Forest

Service

United States Research Map NRS-1

Department of

Agriculture

Border Lakes Land-Cover 

Classification 

Metadata, description of land-cover

classification methods, fold-out map, and digital

versions of the classification
Abstract

This document contains metadata and a description of

land-cover classification for an approximate 5.1 million

acre (2.1 million hectare) landscape bordering

Minnesota, U.S.A., and Ontario, Canada. The product

was created by the University of Minnesota-Geospatial

Analysis Laboratory under grant by, and in collaboration

with, the U.S. Forest Service Northern Research Station

acting as a representative for the Border Lakes

Partnership. The classification focused on the separation

and identification of specific forest-cover types. Some

separation of the nonforest classes also was performed.

The classification was derived from multi-temporal

Landsat TM/ETM+ imagery (collected from the years

1999-2001) using eCognition segmentation software to

identify stands and the kNN algorithm to perform the

spectral classification. Reference data sites were

obtained for Minnesota from the Forest Inventory and

Monitoring (FIM) program dataset and for Ontario from

the Forest Resource Inventory (FRI) program. This

dataset was created for use in a forest landscape model

(LANDIS-II) that was designed to simulate future forest

disturbance and compositional changes over the Border

Lakes landscape.

Disclaimer

United States Department of Agriculture Forest

Service has developed this information for the

guidance of its employees, its contractors, and its

cooperating Federal and State agencies. It is not

responsible for the interpretation or use of this

information by anyone except its own employees.

The use of trade, firm, corporation, or association

names in this publication is for the information and

convenience of the reader and does not constitute an

endorsement by the Department of any product or

service. This product is reproduced from geospatial

information prepared by the U.S. Department of

Agriculture, Forest Service. Geographic Information

System (GIS) data and product accuracy may vary.

They may be developed from sources of differing

accuracy, accurate only at certain scales, based on

modeling or interpretation, or incomplete while being

created or revised.

Using GIS products for purposes other than those or

for which they were created may yield inaccurate or

misleading results. This information was released on

the indicated date. The Forest Service reserves the

right to correct, update, modify, or replace GIS

products without notification.

Manuscript received for publication 

January 2009

Published by:

USDA FOREST SERVICE

11 CAMPUS BLVD SUITE 200

NEWTOWN SQUARE, PA 19073

August 2009

For additional copies:

USDA Forest Service

Publications Distribution

359 Main Road

Delaware, OH 43015

Fax: 740-368-0152

Visit our website: 

www.nrs.fs.fed.us

Cover photo: Clearwater Lake, Boundary Waters Canoe 

Area Wilderness, Superior National Forest, Minnesota, U.S.A.

Photo copyright Airphoto–Jim Wark, all rights reserved, used

with permission.
Border Lakes Land-Cover 

Classification 

Metadata, description of land-cover

classification methods, fold-out map, and

digital versions of the classification

Marvin Bauer, Brian Loeffelholz, Doug Shinneman

The Authors

Marvin Bauer: 

Department of Forest Resources, University of

Minnesota, St. Paul, MN 55108-6112

Doug Shinneman:

Affiliation at the time of the classification work:

The Nature Conservancy, Minneapolis, MN 55415;

and USDA Forest Service, Northern Research

Station, Grand Rapids, MN 55744

Current address: 

U.S. Geological Survey, Forest and Rangeland

Ecosystem Science Center, Snake River Field

Station, Boise, ID 83706

Brian Loeffelholz:

Affiliation at the time of the classification work:

Department of Forest Resources, University of

Minnesota, St. Paul, MN 55108-6112

Current address: 

Wisconsin Department of Agriculture, Trade &

Consumer Protection, Madison, WI 53708

Inquiries also can be directed to:

Brian Palik, U.S. Forest Service Northern Research

Station, 1831 Highway 169 E., Grand Rapids, MN

55744; email: bpalik@fs.fed.us
4

About this Publication

This map product is derived from a Landsat land-cover classification

for the Border Lakes Region in northern Minnesota and

northwestern Ontario—a lake-filled landscape in the southern

boreal forest that covers 5.1 million acres (2.1 million hectares) and

includes Voyageurs National Park, Quetico Provincial Park, the

Boundary Waters Canoe Area Wilderness, other public (state,

provincial, county), First Nation/tribal, and private lands. The

classification was developed by the University of Minnesota for the

U.S. Forest Service Northern Research Station and The Nature

Conservancy. The classification was used as a key map layer for an

ecological modeling research project, known as the Border Lakes

Project (BLP). The BLP was designed to help land management

agencies and other organizations understand the effects of longterm disturbance dynamics, especially fire and timber harvest, on

forest ecosystems. The BLP used a forest landscape computer

model, called LANDIS-II, to simulate various management scenarios,

including collaborative, cross-boundary strategies to manage forest

resources, reduce hazardous fuels, and conserve biodiversity. 

For more information about this research project, please contact:

Brian Palik, Team Leader and Research Ecologist

U.S. Forest Service Northern Research Station

1831 Hwy 169 East

Grand Rapids, MN 55744

bpalik@fs.fed.us

DVD Contents:

• Map in PDF format 

Software required: Adobe

Acrobat Reader

• Map in GeoTIFF format

Software required: Geographic

Information System software

(e.g., ESRI ArcGIS)

• Documentation Booklet in 

PDF format
5

This dataset is provided in three

digital formats (on a DVD) and as two

hard-copy maps (specifications listed

below). The maps and DVD are

located in the back of this publication.

1) A GeoTIFF (*.tif) file. This file

format is a georeferenced raster

image that can be viewed, edited,

queried, and manipulated using a

geographic information system

(GIS). The GeoTIFF data also

includes an attribute table with

cover-type category names and

classification codes (refer to the

metadata for details), and FGDCcompliant metadata (available in

both XML and HTML formats).

2) A 21 inch x 32 inch poster with a

locator map and a legend indicating

major cover types. This product is

provided as both a printable PDF

file and a folded paper poster. 

3) An 8 1

/2 inch x 11 inch poster with

a locator map and a legend

indicating major cover types. This

product is provided as both a

printable PDF file and an attached

paper map (page 14). 

About the Dataset
6

Introduction 

The land-cover classification of approximately 5.1 million acres (2.1

million hectares) of land bordering Minnesota, U.S.A. and Ontario,

Canada (Border Lakes Region) was created by the University of

Minnesota, Geospatial Analysis Laboratory under grant by the U.S.

Forest Service Northern Research Station acting as a representative

for the Border Lakes Partnership. The classification focused on the

separation and identification of specific forest-cover types. Some

separation of the nonforest classes also was performed. The

classification was derived from multi-temporal Landsat TM/ETM+

imagery (collected from the years 1999-2001) using eCognition

segmentation software to identify stands and the kNN algorithm to

perform the spectral classification. Reference data sites were

obtained for Minnesota from the Minnesota Department of Natural

Resources (MNDNR) Forest Inventory and Monitoring (FIM) program

dataset used in forest resource management plans (Minnesota

Department of Natural Resources 2005) and for Canada from the

Forest Resource Inventory (FRI) program (Ontario Ministry of Natural

Resources 2004). This dataset was created to be used as an input

layer in the LANDIS-II forest landscape model for simulation of 

future forest disturbance and compositional change over the Border

Lakes landscape.

There were four objectives: (1) to divide the Landsat imagery into

groups of pixels that represent areas of continuous cover types on

the ground, referred to as stands; (2) to produce a land-cover

classification that separated the land cover into forest, nonforest,

and water classes; and (3) to delineate the forest areas into more

specific forest-cover-type classes (i.e., white pine, jack pine,

spruce/fir, aspen/birch, etc). 

The spatial extent of the data is the ecologically defined Border

Lakes Region, which includes most of the Boundary Waters Canoe

Area Wilderness (BWCAW), Quetico Provincial Park (Ontario,

Canada), and Voyageurs National Park.

The Border Lakes Partnership is an interagency team of

natural resource professionals from land-owning agencies

and other organizations concerned with the management of

the Border Lake region’s resources. The overarching goal of

the group is to develop collaborative, cross-boundary

strategies for managing forest resources, reducing

hazardous fuels, and conserving biodiversity. The group plans

to identify a variety of land management options that will help

agencies to achieve mutual goals. A number of technical

experts have contributed, including: U.S. Forest Service

Northern Research Station and Superior National Forest;

Minnesota Department of Natural Resources; Voyageurs

National Park; Quetico Provincial Park; The Nature

Conservancy; The Nature Conservancy of Canada; Legacy

Forest Project (Canada); Canadian Forest Service; and

Ontario Ministry of Natural Resources. The Partnership also

cooperates with other interagency organizations, including

the Minnesota Forest Resources Council.

Boundary Waters Canoe Area Wilderness, Superior National Forest,

Minnesota, U.S.A. Photo by Doug Shinneman, used with permission.

About the Border Lakes Partnership
7

Classification

Two levels of classification were performed. The first classification,

Level 1, had three classes: forest, nonforest, and water. The second

classification, Level 2, was performed separately on the Level 1

forest and nonforest classes. 

The Level 2 forest classification focused on identifying and

separating forest-cover types. 

The Level 2 nonforest classification focused on separating nonforest

cover types (i.e. wetland, shrubland, grassland, and urban/soil). 

Cover types for the Level 1 and Level 2 classifications are defined in

the cover-type classification scheme listed on page 11.

Data Quality

Level 2 classification accuracy is assessed with a traditional error

matrix. Accuracy assessment sites were randomly selected for each

Level 2 class from field verified datasets. Points were generated from

the existing datasets and the points were then visually compared to

high resolution imagery for location and adjusted when necessary. A

total of 738 accuracy test points were generated. Level 2 accuracy

assessment data were only available for the individual forest-covertype classes. Nonforest cover types were only assessed as a whole.

Accuracy

Producer’s Accuracy — This refers to the proportion of reference

points for each category that were accurately classified. (Cover-type

categories listed below and the numbers in parentheses correspond

to the cover-type category in the attribute table of the GeoTIFF

dataset included on the DVD in this publication.)

• Mixed coniferous/deciduous (1) – 31.7 percent 

• Red pine/white pine (2) – 42.4 percent

• Jack pine (3) – 45.3 percent

• Spruce/fir (4) – 29.8 percent

• Cedar (5) – 41.7 percent

• Tamarack (6) – 44.8 percent

• Mixed/other coniferous (7) – 15.0 percent

• Aspen/birch (8) – 32.8 percent 

• Northern boreal hardwood mix (9) – 22.9 percent

• Lowland hardwoods (10) – 24.3 percent

• Water (11) – 98.9 percent

• Nonforest (12) – 34.9 percent 

User’s Accuracy — This refers to the proportion of the pixels

assigned to each category that were accurately classified.

• Mixed coniferous/deciduous (1) – 17.9 percent 

• Red pine/white pine (2) – 44.6 percent

• Jack pine (3) – 24.0 percent

• Spruce/fir (4) – 32.1 percent

• Cedar (5) – 66.7 percent

• Tamarack (6) – 48.2 percent

• Mixed/other coniferous (7) – 17.7 percent

• Aspen/birch (8) – 19.2 percent 

• Northern boreal hardwood mix (9) – 15.7 percent

• Lowland hardwoods (10) – 60.0 percent

• Water (11) – 100.0 percent

• Nonforest (12) – 86.9 percent 

Overall Accuracy

• 41.3 percent

Kappa 0.42

Horizontal positional accuracy

• RMS error <7.5 m or 1

/4 pixel

Vertical positional accuracy

• RMS error <7.5 m or 1

/4 pixel
8

Imagery and Imaging

Preprocessing

To obtain an acceptable level of classification accuracy at the

species level requires the use of multi-temporal image classification

which utilizes the seasonal phenological variability in land covers to

aid in spectral identification. The Landsat TM and ETM+ images

acquired for this project were multi-temporal image sequences of

spring, summer, and fall imagery. To cover all of the Border Lakes

Region, a minimum of five images for each date were needed. The

Landsat imagery used for the project are as follows:

Patch(P):28 Row(R):26

Spring: 20 April, 2000 - ETM+

Summer: 26 August, 2000 - ETM+

Fall: 21 October, 2000 - TM

P:27 R:26

Spring: 29 April, 2000 - ETM+

Summer: 18 July, 2000 - ETM+

Fall: 22 October, 2000 - ETM+

P:26 R:26

Spring: 25 April, 2001 - ETM+

Summer: 13 September, 2000 - ETM+

Fall: 13 October, 1999 - ETM+

P:27 R:27

Spring: 29 April, 2000 - ETM+

Summer: 12 September, 2000 - TM

Fall: 05 November, 1999 - ETM+

P:26 R:27

Spring: 28 April, 1999 - TM

Summer: 07 August, 2001 - TM

Fall: 13 October, 1999 - ETM+

Imagery was rectified to the Universal Transverse Mercator (UTM)

coordinate and projection system to an root mean square (RMS) error

of less then 1/4 pixel (7.5 meters) using approximately 25 ground

control points per image. The base layer used was the Minnesota

Department of Transportation map of major roads map and the

Ontario roads map. The imagery was nearest neighbor resampled to

a 30-meter pixel size and the coordinates of the final image were

adjusted to values evenly divisible by 30. Format of rectified imagery

is unsigned 8-bit in the UTM, spheroid GRS80, datum NAD83, Zone

15 coordinate system.

Following rectification the imagery was then mosaicked by season.

The five images necessary for complete coverage of the Border Lakes

Region by season were mosaicked to produce a single image for

three seasons (spring, summer, fall) with six bands each (1, 2, 3, 4, 5

and 7).

During the mosaicking process we established spectrally consistent

classification units (SCCUs) to identify where the image boundaries

were located. This is essential when classifying a large area with

imagery from multiple dates and years. Five SCCUs were identified

for the Border Lakes project. The SCCUs will be used for

stratification during the classification process.

Computation of an additional feature, normalized difference

vegetation index (NDVI), was then performed on the separate

mosiacked imagery. NDVI is a spectral radiometric transformation

that is sensitive to the amount of green vegetation. This created

three new images with a single layer of NDVI for each.

Sample from Landsat 7

ETM+ data used to

classify the Border Lakes

Region land cover. This

image was created using

bands 1, 2, and 3 to create

a natural color image.

Source: Landsat 7 ETM+

P27 R26 July 18, 2000.
9

The mosaicked Landsat imagery as well as the NDVI imagery was

then layer stacked to produce a single 21-band image. This image

consisted of the six spectral bands (1, 2, 3, 4, 5, and 7) from each

season (spring, summer, and fall) as well as the three texture layers

from the NDVI images. The layer order is as follows: 

Image Segmentation:

We used eCognition image software to separate the mosaicked

layer-stacked imagery into areas of consistent cover type that

represented stands. This software groups pixels into areas where the

spectral values are the most alike within the group and least like

those out outside the group. The parameter settings for eCognition

segmentation were as follows: 

Scale Parameter - 10

Shape Factor - 0.1

Smoothness - 0.5

Compactness - 0.5

These setting were chosen after several trial segmentations were

performed and the resulting segments compared to stand size

statistics from the Minnesota Department of Natural Resources

(MNDNR) Forest Inventory and Monitoring (FIM) data and Ontario

Forest Resource Inventory (FRI) datasets. The MNDNR-FIM dataset

had mean stand size of 7.2 acres, standard deviation of 10.5 acres,

maximum stand size of 177.1 acres, and minimum stand size of less

then 0.1 acre. The Ontario-FRI dataset had mean stand size of 10.8

acres, standard deviation of 253.5 acres, maximum stand size of

911.0 acres, and minimum stand size of less then 0.1 acre. Using the

previously mentioned parameter settings, the resulting segmentation

stand statistics were mean stand size of 32.88 acres, standard

deviation of 30.97 acres, maximum stand size of 368.3 acres, and

minimum stand size of 0.2 acre.

The SCCU data also were included as a thematic layer in eCognition

during the segmentation process. This ensured that image segment

boundaries stopped at the edges of the SCCUs. 

When the image segmentation was complete, the data were

exported from eCognition as a shapefile, creating a polygon for each

image segment. Attributes of the segments retained during the

export were: (1) the mean spectral value within the individual

segments for each of the 21 layers; (2) the standard deviation of the

spectral value within the individual segments for each of the 21

layers; and (3) the SCCU the segment was within.

band01: Spring/band1

band02: Spring/band2

band03: Spring/band3

band04: Spring/band4

band05: Spring/band5

band06: Spring/band7

band07: Summer/band1

band08: Summer/band2

band09: Summer/band3

band10: Summer/band4

band11: Summer/band5

band12: Summer/band7

band13: Fall/band1

band14: Fall/band2

band15: Fall/band3

band16: Fall/band4

band17: Fall/band5

band18: Fall/band7

band19: Spring/NDVI

band20: Summer/NDVI

band21: Fall/NDVI

Voyageurs National Park, Minnesota, U.S.A. Photo by Cory Mcnulty, Voyageurs

National Park Association, used with permission.
10

Classification Techniques

The Level 1 classification was performed using ERDAS Imagine

maximum likelihood image classification software. The maximum

likelihood algorithm assigns each unknown (target) pixel the field

attributes of the most spectrally similar reference object from the

field reference data. The similarity is defined in terms of the feature

space. The Level 1 classification was performed for each individual

SCCU separately and then combined to create a coverage of the

entire Border Lakes Region. This was performed four times to create

classifications based on the four time combinations:

spring/summer/fall, spring/summer, summer/fall and spring/fall. Of

these four, the primary classification was the three-date

classification. Areas in the three-date classification that had clouds

were substituted with clear portions of the corresponding two-date

classification. This created a complete, cloud-free, Level 1

classification with classes of forest, nonforest, and water.

The Level 2 forest classification was restricted to the area identified

in the Level 1 classification as forest. This classification was

performed using the University of Minnesota’s kNN image

classification software. The kNN software assigns each unknown

(target) object the field attributes of the most similar reference

object(s) for which field data exists. The similarity is defined in terms

of the feature space, typically measured as Euclidean distance. The

nonparametric algorithm of this unique method overcomes low

sampling intensity limitations to produce truly useful maps of forest

attributes collected through field inventory (Franco-Lopez et al. 2001).

The classification was again performed for the four time

combinations: spring/summer/fall, spring/summer, summer/fall, and

spring/fall. Of these four, the primary classification was the threedate classification. Areas in the three-date classification that with

clouds were substituted with portions of the corresponding clear

areas from the two-date classification. This created a complete,

cloud-free, Level 2 classification of the specific forest cover types.

Following classification, the Level 2 forest vector cover type

classification was converted to raster format. The data was

converted to 30-meter by 30-meter pixels. Following the conversion,

the raster data set was spatially adjusted to have coordinates

divisible by 30. This ensured that the Level 2 cover-type pixels would

overlap with other 30-meter pixel datasets.

The Level 2 nonforest classification was restricted to the area

identified in the Level 1 classification as nonforest. This

classification was performed using ERDAS Imagine maximum

likelihood image classification software. Each SCCU was classified

separately as well as for the four time combinations. Of these four,

the primary classification was the three-date classification. Areas in

the three-date classification with clouds were substituted with

portions of the corresponding clear areas from the two-date

classification. This created a complete, cloud-free, Level 2

classification of the specific nonforest cover types. 

Readers should note that forest training sites (used to derive

classification parameters) for Minnesota were obtained from the

MNDNR-FIM dataset and from the Ontario–FRI dataset. We made an

effort to match the cover-type descriptions between the inventory

datasets and the remotely sensed cover type within the limitations of

remote sensing capabilities. Examples: white pine, red pine, oak,

mixed conifer and mixed deciduous.

Boundary Waters Canoe

Area Wilderness, Superior

National Forest,

Minnesota, U.S.A. Photo

by Doug Shinneman, used

with permission.
11

Cover-type Classification

Scheme

Forest: An upland or lowland area of land covered with woody

perennial plants, the tree reaching a mature height of at least 6 feet

with a definite crown. To be considered a forested cover type the

stand must have a combined species minimum volume of 3

cords/acre or 1,251 board feet/acre or 251 stems/acre depending on

size class (MN Common Cooperative Stand Assessment [CCSA]

standards) 

Coniferous: Upland areas whose canopies have a distinct crown

closure of which no less than two-thirds (67 percent), based on

volume measures, should be of the coniferous tree group. If the

broad-leaved deciduous species group is present, it should not

exceed one-third (33 percent) of the canopy), based on volume

measures, (Examples: jack pine, red pine, white spruce, balsam fir,

tamarack.)

Red pine/white pine: No less than 80 percent of the conifer stand

volume should be red pine, white pine or a combination of the two

species.

Jack pine: No less than 80 percent of the conifer stand volume

should be jack pine.

Spruce/fir: No less than 80 percent of the conifer stand volume

should be composed of a single or a mix of black spruce, white

spruce and/or balsam fir.

Cedar: No less than 80 percent of the conifer stand volume should

be northern white cedar.

Tamarack: No less than 80 percent of the conifer stand volume

should be tamarack. 

Mixed/other conifers: In the case of mixed conifers, no less than

80 percent of conifer stand volume is not of a single coniferous

species but rather a mix of coniferous species, but not a mix listed

previously. 

Broad-leaved deciduous: Upland areas whose canopies have a

distinct crown closure of which no less than two-thirds (67 percent),

based on volume measures, should be of the broad-leaved deciduous

tree group. If the coniferous species group is present, it should not

exceed one-third (33 percent) of the canopy, based on volume

measures. (Examples: aspen, oak, maple, birch.)

Aspen/birch: No less than 80 percent of the deciduous stand

volume should be composed of quaking aspen, big-toothed aspen,

balsam poplar, and paper birch. 

Northern boreal hardwood mix: No less than 80 percent of the

deciduous stand volume should be composed of a single or a mix of

quaking aspen, bigtoothed aspen, balsam poplar, paper birch, red

maple, sugar maple, silver maple, basswood, red oak, northern pin

oak, burr oak and/or boxelder. No more than 79 percent of the

deciduous stand volume should be composed of a single or mix of

aspen (Populus spp.) and/or birch (Betula spp.).

Lowland hardwoods: No less than 80 percent of the deciduous

stand volume should be composed of a single or a mix of black ash

and elm.

Mixed coniferous/deciduous: In the case of mixed broad-leaved

deciduous/coniferous forest, canopy percent must have a distinct

canopy closure, of which no more than two-thirds (67 percent), based

on volume measures, should be from the coniferous or broad-leaved

deciduous.

Nonforest

Urban/developed or bare soil: An area of any amount of

impervious cover of manmade solid materials or compacted soils

including areas with interspersed vegetation. Examples: parking lots,

shopping malls, warehouses, industrial parks, highways, sparse

development, single family residential developments, single lane

roads, and mines. Also, any sparsely vegetated exposures of soil,

sand, or rock (less than 33 percent coverage). If vegetation is present,

it is low density and not uniformly distributed throughout the stand.
12

Grassland: An upland or lowland area covered by cultivated or

noncultivated herbaceous vegetation predominated by grasses,

grass-like plants and forbs. Includes nonagricultural upland

vegetation dominated by short manicured grasses and forbs as well

as noncultivated herbaceous vegetation dominated by native grasses

and forbs. Examples: golf courses, lawns, athletic fields, wet and dry

prairies, and pastures.

Wetland: A lowland area with a cover of persistent and

nonpersistent herbaceous plants standing above the surface of wet

soil or water. Examples: cattails, marsh grass, sedges, and peat.

Shrubland: An upland or lowland area with vegetation that has a

persistent woody stem, generally with several basal shoots, low

growth of less than 20 feet in height. Area has less than 251

stems/acre of commercial tree species, the shrub species are fairly

uniformly distributed throughout and the density of the coverage is

moderate to high. (Examples: alder, willow, buckthorn, hazel, sumac,

and scrub oak) Note: all scrubland training sites were obtained from

the MNDNR CCSA data and thus an effort was made to match the

cover-type descriptions between the two data sets within the

limitations of remote sensing capabilities.

Water: An area of open water with none or very little above surface

vegetation. Example: lakes, streams, rivers, and open wetlands.

Reference Sites

Reference sites (used to assess classification accuracy) were

generated from the Minnesota FIM, the Ontario FRI cover type, and

the MNDNR digital line graph (DLG) data. These are polygon

datasets that offered detailed stand-level information about cover

types as well as when and how the data were collected. This dataset

was created as an input layer in the LANDIS forest landscape model

for simulation of future forest disturbance and compositional change

over the Border Lakes landscape. Both Level 1 (forest, nonforest, and

water) as well as detailed Level 2 forest-cover-type data was

extracted from these datasets along with forest stand age

information.

Processing of these datasets was needed to make them suitable for

use as spectral signature references for a Landsat image

classification. First, all the available reference sites were buffered

internally by 30 meters. This ensured that there would not be edge

pixels included in the spectral signature extracted from the Landsat

images for the reference sites. Second, the area of the buffered

reference sites was calculated and retained only those stands that

were 1.0 hectare or greater. This ensured that several Landsat pixels

would be contained within the reference site.

Adjustments also had to be made to the attributes of the reference

datasets before utilization. The reference data originated from

several sources which classified the cover types based on different

definitions, thus a standardization process was needed to create

reference data that were consistent across the entire Border Lakes

Region. This was particularly the case for the forest reference sites.

The reference sites were standardized to cover type definitions

specifically defined for the Border Lakes Project. Standardization was

based on forest species composition information supplied in the

attribute data. The MNDNR-FIM data was standardized using forestvolume attributes and the Ontario-FRI was standardized using

species composition attributes. 

Selection of sites was performed randomly with a goal of 10 to 20

reference sites per cover type for each SCCU. Sites were selected at

the lowest cover-type level and included the following cover types: 

Boundary Waters Canoe Area Wilderness, Superior National Forest, Minnesota,

U.S.A. Photo by Doug Shinneman, used with permission.
13

• Forest (10 types): red pine, white pine, jack pine, spruce/fir, cedar,

tamarack, mixed/other conifers, aspen/birch, northern boreal

hardwood mix, lowland hardwoods, and mixed coniferous/deciduous.

• Nonforest (five types): urban/bare soil, agriculture, grassland,

wetland, and shrubland.

• Water

For those cover types which had fewer then 10 reference sites, all

available sites were used. Also, several cover types did not have any

reference sites for one of several SCCUs due to little or no presence

of the particular cover type in that region. 

Spectral information for each reference site was extracted from the

Landsat imagery using the zonal attributes function in the ERDAS

Imagine software. Spectral information calculated from the imagery

for each reference site included the mean spectral value within each

reference site for all 21 spectral layers as well as the standard

deviation of the spectral values within each reference site for all 21

spectral layers. 

Clouds and cloud shadows were identified by visual interpretation of

the Landsat imagery. Once identified, the boundaries of the

clouds/cloud shadows were digitized. This was performed separately

for each mosaicked date of Landsat imagery. 

Map

This dataset found on the DVD in the back of this publication is

provided in three formats:

• A GeoTIFF (*.tif) file. This file format is a georeferenced raster

image that can be viewed, edited, queried, and manipulated using

a geographic information system (GIS). The GeoTIFF data also

includes an attribute table with cover-type category names and

classification codes (refer to the metadata for details), and FGDCcompliant metadata (available in both XML and HTML formats).

• A 21 inch x 32 inch poster with a locator map and a legend

indicating major cover types. This product is provided as both a

printable PDF file and a folded paper poster. 

• An 8 1

/2 inch x 11 inch poster with a locator map and a legend

indicating major cover types. This product is provided as both a

printable PDF file and an attached paper map.

Acknowledgments

We would like to thank Ebrahim Abdela (University of Minnesota) for

assisting with datasets used to produce this classification; Casey

Souder (The Nature Conservancy) for his data assistance and

cartographic contributions; and Brian Sturtevant (U.S. Forest Service

Northern Research Station), Mark White (The Nature Conservancy),

and others (anonymous) for reviewing earlier iterations of this

classification. We would also like to thank the many organizations

and agencies in Ontario and Minnesota who were engaged in the

Border Lakes Partnership and provided reference data and advice. 

Literature Cited

Definiens Imaging. 2003. eCognition object oriented image

analysis — user guide 3. Munich, Germany: Definiens Imaging.

Franco-Lopez, H.; Ek, A. R.; Bauer, M. E. 2001. Estimation and

mapping of forest stand density, volume, and cover type

using the k-nearest neighbors method. Remote Sensing of

Environment. 77(3): 251-274.

Minnesota Department of Natural Resources. 2005. Border Lakes

subsection forest resource management plan. St. Paul, MN:

Minnesota Department of Natural Resources, Division of Forestry.

www.dnr.state.mn.us/forestry/subsection/borderlakes. [Accessed

May 2009].

Ontario Ministry of Natural Resources. 2004. Forest management

planning manual for Ontario’s Crown Forests. Toronto, ON:

Ontario Minstry of Natural Resources. 440 p.

.
90oW

90oW

91oW

91oW

92oW

92oW

93oW93oW 94oW

49oN

49oN

48oN

48oN

0 5 10 20 30 40 Miles

0 5 10 20 30 40 Kilometers

Scale: 1:1,180,000

(UTM NAD83 Zone 15N)Projection: Transverse Mercator

Minnesota

Ontario Manitoba

Mixed/Other ConifersTamarackCedarSpruce/FirJack PineRed/White PineMixed Coniferous/Deciduous

Urban/Developed or Bare SoilGrasslandShrublandWetlandWaterLowland HardwoodsNorthern Boreal Hardwood MixAspen/Birch

BORDER LAKES REGION 

Land-Cover Classification

Canada

U.S.A

August 2009Cartographer: Casey SouderNorthern Research Station and The Nature Conservancy.Minnesota in cooperation with the USDA Forest ServiceLand-cover classification produced by the University of
Bauer, Marvin; Loeffelholz, Brian; Shinneman, Doug. 2009. Border Lakes land-cover classification.

Research Map NRS-1. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern

Research Station. 14 p. [DVD and printed map included].

This document contains metadata and description of land-cover classification of approximately 

5.1 million acres of land bordering Minnesota, U.S.A. and Ontario, Canada. The classification focused

on the separation and identification of specific forest-cover types. Some separation of the nonforest

classes also was performed. The classification was derived from multi-temporal Landsat TM/ETM+

imagery (collected from the years 1999-2001) using eCognition segmentation software to identify

stands and the kNN algorithm to perform the spectral classification. Reference data sites were obtained

for Minnesota from the Forest Inventory and Monitoring program dataset and for Ontario from the

Forest Resource Inventory program. This dataset was created as an input layer in the LANDIS-II forest

landscape model for simulation of future forest disturbance and compositional change over the Border

Lakes landscape.

KEY WORDS: Minnesota, Ontario, Quetico Provincial Park, Boundary Waters Canoe Area

Wilderness, Voyageurs National Park, land-cover type, Landsat, forest

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