Forest Land Cover Change 

(1975-2000) in the Greater 

Border Lakes Region

Research Map NRS-3

United States 

Department of Agriculture

Forest 

Service
Abstract

This document and accompanying maps describe land cover classifications and change detection for a 

13.8 million ha landscape straddling the border between Minnesota, and Ontario, Canada (greater Border 

Lakes Region). Land cover classifications focus on discerning Anderson Level II forest and nonforest cover 

to track spatiotemporal changes in forest cover. Multi-temporal Landsat Thematic Mapper (TM), Enhanced 

Thematic Mapper Plus (ETM+), and Multi-Spectral Scanner (MSS) data from 1972 to 2000 were used to 

classify forest cover types and disturbances at 5-year intervals. A composite dataset depicting the period of 

forest disturbance was produced using the 1975-2000 sequence of land cover data. These land cover change 

data were produced to facilitate analysis of forest disturbance patterns, to support landscape simulation 

modeling, and to support cross-ownership land management within the region. A double-sided fold-out map 

shows A) forest land cover change across differently managed forests, and B) classified period of forest 

canopy disturbance for the entire study area. Digital versions of the map are available online, as are the 

datasets and code used to produce them.

Published by: For additional copies:

U.S. FOREST SERVICE U.S. Forest Service

11 CAMPUS BLVD SUITE 200 Publications Distribution

NEWTOWN SQUARE PA 19073-3294 359 Main Road

Delaware, OH 43015-8640

November 2012

Visit our homepage at: http://www.nrs.fs.fed.us

Manuscript received for publication May 2012

Printed on recycled paper

Cover photograph: Landscape mosaic characteristic of the greater Border Lakes Region. Aerial image by Peter Wolter, Iowa State 

University, and Clayton Kingdon, University of Wisconsin-Madison; used with permission.
1

Peter T. Wolter, Brian R. Sturtevant, Brian R. Miranda, Sue M. Lietz, 

Philip A. Townsend, and John Pastor

Peter Wolter is an Assistant 

Professor in the Department 

of Natural Resource Ecology 

& Management, Iowa State 

University, Ames, IA 50011

Brian Sturtevant is a Research 

Ecologist, Brian Miranda an 

Ecologist, and Sue Lietz a Forester 

at the U.S. Forest Service, Northern 

Research Station, Institute for 

Applied Ecosystem Studies, 

Rhinelander, WI 54501

Philip Townsend is a Professor 

in the Department of Forest and 

Wildlife Ecology, University of 

Wisconsin-Madison, Madison, 

WI 53706

John Pastor is a Professor in the 

Department of Biology, University 

of Minnesota-Duluth, Duluth, 

MN 55812

Corresponding Author: 

Brian Sturtevant, USDA Forest 

Service, Northern Research 

Station, Institute for Applied 

Ecosystem Studies, 5985 Hwy K, 

Rhinelander, WI 54501 

Email: bsturtevant@fs.fed.us

Telephone: 715-362-1105

Fax: 715-362-1166

Forest Land Cover Change 

(1975-2000) in the Greater 

Border Lakes Region

The Authors
2

About the Dataset

The land cover classifications for 

the Greater Border Lakes Region 

described herein for each 5-year 

interval from 1975 through 2000 

are available online as a U.S. Forest 

Service Research dataset (Wolter 

et al. 2012). The spatial extent of the 

dataset is defined by the combined 

extents of the six imagery scenes 

used for the classifications. Arc 

Macro Language (AML) code used 

to map forest disturbances by date 

(as shown here) from the land-cover 

inputs is also included. The dataset 

will be updated with more recent 

classifications (i.e., 2005, 2010, etc.) 

as time and resources permit.

DESCRIPTION OF MAPS

The two-sided map is available in hardcopy 

format (22 inches x 36 inches) included with 

this publication. The map is also available in 

a digital printable PDF format available for 

download at http://nrs.fs.fed.us/pubs/42187. 

Spatial datasets used to construct the maps 

at the original 28.5 m resolution are also 

available online (Wolter et al. 2012).

2
3

INTRODUCTION

The greater Border Lakes Region comprises about 13.8 million 

ha straddling the international boundary between Minnesota 

and Ontario, Canada (Fig. 1). The region is an expansion of the 

approximately 2.1 million ha Border Lakes ecoregion defined 

by Bauer et al. (2009) and Shinneman et al. (2010). The greater 

Border Lakes Region includes the combined wilderness areas 

of the Boundary Waters Canoe Area Wilderness (BWCAW), 

Quetico Provincial Park, and Voyageurs National Park, as 

well as managed forests across multiple land ownerships in 

Minnesota and managed crown lands of Ontario. The region is 

an ideal context for evaluating forest disturbance because of the 

disparate forest management in the managed forests on both 

sides of the border and within the wilderness areas. Historical 

forest disturbance has been studied and mapped within 

portions of this region (Heinselman 1996), and different land 

management agencies and private industry maintain harvest 

and fire records, but recent disturbance history has not been 

mapped consistently across this region. 

Land cover change studies are becoming increasingly prevalent 

in the scientific literature—particularly given the recent 

accessibility of Landsat-based imagery (Cohen et al. 2010). 

While production of land cover classifications from Landsat 

imagery has become somewhat routine, specialized methods 

are still required to produce land cover maps that are both 

consistent across Landsat scenes and reliable in terms of 

the accuracy of the classification. Research has shown the 

fundamental importance of land use and disturbance history 

as it impacts future vegetation states and landscape recovery 

patterns (Schoennagel et al. 2008, Spies et al. 1994)—

suggesting that knowledge of past land cover and disturbance 

patterns can help guide land management within the current 

landscape. Unfortunately many peer-reviewed land cover 

classifications remain within the research institutions from 

which they were created with limited capacity to distribute the 

mapped information essential for effective land management. 

Such mapped information—both past and present—can 

supplement larger-scale mapping efforts (e.g., National Land 

Cover Database, see Homer et al. 2004) and ownership-specific 

resource inventories to support both subsequent research efforts 

and the coordination of management activities across land 

ownerships (Lytle et al. 2006). 

This document describes land cover classification and change 

detection methods used to produce a series of Anderson Level 

II (Anderson et al. 1976) land cover classifications as spatial 

datasets (Wolter et al. 2012) at 5-year intervals between 1975 

and 2000 across the greater Border Lakes Region. The spatial 

extent of the datasets is the combined extents of the six imagery 

scenes used for the classifications. Land cover changes across 

the 25-year time series were used to produce a continuous, 

consistent mapping of recent (1975-2000) forest disturbance 

across the entire region. Land cover changes are focused 

primarily on stand-replacing forest disturbances. Maps included 

in this publication provide examples of land cover changes 

from three differently managed areas (Minnesota managed, 

Ontario managed, and wilderness), and the composite forest 

“period of disturbance” map for the full extent of the dataset. 

Digital versions of land cover from each of the six dates, and 

Arc Macro Language (AML) code used to produce forest 

disturbances by date from the land cover inputs are available 

online (Wolter et al. 2012). The dataset will be updated with 

more recent classifications (i.e., 2005, 2010, etc.) as time and 

resources permit.

100°0'W 92°30'W 85°0'W

48°0'N 43°0'N

Ontario

Canada

Minnesota

U.S.A.

μ

Figure 1.—Location of the Border Lakes ecoregion (dark gray outline) within the 

full extent of the classified Landsat scenes that define the greater Border Lakes 

Region (dotted black line).
4

METHODS

Imagery and Image Processing

Six Landsat footprints (hereafter referred to as scenes) were 

required to cover the greater Border Lakes Region. These scenes 

correspond to the Landsat Worldwide Reference System (WRS2) path (P) and row (R) combinations: P28 R26, P28 R27, P27 

R26, P26 R26, P27 R27, and P26 R27. To capture changes 

as precisely as possible using Landsat imagery, we (as well as 

Wolter and White 2002) targeted images collected during 

stable growing season conditions, free of clouds, and as close 

to calendar matches from one year to the next (Table 1). In a 

number of cases such precise pairing was not possible. Thus, 

substitute images were acquired either before or after peak 

growing season from the same year or selected growing season 

images from the year immediately before or after the target 

years (i.e., 1975, 1980, 1985, 1990, 1995, and 2000). In the 

case of fire disturbance that occurred between time-steps, off 

time-step images were subsequently selected, after the Wolter 

and White (2002) analyses, to assist identification of these 

spectrally unique, but ephemeral, disturbance signatures.

Original work by Wolter and White (2002) used Landsat TM 

and ETM+ data acquired from the U.S. Geological Survey 

(USGS) Earth Resources Observation and Science Center 

(EROS) prior to open access to the archive. Those image data 

were acquired in raw, unrectified format at 28.5 m nominal 

resolution and geometrically rectified (see Wolter and White 

2002 for details on image pre-processing). 

All additional Landsat images (off-step MSS, TM, and ETM+ 

imagery) used in this recent effort were downloaded from the 

EROS web site (http://glovis.usgs.gov/). These Landsat images 

are convenient as they are precision-orthorectified and geocorrected using Global Land Cover Facility GeoCover data 

(www.landcover.org). The format of these images is unsigned 

8-bit, in Universal Transverse Mercator (UTM) zone 15 

coordinates with World Geodetic System (WGS) 84 ellipsoid 

 Landsat TM, ETM+ Landsat MSS

P: 28 R: 26 P: 27 R: 26 P: 26 R: 26 P: 28 R: 26 P: 27 R: 26 P: 26 R: 26

8/26/2000 7/18/2000 12/10/2000 7/7/1981 2/12/1981 2/11/1981

8/21/1995 7/31/1996 5/16/2000 7/12/1980 7/29/1980 10/8/1980

8/7/1990 7/29/1995 2/4/1998 7/3/1980 7/11/1980 12/16/1979

9/26/1985 7/31/1990 6/9/1997 1/23/1980 7/2/1980 9/17/1979

5/21/1985 10/21/1985 5/19/1995 2/24/1979 6/11/1979 1/9/1977

6/3/1984 4/28/1985 6/1/1994 9/7/1976 7/5/1976 7/4/1974

4/16/1984 5/27/1984 5/10/1992 7/30/1975 5/9/1975 2/11/1976

12/18/1991 3/8/1975 9/26/1974 9/2/1975

8/28/1991 2/29/1974 5/28/1973 5/8/1975

9/26/1990 5/29/1973 2/9/1973 12/16/1972

 10/14/1985 9/18/1972

P: 27 R: 27 P: 26 R: 27 P: 27 R: 27 P: 26 R: 27

9/12/2000 12/10/2000 2/12/1981 5/12/1981

8/19/2000 5/16/2000 5/27/1980 2/11/1981

8/14/1995 2/4/1998 6/11/1976 10/8/1980

7/31/1990 6/9/1997 9/24/1976 12/16/1979

6/2/1986 5/19/1995 2/21/1976 9/17/1979

4/28/1985 6/1/1994 6/23/1975 1/9/1977

6/28/1984 12/20/1992 9/26/1974 7/4/1976

5/27/1984 5/10/1992 2/22/1974 2/11/1976

 8/28/1991 7/3/1973 8/6/1975

 8/12/1991 9/18/1972

 9/26/1990 

Table 1. Landsat images used for this research map series, organized by footprint (i.e., Landsat Worldwide Reference System [WRS-2] path [P] and 

row [R] combinations).

P: 28 R: 27

7/9/2000

8/21/1995

8/7/1990

9/26/1985

P: 28 R: 27

7/3/1980

7/17/1974
5

Recode	
  1990	
  Land	
  Cover	
  

•	
  Anderson	
  Level	
  II	
  

•	
  upland	
  vs.	
  lowland	
  

•	
  hardwood	
  &	
  conifer	
  regenera8on	
  

(17	
  classes	
  	
  BASE)

Anderson	
  Level	
  III	
  Land	
  Cover	
  1990	
  

Forest	
  detail:	
  	
  genus	
  to	
  species	
  

(Wolter	
  and	
  White	
  2002)	
  

Mul8-­‐temporal	
  image	
  analyses	
  

&	
  ancillary	
  wetlands	
  data	
  

Overall	
  Accuracy	
  =	
  75%	
  

Forest	
  Type	
  Accuracy	
  =	
  77%	
  

Overall	
  Accuracy	
  =	
  78.5%	
  

Forest	
  Type	
  Accuracy	
  =	
  82%	
  

Unchanged	
  Pixels	
  

Subs8tute	
  1990	
  BASE	
  classes	
  for	
  

current	
  8me-­‐steps,	
  e.g.,	
  

1st	
  steps:	
  85-­‐90	
  and	
  90-­‐95	
  

2nd	
  steps:	
  80-­‐85	
  and	
  95-­‐00	
  	
  

Five-­‐Year	
  Change	
  Detec?on	
  

TM	
  85-­‐90,	
  90-­‐95,	
  95-­‐00:	
  	
  ∆NDMSI	
  

MSS	
  to	
  TM	
  80-­‐85:	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  ∆Red	
  

MSS	
  75-­‐80:	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  ∆Red	
  

Classify	
  Changed	
  Pixels	
  

•	
  unsupervised	
  classifica8on	
  

•	
  reconcile	
  illogical	
  change	
  

classes	
  against	
  BASE	
  (Q/C)	
  

•	
  88.8%	
  accuracy	
  (Wolter	
  &	
  

White	
  2002)	
  

Classify	
  Disturbance	
  Type	
  

•	
  large	
  fires:	
  unsupervised	
  

classifica8on	
  (incl	
  off-­‐8me	
  steps)	
  

•	
  large	
  blowdown:	
  manual	
  

interpreta8on	
  

•	
  harvest,	
  small-­‐scale	
  natural,	
  &	
  

regrowth:	
  from	
  stage	
  3	
  

	
  	
  	
  	
  No	
  

Change	
  

Change	
  

Stage	
  1	
  

Stage	
  2	
   Stage	
  3	
  

Stage	
  4	
  

Combine	
  Change	
  &	
  No	
  Change	
  

Final	
  8me-­‐step	
  land	
  cover	
  state	
  

Flowchart	
  Figure	
  

Wolteretal.2

Figure 2.—Flowchart describing the land cover 

classification and change detection process used 

to produce the land cover maps from 1975-2000.

and datum. We transformed (nearest neighbor) all newly 

acquired images to UTM15 coordinates, with North American 

Datum (NAD) 83 and Geodetic Reference System (GRS) 1980 

ellipsoid, to 28.5 m to match the older 2002 analyses.

Since it was difficult to find cloud-free images for two pairs of 

scenes (e.g., R26 and R27) along a particular path (e.g., P: 26 

or P: 27) for the same date across six time-steps, we chose to 

process imagery for change detection on a scene-by-scene basis 

rather than mosaic imagery by path or study region (Wolter 

and White 2002). This allowed more freedom to select optimal 

imagery, rather than having to ignore perfect, cloud free images 

because the date-compliment scene to the north or south was 

compromised by clouds. We further accepted images with 

clouds within about 60 km of either the west or east edges of 

scenes (i.e., 32.4 percent of a 185 km wide image), but never in 

the middle 60 km, and then used neighboring images from the 

same time-step to cover gaps due to cloud cover (Landsat path 

side lap is ~37.5 percent or 69.3 km at 47.5o

 north latitude). 

A consequence of this method is that the 5-year time-steps 

between classifications were nominal as precise dates varied by 

scene (Table 1). 

Land Cover Classification and Change Detection 

Techniques

The land cover classification and change detection methods 

presented here are extensions of earlier work by Wolter and 

White (2002) and Pastor et al. (2005), and were completed in 

four distinct stages (Fig. 2). In Stage 1, we simplified Wolter 

and White’s initial classification for 1990, initially classified 

to genus- and/or species-level detail, to Anderson Level II 

classes (Table 2). Upland and lowland vegetation classes were 

differentiated based on intersections with U.S. National 

Wetlands Inventory (Wilen 1990), and the Canadian NationalScale Ontario Land Cover (OLC) wetland classes (see Wulder 

et al. 2003). The 1990 land cover classification served as the 

“base” year for the time series across the whole region (Wolter 

and White 2002). 
6

CLASSIFICATION CODE DESCRIPTION

Water W Surface water including lakes, ponds, rivers, streams, and some wetland areas.

Emergent vegetation EM Wetland areas dominated by Carex spp., Typa spp., Scirpus spp., or similar vegetation 

growing above the surface of wet soil or water.

Lowland grass LG Graminoid-dominated low-lying areas with saturated soils commonly found near 

streams, lakes, and marshes.

Lowland brush LB Areas dominated by short woody vegetation over wet or saturated soil. Examples: 

Ericaceous vegetation, Alnus spp., Salix spp., and Cornus spp.

Sphagnum bog BOG Wetlands dominated by Sphagnum spp. that are commonly associated with very 

sparse, small diameter, stagnant woody vegetation including Ericaceous brush, Picea 

mariana, and Larix laricina.

Upland grass UG Areas dominated by graminoid vegetation and forbs that do not intersect with any of the 

National Wetlands Inventory’s (NWI) moisture modifier data.

Domestic grass DG Areas covered by cultivated or noncultivated herbaceous vegetation dominated by short 

manicured graminoids and/or forbs. Examples: agricultural fields and pastures, golf 

courses, and miscellaneous lawns.

Upland brush UB Areas dominated by short woody vegetation over relatively dry soil that do not intersect 

with NWI moisture modifiers. Examples: Corylus spp., Viburnum spp., Rubus spp., and 

very early successional hardwood regeneration.

Conifer regeneration CREG Early successional coniferous forest that is spectrally distinct from mature forest 

classes, brush classes, and early successional hardwood regeneration.

Hardwood regeneration HREG Early successional hardwood forest that is spectrally distinct from mature forest classes, 

brush classes, and early successional conifer regeneration.

Lowland conifer LC Forested areas composed primarily of wetland conifer species (e.g., Picea mariana, 

Larix laricina, Thuja occidentalis) that intersected with NWI moisture modifier data.

Lowland hardwood LH Areas dominated by hardwood tree species commonly associated with wetlands (e.g., 

Fraxinus nigra, Acer rubrum, Salix spp., Ulmus spp.) that intersected with NWI moisture 

modifier data.

Lowland mixedwood LM Forest areas consisting of lowland mixtures of hardwood and conifer species, as 

specified above, that intersect with NWI moisture modifier data.

Upland conifer UC Forest dominated by upland conifer species (e.g., Picea glauca, Abies balsamea, and 

Pinus spp.) that do not overlap with NWI moisture modifier classes.

Upland hardwood UH Forests dominated by upland hardwood species (e.g., Populus spp., Betula papyrifera, 

Acer saccharum, and Fraxinus Americana) that are not coincident with NWI moisture 

modifier.

Upland mixedwood UM Forest areas consisting of upland mixtures of hardwood and conifer species, as 

specified above, that do not intersect with NWI moisture modifier data.

Forest blowdown* BLOW Stand-replacing forest disturbance due to extreme wind events, such as the July 4, 

1999 blowdown event in northeast Minnesota.

Forest fire scars* FIRE Stand-replacing forest disturbance due exclusively to wildfire events.

Developed DEV Lands dominated by residential housing structures, commercial industrial development, 

and/or copious areas of pavement.

* Identified in the final stage of the classification and change detection process.

Table 2. Land cover classification scheme 
7

In Stage 2, vegetation change between time periods (e.g., 1985 

and 1990) was quantified using the normalized difference 

moisture stress index (NDMSI; see Hunt and Rock 1989, 

Wilson and Sader 2002, Wolter and White 2002) applied to 

leaf-on imagery from the respective time periods. Pixels that 

showed differences in this index >1.5 standard deviations (+/-) 

between time periods were designated as changed pixels; all 

others were considered unchanged. Change analysis between 

dates involving MSS (1975-1980) or MSS and TM (1980-

1985) imagery followed different protocols since the MSS 

sensor did not have a shortwave infrared band required to 

calculate NDMSI. Prior to change analysis, 60 m MSS pixels 

were rescaled (nearest neighbor) to 28.5 m to match TM/

ETM+ spatial resolution. Differences in visible red reflectance 

between image dates were used as a surrogate tool to identify 

change pixels (Desclée et al. 2007). 

In Stage 3, pixels identified as changed were classified using an 

iterative, self-organizing, maximum likelihood classifier into one 

of 17 cover classes (i.e., Table 2), deferring the identification of 

specific disturbance types until the final stage (Fig. 1). Leaf-off 

winter Landsat images were used to identify conifers below 

hardwood overstory to more accurately discern the hardwood, 

conifer, and mixed-wood forest classes (Wolter et al. 2008). 

Logical cover-type transition checks were performed to correct 

unlikely change or illogical change classes. In the first 5-year 

time-steps (backward and forward in time: 1985-1990 and 

1990-1995, respectively), change classes were compared to 

the 1990 base classification to ensure the transition class made 

sense: a 5-year transition from upland grass (UG) to mature 

upland mixed forest (UM), or water to upland conifer are 

examples of unlikely and illogical transitions, respectively. In the 

second set of time-steps from the base year (i.e., 1980-1985 and 

1995-2000), resulting change classes were checked against base 

and the first time-step classifications for illogical transitions. 

For pixels that did not change between the 1990 base year and 

the first time-steps (backward and forward from 1990), the 

associated land cover classification value from of the 1990 base 

classification was transferred to the respective time-step year 

(1985 and 1995). Pixels that did not change from time-step 

1 to 2 (i.e., 1980-1985 and 1995-2000) adopted pixel values 

from the step 1 classifications, and so forth. 

In Stage 4, large-scale (>100 ha) natural disturbances (i.e., fire 

and blowdown) were identified and used to distinguish these 

forest-cover changes from all other disturbances. Recent burn 

scar spectral signatures were readily distinguished from other 

forest disturbance classes using both Landsat TM/ETM+ (dark 

purple-maroon in the 5, 4, 3 band combination) and MSS 

(blackish in 4, 3, 2 band combination) imagery. However, this 

unique spectral signature fades after a few years. Therefore, 

the entire Landsat archive (1972-2000 by each scene) was 

examined to detect evidence of large scale (>100 ha) fire activity 

between 5-year time periods to better capture these ephemeral 

Right: Recreational use characteristic of the wilderness 

area. Photo by Sue Lietz, U.S. Forest Service.

Below: Landscape mosaic characteristic of the greater 

Border Lakes Region. Aerial image by Peter Wolter, Iowa 

State University, and Clayton Kingdon, University of 

Wisconsin-Madison; used with permission.
8

signatures (Fig. 1). Once an off-time-step image (with recent 

fire activity) was discovered, all no-change areas for the timestep containing the fire event were masked out. Then, recent 

fire scars were distinguished from other disturbance types 

using the unsupervised classification methods described for 

Stage 3 above. Large-scale (>100 ha) tree blowdowns were then 

separated from remaining forest disturbances by manual image 

inspection (shape and pattern assessment) and digital recoding, 

rather than by unsupervised image classification. Changed 

pixels were then merged with the unchanged pixels to complete 

the final maps for each time step (Fig. 2).

Harvest disturbances represent the most prevalent form of 

canopy-replacing disturbance within the managed areas of the 

Border Lakes Region (Sturtevant et al.1

). While most of the 

harvest operations were qualitatively distinguishable from other 

disturbance types (i.e., large fires and blowdowns), cutting 

operations range in size from very large to very small, and 

the size distributions differ on each side of the international 

border (Sturtevant et al.1

). Given that the cause of disturbance 

was more difficult to distinguish at the finer spatial scales, we 

did not attempt to classify other disturbance types apart from 

major fire and blowdown events. Other forest disturbances 

include insect disturbance (primarily defoliation), small-scale 

fire and wind disturbances, flooding by beaver impoundment, 

and anthropogenic linear features such as road and utility 

development. Future analysis of the classified maps (Wolter 

et al. 2012) could allow greater separation by disturbance type. 

Notably, the canopy openings caused by the major insect 

defoliators in the region range from ephemeral changes in 

leaf area (e.g., forest tent caterpillar [Malacosoma disstria]) to 

widespread mortality (e.g., spruce budworm [Choristoneura 

fumiferana]), where impacts range widely within a given 

defoliator species. Yet even widespread defoliation mortality is 

patchy in space and time (Kneeshaw and Bergeron 1998), and is 

limited to the specific host tree species within the mixed forests 

of the greater Border Lakes Region (James et al. 2011). Hence, 

methods alternative to those presented here are necessary to 

properly map and quantify the partial canopy disturbances 

caused by defoliation damage (Townsend et al. 2012). 

1

 Sturtevant, B.R., et al. 2012 manuscript in preparation. Processes underlying 

divergent forest disturbance legacies in the Border Lakes Region of Minnesota 

(USA) and Ontario (Canada).

Disturbances in the greater Border Lakes Region. Above: Pagami Creek 

Fire, Sept 4, 2011. Photo by Kari Greer, U.S. Forest Service, Superior 

National Forest. Right: Clearcut example from Minnesota. Aerial image 

by Peter Wolter, Iowa State University, and Clayton Kingdon, University 

of Wisconsin-Madison; used with permission.
9

Land Cover and Disturbance Class Evaluation

Overall accuracy of the full 1990 forest classification (six 

Landsat scenes at near tree species level detail), that served 

as the “base” year for our change analyses, was 75.0 percent 

(Kappa [ ] = 0.74) (Wolter and White 2002). Average 

accuracy among the full complement of forest classes was 78.5 

percent (commission/inclusion) and 74.7 percent (omission/

exclusion). After aggregation to Anderson Level II classes, 

the new overall accuracy was 78.5 percent ( = 0.77), with 

an average accuracy among aggregated forest classes of 82.4 

percent (commission/inclusion) and 80.9 percent (omission/

exclusion) (see Appendix). Accuracy of 1990 to 1995 change 

classification results reported by Wolter and White (2002) was 

88.8 percent ( = 0.87). More details on accuracy assessment 

can be found within Wolter et al. (2012). Because we extended 

Wolter and White (2002) methods of change detection (MSS 

protocol differed slightly as noted above) and unsupervised 

reclassification of change types for other time-steps before 1990 

and after 1995, it is reasonable to expect similar change class 

accuracies for the remaining time-step land cover classifications.

 

Burn scar identification was independently evaluated using 

available fire perimeter records from the Canadian large fire 

database (LFD; Stocks et al. 2002), the Superior National 

Forest, and fire point locations assembled by the Minnesota 

Department of Natural Resources and the Superior and 

Chippewa National Forests (Cardille and Ventura 2001). 

The LFD is a national-scale product with recognized spatial 

inaccuracies—however the dates of fire events are accurate. 

Polygons representing fire perimeters were overlaid with each 

classified image for the time step prior to and immediately 

after the year of each fire. The proportions of land cover types 

within the fire perimeters were tabulated for both time periods 

to determine if fire scars or other disturbances were mapped 

within the perimeter. Validation focused on stand-replacing 

forest fires, defined as those fire polygons that affected primarily 

forested land cover and contained at least 10 percent of its 

area where forest was converted to a transitional cover type. 

A forest fire event was tallied as correctly classified if cells 

identified as “fire scar” were represented within the polygon. 

For fire records for which there were no available perimeter 

data, the public land survey (PLS) section that included the 

ignition location was used as the analysis window instead of a 

perimeter. For those large fire (>200 ha) observations meeting 

the above criteria (n=66), approximately 74 percent contained 

mapped fire scars. The fact that some large fires were not 

mapped as fire scars suggests that rapid revegetation following 

those fires, potentially in combination with understory burning 

that did not impact the canopy, may have masked the fire scar 

signature. In addition, some portions of the burned area (as 

indicated by fire polygons) were classified as some transitional 

type rather than fire scar, indicating regeneration following the 

burn affected the fire scar classification. However 30 additional 

fire scars were mapped that did not correspond to any of the 

available fire records, including fires smaller than the 200 ha 

threshold for the LFD (Ontario), fires in Minnesota that 

occurred prior to the state records, and a subset (less than 10) 

that were apparently missed by the recording agencies.

The accuracy of the forest blowdown class was assessed 

through comparison with an independently derived map of 

wind disturbance severity (Rich et al. 2010). The severity map 

corresponds to a small portion of the full classification extent, 

but overlaps a portion of the area mapped as blowdown in the 

2000 classification year. Only upland areas were included in the 

severity map, and we further masked out obvious image edge 

artifacts as well as all areas mapped as the forest fire scar class 

in either 1995 or 2000 to avoid confusion across disturbances. 

After applying these masks, we quantified the relative frequency 

of wind disturbance severity within the blowdown class and 

across all other classes (Fig. 3). The distribution demonstrates 

that most of the blowdown pixels correspond to mapped high 

severity levels. For a direct comparison between maps, we 

reclassed the severity map to four discreet severity classes with 

thresholds of 10, 34, and 67 percent severity (Rich et al. 2010). 

We then rescaled the severity class map to match the 28.5 m 

resolution of our classification using majority class assignment. 

We assessed accuracy of the blowdown class through direct 

comparison with the highest severity (67-100 percent) class. 

We calculated an overall accuracy of 74.6 percent ( = 0.74), 

with an average accuracy of 67.4 percent (user’s) and 34.8 

percent (producer’s) (See Appendix). These results indicate 

that we can be confident that areas mapped in our blowdown 

class do represent very severe wind disturbance. The blowdown 

class likely does not represent all areas impacted by wind 

disturbance, especially areas with low to moderate disturbance.

Period of Most Recent Forest Canopy Disturbance

A composite map depicting the period of forest canopy 

disturbance was produced using the 1975-2000 sequence of 

land cover data. First, a map of forest disturbance was created 

for each consecutive time period (e.g., 1980-1985). Forest 

canopy disturbance was defined as a forested pixel changing to 

a transitional cover type (i.e., herbaceous, brush, regeneration, 

fire scar, or blowdown) in successive time steps, indicating 

stand-replacing disturbance (i.e., overstory removal). To extend 
10

the forest disturbance series earlier in time, pixels that were 

classified as a transitional cover type in 1975 were considered 

disturbed forest if the pixel changed to a forest type later in 

the time series. Because the actual timing of disturbance is 

unknown for the 1975 disturbed pixels, these were assumed 

to be disturbed prior to 1975 and assigned to a single class. 

Relative density of pre-1975 disturbance pixels within a given 

“patch” may be interpreted as the relative recovery of forest 

there, with the caveat that the original land cover type (i.e., 

forest or nonforest) for “nonrecovered” pixels in the patch 

cannot be determined. The six individual disturbance maps from 

each time period were then merged, with the latest dates given 

priority in case of overlap, to produce a composite map of the 

period of disturbance. To complete the map, undisturbed areas 

were pooled into three stable classes. Forested pixels circa 2000 

that were not disturbed within the timeframe of the data were 

pooled into a stable “forest” class. The nonforested lowlands 

(EM, LG, LB, BOG) circa 2000 were pooled into a stable “open 

wetlands” class. Sites that were nonforest throughout the time 

series but are not open lowlands were grouped into a stable 

“nonforest” class. This class includes developed areas and upland 

openings (natural or manmade). Note that while nonforest 

classes do experience change over the course of the land cover 

time series, the major changes occurring on this landscape 

are linked to natural and human forest conversion factors 

(Sturtevant et al.1

). The research dataset (Wolter et al. 2012) 

includes Arc Macro Language (AML) code used to construct 

the period of most recent disturbance map from the time series 

of land cover classifications, and will be updated to include 

more recent classification dates.

ACKNOWLEDGMENTS

These products were created by the University of MinnesotaDuluth, the University of Wisconsin-Madison, and U.S. Forest 

Service Northern Research Station under the following grants: 

National Fire Plan (Project 01.NCS.C.2); USDA Cooperative 

State Research, Education and Extension Service (CSREES) 

Managed Ecosystems program (2005-35101-16342); and 

National Aeronautics and Space Administration (NASA) 

Carbon Science Program (NNX06AD45G). This product 

benefited from the review of Ajith Perera (Ontario Ministry 

of Natural Resources) and Mark Nelson (U.S. Forest Service), 

as well as personnel from the Superior National Forest (Bruce 

Anderson and Thomas McCann).

3.0%

Blow down

Other upland

2.5%

2.0%

1.5%

1.0%

0.0%

0 10 20 30 40 50

Severity (% mortality)

Relative Frequency (%)

60 70 80 90

0.5%

Figure 3.—Relative frequency of wind 

disturbance severity values (Rich et al. 

2010) among cells classified as forest 

blowdown and all other upland classes, 

for a subset of the greater Border Lakes 

Region classification for the year 2000.
11

Forest Type Mosaic

Figure 4.—Forest types characteristic of the study area. 

Upper left: Upland conifer example; mixed pine stand in the BWCAW. Photo by Sue Lietz, U.S. Forest Service.

Middle left: Lowland conifer example; tamarack forest in northeast Minnesota. Photo by Sue Lietz, U.S. Forest Service.

Lower left: Lowland conifer example; lowland spruce forest in northeast Minnesota. Photo by Peter Wolter, Iowa State University; used with permission.

Upper Right: Upland mixedwood example; white pine and paper birch forest in northeast Minnesota. Photo by Peter Wolter, Iowa State University; used with permission.

Middle Right: Upland conifer example; jack pine mix in BWCAW. Photo by Brian Sturtevant U.S. Forest Service.

Lower Right: Upland deciduous example; aspen stand with balsam fir understory in the BWCAW. Photo by Brian Sturtevant, U.S. Forest Service.
12

APPENDIX 

	 Land Cover	 User’s Accuracy	 Producer’s Accuracy	 Overall Accuracy

1 [W] 0.885 0.975

2 [EM] 0.938 0.565

3 [LG] 0.500 0.778

4 [LB] 0.663 0.663

5 [BOG] 0.752 0.666

6 [UG] 0.654 0.845

7 [DG] 0.939 0.756

8 [DEV] 0.966 0.887

9 [UB] 0.415 0.672

10 [BLOW] 0.674 0.348 0.746

11 [CREG] 0.852 0.821

12 [HREG] 0.853 0.848

13 [LC] 0.828 0.830

14 [LM] 0.735 0.807

15 [LH] 0.915 0.821

16 [UC] 0.788 0.772

17 [UM] 0.883 0.713

18 [UH] 0.740 0.861

19 [FIRE] 0.881 0.743

(3820/4869) = 0.785

Table A1. Accuracy of the greater Border Lakes Region recoded land cover images.
13

Table A2. Accuracy of the greater Border Lakes Region 1990 land cover image (Wolter and White 2002).

	 Land Cover	 User’s Accuracy	 Producer’s Accuracy	 Overall Accuracy

JP 0.733 0.787

JP-H 0.757 0.963

RP 0.741 0.776

RP-H 0.554 0.891

SF 0.662 0.633

SF-H 0.735 0.771

C 0.707 0.630

C-H 0.565 0.795

T 0.815 0.677

BS 0.785 0.878

ABC 0.638 0.744

CREG 0.821 0.852

BA 0.810 0.860

BA-LC 0.874 0.765

BA-UC 0.688 0.855

AB 0.820 0.669

AB-LC 0.785 0.646

AB-UC 0.653 0.845

NH 0.808 0.724

NH-Con 0.826 0.905

NH-UC 0.833 0.714

H-trn 0.733 0.846

HREG 0.838 0.820

BARE 0.748 0.773

W 0.975 0.885

E-aq 0.552 0.941

EM 0.576 0.919

SPHAG 0.793 0.902

UG 0.755 0.346

LG 0.778 0.510

AGRIC 0.756 0.939

UB 0.672 0.415

LB 0.639 0.670

ERIC 0.806 0.509

DEV 0.817 0.946

ROAD 0.927 0.916

lh 0.479 0.897

C-lowBA 0.667 0.848

(3653/4869) = 0.750
14

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15

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Phillip A.; Pastor, John. 2012. Forest land cover change (1975-2000) in the Greater

Border Lakes Region. Research Map NRS-3. Newtown Square, PA: U.S. Department of 

Agriculture, Forest Service, Northern Research Station. 17 p. [Printed map included].

This document and accompanying maps describe land cover classifications and change 

detection for a 13.8 million ha landscape straddling the border between Minnesota, and 

Ontario, Canada (greater Border Lakes Region). Land cover classifications focus on 

discerning Anderson Level II forest and nonforest cover to track spatiotemporal changes 

in forest cover. Multi-temporal Landsat Thematic Mapper (TM), Enhanced Thematic 

Mapper Plus (ETM+), and Multi-Spectral Scanner (MSS) data from 1972 to 2000 were 

used to classify forest cover types and disturbances at 5-year intervals. A composite 

dataset depicting the period of forest disturbance was produced using the 1975-2000 

sequence of land cover data. These land cover change data were produced to facilitate 

analysis of forest disturbance patterns, to support landscape simulation modeling, and to 

support cross-ownership land management within the region. A double-sided fold-out map 

shows A) forest land cover change across differently managed forests, and B) classified 

period of forest canopy disturbance for the entire study area. Digital versions of the map 

are available online, as are the datasets and code used to produce them.

KEY WORDS: Land cover classification, land cover change, forest disturbance, land 

cover change map

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