LATE PLEISTOCENE GLACIAL HISTORY OF CENTRAL MARQUETTE ANDNORTHERN DICKINSON COUNTIES, MICHIGAN

By

Robert S. Regis

A DISSERTATION

Submitted in partial fulfillment of the requirements

for the degree of

DOCTOR OF PHILOSOPHY

(Geology)

MICHIGAN TECHNOLOGICAL UNIVERSITY

1997

This dissertation, "Late-Pleistocene Glacial History of Central Marquette and Northern

Dickinson Counties, Michigan", is hereby approved in partial fulfillment of the

requirements for the degree of DOCTOR OF PHILOSOPHY in the field of geology.

DEPARTMENT: Geological Engineerinjand Sciences

Thesis Advisor

HewroT Department

April 28, 1997Date

PREFACE

Glacial processes affected broad regions in the northern part of North America and

many parts of the world, yet in many areas, little is known about the details of glacier

movements. To better understand the glacial history of the central Upper Peninsula of

Michigan and to identify new techniques that will help improve interpretations of the

glacial history in any region, three related studies were conducted. The first study used

diverse digital datasets and computer image processing techniques in place of traditional

maps and aerial photographs for interpreting and mapping the spatial distribution of glacial

landscape features. The second study incorporated field and laboratory data with the

interpretations made in the first study to describe the movements of glacial ice within the

central Upper Peninsula. The third study explored a relationship between drumlin

orientation and form to underlying bedrock geology that was first recognized in the image

processing study.

In the first study, several diverse digital datasets were pre-processed so that they

could be easily combined. Image processing algorithms such as the Intensity-Hue-

Saturation (IHS) transformation were applied to the dataset combinations to test their

utility for interpreting and mapping glacial landforms. Digital elevation model (DEM),

Side-Looking Airborne Radar (SLAR), Thematic Mapper (TM), and several ancillary

datasets proved to be effective replacements for traditional interpretation tools such as

aerial photographs and topographic maps. From this study, it was concluded that digital

image processing of diverse datasets can be used to effectively analyze glacial landscapes

and in many cases, replace traditional interpretation tools.

The second study was undertaken to interpret the movements of glacial ice

through the central Upper Peninsula region utilizing information from the image

processing study combined with field and laboratory studies of sediments and geomorphic

features. The stratigraphy of geomorphic features identified in the image processing study

was examined and sediment samples were collected, and analyzed in the lab to determine

texture and lithology. From this study, it was interpreted that two ice lobes initially

advanced across the region and then retreated into the Lake Superior basin. In the

northern and northwestern parts of the study area, the Michigamme lobe moved forward

from the northeast. In the southeast and southern parts of the study area, the Green Bay

lobe moved initially toward the southwest, but refracted so that it finally moved in a

westward direction. After they retreated, the Superior lobe advanced and deposited a

large moraine and outwash plain that parallels the modern Lake Superior shoreline about

10 km inland. The lobe retreated into the Lake Superior basin and the ice never returned.

A compelling relationship between drumlin form and orientation with bedrock

geology is the subject of the third chapter. When Side-looking Airborne Radar (SLAR)

data was combined with a scanned geologic map, it was noted that characteristics of

drumlins change precisely at the contact between Ordovician limestone (east) and

Cambrian sandstone (west). Drumlins over limestone have mean length/width ratios of

6.0 and are oriented toward the south-west, those over sandstone have length/width ratios

of about 4.0 and are oriented toward the west. Variation in porewater pressure in the

subglacial sediments, which affected their shear strength, is thought to be responsible for

the differences.

Ill

ACKNOWLEDGMENTS

Most of all, I would like to thank my major advisor, Jackie Huntoon. Jackie led

me through my graduate tenure with grace. She always seemed to know when to give me

some words of encouragement. Even though the editing process must have been a real

chore at first, because of the poorly written text I imposed upon her, her commitment to

my success was always apparent. This dissertation was improved immeasurably by your

suggestions. I can't thank you enough.

I would also like to express thanks to Steve Shetron. Steve became a mentor and

friend early in my research. Thank you for the invigorating discussions, field trips, and for

your library. You always kept me "on my toes" by pointing out alternatives to my

explanations, which improved this dissertation greatly, and made a better researcher out of

me.

Ann Maclean deserves a lot of thanks for this dissertation because she helped to

improve my methodology for delineating glacial landforms. From the beginning of my

research, she provided me with an "open door" to her image processing lab, and to her

expertise. Because of that, my research was improved substantially. Thank you, Ann.

To the fourth member of my advisory committee, Bill Rose, thank you for listening

to, and for critical review of potential dissertation proposals, and for allowing me the

freedom to "march to my own drummer". Also, thank you for your support, especially in

the important first year of the program. I'm sure that you were instrumental in acquiring

most of my funding.

IV

I would also like to recognize others who helped me through the program. First of

all, I want to thank Neil Hutzler, who volunteered to serve on my written and oral

qualifying exam committees, and for helping me to sustain funding through the GEM

grant. Thank you John Gierke and Sue Beske-Diehl for volunteering to serve on my

written and oral qualifying exam committees, and to John Hughes for serving on my

written qualifying exam committee, for being a mentor and friend, and for inspiring me to

undertake the study of glacial geology.

A special thank you goes to Bonnie Gagnon, who sheltered me from institutional

bureaucracies and red tape, made sure my records were submitted in the right order, even

though they weren't always prepared in the right sequence, and generally made the

administrative chores less painful.

Thank you to all of the faculty in the Department of Geography, Earth Science,

Conservation, and Planning at Northern Michigan University, especially Pat Farrell and

Fred Joyal. Your support by hiring me before completing this dissertation was greatly

appreciated. Your support also got me through some rough times while trying to perform

my full-time job with many new classes, at the same time setting up and maintaining a new

computer lab, all the while trying to find time to complete this dissertation.

This project benefitted from intellectual discussions with many people during my

research. Thank you to faculty members Ted Bornhorst, Jimmy Diehl, Doug McDowell,

Bill Gregg, Chuck Young, and Alex Mayer, and to fellow graduate students Dave

Schneider and Drew Pilant. Thanks also to Bob McCarthy for help with laboratory work.

Last, but not least, thanks to my family. To my wife Monica, and to Brent and

Stephanie, who followed me to three different universities, sometimes with enough

resources to barely cover the moving expenses, we can finally look back on the rough

times, the good times, and see humor. I wouldn't trade them for anything. Finally, I want

to thank my parents, George and Becky, who instilled the perception that if I believed in

my own capabilities, success would surely follow. As usual, they were right.

VI

TABLE OF CONTENTS

Chapter 1: Use of DEM, SLAR, and TM Data for Interpreting and MappingGlacial Landscape Units, Central Upper Peninsula, Michigan.

ABSTRACT 2

INTRODUCTION 4

BACKGROUND 6Study Area 6Surface Geology 6

SYSTEM 8

DATA 9Digital Elevation Model (DEM) 9Thematic Mapper 11Side-Looking Airborne Radar 11Digital Geologic Map 12Other Digital Datasets 12

METHODOLOGY 13Digital Elevation Model Derivatives 13

Texture images 13Relief images 16Perspective views 17

Thematic Mapper Derivatives 18Intensity-Hue-Saturation Transformation 18Additional Image Transformations 19Classification 20

IDENTIFICATION OF GLACIAL LANDSCAPE UNITS 24Perspective Views 31Classification 34

CONCLUSIONS 42

REFERENCES 44

Vll

Chapter 2: Late Pleistocene Glacial History of the Central Upper Peninsula,Michigan

ABSTRACT 48

INTRODUCTION 51

METHODOLOGY 56

GEOLOGIC SETTING 60Bedrock Geology 60Bedrock Topography 60

DATA 64Topography 64Sediments 67Moraines, ice contact features, and outwash plains 73Interlobate deposits 85Proglacial lake landforms 89Ablation hills 93Drumlins 95Loess 96

TEMPORAL MOVEMENTS OF GLACIAL ICE 96Retreat from the late Sagola and Republic moraines 98Retreat from the Green Hills and Ishpeming positions 100Gribben Interstadial 102Gribben forest 103Marquette Stadial 112

CORRELATION 122

CONCLUSIONS 127

REFERENCES 130

vm

Chapter 3: Bedrock Control of Drumlin Morphology and Orientation

ABSTRACT 135

INTRODUCTION 137

STUDY AREA 143

METHODOLOGY 147

DRUMLIN MORPHOLOGY AND ORIENTATION 149Relationship to bedrock geology 149

STATISTICS 153

DRUMLIN SEDIMENTOLOGY AND STRATIGRAPHY 154

NON-DRUMLIN LANDFORMS 160Troughs between drumlins 160Transverse feature 163Outwash fan and tunnel channel 166

BEDROCK CONTROL ON WATER PRESSURE 169

DISCUSSION 179

CONCLUSIONS 182

REFERENCES 184

IX

LIST OF FIGURES

Chapter 1.

Egure1. Study area location and size 72. Methodology flowchart 233. IHS transformation (DEM, relief; east and north illumination) 254. IHS transformation (DEM, SLAR, PCI) 275. IHS transformation (PC 1, DEM, relief) 296. IHS transformation (geology, PCI, SLAR) 307. Perspective view, DEM with relief (westerly look direction) 338a. Classification image: 3-band 378b. Classification image: 4-band 388c. Classification image: 5-band 408d. Classification image: 8-band 41

Chapter 2.

Figure1. Study area location and size 522. Bedrock geology of the study area 613. Contour and perspective view maps of bedrock topography 624. Perspective view of surface topography 655. West-east surface profile across the study area 666. Textural triangle showing till samples 697. Ternary plot: percentage of lithologic components in till 708. Contour map showing distribution of lithologic components 729. Photo of sediment exposure: outwash sand over till 76lOa. Photo of sediment exposure: foliated till over outwash 77lOb. Close-up of from area just to right of Figure 10(a) 7911. Tributary (parallel) channels of the Escanaba River 8112. Green Hills topographic map (north part) 8613. Portion of the Cataract Basin topographic quadrangle 8814. Boulder train 9015. Green Hills topographic map (south part) 9216. Portion of the Sands topographic quadrangle 9417. Loess filling a small postglacial channel 9718. Interpretation of ice marginal positions 9919. Location and size of trees unearthed in the Gribben Basin in 1978 10520. Photo of in situ spruce tree unearthed in the Gribben pit 110

Chapter 2. (continued)

21 . Cross section showing sediments and standing trees 1 1 122. Location of the Gribben forest 1 1323. Locations and C14 dates of organic materials 1 1424. Ponding of water in front of advancing Marquette ice 11625. Maximum position of Marquette ice 11726. Position of ice while outer Marquette moraine formed 1 1 827. Position of ice while inner Marquette moraine formed 12028 . Map of Marquette moraines 1 2 129. Regional correlation of moraines 1 23

Plate

1 . Glacial landscape units in pocket

Chapter 3.

Figure1 . Drumlin formation resulting from rheological differences 1392. Impact of porewater pressure on subglacial sediments 1413. Study area 1444. Generalized bedrock geology 1455. Overburden thickness/geology/drumlin relationship 1486. SLAR/geologic map combined image 1507. An example of some drumlin forms 1 528. Photo of gravel pit exposing a drumlin interior 1 569. Close-up view to the right of Figure 8 1 5710. Drumlin interior exposure 1 591 1 . Topographic map (Northland, MI quadrangle) 1611 2. Photo of gravel pit 1 6213. Cross section of asymmetrical drumlins 1 6414. Cross section through transverse (to ice flow) feature 1651 5. Close-up of contorted strata 1 671 6. Landsat TM image 1 6817. SEFTRAN finite element numerical groundwater model output 1 78

XI

LIST OF TABLES

Chapter 1.

Table1. Data characteristics 102. Datasets and their information content 213. Maximum-likelihood classifier accuracy assessment 35

Chapter 2.

Table1. Gribben forest radiocarbon dates 1042. Gribben forest paleosol description #1 1073. Gribben forest paleosol description #2 1084. Regional correlation of moraines 124

Chapter 3.

Table1. Drumlin statistics 1542. Glacial sediment characteristics 1743. Values of hydraulic conductivity 177

Chapter 1

Use of DEM, SLAR, and TM Data for Interpreting and MappingGlacial Landscape Units, Central Upper Peninsula, Michigan

ABSTRACT

New techniques for mapping glacial landscape units located in the central Upper

Peninsula of Michigan were developed using image processing software. Digital Elevation

Model (DEM), Side-Looking Airborne Radar (SLAR), Landsat Thematic Mapper (TM)

and overburden thickness (OBT) datasets were used. Many combinations of the DEM,

SLAR, and TM datasets using the Intensity-Hue-Saturation (IHS) and Principal

Components Analysis (PCA) transformations were valuable for visual interpretation of

glacial landscape units. Such combinations showed relative elevations of landscape units,

relief variations, and surface cover types in a single image. Also in the study, relief images

and three-dimensional perspective views derived from the DEM were used to map ice-

marginal positions and interpret how glacial ice receded from the area. The stair-step

appearance of glacial outwash terraces at progressively lower elevations toward the east

became evident using the perspective view technique. Visualization of glaciated terrain

using these datasets in an image processor proved to be more effective for interpreting

glacial landscapes than traditional topographic map or aerial photograph analyses.

Texture analysis of the DEM was used to provide a measure of terrain ruggedness

(or roughness) as input to a supervised maximum likelihood classification algorithm.

Standard deviation of the DEM was assessed as a measure of texture in four moving

windows of the following sizes; 64 pixels2, 32 pixels2, 16 pixels2, and 3 pixels2. Windows

of different sizes were used to match the frequency of natural variation in size and spacing

of features that comprise each of the landscape units in the study area. Texture files were

combined with the TM, DEM, and OBT datasets into a single multi-band file. The

3

maximum likelihood classification algorithm was then applied to the multiple-dataset file.

The algorithm was first applied only to the two principal components (PCI and PC2) of

the TM's six non-thermal bands, then each remaining dataset was added, one at a time, and

the algorithm was re-applied until all eight datasets (PCI, PC2, DEM, OBT, and the four

texture datasets) were used. When compared to ground truth data, classification accuracy

utilizing all eight datasets reached a maximum of 68.6% correctly classified pixels.

Without any textural measure included in the classification (only using PC's, DEM, and

OBT), overall accuracy was 54.2%. The addition of each dataset significantly improved

the overall performance, suggesting that when classifying glacial landscape units, land

cover, topography, overburden thickness, and a measure of surface roughness improves

the accuracy of glacial landscape classification.

INTRODUCTION

Identification and classification of landscapes is based on the premise that areas of

the earth's surface may be characterized by a unique set of internally homogeneous

properties such as slope, form, hydrologic characteristics, etc. (Townshend, 1981). Such

areas are composed of assemblages of feature types that collectively form a landscape unit.

In the heavily-wooded, glaciated Superior Upland province of North America, landscape

units are mapped via relief, topographic form of individual features, associations of

multiple features, vegetation patterns, and distribution of soils.

Topographic maps and stereoscopic aerial photographs are traditionally the

primary sources of information for geologists and physical geographers who interpret and

map glacial landscapes. These types of data provide researchers with inexpensive, readily

accessible spatial (X,Y and Z) information for interpretation and feature identification.

However, there are some problems associated with using these media types. To interpret

topographic maps, isolines of elevation on the map must be mentally converted to a three-

dimensional image of the terrain. The visualization and interpretation of earth surface

features made from isolines is a subjective process that is often associated with error,

particularly if the analyst is not highly trained. Aerial photographs, when used for

mapping land use/cover, also present interpretation problems since they are often acquired

"leaf-on". The vegetation masks subtleties of geomorphic form, making accurate landform

interpretation difficult. Another problem specific to interpreting glacial landscapes with

each of these tools is that much of the geomorphic detail is lost as the scale of the map

and/or photograph becomes smaller.

In contrast to topographic maps and aerial photographs, a computer-assisted

image processing system provides the hardware and software necessary for improved

visualization and analysis of geomorphic terrains when used with digital data. Image

processing and geographic information system (CIS) techniques may be used to

manipulate large digital datasets and to improve interpretability of the data. The

computer-assisted approach reduces interpreter subjectivity and biases inherent in

interpretations of topographic maps and aerial photographs. However, to adequately

characterize a glacial landscape using image processing techniques, the selection of proper

datasets, and the selection of appropriate processing algorithms are critical.

In a geomorphological context, many studies have shown that landforms can be

identified through the surrogate of vegetation using spectral data alone (Siegal and Goetz,

1977; Mussakowski et al., 1991). However, interpretation or classification of geomorphic

landscape units based solely on satellite spectral imagery is inadequate because not all of

the features comprising the whole unit (relative elevation and relief, for example) are

expressed. It is necessary to incorporate geomorphic and topographic parameters with the

satellite imagery to accurately map the landscape units. Digital Elevation Model (DEM)

and Side-Looking Airborne Radar (SLAR) data provide topographic and/or relief

information about surface features at a spatial resolution required for discrimination of

small landforms. Additionally, these datasets cover a sufficiently large surface area

necessary to characterize glacial landscape units.

BACKGROUND

Study Area

The study area is located in the Upper Peninsula of Michigan, bounded by the

parallels 46° OO'N and 46° 30'N latitude, and 87° 15'W to 88° OO'W longitude (Figure 1).

This area is equivalent to the western 3/4 of the Gwinn, Michigan 1:100,000 topographic

sheet.

Surface Geology

There are several distinct glacial landscape units within the study area that record

the movements of two separate ice lobes: a northern lobe that entered from the northeast,

and a southern lobe that moved in from the east (Peterson, 1986). Landforms and

sediments deposited by the respective lobes are distinctly different from one another. The

northern lobe transected high-relief, crystalline igneous and metamorphic bedrock.

Deposits of this lobe are thin, discontinuous, and are difficult to map. The southern lobe

transected limestone and sandstone lithologies of the Michigan Basin rocks in the east and

central portions of the study area, but terminated on crystalline rocks in the far western

portion. Drumlins and well-defined moraines were formed by this lobe. At the confluence

of the two lobes, an interlobate tract composed of high-relief landforms was deposited.

This landscape unit trends east-west across the central portion of the study area.

Several moraines and ice-contact scarps were constructed where the ice margins of

each respective lobe persisted at a given position for a significant period of time. The

recessional moraines deposited by the northern lobe trend northwest-southeast in the

UPPER PENINSULA

DF MICHIGAN

BEDROCKOUTCROPS

DRUMLINSAND FLUTES

ICE LDBEMOVEMENTS

MORAINES ANDICE-CONTACT SCARPS

DUTWASH PLAINS

STUDY AREA WITHGLACIAL FEATURES

L A K E S U P E R I O R

Figure 1. Study area. The study area comprises about 3300 sq. km in the central Upper Peninsula of Michigan.Features near KI Sawyer (KIS) Air Force base are younger than those to the west.

northern part of the study area. They become discontinuous between the bedrock knobs

prevalent in that region. In the southern half of the area, recessional moraines deposited

by the southern lobe trend approximately north-south and are curvilinear (concave toward

the east). Successively younger recessional moraines of both lobes occupy lower

topographic positions because the regional slope is toward the north and east in the

direction of ice margin retreat. Low-relief, large outwash plains accumulated beyond the

margins of each moraine. Continuous outwash plains join pairs of time-equivalent

moraines, one formed by each of the ice lobes, suggesting both lobes retreated uniformly

from the area. The moraine-outwash plain combinations form a distinct "stairstep" pattern

decreasing in elevation toward the north and east. Additionally, a single lobe deposited a

final, large moraine-outwash plain combination is found in the extreme northeastern

portion of the study area (Figure 1).

SYSTEM

An IBM compatible personal computer supporting Earth Resources Data Analysis

System (ERDAS®) software was the main system used in the analysis. The system

included a 48 x 36 inch (active-area) digitizer, an Eikonics digital color scanner (4096

pixels2), and a high-resolution (1024 pixels2) 24-bit color monitor. The ERDAS software

is a raster-based application that has the capability of both image processing and GIS

functions.

DATA

Several commercially available digital datasets and photo-products were used in

the study. Their characteristics are described below and are summarized in Table 1.

Additionally, a digital geologic dataset was created from a scanned analog geologic map,

and an overburden thickness dataset was constructed from water-well drilling records,

geophysical, and bedrock outcrop data.

Digital Elevation Model (DEM)

The west half of the Marquette, Michigan 3-arcsecond DEM was used in this

study. It is a level 3 product of the USGS. Each pixel of the data in its raw form

represents about 80 m2 of ground area. Absolute elevation accuracy of these data is Vz the

contour interval (50 feet) of the parent topographic map product at a 90% confidence

level (U.S.G.S., 1987). For this study, it equates to nominal elevation accuracy of

approximately ±8 m. Relative elevation accuracies within a region, however, are generally

much better (U.S.G.S., 1987). This is advantageous for the present study because

absolute elevation is not as important as changes in elevation (relief) for identification of

glacial landscapes.

10

Table 1. Data and characteristics.

DATA TYPE

Digital Data

DEM

SLAR (Aeroservice)

SOURCE

U.S. Geological Survey

U.S. Geological Survey

LANDS AT TM EOSAT Corp.

Photo Products

SLAR U.S. Geological Survey

LANDSAT TM EOSAT Corp.

CHARACTERISTICS

1:250,000 (3-arcsecond)approx. 80x78 m pixelsMarquette, MI (west Vi)

X-Band (2.5 cm wavelength)HH polarizationSouth lookFar rangeapprox. 10m2 pixelsAcquisition date 6-88

Bands 1-728.5 m2 pixelsAcquisition date 8-13-85Path 24, Row 28, Quad 2Cloud cover = 0%

1:100,000 scaleSouth lookFar rangeStrip mosaicAcquisition date 6-88

Bands 5,4,3 (R,G,B)Acquisition date 8-13-851:100,000 scalePath 24, Row 28, Quad 2Cloud cover = 0%

11

Thematic Mapper

The Thematic Mapper (TM) digital data used in the study was collected on August

13, 1985, and was cloud-free within the study area (scene I.D.#5024028008522510). TM

photo products of the same scene were also used for photogeologic interpretation. The

false-color photo product was derived from band combinations 5,4,3 (Red, Green, Blue

(RGB)) and produced at a scale of 1:100,000. It provided high spatial resolution

information and was useful for comparison with the computer images produced through

analysis of the digital data. Thematic Mapper data was used because of its low cost and

availability, and because it provided sufficient spectral and spatial resolution to delineate

variations in land cover types.

Side-Looking Airborne Radar

The SLAR data was acquired by Aeroservice in June, 1988 for the U.S.

Geological Survey. It is an X-band (2.5 cm wavelength) south-look direction dataset,

with a nominal spatial resolution of about 10 m2. A digital mosaic was created from

portions of five east-west oriented flight lines that encompass the study area. A 1:100,000

scale panchromatic SLAR photo-product of the same area was produced from the same

dataset and used for comparison with computer generated images. Far-range (low

depression angle) data were selected for both digital and paper products because this look

angle enhances the relatively low relief within the study area.

12

Digital Geologic Map

A color, paper geologic map (Harrison et al., 1982) at 1:1,000,000 scale was

scanned at a resolution of 2048x2048 pixels using a high-resolution Eikonics scanner.

The digital geologic map was geometrically corrected to 28 ground control points using a

linear least squares transformation (RMS error <1.0). Pixel size was resampled to 28.5 m2

via a cubic convolution spatial interpolation technique.

Other Digital Datasets

Overburden thickness data were compiled from water-well drilling records

acquired from the local health department, bedrock outcrop data from field observations,

geophysical resistivity measurements (Young et al., 1982), and from hydrogeologic

studies (Stuart £f al., 1954; Granneman, 1984). Eight-hundred ninety-three data points

(X,Y, and thickness) were entered into a spreadsheet software program, exported into a

software package (Surfer) and kriged to produce a regularly-spaced grid of output values

(dimensions = 500 lines by 500 columns) to encompass the entire study area. Finally, the

data were converted to ERDAS image format. The data were then resampled using the

cubic convolution spatial interpolation technique before incorporation into classification

and enhancement procedures. The dataset that resulted from this procedure is a

generalization of overburden thickness, because the distribution of data points across the

study area was non-uniform. However, it is thought to be an acceptable representation of

overburden thickness conditions because in this study, glacial landscape units are defined

13

by only a few thickness classes (for example, "thin overburden" of 0-10 m, "moderate

thickness" of 10-20m, and "thick overburden" of >20 m).

METHODOLOGY

Digital Elevation Model Derivatives

Texture images

Textural analysis was performed to assess variations in spatial homogeneity of the

DEM. Texture is defined as "virtually any type of spatial variation" (Townshend, 1981).

With textural analysis, a movable window centers on each pixel and a user-defined

mathematical function is applied to the digital numbers (DN's) of all the pixels within the

window. The size of the window (specified in units of number of pixels but equated to

ground units, or distance) assessed at one time is also selected by the analyst. Various

algorithms may be applied within the window as measures of texture, such as minimum-

maximum difference or standard deviation of the elevation values.

The derivation of textural information from digital datasets has been shown to

greatly improve classification performance when using a DEM or spectral data such as

Landsat Multispectral Scanner (MSS, which has 4 bands of spectral data). Franklin

(1987) reported improvement in classification of subarctic landscapes by adding five

geomorphic derivatives (elevation, convexity, relief, slope, and incidence) of a DEM to

MSS data. When using MSS data alone for nine landscape classes, overall accuracy was

46%. However, when the DEM data and its derivatives were added to the spectral data,

classification accuracy rose to 75%. In a similar study, applied explicitly to a DEM,

14

Franklin and Peddle (1987) presented a C-language program for textural analysis, and

applied it to a DEM of a mountainous region in Canada. They concluded that texture

information provides an independent measure of surfaces, and can be interpreted alone.

Franklin (1989), continuing his work on terrain analysis via image processor,

demonstrated that classification accuracy for land systems mapping improved from 40%

using only MSS data to 85%, simply by including elevation and slope data derived from a

DEM. Shih and Schowengerdt (1983), in a study of terrain units in Arizona, showed that

texture measures were extremely valuable for classifying geologic/geomorphic surfaces.

However, they only used Landsat MSS data as the source for texture analysis.

Hutchinson (1982) summarized the advantages and disadvantages of several methods for

combining spectral (MSS) and ancillary datasets. For natural resource applications, the

author suggested the use of ancillary datasets with computer-assisted classification

techniques holds "much promise". However, in contrast to most other studies, he

stipulates that "the simple addition of new observations increases computer time and does

not appear to improve classification accuracy with any consistency" (p. 128). As

microprocessor speeds increase, computer time required for classification of large, multi-

band datasets becomes less of an issue. Classification accuracies are, however, heavily

weighted by the quality of training samples selected to represent the classes involved, and

the spectral and ancillary datasets selected for the study at hand. Weszka et al. (1976)

cited a 90% classification accuracy of geologic features using only the textural derivatives

from a series of scanned black & white air photos. The authors compared three

approaches to derive textural information from the photos (Fourier transform, second-

15

order grey-level statistics, and first order grey-level statistics). In general, the Fourier

transform performed poorly. The reason cited was that terrain characteristics are usually

not periodic. Second and first-order grey-level statistics were found to perform well. In

fact, the simpler, first-order statistics such as standard deviation, consistently out-

performed more complex second-order statistics, such as convexity. Evans (1972, p.31),

in a discussion of statistical methods for measuring geomorphometry, states that "it is

logical that relief...should be measured by the standard deviation of altitude".

Standard deviation (S.D.) of the DEM was used to measure terrain ruggedness

(relief) in the present study. Standard deviation has been shown to be an effective

measure of relief, but using only one window size (corresponding to a finite area on the

ground) emphasizes landscape features that are about the same size as the window. For

example, a window designed to emphasize outwash plains would not be useful for

delineating a drumlin field because individual drumlins are much smaller than an outwash

plain. The use of several different areas of coverage for a complete relief analysis within a

region have been suggested in the literature, but not specifically for use in the image

processing context. Trewartha and Smith (1941), for example, suggested that the size, or

area of coverage, should be adjusted to match areas of different topographic frequency. In

a similar manner, Clark and Boulton (1989) showed how multi-scale remote sensor

datasets were necessary to match the frequency of natural variation of surface glacial

features and that a single dataset would be insufficient. In this study, four different

window sizes were applied to the DEM dataset to measure texture of the land surface

(relief). The size of the windows were designed to match the frequency and size of natural

16

relief variation within landscape units of the study area. After careful assessment of

geomorphic regions apparent on the digital and paper images, windows of several sizes

(64, 32, 16, and 3 pixels square) were chosen. The standard deviation algorithm of

ERDAS was applied to all the pixels within each respective window. In an outwash plain,

a small S.D. would be calculated for a large window (64 pixels square) of DEM data

because the feature is relatively flat and large (low total variation in elevation compared to

the number of pixels). Because stream channels that dissect the plain are of much smaller

size, these features add little to the total variance in the 64 pixel square window, unless

there are a great number of them. Stream channels are more apparent, however, in a

smaller (16 or 3-pixel square) window dataset. This is because the total number of pixels

is reduced, and a large percentage of them represent the variability in elevation of the

high-relief stream channel. New image files were created from texture analysis of the

DEM, corresponding to each window size and incorporated into the classification

procedure.

Relief images

An ERDAS relief algorithm (TOPO module) was applied to the rectified DEM

dataset. The relief product of the DEM is similar to a slope image, in that areas of high

and low relief are expressed by varying gray-scale levels. The relief algorithm provides the

analyst with an additional level of control in that artificial illumination may be applied from

a source whose coordinates and intensity are specified by the user. Thus, the analyst may

emphasize relief features with a particular orientation by altering the parameters of

17

illumination. In the present study, low-altitude illumination from east and north azimuths

were used because the regional slope is toward the northeast. East and north-facing

slopes (such as moraines and ice contact scarps) were brightly illuminated using these

parameters, while outwash plains (that slope toward the west) were darker-toned.

Perspective Views

Perspective views of digital datasets aid interpretations by allowing visualization of

relative spatial changes in Z-values across a surface. Perspective views that are created

from a DEM are a manifestation of the terrain. When a relief image is "draped" over a

perspective view, variations in tone that correspond to degree of slope further enhance the

separation between low-elevation and high-elevation areas.

Perspective views eliminate many problems inherent in other methods of

geomorphic interpretation. Data are displayed in a form that the analyst can easily

perceive, with geomorphic features emphasized by variations in grey-scale tones. Thus,

the investigator can spend more time analyzing the image, and less time converting

information from another format such as topographic maps. In the present study,

perspective views of the DEM were used to analyze the spatial associations (relative

elevations and relief) between moraines and outwash plains, interlobate regions, bedrock-

controlled topography regions, and reconstruction of dissected outwash plains.

18

Thematic Mapper Derivatives

Principal Components Analysis (PCA) was applied to the TM dataset to reduce

dimensionality and redundancy of the six spectral bands. Principal components one (PCI)

and two (PC2) were incorporated into the classification procedure (for an explanation of

PCA, Lillesand and Kiefer (1994) give an excellent overview). These two components

explained 89% of the spectral scene variance. Principal components analysis was also

applied to a file containing the spectral TM bands (bands 1-5 & 7), DEM, and relief

datasets. Then, PC 1-PC3 were displayed on the monitor for visual analysis. This

technique resulted in a single image that contained both land cover and topographic

information. Moraines and outwash plains at different elevations were easily delineated

based on variations of color as displayed on the computer monitor.

Intensity-Hue-Saturation Transformation

In recent years, the In tensity-Hue-Saturation (IHS, or sometimes, HIS)

transformation has been widely used as an enhancement tool in geologic studies (Rheault

etal., 1989; Jaskolla and Henkel, 1989; Harris et «/., 1990; Harris, 1991). All the studies

reported positive results using the technique for geologic interpretation. Sabins (1987, p.

287) and Lillesand and Kiefer (1994, p. 579) offer excellent descriptions of the IHS

transform and its functionality. The methodology described by Harris et a/., (1990) has

proven quite useful, and was adapted for the present study.

Several different combinations of datasets using the IHS transform are useful for

landscape analysis. In particular, combinations of bedrock geology or overburden

19

thickness with SLAR and TM datasets clearly shows relationships that would otherwise

not be apparent. Also, IMS combinations of DEM with relief images provided valuable

information for reconstructing outwash plains that formed contemporaneously but are now

dissected by stream valleys. Combinations of the DEM and SLAR with TM through the

IHS transformation expressed topographic information the TM does not provide, allowing

more accurate interpretation of moraines, outwash plains, and drumlinized terrain.

Additional Image Transformations

Simple algebraic manipulations of SLAR and TM datasets are quite useful for

enhancing geomorphic forms and aiding interpretations. For example, one of the most

useful enhancements for studying the drumlin landscape found in the southeast quadrant of

the study area was multiplication of the single SLAR dataset with three separate TM

bands (7,5,4 or 5,4,3 in R,G,B) to produce an enhanced color-composite image.

Mussakowski et at. (1991) reported similar findings. This technique preserves the original

spectral information of the TM data, but the benefits of the SLAR data (relief information)

are incorporated. Distribution of surface features, such as vegetative associations, rivers,

and lakes are clearly apparent in the TM data, but not easily perceived by viewing the

SLAR data alone. The data mutually compliment each other.

The application of edge-enhancing filters improved the visual appearance and

information content of nearly all datasets. DEM and TM datasets were improved by

passing a 5x5 non-directional edge-enhancing filter over them. The SLAR data, which has

a very high spatial frequency, was improved by first applying a 5x5 high-pass filter to

20

further increase the spatial frequency, and then applying a 3x3 low-pass filter to smooth

the dataset. The procedure retains the general spatial variations in the SLAR data that

correspond to landforms, but smooths the dataset by decreasing the high spatial contrast

between adjacent pixels.

Classification

The "logical channel" approach (Strahler et al., 1978), also called the

"probabalistic method" (Franklin, 1989), of classification was used in this study. This

involves the simple addition of ancillary data (in the present study, DEM, texture

measures, and overburden thickness) to spectral (TM) data in a statistical classification

procedure. A maximum likelihood (Mahalanobis) supervised classification algorithm was

applied to a file containing the eight datasets in Table 2. These datasets were chosen

because each represents a unique component of glacial landscape units. For example, the

DEM data was used to delineate outwash plains and moraines at different elevations.

Textural measures aided separation of landscape units with differing topographic

frequencies. Principal components added spectral response information

for separability based mainly on vegetation changes. The overburden thickness

information helped the classifier to differentiate between moraine (thick deposits of

sediment) and thin drift classes (such as thin drift over bedrock). Both of these classes

21

Table 2. Datasets and their information content for analyzing GLU's.

DATASET INFORMATION CONTENT

1)DEM2) Texture (642 pixel SD of DEM)3) Texture (322 pixel SD of DEM)4) Texture (162 pixel SD of DEM)5) Texture (32 pixel SD of DEM)6) Overburden Thickness7)TMPC18) TM PC2

Elevation and reliefLarge-area relief variationsMedium-area relief variationsMedium-area relief variationsSmall-area relief variationsThickness of sediment depositsLand cover typesLand cover types not in (7).

have similar relief characteristics, are located at comparable elevations, and have similar

land cover types.

Eleven distinctly different geomorphic units (classes) were identified through use

of the hard copy TM product and SLAR, topographic maps, and field reconnaissance. At

least three characteristic areas of each class were identified and digitized into polygons and

saved as training areas. Statistics were extracted for use in the classification algorithm.

The maximum likelihood classification algorithm was systematically applied to

various combinations of the datasets to assess their utility in classification of glacial

landscape units. Using the work of Franklin (1987, 1989) and Franklin etal. (1987) as a

guide, many combinations were tried and assessed visually before a standardized

procedure was established. First, only the principal components pair of datasets (TM PC 1

and PC2) were classified by extracting statistics from training area polygons in those two

"bands", then applying the classification algorithm to those "bands" only. The resulting

file was identified by the name "2-Band". Training area polygons were then applied to

22

PCI and PC2 with the DEM added (3-Band). Statistics were extracted and the scene was

classified. The same procedure was applied to combinations of PCI, PC2, DEM with

OBT added (4-Band), with PCI, PC2, DEM, OBT, and the 64 pixel2 S.D. textural

measure added (5-Band). The procedure was continued by adding individual texture

datasets until finally, the area was classified using the entire, eight-band dataset (8-Band).

A single accuracy assessment table was produced for evaluation of all classified

scenes. Five hundred (software maximum) stratified random pixels (software-selected)

were displayed on the monitor. Interactively, with "ground truth" such as topographic

maps and aerial photographs, and with field verification, "correct" classes were assigned to

each reference pixel. Accuracy of each classified scene using the different band

combinations was assessed through comparison of the reference and classified pixels.

A flowchart (Figure 2) shows the procedure for analysis of the datasets. All data

were registered to a common geographic coordinate system (Universal Transverse

Mercator, UTM), bounding coordinates, and pixel size. This allowed combinations and

interactions between the datasets to be performed freely. The DEM, TM, and overburden

thickness data were rectified and resampled to 56x54 m2 per pixel as a compromise

between the coarse resolution of the DEM and the finer resolution of the other datasets,

and so the entire (non-square) study area could be displayed on the 10242 pixel resolution

monitor without reduction. For more detailed analysis in specific areas, the TM and

SLAR datasets were rectified and resampled to 28.5 m2. The DEM dataset was not used

in those analyses.

PERSPECTIVEVIEWS

!HSTRANSFORM

GEOLOGIC MAP

CLASSIFICATIONACCURACY

ASSESSMENT

Figure 2. Methodology flowchart. Datasets used as input are enclosed in ellipses, functions performed on those data setsare within the small rectangular boxes (GCP = ground control points), outputs are in large rectangular boxes. The mainoutputs were perspective view images, images from the IHS transformation, and classified images.

to

24

IDENTIFICATION OF GLACIAL LANDSCAPE UNITS

In particular, the DEM dataset and its derivatives proved extremely valuable for

analyzing glacial geomorphic features and landscape units. The prime determinant of any

geomorphic form is spatial change in elevation (relief), justifying the use of DEM. The

raw DEM is useful by itself for initial landscape characterization, but is most useful after

application of a high-pass (edge-enhancing) filter. A filtered IMS transform combination

of DEM and relief images (east and north-illumination) emphasizes the assets of both. An

example of the IHS combination is shown in Figure 3; absolute elevation is portrayed as

gray-scale tones, with lower elevations as dark tones, high elevations in light tones. High-

relief features appear relatively bright because they are displayed in both the intensity

(north-illumination relief image) and saturation (east-illumination relief image) bands.

Outwash plains and other low-slope areas are represented by uniform-toned areas on the

image.

Most outwash plains at their respective elevation are bounded on the southeast and

northeast by linear, high-relief features. These are moraines and ice-contact scarps that

are expressions of the ice margin position at the time the corresponding outwash plain was

being constructed. The occurrence and spatial relationships of these features are evidence

that two ice lobes entered the area, one from the northeast, the other from the east, as

previously suggested by Peterson (1986). Continuous outwash plains bounded by the

moraine pairs indicate that two separate lobes were in place simultaneously, both

contributing to the formation of the plain.

25

46'30'

46°00'87*15'

54321O 5 10Kilometers

Figure 3. IHS transformation applied to DEM and relief datasets.

26

Post-glacial channels that cut into the moraines and outwash plains are indicated

by higher-relief (brighter-tone), linear, sometimes dendritic patterns that are lower in

elevation than the features they dissect. A large moraine and associated outwash plain

dissected by a northeast-southwest oriented post-glacial stream, is found in the upper-right

corner of the image. Additionally, large drumlins can be seen in the lower-central portion

of the image.

Figure 4 is an IHS combination of SLAR, DEM, and TM PCI datasets. As in the

previous image, it is possible to identify surface features in the context of relative changes

in elevation. The PCI dataset shows spectral variation at the surface while the SLAR

dataset enhances geomorphic characteristics. Elevation variations are expressed as

variations in hue. Blue and magenta hues signify (relatively) high elevations, green hues

show areas of lower elevations. In this scene, clearly, the overall slope of the terrain is to

the east. Oriented glacial features apparent at the bottom center of the scene maintain

their orientation approximately parallel to the regional slope, and suggest ice motion

around the higher elevation areas of the study area (blue hues, in the center). The major

moraines of the region are oriented perpendicular to the local slope.

From this scene, it is possible to interpret how the ice lobes traversed the terrain,

and why the ice progressed to certain positions and then halted. Because the ice was

generally moving from east to west (Peterson, 1986), its progress was impeded by the

regional slope. In such circumstances, forward motion of a glacier is associated with high

levels of frictional resistance, but downslope recessions are more likely to be episodic

because of ice stagnation and melting. The higher-elevation moraines and outwash plains

27

46'30'

88=00'

5 4 3 2 1 OMiles 10

543210 5 10Kilometers

15

N

Figure 4. IHS transformation of SLAR, DEM and TM PCI datasets.

28

in the west are evidence of earlier, more vigorous advances spared destruction by later

advances, or burial by sedimentation during retreat, because of the sloping terrain.

Combining the DEM and relief images with PCI provides the user with relief,

elevation, and spectral information. In Figure 5, an IHS transformation of PCI, DEM,

and relief, not only are relief features and absolute elevation apparent, as in Figure 4, but

the spatial relationships between surface land cover types and relief and elevation are

evident. The analyst can distinguish relationships between the distribution of deciduous

vegetation (brighter tones) and high relief (therefore usually drier) glacial features.

Likewise, poorly drained areas support moisture-tolerant species (spruce, cedar, etc.) and

are dark-toned on the image. Red and Jack pine dominate on outwash plains and appear

as an intermediate tone. The utility of using vegetation as a surrogate to surface geologic

and soils mapping has long been recognized (Tomlinson and Brown, 1962; Siegal and

Goetz, 1977). The combination of these datasets provides the capability of viewing

several different types of information at once. Thus, a more accurate interpretation may

result. For instance, in the right-center portion of the image is an interlobate tract of high-

relief ice-contact deposits. That feature is separable, in terms of relief, elevation, and

vegetation, from the terrain to the north or south. Also, stagnant-ice features in the

southwestern part of the study area (lower left) are clearly high-relief, deciduous-

supporting features.

Figure 6 shows another application of diverse data combination through use of the

IHS transformation. In this image, the scanned geologic map is represented by variations

in hue, consistent with the colors of bedrock lithologic units as portrayed on the original

,f I

Bedrock

Outwash 'terraces

1 #1 ;,'« SI #3

i^

Moraines and *'tee-contact Scarps

Ablation hillsDrumiins

88"00'

5 4 3 2 1 O MileS

54321O 5 1OKilometers

10N

15w-O-

46'00'87°15'

Figure 5. IHS transformation of PCI, DEM, and relief datasets.

Cambrian

30

. 46°30'

Mefasedtmenis

88°00'

r 1 r i i5 4 3 2 1 0 MHeS 5

543210 5 10Kilometers

10

15

OrdovicianLimestone

Dfumlins46W

87° 15'

N

Figure 6. IHS transformation applied to geology, TM PCI, and SLAR datasets.

31

map. The intensity and saturation components are represented by PCI and SLAR. The

original values of PC1 are retained, while relationships between the surface features and

bedrock geology become apparent. The orientation of drumlins (the small, linear forms in

the south-central part of the image) change from southwest/northeast in the eastern half of

the study area to northwest/southeast in the western half. The change in orientation

occurs over the contact between Ordovician limestone and Cambrian sandstone bedrock

lithologies (indicated by hue change). This is significant in that spatial variations in

bedrock geology are expressed through surface morphology. Drumlins are subglacially

formed features oriented in the direction of ice motion. Apparently, the bedrock geology

may have played a role in the formative processes of the drumlins. For field geologists,

exploration geologists, and geomorphologists, this finding is encouraging. Once more is

known about the processes of subglacial bedform construction and specifically, the effect

bedrock may have on their formation, remote sensing techniques may aid in mapping of

bedrock geology in glaciated terrain where other types of data are scarce. Although this

technique has been applied in an analog fashion in the past (map overlays), it is doubtful

that the data would show this relationship as clearly. Also, problems with the analog

method, such as combining maps of different scale, are less of an issue when using digital

image processing.

Perspective views

The combination of DEM with relief through the 3-D, or perspective view

functions of the image processor provide yet another way to perceive and analyze glacial

32

landscape units. Figure 7 is a perspective image of the DEM overlain by an east-

illumination relief image. The view direction is from south-southeast toward the north-

northwest. Through this function, terrain is represented in a visually familiar way. Mental

conversions are not necessary to perceive elevation changes, or relationships between

features. The outwash plains described previously are apparent at several, successively

lower elevations on this image. In this image, at least six separate levels are recognized,

indicating six or more separate stillstands (or minor re-advances) of the ice fronts.

Because the outwash plains at each level are continuous across the deposits of both ice

lobes, they are interpreted to have formed contemporaneously. In order for the glaciers to

construct outwash plains of this extent, it is interpreted that the ice must have persisted at

their respective positions for a significant period of time. The series of stair-stepped

outwash plains is also meaningful, as it records the recession of the ice masses

northeastward into the Lake Superior basin. Thus, it is interpreted that the recession of

glacier ice from the region was not uniform, and the intervals between adjustment to a

new equilibrium position are noteworthy.

Valleys of streams flowing eastward, that are currently dissecting the outwash

plains and moraines, are displayed as bright areas typical of rugged topography. In many

areas these stream valleys dissect the outwash plain to the extent that only separated,

remnant terraces remain. The perspective images are very useful in reconstructing the

former extent of outwash plains from isolated outwash terraces. On topographic maps or

aerial photographs, it is extremely difficult to mentally visualize these features. Most

often, only a portion of one remnant terrace may be found on a single topographic map.

Figure 7. Perspective view, DEM with relief (westerly look direction). Note the terraces at progressively lower elevationstoward the east (toward the viewer). Terrace numbers are described in the text.

34

Reconstruction of the entire terrace from isolated bits of information requires a good deal

of experience and imagination. On the computer image, however, the relationships

between disjointed terraces become very apparent.

Also on Figure 7, large hills are seen at a position where the two ice lobes met

(right-central part of the image, called the Green Hills on local topographic maps). They

formed where runoff of supraglacial meltwater was concentrated. These are ice-contact

interlobate deposits and comprise a distinct glacial landscape unit (based on relief and

spatial association).

Classification

Classification procedures applied to the datasets proved useful for automatically

mapping glacial landscapes. Table 3 provides a classification summary for each class and

combination of datasets. The maximum likelihood classifier yielded only 16.4% accuracy

in errors of omission (pixels that should have been included in a category but were

omitted) using only PCI and PC2 (identified as "2-Band"). Errors of commission and

percent correct, also shown in Table 3, are those associated with pixels that were

improperly included in a class (Lillesand and Kiefer, 1994). The moraine/outwash plain

separability was adequate using "2-Band", probably because those features support

completely different vegetation. When PCI, PC2, and DEM was used, classification

accuracy rose to 38.2% ("3-Band"). Significant improvements were realized in the

identification of all landscape unit classes except the moraine and outwash classes. These

became worse because of confusion with other classes (for example, pixels that should

Moraine 1

Moraine!

Outwash 1

Outwasb2

Thm Drift/Bedrock

Ground Moraine 1

Ground Moraine 2

Ablation Drift

Ice Coatxt Drift

Thin Drift/Wetland

Beach Deposits

Omission

100.030.360.061.893.1

100.0100.076.9100.0100.092.3

PC1 + PC2

Commission

83.792.582.953.3100.0

72.9100.0

96.4

% Correct

0.0

69.740.058.26.90.00.09.60.00.07.7

Omission

70.845.657.789.155.937.566.711.175.743.90.0

PC1 + PC2+ DEM

Commission

72.768.421.453.844.476.782.063.280.431.931.6

PC1 + PC2 + DEM PC1+PC2 + DEM PC1 + PC2 + DEM

% Correct 1

29.2 I

54.4 1

42.3 110.9 144.1 162.5 133.3 122.3 124.3 156.1 i100.0 1

Omission

54.272.765.487.316.731.344.457.462.219.30.0

+ Thickness

Commission

45.057.110.063.218.371.451.645.281.16.1

40.9

+• Thkkness + Texture + Thickness + Texture(one window) (all windows)

% Correct

45.827.334.612.783.368.755.642.637.880.7100.0

Omissiop Commission % Correct Omission Commission % Correct

67.7 57.9 33.3 45.8 38.1 54.272.7 50.0 27.3 36.4 22.2 63.680.8 28.6 19.2 15.4 21.4 84.683.6 35.7 16.4 12.7 40.7 87.315.7 14.0 84.3 37.3 3.0 62.737.5 64.3 62.5 67.7 10.0 33.340.7 32.9 59.3 28.1 60.3 71.942.6 46.0 57.4 39.4 14.9 60.656.8 80.7 43.2 45.9 71.0 54.112.3 5.7 87.7 3.5 5.2 96.50.0 40.9 100.0 15.4 26.7 84.6

Overall Accuracy 16.4% 38.2% 54.2% 57.2% 68.6%

Table 3. Maximum-likelihood classifier accuracy assessment. This table shows errors of omission, commission, and totalaccuracy in percentages for each landscape unit. Progressively adding datasets improved accuracy to a maximum of 68.6%.

36

have been classified as moraine, but were classified as something else). When overburden

thickness was added ("4-Band"), classification accuracy increased 16%, to 54.2%. The

two "thin drift" classes gained most from the addition of this dataset. A slight

improvement in overall accuracy (54-58%) was realized by the addition of any one

textural dataset (the greatest improvement using a single textural measure was gained by

using the 64 pixel2 window dataset; "5-Band"). In fact, accuracy decreased in some

classes due to errors of omission. When all datasets were classified ("8-Band"), the

overall accuracy level in classifying glacial landscapes escalated to 68.6%. The most

significant gains were realized in the outwash and moraine classes, where errors of

omission were reduced by as much as 70%. One class, ground moraine 1, actually fell

significantly with the addition of the textural measures. The classifier confused pixels with

the similar, and spatially adjacent, ground moraine 2.

Figure 8 (a-d) shows the classified images "3-Band", "4-band", "5-band", and "8-

Band" for the entire study area. The classified images "2-Band", "6-Band", and "7-Band"

have little additional value, and are omitted. Color-coding of the classes is indicated by

the legend, and is consistent for all the images. Refer to Plate 1 as a comparison for

"correct" landscape units. Note in Figure 8(a) that very little of the scene is clustered into

homogeneous areas that reflect individual landscape types. The DEM imparts a

dominance to the classification that is apparent through the northeast-southwest oriented

linearity of classes, because elevation generally increases to the northwest. Individual

cover-types are emphasized, rather than homogeneous landscape units. In Figure 8(b),

grouping of pixels into contiguous landscape units is more apparent. The addition of

37

46°30'

8800'

• [Moraine 1

I Moraine 2

I Outwash 1

i Outwash 2

5 4 3 2 10

Thin Drift/Bedrock

I Ablation Drift

1i Ice Contact Drift

Miles ,-

Ground Moraine 1

Ground Moraine 2

Thin Drift/wetland

Shoreline Deposits

10

N

543210 5 10Kilometers

15

Figure 8(a). 3-Band: PC 1, PC2 & DEM. This combination yielded only 38.2%classification accuracy for delineating glacial landscape units.

46° 30'

88°00'

I

aine 1 I 1 Thin Drift/Bedrock

mine 2 ! Ablation Drift

wash 1 I I Ice Contact Drift

wash 2

5 4 3 2 10 Mlles 5 10' 'i — i — i — i — i — i 1 1 1

Ground Moraine 1

Ground Moraine 2

Thin Drift/wetland

Shoreline Deposits

N

W - « V

YS

543210 5 10Kilometers

Figure 8(b). 4-Band: PCi, PC2, DEM &OBT. This combination yielded 54.2%classification accuracy for delineating glacial landscape units.

39

overburden thickness to the classification emphasizes the importance this dataset has in

discriminating glacial landscapes. For instance, moraines and thin drift with bedrock near

the surface are difficult to discriminate based on spectral or textural data alone. Both are

high-relief landscapes and support similar vegetation. The inclusion of the overburden

thickness dataset adds a necessary component to the classification decision rule that

further homogenizes pixels into contiguous groupings (landscapes).

When a single textural measure is added to the classification (Figure 8c), the

overall accuracy level increases only slightly. A single window size is thought to be

inadequate for discriminating homogeneous glacial landscapes because different types of

glacial landforms have many different sizes and have unique relief characteristics.

Drumlins, for example, are generally small, individual features separated by short

distances. They occur in groups called "swarms" that collectively comprise a landscape.

Conversely, ablation features are typically large, individual mounds of till that melted-out

as the ice receded. The size and separation distance between these features are far greater

than drumlins, and comprise a distinctly different landscape unit based on size and relief.

A single-window textural measure is inadequate to automatically discriminate both

landscape units. Windows must be designed to match the frequency of natural variation,

and several individual window sizes are needed to account for most glacial landscapes.

When four windows were added to the routine to match the different glacial landscapes of

the study area (Figure 8d), overall classification accuracy improved to 68.6%. Note the

improvements in the moraine/outwash plain discrimination by addition of the textural

measures. Also, the delineation of landscapes formerly difficult to classify, such as the

40

46"30'

88°00'

^H Moraine 1

•r

Moraine 2

i I Outwash 1

I Outwash 2

5 4 3 2 10

Thin Drift/Bedrock

]I Ablation Drift

I Ice Contact Drift

Miles ,-

Ground Moraine 1

Ground Moraine 2

Thin Drift/wetland

Shoreline Deposits

N

10

5432 10 5 1OKilometers

15

Figure 8(c). 5-Band: PCI, PC2, DEM, OBT and 64 pixel texture window. Thiscombination yielded 51.2% accuracy for delineating glacial landscape units.

41

46°30'

Moraine 1r

Moraine 2

rOutwash 1

Outwash 2

5 4 3 2 10

J Thin Drift/Bedrock1I Ablation Drift

I Ice Contact Drift

Miles cr

Ground Moraine 1

Ground Moraine 2

Thin Drift/wetland

Shoreline Deposits

N

10~TT~T—r~T5432 10 5 10

Kilometers15

Figure 8(d). 8-Band: PCI, PC2, DEM, OBT and four texture windows. Thiscombination yielded 68.6% accuracy for delineating glacial landscape units.

42

two ground moraine classes were improved. Boundaries between all classes became more

accurately positioned.

It is recognized that per-pixel classifiers are not entirely adequate for landscape

mapping because they ignore the inherent high-degree of spatial correlation typical of

pixels of satellite imagery (Franklin and Wilson, 1992). Single pixels are classified based

on parametric statistics without regard to surrounding pixels. The maximum likelihood

classifier is the best commonly available algorithm for general classification, but results are

not satisfactory for landscape delineation unless datasets such as the textural measures

incorporated into this study are used. Techniques based on the layer-classifier principle

hold promise for improved automatic classification of spatially related phenomenon such

as glacial landscape units (Graff and Usury, 1993).

CONCLUSIONS

Glacial landscape units may be differentiated and classified using several digital

datasets and techniques. Glacial landscape units are characterized through variations in

surface features, elevation, overburden thickness, and relief (texture). Both visualization

and automatic classification routines are useful for glacial geomorphic mapping with the

datasets and techniques described. Perspective views using 3-arcsecond DEM, overlain

by relief images, clearly show outwash plains at different elevations. It is interpreted that

the features are indicative of temporal variations in the position of the ice margin.

Correlations of terraces at the same elevation, now widely separated by post-glacial

erosion, are better than the interpreted correlation based on topographic maps. Terraces

43

at the same elevation have similar tonal intensities when they are displayed in 3-D

perspective views or as relief images on the image display monitor. Thus, they are much

easier to perceive than isolines and numbers on maps. Also, the processor subdues much

of the "noisy" details (such as stream valleys) that may obscure the desired information.

Combination of datasets through the IHS transformation (such as DEM with relief images,

DEM combined with TM and SLAR, or the scanned geologic map with TM)

quantitatively and qualitatively express variations in form, and elevation, of glacial

landscapes and show relationships difficult to perceive when viewing only one dataset.

Classification of glacial landscapes requires several datasets to provide sufficiently

accurate results. DEM, TM, overburden thickness, and textural information are needed.

Textural information must account for variations in scale of internal features that comprise

the individual landscapes within the study area. It is necessary to measure the size and

separation distance between features in the landscape, and design window sizes to match

those landscape regions. When the first two principal components, DEM, overburden

thickness, and four different-window size textural measures were used in a maximum

likelihood classification algorithm, landscape units were correctly classified 68.6% of the

time. Accuracy levels dropped significantly with removal of any of the datasets.

Image processing provides a useful tool for landscape mapping. Combinations of

these datasets clearly show relationships between surface form, cover types, and geology

that comprise a landscape unit. These techniques help by portraying the earth surface in a

way that is both quantitative (absolute elevations may be extracted) and qualitative

(relationships between features).

44

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