas. The summary list of classes trained 

within each classified scene reported in Table 9 shows a net gain of 58 training sites in scene TM7 

when compared with scene 7018. However, there was a net loss of 101 training sites (40%) when 

compared with scene 7119. While the newer imagery improved estimation of land cover type 

classes in the area previously covered by 7018, it resulted in a decreased number of classes in 

that portion of the project area previously covered by 7119. This was due to the decreased area of 

overlap between 7219 and the TM7 data, as opposed to the original area of overlap between 7219 

and 7119, in which a significant number of training sites were located (see Figure 14). 

Figure 14: Original and Final Scene Extent 

Unfortunately, the portion of the project area covered by scene 7018 was only about 1% of the total 

area, while scene 7119 covered approximately 52% of the project area. Scene replacement 

translated into a reduced variety (from 60 to 49) of the available surveyed training areas/classes. 

Particularly affected classes were “Open Spruce” and “Spruce/Deciduous,” whose available 

training areas were reduced from 6 to 2 and 9 to 0, respectively. Scene TM7 had 86 fewer training 

sites/classes used in final classifications than scene 7219. The overall result is that the pixel 

classification in the TM7 coverage may be more generalized due to fewer classes. In addition, the

unsupervised classification also contributed a slightly larger percentage of pixels in this area than

in 7219 – 15% of the pixels as opposed to 13%. 

 50
However, a clear benefit from scene replacement was the reduction in the confusion between 

“Spruce” and “Wet-Herbaceous” classes. The newer imagery allowed a cleaner separation of 

these classes during classification. However, this may have been caused by the reduction in 

available “Spruce” class training sites in the new imagery as there were only 2 “Spruce” classes in

the replacement training data set, as opposed to 6 in the original training data set. Whatever 

confusion remained between these classes was controlled by means of the spruce mask, as 

described earlier. The 2000 imagery may also have enabled the removal of differences in 

senescence that may have affected the classifications, thereby ensuring a better classification 

performance of the training areas surveyed in August of 2000. Unfortunately, time did not allow for 

a pixel-by-pixel comparison between the original classification data and the final classification data 

to quantify these differences. 

Positional Accuracy of Results

The replacement data was offset significantly from the original imagery. The apparent shift was 

approximately 3 pixels in an easterly direction and 5 pixels in a northerly direction. All replacement 

data were shifted to coincide, as best as possible, with the location of the original pixel data. Of 

particular interest was that the replacement imagery, in terms of positional accuracy and alignment 

to USGS DEM data, did not exhibit the same alignment that either the replacement imagery 

acquired from the EROS Data Center, or the original imagery reflected. Conversations with RGI 

indicate that it may not be possible for a third-party to achieve the same alignment without 

obtaining the same control data used by EROS Data Center for their internal processing of the 

imagery. This lack of consistency potentially compromises the spatial location of the image data 

and the resulting pixel classification maps. Without a conflicting data set that enabled the prompt 

identification of a sizable data offset, the user of such image data must be careful to check the 

location of the imagery with respect to its true position on the earth. Such discrepancies could 

dramatically effect the development of training classes; if training sites are accurately located with

a GPS and then referenced to an inaccurately located image data set, the training data might 

reflect spectral data that was not really present at the training site. Ideally, EROS Data Center 

would change its internal image processing policy to use nearest neighbor instead of cubic 

convolution for re-sampling of LANDSAT scenes, so that a third-party firm would not be needed to

orthorectify the imagery. However, until such time, future projects may have to either accept the 

‘convoluted’ data or turn to private sources (IKONOS) and third-party companies for comparable or 

improved image products. Care will need to be exercised to assure that future data are accurately

located. 

Training Data Collection Efforts 

Uncertainty of Training Site Locations

One of the major issues potentially impacting the results of this mapping effort is the reduced 

confidence in the true ground location of aerial survey training sites. The failure of the PLGR GPS 

units to receive and record spatial coordinates made the location of training sites much more 

difficult and potentially inaccurate. The need to “make do” with the aircraft’s GPS unit for 

determining survey locations (as opposed to the more accurate PLGR unit) introduced an unknown 

amount of error in the assessment of the latter during image training efforts. The difference in 

format and datum between the two devices led to uncertainty during the first three days of data 

collection efforts, until the coordinate differences had been properly verified. This uncertainty was 

compounded by the fact that helicopter GPS values were shown to often be 100 meters, or further, 

from the true location of the training site in any direction. The potential impact of this uncertainty is 

that training data may have been collected in the wrong locations. The potential inclusion of 

erroneous nearby pixels during region growing operations, due to erroneous placement of 

inaccurate training site locations (shifted toward the edge of target areas) might have introduced 

 51
unwanted confusion between apparently unrelated classes. This additional confusion required 

multiple reviews to resolve, and in some cases the problems never could be resolved. Table 20 

lists those training areas excluded from final classification processes due to confusion that was 

likely due to the positional uncertainty of the training site location. 

Although for different reasons, the same 

uncertainty of training site locations 

applied to some of the Dr. Thomas Smith 

(TSMITH) data collection sites that were 

included in the project. In these cases, 

uncertainty existed as to the actual extent 

(and location) of the training areas, 

particularly for those sites for which no 

photographic record was available. 

AKSO staff reviewed all TSMITH data 

and locations and those sites that were 

deemed inaccurately located were 

withheld from use as training data 

classes. 

In future projects of this nature, care must 

be taken to perform an actual test of GPS 

equipment under conditions identical to 

actual field sampling (as opposed to hearsay accounts of its use) to ensure proper functioning of 

such a critical instrument. While the PLGR units were used successfully on the ground, and even 

in a moving truck, during the days immediately prior to the aerial survey efforts, these conditions 

were not the same as those in the helicopter. Prior knowledge of this problem would have 

eliminated some of the problems and uncertainty of locations visited during the first few days of the 

project. 

Table 20: Untrained Sites 

trsite_id cover_type_des 

----------- ------------------------------------- 

80726 Alder-Willow:Tall:Open 

80803 Balsam Poplar:Open 

80711 Balsam Poplar:Open 

80703 Birch:Woodland 

80402 Dwarf Shrub-Bare Soil 

80634 Dwarf Shrub-Sedge 

80636 Dwarf Shrub:Mixed 

80722 Dwarf Shrub:Mixed 

80810 Herbaceous:Freshwater 

80601 Spruce:Open 

80720 Willow:Low:Open 

80502 Willow:Low:Open 

Land Cover Data Estimates

Another issue regarding the collection of field data regards the estimation of land cover component 

that formed the basis of the Land Cover Type Classification. The approach that was applied during 

this project was that the cover components would be estimated separately on the field data 

collection forms and the Land Cover Type class would be assigned on the basis of the type 

components. The land cover estimates were recorded on the aerial field survey sheets from a 

helicopter, as it slowly circled the area of interest. Two issues arose with this method of data 

collection. The first issue concerns verification of the cover estimates. None of the aerial 

estimates were field verified to determine their accuracy. While it is entirely possible to develop 

accurate estimates in this manner, these estimates should be verified to determine that they are 

accurate. It is doubtful that estimates of this nature can be much more accurate than ± 10 percent 

of the true value (on the average), as this is the typical level of error for a ground cover survey 

using 100 point transects. There were instances where either too much or too little cover may 

have been estimated for a particular species, possibly resulting in mistyping of the site. An 

illustrative example of this scenario was training site 80607. The cover components of this site 

included an estimate of 10% spruce cover, resulting in a type assignment of “Spruce:Woodland.” 

However, this site was confused with a number of Dwarf Shrub sites. A review of the photo record

for this site indicated that there was very likely much less than 10% spruce cover. This training 

site, with less spruce cover would have been typed as a “Dwarf Shrub” type, a type which would 

have been quite consistent with its confused types. 

 52
While there will always be cases such as this one, where estimates span thresholds of very 

different types (tree type versus a shrub type), care must be exercised to accurately estimate the 

key cover components that determine the eventual type designation of an area. 

Other concerns involved the estimation of field data without verification for consistency of the 

estimates. In several situations, all data were collected in the field only to find out several weeks 

later that the cover data did not add up to 100% cover, as was our stated goal. The plot was then

reviewed with respect to all other field notes and photos to estimate the missing cover. Errors may 

have been introduced by fixing these anomalies in the office, rather than in the field as the data 

were collected. Fixing these errors in the office not only introduced possible error, but also took a 

substantial amount of time as all data were subjected to rigorous checks and editing. In addition,

some types were assigned to data that were inconsistent with the cover components that had been 

estimated. This was evident, for example, in training site number 80401, where the assigned cover 

class was “Spruce:Woodland;” even though it was attributed with 25% of Spruce cover, which 

made it an “Open” type. These discrepancies nearly always involved type, species, or density 

class thresholds. These discrepancies are significant, as the field personnel use their calls or type 

assignments to categorize different land cover types with their visual observations – they train their 

eye to recognize the different types. If the assignment that has been made is wrong, and not 

corrected until after leaving the field, then the field observer may be associating field observations 

with incorrect types or density classes, thereby introducing potential errors into the data. For 

example, if a field observer estimates tree cover at 20 percent, and the observer is consistent in 

calling other areas with similar tree cover as 20 percent cover, that doesn’t make them 20 percent 

cover trees when they are really 30 percent cover. Being consistently inaccurate is not the same 

as being right. Similar errors can occur with species composition, size/height, and specie 

designation (aspen versus balsam poplar). 

Although these situations and other obvious ones were detected, their existence indicated that 

methods used to estimate and record land cover data for each survey site may have allowed less 

obviously inaccurate data into the project’s database from which subsequent cover data were 

developed. Uncertainty regarding cover component data and class calls should be avoided 

through the incorporation of procedures to verify land cover data estimates on site in future 

projects. Possible solutions could include: ground verification of a certain percentage of aerial 

survey sites to provide verification of estimates and help the field observers calibrate their ‘eyes’; 

hovering directly over the center of each training site, and/or mounting a remote-controlled highresolution stereo-capable film camera on the aircraft (which would obtain high resolution, stereo

pairs for subsequent photo-interpretation to verify observations). In addition, field survey personnel 

should be trained using paired ground/air plots to recognize differences in cover, species, and 

size/height immediately prior to making the actual training site estimates. Problems concerning 

data records are another matter than could be avoided through the used of hand-held data 

recorders. Rather than log field estimates on field forms with written notes and numbers the use of 

data recorders is strongly recommended. These data recorders may be programmed to perform 

data validity checks to be certain that not only are all the data collected, but that the data are 

consistent with each other. All instances where the “bird’s-eye” cover estimates did not total 100% 

would be resolved before leaving the site. Inconsistent type calls would be flagged for review and 

correction in the field. Erroneous data would be avoided eliminating potential data inaccuracies as 

well as the time required to review and resolve these problems. Some of these problems were not 

uncovered until an inconsistency in the fidelity test or confusion report identified the problem. 

Elimination of erroneous data would have saved considerable efforts as field data tables were 

reviewed, corrected, and exchanged at least four or five times prior to resolving every last problem. 

These problems amounted to several weeks worth of effort over the course of the project in 

additional data review, verification, and translation of the data, as well as redoing all associated 

reports and analyses. 

 53
Aerial Survey Data Collection

A total of nine days were dedicated to collection aerial survey data. The first possible day was lost 

to weather, resulting in eight days of extensive aerial survey work. During these eight days a total 

of 32 hours of actual aerial survey time were logged, or an average of four (4) hours/day. Other 

flight time was spent (lost) due to refueling flights, transportation of ground personnel, and down 

time due to limited refueling opportunities. The average number of aerial survey training sites 

visited per day was 27. Of the total of 222 sites surveyed from the air, 210 (95%) were ultimately 

used in the supervised classification efforts. 

Reduced aerial survey time resulted in fewer surveyed areas than was originally expected. This 

limitation in training area number came to bear in particular when 2000 imagery replaced the 

original scenes around which data collection efforts was planned and implemented. Training sites 

in the replacement imagery were limited due to virtually no scene overlap with scene 7219 and the 

fact that only two days of aerial survey efforts were conducted in the eastern portions of the project 

area. On the basis of these field data collection efforts and taking into account such factors as 

weather, safety, and fuel availability, as many as 40 sites could be visited during a full aerial 

sampling day (i.e., nearly 75% flight time dedicated to aerial training site surveying instead of 

50%). One could conservatively estimate a 50% increase in the total number of aerial survey 

training areas, if such a schedule could have been followed, but this would require sacrificing other 

uses of the helicopter. This estimated level of productivity should be kept in mind for future 

projects for which a minimum number of training areas would be necessary and other data 

sources, such as the TSMITH ground plots, are not available to supplement data collection efforts. 

Image Processing Issues 

Water and Non-Water Confusion in Mountainous Regions 

Image classification efforts were based on a number of training sites in order to reduce confusion 

and misclassification of data. Particularly noteworthy was the need to partition training sets into 

“water” and “non-water” training data sets to reduce the assignment of “Water” pixels in higher 

elevation, flat to gently sloping, non-water situations, as occurred in scene 7219 (this was a 

situation not related to the “terrain shadowing” dark pixels assigned Water values). While this 

situation was resolved in this manner, further analysis revealed the cause of this problem. A few 

“Water” training areas were collected in areas that inadvertently included very shallow water. 

These shallow-water training areas often ran up to the edge of the water bodies, and appear to 

have mistakenly absorbed training pixels from adjacent “barren” pixels along the shoreline of the 

water bodies during the region growing process. These training classes actually had a mix of 

water and barren spectral data that led to the classification of some valid “Barren” areas as “Water” 

type pixels. While removal of the water training areas resolved this situation in this case, care 

should be exercised to not include these very shallow water types if the training data comes ashore 

onto the neighboring “barren” pixels. In particular, in large bodies of shallow water this may be a 

serious concern, as the region growing processes more easily absorb these different edge pixels 

when the area size is larger (the larger size creates a large sample size and may easily absorb 

variant pixels without exceeding variance thresholds). Building training areas in riverine situations

with combinations of shallow water and gravel is another situation to avoid. Splitting the training

sets solved the problem this time, but less work is involved if greater care is taken in building the 

water training classes. 

 54
Water and Isolated Herbaceous Type Pixels

In certain water situations “Herbaceous” or “Sparse Vegetation” classes were assigned resulting in 

pixilated water bodies. In this situation, the vegetation cover attributed to the pixel data was 

emergent grasses and other aquatic vegetation in types that were dominated by water. 

Unfortunately, in the classification rules, “Herbaceous” or “Sparse Vegetation” types were 

designated if either the herbaceous cover was greater than 25% cover, or if the sum of the 

vegetation cover was greater than 15% cover. Therefore, a “Herbaceous” type could be assigned 

if the class had 25% herbaceous cover and 75% water, and a “Sparse Vegetation” class with only 

15% vegetation cover and 85% water. These classification rules resulted in a few training classes 

that looked spectrally similar to water, but were classified as a non-water pixel class. In these 

situations, the classification thresholds may warrant adjustment if the assignment of these nonwater classes is too much of a problem. As with some of the other pixelation issues, aggregation 

or modeling are other ways to address resolution of these issues if the end user finds the results 

distracting or confusing. The presence of the cover by component in the database record is a 

tremendous help in both identifying and resolving this situation. 

Spruce/Wet Herbaceous Confusion 

Throughout the image training process, another stubborn confusion between a few “Wet 

Herbaceous” and “Spruce” types became apparent. After undertaking exhaustive spectral training

efforts to separate these types some confusion still remained. In order to resolve this issue spruce 

masks were developed, one for each scene. These masks were used to identify where spruce 

was thought to exist, as opposed to where it was not present. In this way, training areas containing

spruce cover were excluded from areas known to lack any significant spruce cover. However, 

confusion remained between these two types within the spruce-inclusive areas of each scene. 

This was particularly evident in scenes 7219 and 7119, which contained larger numbers of spruce 

and wet herbaceous training areas (41 versus 13 and 20 versus 24 respectively). The newer TM7 

mosaiced imagery did not show much confusion between these types. While this may have been 

due to the earlier acquisition date and lack of senescence in the “Wet Herbaceous” type pixel, this 

reduction may also have been due to the reduced number of available spruce training areas in the

replacement imagery as compared to the original 7119 scene. Scene 7219 retained some of the 

Spruce/Wet Herbaceous confusion that prompted the use of spruce masks to isolate areas unlikely 

to contain spruce. “Spruce” inclusions within “Wet Herbaceous” areas should definitely be viewed

with suspicion. Where “Spruce” pixels are isolated and surrounded by “Wet Herbaceous” types, an 

aggregation or modeling process could be used to identify and resolve these inconsistencies. 

 55
Bibliography

Civco, D. 1989. “Topographic Normalization of Landsat Thematic Mapper Digital Imagery,” 

Photogrammetric Engineering and Remote Sensing, Vol. 55(9), pp. 1303-1309. 

Colby, J., 1991. “Topographic Normalization in Rugged Terrain,” Photogrammetric Engineering

and Remote Sensing, Vol. 57(5), pp. 531-537. 

Geographic Resource Solutions, 1993. Klamath Province Mapping Pilot Study Report. Arcata, 

CA. Prepared for the California Department of Forestry and Fire Protection. 78 pp. 

Swain, P. and S. Davis, 1978. Remote Sensing: The Quantitative Approach. McGraw-Hill Book 

Company, New York, NY. 

Viereck, L.A. et al., 1992. The Alaska Vegetation Classification. U.S. Department of Agriculture. 

Forest Service. Pacific Northwest Research Station General Technical Report: PNW-GTR-286. 

 56
Appendices 

Appendix A: Katmai Mapping Project Aerial Survey Form

 57
Appendix B: Katmai Mapping Project Land Cover Type Classification 

Specific designation, as identified by cover components

no

no

no

no

yes

yes

yes

Specific specie group if > 75% of Shrub Cover

Specific specie group if > 75% of Shrub Cover

Specific designation, as identified by cover components

no

Stunted if > 75% of Conifer Cover

Specific specie group if > 75% of Broadleaf 

yes

yes

no

no

no

no

yes

yes

yes

Tree Cover yes yes >=

 10% Cover

Conifer >=

 75% of Tree

Cover

Shrub Cover >=

 25% Cover

Hardwood >=

 75 % of Tree

Cover

Deciduous Balsam Poplar

 Birch

 Mixed

Conifer/Deciduous

Tall Shrub >=

 25 % Cover

yes

Low Shrub >=

 25 % Cover

yes

Tall Shrub

 Alder

 Alder-Willow

 Willow

 Mixed

Low Shrub Alder-Willow

 Willow

 Mixed

Herb. Cover >=

 25% Cover

no

Wet Regime ?

Veg Cover >=

 15% Cover

Wet Herbaceous

Forb Graminoid

 Lichen

 Moss

Sparse Vegetation

no

Barren Cover >= 

25% Cover Barren

Water Cover >= 

25% Cover Water

Unknown

Dwarf Shrub >=

 25% Cover

no

 Dwarf Shrub Mixed

 Rock

 Tussock

 Sedge

 ....

 Conifer White Spruce

 Stunted Spruce

Snow/Ice Cover

>= 25% Cover Snow/Ice

yes

Type Assignment Fortran Source Code: 

c 

c Katmai type rules

 58
c 

 type='Unk' 

 maxden=0 

c 

c tree ?? 

c 

 if ( (cfsum + hwdsum) .ge. 25.0 )then 

c 

c have a tree type 

c 

 maxden=0 

 densmx=0 

c 

 do i = 1,maxtree 

 if(sumcov(i,m1).gt.densmx)then 

 densmx=sumcov(i,m1) 

 maxden=i

 end if 

 end do 

c 

c conifer ?? set default to mixed spruce type 

c 

 if( denscf .gt. 75. ) then 

 type='UnP' 

c 

c white spruce 

c 

 if(sumcov(1,m1)/denscf .ge. 0.667)then 

 type='PGl' 

 else

c 

c black/stunted spruce 

c 

 if(sumcov(2,m1)/denscf .ge. 0.667)then 

 type='pgs' 

c 

c uspecified spruce - leave default as is

c 

 endif 

 endif 

 else

c 

c hardwood 

c 

 if( denscf .lt. 25.)then 

 type='Hwd' 

 else

c 

c hardwood/conifer mix 

c 

 type='PHw' 

c 

 endif 

 endif 

c 

c end of tree 

c 

 else

 59
c 

c non-tree type

c 

c shrub 

c 

 if ( shrsum .ge. 25. )then 

 maxden=0 

 densmx=0 

c 

c set default to low shrub for katmai 

c 

 type='LSh' 

 ttlsum=sumcov(26,m3)+sumcov(27,m3)+sumcov(28,m3) 

 tlwsum=sumcov(29,m3)+sumcov(30,m3)+sumcov(31,m3)+sumcov(32,m3) 

 if(ttlsum .ge. 25.)then 

 type='TSh' 

 mmnshr=26 

 mmxshr=28 

 else

 if( tlwsum .ge. 25.)then 

 type='LSh' 

 mmnshr=29 

 mmxshr=32 

 else

 if( (tlwsum+ttlsum) .ge. 25. .and. sumcov(33,m3).lt.25.)then

 type='LSh' 

 mmnshr=26 

 mmxshr=32 

 else

 type='DSh' 

 mmnshr=33 

 mmxshr=33 

 end if 

 end if 

 end if 

 do i = mmnshr,mmxshr 

 if(sumcov(i,m3).gt.densmx)then 

 densmx=sumcov(i,m3) 

 maxden=i

 end if 

 end do 

c 

 else 

 if(hrbsum .ge. 25. )then 

 maxden=0

 densmx=0 

c 

c set default to forb for katmai 

c 

 type='Frb' 

 do i = minherb,maxherb 

 if(sumcov(i,m3).gt.densmx)then 

 densmx=sumcov(i,m3) 

 maxden=i 

 end if 

 end do 

 60
 if(maxden .eq. 36)type='Grm' 

 if(maxden .eq. 37)type='Frb' 

 if(maxden .eq. 38)type='WMo'

 if(maxden .eq. 39)type='Lch'

c 

 else

c 

c Sparse Vegetation 

c 

 if( (shrsum+hrbsum+cfsum+hwdsum+aqusum) .ge. 15. )then 

 densmx=0 

 maxden=0

 type='SVg' 

 do i = mincon,maxaqu 

 if(sumcov(i,m3).gt.densmx)then 

 densmx=sumcov(i,m3) 

 maxden=i 

 end if 

 end do 

c 

c major component > 10 percent ? 

c 

 if(densmx .lt. 10.)then 

c default to misc forb 

 maxden=40 

c 

 if(shrsum .ge. hrbsum.and. 

 * shrsum .ge. aqusum )maxden=32 

c 

 if( (hwdsum+cfsum).ge.shrsum.and. 

 * (hwdsum+cfsum).ge.aqusum.and. 

 * (hwdsum+cfsum).ge.hrbsum )maxden=24 

c 

 if(hwdsum .ge. cfsum .and. 

 * hwdsum .ge .aqusum .and. 

 * hwdsum .ge. shrsum .and. 

 * hwdsum .ge. hrbsum )maxden=25 

c 

 if(cfsum .ge. hwdsum .and. 

 * cfsum .ge .aqusum .and. 

 * cfsum .ge. shrsum .and. 

 * cfsum .ge. hrbsum )maxden=20 

 endif 

c 

 else

c 

c non-veg type 

c 

 densmx=0. 

 type='???' 

c 

c 

 if(barsum.gt.densmx.and.barsum.ge.25.)then 

 densmx=barsum 

 type='Bar' 

 typmax=0. 

 do i = minbar,maxbar 

 61
 if(sumcov(i,m3).gt.typmax)then 

 typmax=sumcov(i,m3) 

 idxtyp=i 

 end if 

 if(idxtyp .eq. 57)type='SnG' 

 if(idxtyp .eq. 58)type='Bar' 

 enddo

 endif 

c 

 if(watsum.gt.densmx.and.watsum.ge.25.)then 

 densmx=watsum 

 type='H2O' 

 typmax=0. 

 do i = minwat,maxwat 

 if(sumcov(i,m3).gt.typmax)then 

 typmax=sumcov(i,m3) 

 idxtyp=i 

 end if 

 enddo

 endif 

c 

c undefined - blank out 

c 

 if(othsum.gt.33.33)then 

 densmx=0 

 type='???' 

 typmax=0. 

 do i = minundf,maxundf 

 if(sumcov(i,m3).gt.typmax)then 

 typmax=sumcov(i,m3) 

 idxtyp=i 

 end if 

 if(idxtyp .eq. 59)type='ClS' 

 enddo

 endif 

c 

 endif 

 endif 

 endif 

 endif 

c 

 endif 

 endif 

c 

c end of rules 

c 

 62
Appendix C: Katmai Mapping Project Land Cover Density Classification 

yes

yes

yes

yes

yes

no

no

no

no

yes

Tree Cove yes r >=

 10% Cover

Tree Cover >= 

60%

Shrub Cover >=

 25% Cover

Tree Cover >= 

25% Open

Woodland

Tall Shrub >=

 75 % Cover

Tall Shrub >=

 25 % Cover

Closed

Open

Closed

Low Shrub >=

 75 % Cover

Closed

no

Open

no

No Value

no

 63
Appendix D: Table Item Listings and Definitions 

Items for table katmai_lcover.vat: 

 

COLUMN ITEM NAME WIDTH OUTPUT TYPE N.DEC INDEXED? 

 1 VALUE 4 10 B - Indexed 

 5 COUNT 4 10 B - - 

 9 SOURCE 20 20 C - - 

 29 SPEC_CLASS 50 50 C - - 

 79 GRIDCLVAL 11 11 I - - 

 90 MAJOR_CLASS 50 50 C - - 

 140 REGIME 20 20 C - - 

 160 MODIFIER 20 20 C - - 

 180 REMAP_VAR 6 6 I - - 

 186 TYPE 6 6 C - - 

 192 PREDOMINANT_COMP 20 20 C - - 

 212 PR_COMP_COVER 8 23 F 4 - 

 220 COVER_CLASS 10 10 C - - 

 230 TREE_COVER 8 23 F 4 - 

 238 OTHER_VEG_COVER 8 23 F 4 - 

 246 CONF_COVER 8 23 F 4 - 

 254 HDWD_COVER 8 23 F 4 - 

 262 SHR_COVER 8 23 F 4 - 

 270 TSH_COVER 8 23 F 4 - 

 278 LSH_COVER 8 23 F 4 - 

 286 DSH_COVER 8 23 F 4 - 

 294 HRB_COVER 8 23 F 4 - 

 302 BAR_COVER 8 23 F 4 - 

 310 OTH_COVER 8 23 F 4 - 

 318 TRSITE_ID 11 11 I - - 

 329 CALC_CLASS 50 50 C - - 

 379 ALNUS_COVER 8 23 F 2 - 

 387 SALIX_COVER 8 23 F 2 - 

Items for table cover_info.dat: 

 

COLUMN ITEM NAME WIDTH OUTPUT TYPE N.DEC INDEXED? 

 1 TRSITE_ID 11 11 I - - 

 12 SOURCE 20 20 C - - 

 32 SPECIES 50 50 C - - 

 82 SPCODENUM 6 6 I - - 

 88 SPCODE 10 10 C - - 

 98 COVER 8 23 F 4 - 

 106 STRATA 10 10 C - - 

 116 COMMENT 50 50 C - - 

 166 IMA_ID 6 6 I - - 

 172 GRIDVAL 11 11 I - - 

 64
Items for the table trsites.pat 

 

COLUMN ITEM NAME WIDTH OUTPUT TYPE N.DEC INDEXED? 

 1 AREA 8 18 F 5 - 

 9 PERIMETER 8 18 F 5 - 

 17 TRSITES# 4 5 B - - 

 21 TRSITES-ID 4 11 B - Indexed 

 25 TRSITE_ID 11 11 I - - 

 36 SOURCE 20 20 C - - 

 56 ISO_CLASS 11 11 I - - 

 67 ELEVATION_FT 6 6 I - - 

 73 SLOPE_DEGREES 6 6 I - - 

 79 ASPECT 6 6 I - - 

 85 X_COORD 8 23 F 4 - 

 93 Y_COORD 8 23 F 4 - 

 101 PHOTO_ID 8 8 C - - 

 109 SCENE_IND 4 4 C - - 

 113 IMA_ID 11 11 I - - 

 124 COVER_TYPE_DES 40 40 C - - 

 164 GRIDVAL 11 11 I - - 

 175 GRIDCLVAL 6 6 I - - 

 181 HABITAT 55 55 C - - 

 236 REGIME 20 20 C - - 

 256 MODIFIER 20 20 C - - 

 276 MAJOR_CLASS_DES 40 40 C - - 

 316 SCN_7219 1 1 C - - 

 317 SCN_7119 1 1 C - - 

 318 SCN_7018 1 1 C - - 

 319 SCN_TM7 1 1 C - - 

 65
Definitions: 

ALNUS_COVER The estimated cover of Alnus sp. 

ASPECT The estimated aspect(azimuth) at the training site location 

BAR_COVER The estimated cover of barren area 

CALC_CLASS The Land Cover Class estimated through application of the 

 Land Cover Classification rules 

COMMENT Comment field 

CONF_COVER The estimated percent of tree cover comprised by conifers 

COUNT The frequency of the pixel value in the map 

COVER The estimated percent land cover of a component 

COVER_CLASS The density class of the Land Cover Type: 

 Woodland = 10 - 24.9% tree cover 

 Open = 25 - 74.9% shrub cover 

 = 25 - 59.9% tree cover

 Closed >= 75% shrub cover

 >= 60% tree cover

COVER_TYPE_DES The field estimated Land Cover Type 

DSH_COVER The estimated cover of Dwarf Shrubs

ELEVATION_FT The estimated elevation at the training site location 

GRIDCLVAL Major class grid value - numeric code for MAJOR_CLASS 

GRIDVAL (VALUE) The pixel value of this class in the final pixel map 

HDWD_COVER The estimated percent of tree cover comprised by hardwoods 

HRB_COVER The estimated cover of herbaceous plants 

IMA_ID For TSMITH sites - the original IMA ID number 

ISO_CLASS The original iso_class number used during candidate 

 site development and selection 

LSH_COVER The estimated cover of Low Shrubs 

MAJOR_CLASS The major land cover class descriptive name - a 

 generalized Land Cover Type 

MODIFIER Modifier (morphologic) Code 

OTHER_VEG_COVER The sum of all non-woody vegetation cover (shrub + forb) 

 66
OTH_COVER The estimated other cover, including water 

PHOTO_ID The digital photo name for the image of the training site 

PREDOMINANT_COMP The predominant component of the land cover - specific to 

 species or group of similar species 

PR_COMP_COVER The estimated percent land cover of the 

REGIME Hydrologic Regime 

REMAP_VAR ReMap Value - scratch column used to store sql derived 

values and generate different map representations 

SALIX_COVER The estimated cover of Salix species 

SCENE_IND Indicator value to identify in which scenes a training site 

 was trained and used: 

 0 = not trained 

 1 = trained

 

Scenes are indicated by the column of the value - 

From left to right: 7219, 7119, 7018, and TM7. 

SCN_7219 The indicator that the site was used in this scene

 (0=No/1=Yes)

SCN_7119 The indicator that the site was used in this scene

 (0=No/1=Yes)

SCN_7018 The indicator that the site was used in this scene

 (0=No/1=Yes)

SCN_TM7 The indicator that the site was used in this scene

 (0=No/1=Yes)

SHR_COVER The sum of Tall and Low Shrub cover 

SLOPE_DEGREES The estimated slope(degrees) at the training site location 

SOURCE Source of the training data: 

 GRS_00 = Aerial Survey 

 AKSO_00 = AKSO ground survey 

 TSMITH = Dr. Thomas Smith IMA data 

 GRS/AKSO_01 = supplemental sites GRS added 

 UNSUP = unsupervised classes 

SPECIES The species/name of the land cover component 

SPEC_CLASS Specific class descriptive name assigned in the field 

 67
SPCODENUM The species code number of the land cover component 

SPCODE The species code symbol of the land cover component 

STRATA The vegetation profile of the land cover component: 

 

 TR_CNFR = Conifer 

 TR_BDLF = Broadleaf 

 TS = Tall Shrub 

 LS = Low Shrub 

 DS = Dwarf Shrub

 H = Herbaceous 

 M = Moss 

 O = Other 

TREE_COVER The estimated percent tree cover (density) 

TRSITE_ID or Training site id number - the unique identifier 

TRSITE-ID of the training site 

TSH_COVER The estimated cover of Tall Shrubs 

TYPE Land Cover Type Code:

 PGl = Spruce

 Pgs = Stunted Spruce 

 PHw = Conifer/Broadleaf 

Hwd = Broadleaf/Hardwood 

 TSh = Tall Shrub 

 LSh = Low Shrub 

 DSh = Dwarf Shrub

 Hrb = Forb:Herbaceous 

Grm = Graminoid

 Lch = Lichen 

 WMo = Wet Moss

 SVg = Sparse Vegetation 

 Bar = Barren

 SGl = Snow/Glacier

 H2O = Water

 Unk = Unknown

VALUE (GRIDVAL) Pixel value used to associate grid to attribute tables 

X_COORD The X coordinate value (Albers Projection) at the training 

 site location

Y_COORD The Y coordinate value (Albers Projection) at the training 

 site location

 68

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-118, June 2003
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 

EXPERIENCE YOUR AMERICA TM