Integrated Terrain Mapping 

With Digital Landsat Images 

in Queensland, Australia

GEOLOGICAL SURVEY PROFESSIONAL PAPER 1102

Prepared in cooperation with the Queensland 

Department of Primary Industries, Australia

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Integrated Terrain Mapping 

With Digital Landsat Images 

in Queensland, Australia

By CHARLES J. ROBINOVE

GEOLOGICAL SURVEY PROFESSIONAL PAPER 1102

Prepared in cooperation with the Queensland 

Department of Primary Industries, Australia

UNITED STATES GOVERNMENT PRINTING OFFICE, WASHINGTON : 1979
UNITED STATES DEPARTMENT OF THE INTERIOR 

CECIL D. ANDRUS, Secretary

GEOLOGICAL SURVEY

H. William Menard, Director

Library of Congress Cataoging in Pubication Data

Robinove, Charles Joseph, 1931-

Integrated terrain mapping with digital Landsat images in Queensland, Australia.

(Geological Survey professional paper ; 1102)

Bibliography: p.

1. Land use—Australia—Queensland—Classification. 2. Cartography—Data processing. 3. Landsat satellites. 

I. Queensland. Dept. of Primary Industries. II. Title. III. Series: United States. Geological Survey. Professional paper ; 1102.

HD1039.Q83R6 333.7'09943 78-11682

For sale by the Superintendent of Documents, U.S. Government Printing Office 

Washington, B.C. 20402 (Paper cover)

Stock No. 024-001-03157-4
CONTENTS

Page

Abstract __________________________________________________ 1

Introduction ________________________________________ 1

Acknowledgments __________________________________________ 2

Methods of terrain classification ____________________________________ 2

Hypotheses being tested ________________________________ 3

Characteristics of Landsat data _________________________________ 4

The Queensland experiment ______________________________ 5

Wet season image analysis ________________________ 6

Subscene 1 __________________________________ 6

Subscene 2 _____________________________________ 12

Subscene 3 ________________________ __ ______ __ _ 13

Contrast stretching _________________________ 16

Multispectral classification _________________________ 17

Correspondence of multispectrally classified themes with terrain

features _______________________________ 19

Extrapolation of partial scene classifications to a full Landsat scene __ 28

Dry season image analysis ____________________________________ 29

Unsupervised classification of a full Landsat scene ________________ 29

Conclusions —________________________________________ 31

Selected bibliography __________________________________ 33

ILLUSTRATIONS

Page

FIGURE 1. Methods of mapping used with Landsat data ______.———_———————————— —————— ——— ——— 5

2. Land system map information was available for almost all of the area covered by the Landsat image

used _______________________________—_._——_—_—-—_-_—-—-__—_-_--_—___—_ 6

3. Landsat Image 1365-23570 (dry season) showing the four bands __ — _—_—————__——————————— 7

4. Color composite of Landsat Image 1365-23570 showing dry season conditions (July 1973) ——————— 8

5. Landsat Image 1563-23530 (wet season) showing the four bands ——————————————————————— 9

6. Color composite of Landsat Image 1563-23530 showing wet season (February 1974) conditions _____ 10

7. Subscene 1 is displayed as a composite of bands 4, 5, and 7. All pixels in the 512 by 369 area are displayed. The land systems map is overlaid on the image ________ — ___________ — __——_ 11

8. The Arrabury and Santos land systems are multispectrally classified ___ —— __ —— ____ — ——————— 12

9. Arrabury and Santos land systems. Aerial oblique photos from 300 meters altitude _______—___ — __ 15

10. Subscene 2, a 3022 by 738 pixel array with 25 percent of the pixels displayed, is overlaid with the land

systems map and is used for multispectral classification ____________________________ — — — 16

11. Six category classified map of subscene 2 with land systems map overlay ________________________— 17

12. Histograms of the six classes in subscene 2 which are mapped in figure 11 __________________ 18

13. Standard color composite of subscene 3 and contrast-stretched composite __________________ —————— _ 20

14. Histograms of the unstretched and contrast-stretched subscene 3 _______________________—————_ 22

15. Land system map of subscene 3 ______________._________________________________________ 23

16. Seven multispectrally classified themes in subscene 3__._______________________________ 24

17. Parallelepiped classification and separability of seven themes in the northwest subscene 1365-23570 ___ 25

18. Seven themes classified in subscene 3 with examples of interpretive boundaries around homogeneous

classes and heterogeneous classes ___________._________________________________________ 27

19. Classification of the upland areas of Landsat scene 1563-23530, February 6, 1974 _____________ 28

20. Classification of the alluvial valley of Cooper Creek, Landsat scene 1365-23570, July 25, 1973 _____ 30

21. Unsupervised classification of sampled Landsat image 1563-23530 ____________ —— ____________——— 32

in
IV CONTENTS

TABLES

Page

TABLE 1. Lower radiance boundaries (LB) and upper radiance boundaries (UB) of training sets in the Arrabury land system in subscene 1 and number of pixels classified with large, medium, and small 

training sets ___________________________.____________________________ — ___________ 12

2. Statistical summary of seven themes classified for subscene 3 _____________________________ — ______ 19

3. Bands in which separability is produced between pairs of themes in the northwest subscene 1365-23530 19

4. Correspondence of multispectral themes with mapped land systems _________ — ___ — ___ — ____________ 26
INTEGRATED TERRAIN MAPPING WITH

DIGITAL LANDSAT IMAGES IN

QUEENSLAND, AUSTRALIA

By CHARLES J. ROBIN OVE

ABSTRACT

Mapping with Landsat images usually is done by selecting 

single types of features, such as soils, vegetation, or rocks, 

and creating visually interpreted or digitally classified maps 

of each feature. Individual maps can then be overlaid on or 

combined with other maps to characterize the terrain. Integrated terrain mapping combines several terrain features 

into each map unit which, in many cases, is more directly 

related to uses of the land and to methods of land management than the single features alone. Terrain brightness, as 

measured by the multispectral scanners in Landsat 1 and 2, 

represents an integration of reflectance from the terrain 

features within the scanner's instantaneous field of view and 

is therefore more correlatable with integrated terrain units 

than with differentiated ones, such as rocks, soils, and 

vegetation.

A test of the feasibilty of the technique of mapping integrated terrain units was conducted in a part of southwestern Queensland, Australia, in cooperation with scientists 

of the Queensland Department of Primary Industries. The 

primary purpose was to test the use of digital classification 

techniques to create a "land systems map" usable for grazing land management. A recently published map of "land 

systems" in the area (made by aerial photograph interpretation and ground surveys), which are integrated terrain units 

composed of vegetation, soil, topography, and geomorphic 

features, was used as a basis for comparison with digitally 

classified Landsat multispectral images. The land systems, 

in turn, each have a specific grazing capacity for cattle (expressed in beasts per km2 ) which is estimated following 

analysis of both research results and property carrying 

capacities.

Landsat images, in computer-compatible tape form, were 

first contrast-stretched to increase their visual interpretability, and digitally classified by the parallelepiped method into 

distinct spectral classes to determine their correspondence to 

the land systems classes and to areally smaller, but readily 

recognizable, "land units."

Many land systems appeared as distinct spectral classes 

or as acceptably homogeneous combinations of several spectral classes. The digitally classified map corresponded to the 

general geographic patterns of many of the land systems. 

Statistical correlation of the digitally classified map and the 

published map was not possible because the published map 

showed only land systems whereas the digitally classified map 

showed some land units as well as systems.

The general correspondence of spectral classes to the integrated terrain units means that the digital mapping of the 

units may precede fieldwork and act as a guide to field sampling and detailed terrain unit description as well as measuring of the location, area, and extent of each unit.

Extension of the Landsat mapping and classification technique to other arid and semi-arid regions of the world may 

be feasible.

INTRODUCTION

Maps and descriptions of "land" as a resource in 

order to provide basic information on its capabilities 

and limitations are presented in numerous ways. 

These include geologic maps, soil maps, vegetation 

maps, topographic maps, water resource maps, and 

a multitude of other thematic maps. Each one is 

usually specific to a single scientific discipline or to 

a single factor that characterizes the land. A method 

of describing and characterizing land which is widely used throughout the world, but is only minimally 

used in the United States, is the Australian land 

systems approach.

The Australian approach characterizes land as 

"* * * the land surface and all of its characteristics 

of importance to man's existence and success. It is 

the integration of all these factors rather than mere 

likeness or unlikeness in some of the more observable characteristics which determine the similarity 

or dissimilarity of areal subdivisions in respect to 

land use potential" (Christian and Stewart, 1968, p. 

238). Christian (1958) and Christian and Stewart 

(1968) provide a complete description of the philosophy, methods, and results of surveys using the 

Australian land systems approach.

Since 1972, Landsat satellite imagery has been 

available and has been used for many types of land 

surveys. The imagery is uniform, repetitive, available worldwide, and is eminently suitable for reconnaissance investigations of large areas. It has the
INTEGRATED TERRAIN MAPPING WITH DIGITAL LANDSAT IMAGES

further advantage of being ideally suited for providing basic map data for surveys conducted by the 

Australian land systems approach. 

The purpose of this report is to:

1. Describe a land mapping basis by which land can 

be classified, described, and mapped by the use 

of Landsat imagery.

2. Relate the description of land mapping by Landsat imagery to land system mapping by the 

well established Australian methods.

3. Describe a land system mapping experiment using Landsat images in southwestern Queensland, Australia, where conventional land system mapping was being done in 1975 and 

1976.

4. Compare conventional and experimental methods 

of land mapping.

5. Recommend operational methods for use of 

Landsat imagery for land system mapping.

ACKNOWLEDGMENTS

The author is grateful for the advice and assistance given by many colleagues during this study. 

Special thanks go to William A. Fischer for many 

stimulating discussions of integrated terrain mapping principles. The cooperation of Noel Dawson, 

Desmond Boyland, Keith Hughes, and John Mills of 

the Queensland Department of Primary Industries 

was invaluable in the field and the office, and by 

correspondence. Since this study is based on their 

original mapping and descriptions of the terrain of 

southwestern Queensland, their help and advice 

made the study possible. The staff of the Data 

Analysis Laboratory at the EROS Data Center, including Fred Waltz, Dave Greenlee, Charlotte 

Muchow, and Charles Nelson made possible the 

analyses reported here. David Carneggie of the 

EROS Data Center provided an especially critical 

and helpful review of the manuscript.

METHODS OF TERRAIN CLASSIFICATION

Terrain classification, whether for general or specific purposes, may be based on genetic, parametric, 

or landscape criteria. Each of these has advantages 

and disadvantages related to mapping methods, format for display of map information, and usability 

by the reader of maps. Mabbutt (1968) has denned 

and explained the three approaches and has wisely 

predicted "Ultimately, there comes a level of investigation at which the greater precision and reliability 

of the parametric approach are needed and to the 

extent that improvements in (remote sensor) scanning render the method more comprehensive, its inherent advantages of reliability will be exploited 

even at the reconnaissance (landscape) level of investigation." (Mabbutt, 1968, p. 26).

Genetic methods of terrain classification are described by Mabbutt (1968, p. 12) who states, "Attempts to arrive at distinctive land units by repeated 

subdivision on the basis of causal environmental factors may be grouped as the genetic approach." 

Normal geologic mapping and geomorphological 

mapping are examples of the genetic approach. Maps 

made on the basis of genetic classification do not 

generally indicate land potential or land capability 

and, although such information can be derived in 

part from them, such derivation generally requires 

large amounts of corollary information from other 

sources and a reasonable means of correlating such 

information.

Parametric classifications are defined by Mabbutt 

(1968, p. 21) as "* * * the division and classification 

of land on the basis of selected attribute values." A 

slope map, depth-to-water-table map or lithologic 

map are examples of parametric maps. The reflection 

of light from the land surface can be considered as 

an attribute of the land, and thus the reflectance in 

several wavelength bands as measured by the multispectral scanner in Landsat can be mapped as "attributes" or "parameters." Individual parametric 

maps are generally prepared for a specific purpose 

of land development and management and may be 

difficult to correlate quantitatively or even qualitatively with other parametric maps. Correlation is 

generally done by means of map overlays. Maps of 

a given area may be made at different times by 

specialists in different disciplines, and at different 

scales. Such maps are often difficult to correlate to 

obtain information bearing on a specific development problem, although they may individually contain important and useful information.

The parametric approach provides, in its most 

rigorous application, a numerical terrain measurement which is calibratable and generally repeatable. 

This approach is more objective than landscape mapping but is of necessity more expensive and time 

consuming and, therefore, limited in application to 

large areas.

The landscape approach classifies land, particularly at the reconnaissance level, on the basis of a complex of factors and attributes. Mabbutt (1968, p. 16) 

states, "The land complex as a whole is the object 

of study, even where a particular attribute may be 

of prime interest to a land classifier."
HYPOTHESES BEING TESTED

Two major factors account for the success of the 

landscape approach; the use of integrated mapping 

units which combine geomorphic features, soils, and 

vegetation as the basis for mapping and the formation of an integrated mapping team consisting of 

specialists in several disciplines working in concert 

to produce the landscape maps and land descriptions. 

The approach is well described in two books, "Aerial 

surveys and integrated studies," (UNESCO, 1968) 

and "Land evaluation" papers of a Commonwealth 

Scientific and Industrial Research Organization of 

Australia (CSIRO) symposium organized in cooperation with UNESCO, (Stewart, 1968). Both books 

are listed in the references in this report and the 

reader is referred to them for details of the mapping, philosophy, methods, and results.

The landscape approach, developed by the CSIRO 

is now readily utilized by the Australian States and 

by workers in the United Kingdom and other countries. Similar parallel approaches are used in the 

Soviet Union (Mitchell, 1973).

Terrain classification in the landscape approach 

is based on four hierarchical categories as proposed 

by Christian and Stewart (1968). A site is part of 

the land surface which is for all practical purposes, 

uniform throughout its extent in landform, soil, 

and vegetation. A land unit is usually a group of 

related sites which have a particular landform 

within the land system and wherever the land unit 

occurs again it would have the same association of 

sites. A land system is an area or group of areas, 

throughout which there is a recurring pattern of 

topography, soils, and vegetation. A land zone is a 

grouping of genetically related land systems.

The landscape approach always involves some subjectivity in the assignment of each area of land to a 

specific unit, system, or zone—but particularly 

where an area is described as being a mixture of 

two or more systems or where two areas belonging 

to the same system contain identical land units but 

in different proportions.

In the approach used by the Queensland Department of Primary Industries in the arid lands, the 

attribute of prime interest is the grazing capacity 

of the land (beasts per km 2 ) in wet and dry years 

and the reaction of the land to grazing pressure, and 

yet the landscape approach is particularly suitable 

because the ultimate numerical parameter of interest 

can only be arrived at by integration of a number 

of other attributes, some of which are difficult or 

impossible to measure quantitatively. The main interest is how the land will react or how it has reacted to different land uses, particularly grazing,

and how productivity might be increased or decreased to ensure long term stability. This is the 

basis for property planning.

Remote sensing has the capability to provide some 

numerical measurements economically and efficiently 

for very large areas and thus is capable of forming 

a bridge between the objective (parametric) and 

subjective (landscape) approaches.

The hypotheses and experiment described in this 

report are an application of the parametric (quantitative) methods to the landscape approach.

HYPOTHESES BEING TESTED

One major and several minor hypotheses have 

been formulated and tested in this research on application of Landsat images to integrated terrain 

mapping. The major, and fundamental, hypothesis 

governing this study is that the radiance measured 

in a single Landsat picture element is an integration 

of the radiance of all features within the 0.45 ha 

area measured on the ground, such as vegetation, 

soil, rock, water, and artifacts, and a group of pixels 

therefore is statistically indicative of an integrated 

mapping unit that can characterize the similarities 

and differences of natural terrain units. Since the 

Australian land systems approach is based on integrated terrain units, the correspondence between 

maps made by the two systems should be high. Minor 

hypotheses are that the upland terrain units in the 

study area should be more readily differentiate in 

the wet season than in the dry and that repetitive 

monitoring can characterize changes in land systems 

that are of importance in land management decisions. Indeed, it is quite important to analyze data 

in the right year, usually one in which vegetation 

production is at a maximum.

It is further hypothesized that spectrally homogeneous units seen in Landsat images may actually 

be coherent terrain units (and consequently usable 

land management units) and can be properly named 

and described. This is the antithesis of the normal 

mapping method of deciding on a terrain inventory 

classification which will be used in a given area and 

then mapping and describing the features in accordance with the selected classification.

The combination of the integrated radiance measurement-integrated mapping unit may logically be 

extended to land inventory units, and from that to 

land capability units. One would not expect that the 

integrated units would correspond exactly in boundaries or descriptions with those mapped by the 

Australian land system approach or by other similar
INTEGRATED TERRAIN MAPPING WITH DIGITAL LANDSAT IMAGES

methods, but they may well be reasonable and usable 

units.

The Australian land system approach utilizes 

mapping units which include geomorphic features, 

soils, and vegetation in the description of each unit, 

rather than separate mapping of each category of 

information. Each Landsat pixel, which has the dimensions of approximately 59x79 meters on the 

ground (0.45 ha), measures the radiance of the 

terrain in four bands (green, red, and two bands in 

the near or reflected infrared). Because the measured radiance is that of all features of the ground, 

it is an areally integrated radiance unit. The major 

question is then: what is the relationship of an integrated radiance mapping unit (pixel) to the integrated attributes of the terrain mapping unit ? Because the Australian land systems approach is a 

widely used one which utilizes integrated terrain 

units, this experiment is designed to see how closely Australian land systems maps can be made on 

the basis of integrated radiance measurements.

CHARACTERISTICS OF LANDSAT DATA

Landsat satellites provide worldwide multispectral 

images on a repeated basis. Landsat 1 operated between July 1972 and January 1978. Landsat 2 began 

operation in January 1975. The major sensor on 

both Landsats is a multispectral scanner which provides images of 185x185 km areas on an 18 day 

cycle. Each scene consists of four separate images:

Image Spectral Interval Spectral Range

Band 4 ________Green __________SOO/on-GOO^m 

Band 5 ___________Red _________________600/im—700/im

Band 6 ________Infrared _______700jum-800jum 

Band 7 __________Infrared ________800Aan-l,100Aim

Each band of each image set consists of 2,340 scan 

lines (perpendicular to the orbital track) and 3,240 

picture elements (pixels) along each scan line, a 

total of 7.58x10 G pixels per band per frame. Each 

pixel represents an area of approximately 0.45 ha 

on the ground.

¥

One hundred twenty-eight radiance levels (7 bits) 

are recorded in bands 4, 5, and 6, and 64 radiance 

levels (6 bits) are recorded in band 7. The images 

may be produced in photographic form and analyzed 

either by photointerpretive techniques (including 

photo-optical image enhancement) or may be produced as computer-compatible magnetic tapes 

(CCT) and be analyzed by digital techniques. Further details of the Landsat image characteristics 

may be found in the "ERTS Data Users Handbook" 

(General Electric, 1972).

Landsat images in photographic form can be enlarged for practical purposes to scales as large as 

1:100,000. Larger scales have been used by some 

investigators, but they require highly accurate 

photographic equipment and quality control. Digital 

images can be displayed at any reasonable scale, but 

the smallest area for which data is available is 0.45 

ha. Once an image has been digitally enlarged and 

displayed so that each pixel can be seen by the eye, 

no further information is gained by further enlargement, but the images can be matched to larger scale 

maps. Landsat images are ideally suited to medium 

and small-scale mapping because of their uniformity 

over large areas.

Three methods of mapping with Landsat images 

are commonly employed. Each has its advantages 

and disadvantages which depend upon the purpose 

of the mapping, the skill and experience of the 

mapper, the interpretive and computer facilities 

available, and the cost.

Figure 1 shows schematically the three methods. 

The basic method (A) involves visual interpretation 

of the photographic images in color composite form 

at the desired scale. Interpretation for some purposes from the black and white prints is preferred 

by some interpreters but in general the color images 

are much more useful. Normal photointerpretive 

methods using photographic tone, texture, pattern, 

and spatial association are used, keeping in mind 

the small scale and large area portrayed.

The second method (B) involves enhancement of 

Landsat images to increase their contrast for greater 

discriminability and recognizability of features, 

ratioing of spectral bands of images to locate new 

interpretable combinations of radiance values, and 

scaling of brightness values of individual bands, 

which is a form of contrast enhancement. Contrast 

stretching can be done either photographically or 

digitally, but the digital processes can be calibrated 

and repeated more precisely and accurately than the 

photographic processes.

The third method (C) uses the radiance values 

measured by the multispectral scanner to classify 

the terrain and produces graphic maps (rather than 

solely enhanced images). Landsat computer-compatible tapes are utilized in a digital computer 

usually with an interactive image display. Successive iterations of radiance classifications are made 

and checked with ground information until the 

analyst is satisfied that a satisfactorily accurate map 

has been made and it is ready for field checking. A 

number of algorithms have been developed for multispectral classification ranging from the simple
THE QUEENSLAND EXPERIMENT

LANDSAT

IMAGE

COLOR

COMPOSITE

BASE MAP

FILM POSITIVE

OVERLAY

BASE MAP 

FILM POSITIVE

INPUT SCANNER

T

MULTISPECTRAL 

IMAGE ANALYZER

PHOTOGRAPHIC PRODUCT FOR VISUAL INTERPRETATION

CONTRAST STRETCH

RATIOING

SCALING

PHOTOGRAPHIC PRODUCTS FOR 

INCREASED INTERPRETABILITY OF 

TERRAIN FEATURES

BASE MAP 

FILM POSITIVE

INPUT SCANNER

MULTISPECTRAL 

IMAGE ANALYZER

A. INVOLVES SIMPLE VISUAL INTERPRETATION OF LANDSAT 

PHOTOGRAPHIC PRODUCTS WITH A FILM POSITIVE BASE 

MAP OVERLAY FOR LOCATION

B. INVOLVES DIGITAL LANDSAT IMAGE PROCESSING TO 

CREATE PHOTOGRAPHIC PRODUCTS WITH 

INCREASED COLOR CONTRAST TO IMPROVE VISUAL 

INTERPRETABILITY

CONTRAST

MULTISPECTRAL

MATION

 PHOTOGRAPHICALLY PRODUCED MAPS

 ALPHANUMERIC THEMATIC MAPS

 DOT PRINTOUT THEMATIC MAPS

 HISTOGRAMS

 THEME AREA SUMMARIES

C. INVOLVES DIGITAL LANDSAT IMAGE PROCESSING 

FOLLOWED BY MULTISPECTRAL CLASSIFICATION 

TO PRODUCE THEMATIC MAPS OF LAND FEATURES

FIGURE 1.—Methods of mapping used with Landsat data.

parallelepiped method to the more complex maximum likelihood classification method.

Images and maps shown in this report were produced by the General Electric Image 100 1 multispectral analysis system at the EROS Data Center, 

Sioux Falls, S.D., and by the computers and programs of the Laboratory for Applications of Remote 

Sensing at Purdue University.

THE QUEENSLAND EXPERIMENT

Recent land systems maps in western Queensland 

(Division of Land Utilization, 1974) made by aerial 

photointerpretation and field checking were used as 

the basis for an experiment in application of Landsat images to land systems mapping. An area of 

about 148,000 km 2 was mapped at a scale of 

1:500,000 and described by the Queensland Depart1 Trade names used in this report are solely for purposes of identification and do not constitute endorsements by the U.S. Geological Survey.

ment of Primary Industries. At the time of this 

project, the map of Part 1 was published, but the 

map of Part 2 was in manuscript form and was used 

with permission of the authors. Landsat images were 

available for the area, and a set of Landsat images 

that covered portions of both Part 1 and Part 2 

areas was selected for detailed analysis. Figure 2 

shows the area covered by Landsat images and the 

available published and manuscript land system 

maps.

Cloud free Landsat images of the western part 

of the mapped area were taken on March 1, 1973, 

July 23, 1973, and February 6, 1974. The July 1973 

image was taken during the dry season; the February 1974 image was taken during a major flood 

which inundated almost all of the Cooper Creek 

Valley. These two scenes (1365-23570 and 1563- 

23530) were analyzed as being representative of 

the dry and wet seasons. Unfortunately, the previous
INTEGRATED TERRAIN MAPPING WITH DIGITAL LANDSAT IMAGES

AREA ANALYZED 

WITH LANDSAT 

IMAGES

FIGURE 2.—Land system map information was available for 

almost all of the area covered by the Landsat image used. 

Part 1 was published (Division of Land Utilization, 1974) 

and Part 2 was in manuscript form when the study was 

done.

years were dry years, and it was not until 1975 and 

1976 that full development of vegetation occurred. 

The greater growth of vegetation would have made 

it easier to separate land units in the silcrete-covered uplands.

In the Part I mapped area, 93 land units are 

grouped into 53 land systems, which in turn are 

grouped into 10 land zones. For example, the "dune 

fields" land zone includes eight types of sand-dune 

fields with varying geomorphic characteristics, vegetation, and soils. Each of the eight dune-field land 

systems is a fairly distinctive type, such as longitudinal dunes or reticulate dunes, but they may 

grade into each other without sharp boundaries. 

Within each of the dune-field systems various land 

units occur, such as the mobile dune crest, vegetated 

dune flank, and scalded dune margin. The available 

land systems maps are at a scale of 1:500,000 and 

show only the distribution of the land systems. A

one-to-one correspondence was not expected between 

the map and multispectral classification of the Landsat images. The smallest area classified on the Landsat images is 0.45 ha (one pixel), too small to be 

shown on a 1:500,000-scale map.

The two Landsat images used in the experiment 

are shown in figures 3-6 and indicate the locations 

of the subscenes analysed in the following sections.

The image analysis method used is outlined as 

follows:

1. A selected sample of a Landsat scene was extracted from the digital tape, placed in the 

analysis system, and displayed on the cathoderay color-display tube.

2. A film positive of the land systems map of the 

subscene area was scanned by a television 

camera, digitized, stored, and registered to the 

Landsat subscene by visual correlation of 

ground features.

3. Multispectral supervised parallelepiped classification of the subscene into a number of units 

was done and the resulting classified image 

was checked with the land systems map. The 

process was repeated until a map was obtained 

that corresponded closely to the land systems 

map or showed consistently mappable units 

whose specific identification required field 

checking.

WET SEASON IMAGE ANALYSIS

Landsat image 1563-23530 was taken during the 

wet season and during a major flood of Cooper 

Creek. It was selected for analysis of the upland 

areas because the vegetation is more vigorous and 

therefore more recognizable than during the dry 

season. Several subscenes of various sizes and the 

entire scene were analyzed to determine the recognizability and separability of various terrain types 

with various levels of sampling of the image data.

SUBSCENE 1

The initial subscene, an area in the uplands between Cooper Creek and Lake Yamma Yamma was 

selected for analysis and displayed on the screen. 

This subscene is a 512 by 369 pixel area, covers 

85,000 ha, and was analyzed using every pixel. Figure 7 shows the subscene as it is displayed on the 

cathode ray tube. It is part of scene 1563-23530 

that was taken during a major flood in the valley 

of Cooper Creek. The northwest and northeast 

corners of the subscene show the flood water.
THE QUEENSLAND EXPERIMENT

BAND 4 BAND 5

BAND 6

FIGURE 3.—Landsat Image 1365-23570

The major land systems within the dune field zone 

were readily differentiated in this subscene. Figure 

8 shows maps of 2 of the land systems as classified 

from small training sets of 4 to 12 picture elements 

that were located within each land system.

BAND? 

(dry season) showing the four bands.

The descriptions of the two land systems as given 

in the legend of the published map are:

Arrabury-Plains with longitudinal dunes 5-19 meters high, 

some converging and diverging with mobile crests; spinifex 

shrubby hummock grassland; sandy red earths and red
INTEGRATED TERRAIN MAPPING WITH DIGITAL LANDSAT IMAGES

5026-30! El'a l-30l El-32-001 23JUL73 C S25-<25/E|41-<a? N S25-a8/EI<ai-51 MSS <• ' R SUN EL29 P20^ 169-5093-N-' -N-?-?'. NflSP ERTS E- i365-235'7g- ^ (?

FIGURE 4.—Color composite of Landsat Image 1365-23570 showing dry season conditions (July, 1973) using bands, 4, 5, and 7.
THE QUEENSLAND EXPERIMENT

36FEBl&'cl ~slH56/f:i

BAND 4 BAND 5

BAND 6 BAND?

FIGURE 5.—Landsat Image 1563-23530 (wet season) showing the four bands.

earthy sands occur on the interdune area with red siliceous 

sands on the dunes.

Santos-Plains with converging and diverging dunes 4—8 

meters high, sporadic mobile crests, spinifex shrubby hummock grassland; red earthy sands, sandy red earths and red

siliceous sands with grey clays and texture contrast soils 

on the interdune clay pan.

From the above descriptions, one might not expect to see significant spectral differences between
10 INTEGRATED TERRAIN MAPPING WITH DIGITAL LANDSAT IMAGES

C S25-5B/El«l-57 N S25-5':I MSS 57 R SUN EL*8 R208^ 189-785^-N- 1 -N-D-2L NRSR ERT5 E- 1 563- 23530- *t 01

FIGURE 6.—Color composite of Landsat Image 1563-23530 showing wet season (February, 1974) conditions using bands 4, 5, and

Subscenes analyzed are outlined in black.
THE QUEENSLAND EXPERIMENT 11

FIGURE 7.—Subscene 1 is displayed as a composite of bands, 4, 5, and 7. All pixels in the 512 by 369 

area are displayed. The land systems map is overlaid on the image.

the two land systems except those caused by the 

soils in the clay pans, and yet they are readily differentiated by classifying the digital Landsat data.

Figure 9 shows photographs of the two land systems taken by the author from an aircraft at an altitude of about 300 meters. The differences in the 

multispectral data for the two systems are probably 

due to the shape of the dunes and interdune areas, 

to differing density of the vegetation, and to the clay 

pan soils.

The conclusion from the analysis of subscene 1 is 

that two land systems within the same land zone, 

which are basically similar, can be differentiated on 

the basis of a multispectral classification.

In the initial processes of classification, large 

training sets were used as the basis for classification. 

It was found, however, that a closer correspondence 

betwen the ground information and the classified 

Landsat data was achieved when smaller trainig 

sets were used. Table 1 compares the training set 

sizes and the multispectral data for classification of 

the Arrabury land systems with three sizes of training sets. Fewer errors of commission were present 

when smaller training sets were used, and although 

errors of omission increased slightly with the small 

training sets, the boundaries of the land system were 

more rigorously mapped with small training sets 

than with large ones.
12 INTEGRATED TERRAIN MAPPING WITH DIGITAL LANDSAT IMAGES

FIGURE 8.—The Arrabury (left, yellow) and Santos (right, light blue) land systems are multispectrally 

classified. Although the two land systems are quite similar except for the shape of the sand dunes 

and the density of vegetation, the multispectral separation is quite distinct.

TABLE 1.—Lower radiance boundaries (LB) and upper radiance boundaries (UB) of training sets in the 

Arrabury Land system in subscene 1 and number of pixels classified with large, medium, and 

small training sets

6372 pixel 

training set

Band 

Band 

Band 

Band

Pixels

4

5

6

7

classified __

LB

27 

26 

39 

30

UB

-37 

-53 

-62 

-46

A

11

28 

24 

17

90,132

364 pixel 

training set

LB

31-

36

42- 

36-

89

UB

36 

49 

57

48

,350

A

6 

14 

16 

13

40 pixel 

training set

LB

30

31

42 

32

UB

-34

47

-52

-44

A

5 

17 

11 

13

57,480

SUBSCENE 2

A 1,022 by 738 pixel area (340,000 ha), including 

subscene 1 described in the previous section, was 

selected and displayed with the land systems map as

ground control. Owing to the limited memory and 

display capacity of the multispectral analysis system, every other pixel in each row and column, or 

25 percent of the total pixels in the subscene, could 

be analyzed and displayed. This provided an opportunity to test whether or not the classification accuracy would be as satisfactory as that achieved in 

subscene 1 where 100 percent of the pixels were 

used. Figure 10 shows this subscene in a composite 

of bands 4, 5, and 7 with an overlay of the land systems map. Training sets ranging in size from 8 to 16 

pixels were used to develop 6 non-overlapping categories that covered a high percentage of the land 

area in the scene. The classified map is shown in figure 11. Note that almost all of the land area is
THE QUEENSLAND EXPERIMENT 13

FIGURE 8.—Continued.

classified and that there is a reasonably good correspondence with the land systems map. Histograms 

showing the number of pixels of each brightness 

value for the six classes are shown in figure 12. 

Many of the histograms have small intervals indicating classes that may be practically unique.

Supervised classification produced a map that resembles the land systems (ground control map) in 

its gross characteristics, such as the boundaries of 

land systems, and correct classification of a large 

part of each land system. In addition, the use of 

only 25 percent of the available pixels provided an 

adequate sampling for analysis. The thematic map 

should, however, not be considered as a final product 

because additional interpretive judgment is needed.

SUBSCENE 3

The third subscene analyzed was a 1,536 by 1,107 

pixel area (about 765,000 ha) covering the northwestern quarter of scene 1563-23530 that was 

analyzed on the multispectral system using as a 

sample every third pixel in every third line, or oneninth of the pixels. Image enhancement by contract 

stretching and multispectral classification were both 

used. Figure 13 shows the standard color composite 

of bands 4, 5, and 7, and the contrast-stretched composite of the same bands. Figure 14 shows the histograms of the two data sets. The contrast-stretched 

image is displayed on the screen, the image data is 

classified, and each class is displayed as an overlay 

on the image. Decisions on the "correctness" of the
14 INTEGRATED TERRAIN MAPPING WITH DIGITAL LANDSAT IMAGES

'•-''
THE QUEENSLAND EXPERIMENT 15

•1 *J

!fi

I f

*t

-n 4.

i

* «P

I

^M /: i (\ ' 1 |, t>-M/« 

i* 1 \ *^ if

i«

* J

H

Mf

•1

:l

^•'4%^ 

1»'

'/ »

it 

f.

> > -.f *T * • "tt "

E0) 'O 

p) •s

^c

6-I 

o 5

O Q)

co So

o *S

^ o

^ >,
16 INTEGRATED TERRAIN MAPPING WITH DIGITAL LANDSAT IMAGES

FIGURE 10.—Subscene 2, a 1022 by 738 pixel array with 25 percent of the pixels displayed, is overlaid 

with the land systems map and is used for multispectral classification.

classification are made on the basis of both ground 

information and photointerpretation of the image. 

The contrast-stretched image is more suitable for 

this purpose than the image displayed from the raw 

data.

Figure 15 shows the land systems map covering 

the area of subscene 3. The legend for the manuscript land systems map is not shown because of its 

detail. The important comparison to be made is between the features that are readily discriminated in 

the image and the general pattern shown on the 

land systems map. The purpose of the analysis of 

subscene 3 was to determine if a satisfactory classification could be achieved using only one-ninth of 

the pixels. This would be desirable for efficiency in

computer mapping and in cartographic display of 

the classified map units. At a scale of 1:500,000, the 

normal publication scale of the Australian land systems maps, the area covered by a 3 by 3 pixel array 

is 0.36 mm by 0.48 mm (approximately 4 ha), which 

is about the size limit that can be shown cartographically and is also at about the limit that can 

readily be seen on a map with the naked eye.

CONTRAST STRETCHING

Subscene 3 was displayed on the screen and contrast stretched to increase the color contrast of the 

terrain features and to increase their visual interpretability. The contrast-stretch program used was
THE QUEENSLAND EXPERIMENT 17

FIGURE 11.—Six category classified map of subscene 2 with land systems map overlay.

the proportional frequency distribution prog-ram,2 

which assigns an equal number of pixels to each unequal interval of digital counts. The contrast stretch 

utilizes the entire digital dynamic range and thus 

increases contrast in each band and subsequently in 

a multiband color composite. It does not affect the 

accuracy or precision of subsequent classification.

Comparison of the contrast stretched image with 

the standard digital display (fig. 12) and the land 

systems map (fig. 15) show that many terrain features are more highly contrasted with their surroundings. The dune fields and sand plains, particularly, show in green tones in contrast to the surrounding- reddish and brownish tones. Distinctions

2 Also termed "equal area stretch" or "histogram equalization stretch." 

A standard name has not yet been adopted.

between alluvial valleys and upland areas are more 

visible on the stretched image.

Visual interpretation of the stretched image 

should be done along with mapping of the land systems on the basis of enhanced spectral reflectance, 

texture, and shape. Because the color contrast is 

much greater than that of the standard color composite, the recognition of some, if not most, land 

systems should be easier and more reliable.

MULTISPECTRAL CLASSIFICATION

Classification of subscene 3 was done by supervised training methods. A training set was selected 

that represented a single homogeneous terrain feature and the multispectral analysis system searched
18 INTEGRATED TERRAIN MAPPING WITH DIGITAL LANDSAT IMAGES

tEcooH

^

D

* LB UB DEL PEAK MEAN

 4 26 48 23 

TRAINING AREAUAR 

2 5 

38 2 12 2 

46 1 30 4

*** OUERUIEU *** 

* LB UB DEL PEAK MEAN

2 34 4S 12

3 SS 65 11

4 50 62 13 

TRAINING AREA38 7 15 7

60 4 15 7

54 7 30 9

PIXELS

BRIGHTNESS VALUES

SUBS7 T2 DUNEFIELDS

ALARMED AREA- 12148 PIXELS* 4 6X) + 

TVPE CHANNEL * OR E(X)IT SUBS7 T4 CENTRAL DUNEFIELDS

*** OUEPUIEU ***

* LB UB DEL

1 38 44 7

2 39 53 15

TRAINING AREA- 16 PIXELS

SUBS7 TS PENINSULA HIGHLANDS

4 52 64 13

TRAINING AREA- 12 PIXELS 

ALARMED AREA- 10641 PIXELSC 

TYPE' CHANNEL » OR E<X)IT

BRIGHTNESS VALUES'

SUBS7 T6 PENINSULA EDGE

*** OUERUIEU *** 

» LB UB DEL PEAK MEAN UAR

t=

CO —J""1

LUx0.

ccLU03-

5-

3 52 57 6

4 44 46 3 

TRAINING AREAALARMED AREATVPE' CHANNEL

34 4 

2 48 0 

2 S4 0 

4 45 0 

8 PIXELS 

5328 PIXELS( 

I OR E(X)IT

*»» OUEPUIEU »*» 

t LB UB DEL PEAK MEAN UAR

SUBS7 T7 DUNE CRESTS

3 41 S2 12

4 40 50 11 

TRAINING AREAALARMED AREATVPE CHANNEL :

2 26 6

3 44 7 

3 43 5 

8 PIXELS 

1316 PIXELSC 

> OR EtXUT

9 0

17 2

12 7

BRIGHTNESS VALUESSUBS7 T8 INTERDUNE AREAS

FIGURE 12.—Histograms of the six classes in subscene 2 which are mapped in figure 11.
THE QUEENSLAND EXPERIMENT 19

TABLE 2.—Statistical summary of seven themes classified for subscene 3

Theme

Radiance

Band Lower 

bound

Upper 

bound

Difference Mean Variance Pixels 

classified

Percent

pixels

classified

Hectares

1 Dunefields __ ___ 4 62 72 11 68 0

5 84 100 17 89".3

6 90 110 21 99.9

7 76 88 13 83.6

2 Bare ground _____ 4 68 84 17 76.2

5 101 154 54 108.5

6 92 168 77 120.6

7 76 136 61 102.9

3 Lowlands ______ 4 60 68 9 67.0

5 74 90 17 81.0

6 106 110 5 108.2

7 96 100 5 97.5

4 Bright vegetation _ 4 60 73 14 67.4

5 46 67 22 61.6

6 100 118 19 107.8

7 99 124 26 104.9

5 Bright valleys ____ 4 88 108 21 93.7

5 92 148 57 117.5

6 118 150 33 132.5

7 104 136 33 114.8

6 Medium vegetation 4 64 73 10 68.5

5 58 76 19 68.7

6 70 105 16 97.2

7 64 96 33 87.8

7 Divides ________ 4 72 80 9 73.6

5 74 86 13 79.9

6 106 112 7 108.5

7 91 100 10 96.9

Total ________ __ _ __ __ __

7.2

17.8

22.4

14.9

24.5

42.3

74.3

76.9

4.2

28.1

2.8

3.7

12.6

25.2

20.4

26.4

31.0

133.6

64.0

51.5

7.2

25.0

19.3

36.9

5.7

17.1

4.2

8.5

23,256

9,362

4,442

8,897

4,025

28,975

6,398

12.c

5.0

2.3

4.7

2.1

15.3

2.4

94670

38150

18080

36210

16410

117900

26050

85,355 45.1

the subscene for pixels whose combination of radiance values were within those of the training set 

pixels. It then classified each pixel according to those 

values and displayed them on the scene as a single 

theme. Table 2 shows a statistical summary of seven 

themes that were classified in the subscene. Figure 

16 shows the combination of the seven themes.

The number of pixels, acres, and hectares for each 

theme are calculated by counting the number of 

pixels classified in each theme and are shown in the 

following table:

Number

of 

pixels

Acres Hectares

Theme 1 ____ 209,241 234,000 94,700

Theme 2 ____ 84,312 94,300 38,100

Theme 3 ____ 39,951 44,700 18,100

Theme 4 ____.. 80,037 89,500 36,200

Theme 5 ____ 36,261 40,500 16,400

Theme 6 ____ 260,613 291,000 118,000 

Theme 7 _________57,573_____64,000_____26,000

Total __ 767,988

Separability of the seven themes is established by a 

lack of overlap of the upper and lower radiance 

limits of pairs of themes in any single band although they may overlap in other bands. For example, themes 1 and 2 are separable in band 5, although they overlap in bands 4, 6, and 7. Table 3 

shows the separability of the seven themes.

TABLE 3.—Bands in which separability is produced between 

pairs of themes in the northwest subscene, 

1365-23530

Themes 12 3 4 567

5

7

5,7 

4,6,7 

5

7

5 

4, 5, 6, 7

6

4

4,5

7

5

4, 5, 6, 7 

4, 5, 6, 7

Figure 17 shows the range, size, and relation of 

the brightness values of the seven themes based on 

their parallelepiped classification in bands 4, 5, and 

7. The four-dimensional boundaries of the classes 

cannot be shown because of the three-dimensional 

nature of the diagram, but it does illustrate the 

closeness of the themes. Because only bands 4, 5, 

and 7 were used in construction of the diagram, it 

does not show well the separability of themes 3 and 

6 and themes 6 and 7, and because they are separated only on the basis of band 6.

CORRESPONDENCE OF MULTISPECTRALLY 

CLASSIFIED THEMES WITH TERRAIN FEATURES

The themes that have been classified and mapped 

with the Landsat data are groupings of pixels with 

similar brightness values. In order to describe them
20 INTEGRATED TERRAIN MAPPING WITH DIGITAL LANDSAT IMAGES

FIGURE 13.—Standard color composite of subscene 3, using every third pixel in every third row, of bands 4, 5, and 7 (left)

and contrast-stretched composite of bands 4, 5, and 7 (right).

adequately in terms of terrain features, it is necessary to relate them to the previously mapped land 

systems. This was done by overlaying a film positive 

of the land systems map over a photographic print 

of the thematic map to see which themes occurred 

within each of the various land systems. Table 4

shows a descriptive comparison of the themes with 

the land systems.

No statistical correlation was attempted for several reasons. First, the original mapping was somewhat subjective and arbitrary. The aerial photo interpreter or field mapper may assign a given terrain
THE QUEENSLAND EXPERIMENT 21

FIGURE 13.—Continued.

area to a given land system rather than to another 

based upon a subjective similarity or may group 

some units because of the small map scale. Second, 

some land units, rather than the larger land systems, are recognizable multispectrally and the same 

unit may occur in several land systems. An example

is the mobile, unvegetated crests of dunes which 

occur in each of the eight dune field land systems. 

Third, a single feature may control the spectral reflectance and yet it may be only a single factor used 

in assigning a given area to a specific land system. 

As an example, dense vegetation or silcrete that com-
22 INTEGRATED TERRAIN MAPPING WITH DIGITAL LANDSAT IMAGES

ALARM FESOLUTIOri 128 

t LB UB DEL PEAK

89 73 5S706. 

118 113 15359. 

106 101 13133.

90 91 23612.

MEAN

36.0

 41.3

51.1

44.2

TRAINING AREA-185196. PIXELS 

ALARMED AREA-188938.PIXELS' 72.1?J> + 

TVPE CHANNEL <1-4>, OR EfX)IT NU SUBSCENE

12

1

6

0

t-.

LU

X

CL

u_ 

O_

oc _

LU 

CD —

ALARM RESOLUTION 128 128 12S 128 

t LB UB DEL PEAK M1EAN UAR 

1 1 127 127 58716. 64.01275.2 

E 1 127 127 15355. 64.01326.2

3 1 127 127 13138. 64.01329.4

4 1 127 127 23603. 64.11326.9 

TRAINING AREA-185196. PIXELS 

ALARMED AREA-188928.PIXELS( 72.1*>+ 

TYPE CHANNEL <\-A), OR E<X)IT

BRIGHTNESS VALUES

NU SUBSCENE EG. AREA STRETCH

FIGURE 14.—Histograms of the unstretched and contrast-stretched subscene 3.
THE QUEENSLAND EXPERIMENT 23

FIGURE 15.—Land system map of subscene 3. (Unpublished map from Queensland Department of Primary Industries.)

pletely covers the ground will control the spectral 

reflectance although the soil type may be highly significant in a land system assignment. The relation 

shown in Table 4 is valid in many cases, but it is 

not always highly correlated because of the reasons 

noted above.

The correspondence indicates that the map produced by digital classification may not represent a 

classical "land systems" map. However, it may be 

used for land management decisions. If, for some 

areas, the bedrock-soils-vegetation complex controls 

the spectral reflectance, the map is similar to a land 

systems map but if only a single terrain factor controls the reflectance at a particular point in time,

that factor may not only be dominant in the analysis 

but may be a dominant control on land management 

and use.

A second method of correlating the multispectral 

analysis results with the published map involves a 

visual interpretation and mapping of data on the 

thematic map. Figure 18 shows subscene 3 (the 

northwest subscene) with boundaries drawn around 

single or multiple themes to create a generalized 

map. Varnes (1974, p. 4) states "The essence of mapping is to delineate areas that are homogeneous or 

acceptably heterogeneous for the intended purpose 

of the map" [italics are the author's]. Each theme 

(individual color) is homogeneous with respect to
24 INTEGRATED TERRAIN MAPPING WITH DIGITAL LANDSAT IMAGES

FIGURE 16.—Seven multispectrally classified themes in subscene 3.

a single terrain attribute, the brightness value in 

each band. Where a reasonably sized geographic area 

consists of a single theme, it may be considered to 

be a terrain mapping unit. Where a reasonably sized 

geographic area consists of a random appearing set 

of two or more themes, it may also be considered as 

a terrain mapping unit with "acceptable heterogeneity." Each of the lettered regions on the map

is either "homogeneous" (A) or "acceptably heterogeneous." (B and C)

It should be recognized that mapping by the digital analysis of multispectral imagery is an a priori 

method of creating homogeneous map units (with 

respect to brightness values). Normal mapping 

methods involve drawing boundaries around areas 

which are determined to be internally homogeneous
THE QUEENSLAND EXPERIMENT 25

BAND 7 (BRIGHTNESS VALUES)

FIGURE 17.—Parallelepiped classification and separability of seven themes in the northwest subscene 1365-23570. Band 6 

is not shown in the diagram but it provides the separability between themes 3 and 6 and between themes 6 and 7.

or acceptably heterogeneous by some limited sampling and testing method. However, multispectral 

analysis identifies areas which conform to rigorous 

numerical limits of radiance, which is an attribute 

of the terrain unit. Only after the area of a unit of 

similar (homogeneous) radiance is mapped is a

boundary drawn around it. The choice of the boundary location can then be based on (1) drawing a 

boundary around a homogeneous radiance unit 

which can be considered representative of a terrain 

unit for the purposes of the map, or (2) drawing 

a boundary around a group of several individually
26 INTEGRATED TERRAIN MAPPING WITH DIGITAL LANDSAT IMAGES

TABLE 4.—Correspondence of multispectral themes with mapped land systems

Theme 

classified

Land systems

mapped and described

on published map

Description 

of theme

Dominant feature 

of theme 

reflectance

Remarks

1. Dunefields Dl Arrabury and 

Kidd, D2 Poongamulla, S6 Galway 

with some mixed.

2. Bare ground 

(mainly red 

soils).

3. Lowlands

4. Bright

vegetation.

5. Bright valleys __

6. Medium

vegetation.

7. Divides

Occurs within almost 

every land system. 

Bare ground is not a 

land system in itself, 

but is diagnostic of 

lands that do not or 

cannot support vegetation and which 

have a light color.

Primarily Durham 

with some areas of 

alluvium of small 

streams and hard 

mulgas.

Fl-4 Downs with some 

in A5 Dingera, H5 

Noccunda, and S6 

Galway.

A2 Eromanga and A5 

Dingera—alluvial 

valleys and in addition scalded areas in 

dunefields and in 

downs.

Primarily associated 

with Downs (F) and 

gidgee lands (G).

Occurs in conjunction 

with almost all land 

systems, generally 

along divides.

Sand dunes, both longitudinal and reticulate with spinifex 

hummock grassland, 

predominantly red 

earthy sands with 

some siliceous sands. 

Includes numerous 

areas of bare ground 

which are dune 

flanks, scalds, or clay 

pans.

Bare ground with little 

or no vegetation.

They are quite

scattered and interspersed with other 

themes. It may represent bare rock with 

a dark color. 

May be areas of dense 

vigorous vegetation 

whether grasses or 

shrubs. Its occurrence in the downs 

indicates that it may 

be Mitchell grass or 

salt bush. It also 

occurs where mulga 

is abundant. 

In the stretched color 

composite it shows 

as the brightest 

red areas. 

Alluvial plains of 

minor streams and 

scalded areas with 

little or no vegetation. Similar to clay 

pans and scalded 

areas.

Areas of. trees on 

plains with a low 

density of vegetation.

Difficult to describe 

because of its widespread occurrence in 

small areas and association with numerous land systems.

Moderate reflectance 

in all four bands. 

The reflectance of 

the red sands is 

modified by the reflectance of the vegetation. This in turn 

is controlled, not by 

the type of vegetation, but basically by 

its density and cover.

Low to moderate reflectance in the infrared bands indicating lack of vegetation.

Moderate reflectance 

with narrow boundaries in all four 

bands.

High reflectance in the 

infrared bands.

High reflectance in all 

bands. Usually white 

on standard color 

composite image.

Somewhat narrow reflectance intervals in 

all four bands.

Very narrow radiance 

intervals with 

moderate reflectance.

May be a highly significant theme for 

monitoring purposes 

to detect changes in 

size of bare areas or 

detection of newly 

bare areas which indicate degradation of 

the vegetation by 

seasons or over grazing. May be related 

to increased erosion.

Because it occurs in 

both grassland and 

shrub areas it is 

diagnostic only of 

dense vigorous vegetation rather than 

any specific types.

May be useful for 

monitoring changes 

and increase in 

erosion.

Much lower radiance 

in band 7 than for 

theme 4, bright 

vegetation.

Silcrete cover could be 

masking soil and 

vegetation differhomogeneous radiance units and defining their heterogeneity of radiance as representative of a terrain 

unit for the purposes of the map.

The method that follows from this description is 

that the digital mapping of areas should be done as 

a first step, boundaries should be drawn in a reasonable manner around various areas as a second 

step, and only then should the sampling and testing 

strategy (that is, field mapping) be applied. At this 

point, the mapper is aware of the relative proportions of the whole area that are described by each 

class and can plan the number and site of his field
THE QUEENSLAND EXPERIMENT 27

•*C , -f »~.-ft~*\ ' " ' «

v?Y"^i^* A- *\ "r*?w t • ^ * t

«A ^"

*,.^ \i^*'' X

FIGURE 18.—Seven themes classified in subscene 3 with examples of interpretive boundaries around homogeneous classes

and heterogeneous classes.

observations in accordance with a statistically valid 

sampling scheme and with due regard for field accessibility, logistics, and efficiency.

Figure 18 is presented as a first step in grouping 

themes into terrain categories. Figure 18 is the 

same as figure 16 with the addition of boundaries 

drawn around three areas, each presenting an example of a different decision for grouping themes. 

Area A is composed of one theme which occupies a 

reasonably sized mappable area and may be considered as a homogeneous theme. Area B consists 

of three themes (plus water) with the theme shown 

in red occupying over 70 percent of the area. It may 

be considered to be an "acceptably heterogeneous"
28 INTEGRATED TERRAIN MAPPING WITH DIGITAL LANDSAT IMAGES

FIGURE 19.—Classification of the upland areas of Landsat scene 1563-23530, February 6, 1974. Band 5 is shown in black 

and white. Eight themes developed by multispectral classification are shown in color for upland areas. The alluvial 

valley of Cooper Creek is not classified because it is flooded. Areas are automatically computed from theme pixel 

counts. Unmapped areas are shown by the background image.

theme for the purpose of the map. Area C is occupied by a heterogeneous, but well distributed, 

mixture of five themes. It also may be considered to 

be "acceptably heterogeneous" for the purpose of 

the map.

EXTRAPOLATION OF PARTIAL SCENE 

CLASSIFICATIONS TO A FULL LANDSAT SCENE

Multispectral classification of the total Landsat 

scene can be done on a large general-purpose computer but it requires much time and is quite expen-
THE QUEENSLAND EXPERIMENT 29

LEGEND

Dunef ields

Bore ground

Lowlands

AREA 

Hectares

178. 000 ha

, 000 ha

38, BOO ha

Bright vegetation 79, 200 ha

Bright valleys 82, 800 ha

Medium vegetation 375, 000 ho

Divides

' Unknown

88, BOO ha

94, 900 ha

sive since either 7.58 by 10 b pixels must be analyzed 

or the scene must be sampled. The analytical device 

used for the analysis in this report can only display 

and store an entire scene if a sampled array of 512 

by 369 pixels issued. This is only 2.49 percent of the 

available data.

When the sampled image of an entire scene was 

used for supervised classification by selecting training sets, the resulting classification was very poor, 

with large errors of omission and commission. In 

order to overcome these errors, the brightness values 

in each band for each of the seven themes (which 

are listed in table 2) were read into the computer 

and the sampled full scene was then classified and 

mapped. Interpretive inspection showed few errors 

of commission but a moderate part of the full scene 

was not classified. A training set was then selected 

in the unclassified region, and this region was, for 

the most part, classified as an eighth theme. Figure 

19 shows the eight themes overlaid on the band 5 

image. The same procedures can be used to evaluate 

the full scene image as those used for the northwest 

subscene 3 previously.

DRY SEASON IMAGE ANALYSIS

Landsat scene 1365-23570 was imaged on July 

23, 1973, during the dry season, and consequently 

is suitable for mapping land systems in the alluvial 

valley of Copper Creek, because the only water 

present is in water holes along deep reaches of the 

stream channels. Because of the simplicity of the 

alluvial land systems, it was not believed necessary 

to analyze a small subscene and extrapolate to the 

entire Landsat scene, but to analyze directly the 

whole scene. This was done by sampling and displaying 2.49 percent of the pixels and classifying 

the alluvial valley into four themes. Figure 20 shows 

the classification, which corresponds rather closely 

with the published map.

Mr. Brian Senior of the Geological Branch, Bureau 

of Mineral Resources, Geology, and Geophysics, 

Australia, has commented on this classification (personal communication, 1976)

The vegetation classification of the alluviated lowlands is 

most convincing. Dark vegetation * * * corresponds to belts 

of large river gums which line trunk sectors of the major 

channels. Bright vegetation * * * corresponds with levees 

and flood plains which in favorable seasons support a thick 

cover of grass and flowering annuals. Both former categories 

correspond with land form unit Qa2 (reticulate channel, 

pointbar, and flood plains) on the 1:250,000-scale geologic 

and geomorphic map. The swamp areas * * * correspond 

with land form unit Qa3 (distributaries, floodouts, and 

marginal flood plains). Dry vegetation * * * coincides with 

'islands' of aeolian sand which remain largely above the influence of flooding and support a thin cover of herbaceous 

perennials and a variable seasonal ground cover. For studies 

of land cover, the presentation is judged to be potentially 

useful as a planning document to guide more detailed work 

including ground checking.

The classification of the alluvial valley is not a 

difficult one and could be done by visual interpretation of the Landsat color-composite image. The 

digital classification, however, has a major advantage of consistency throughout the image which, 

when combined with the judgment of the interpreter, provides a more complete analysis than visual interpretation alone.

UNSUPERVISED CLASSIFICATION OF A FULL 

LANDSAT SCENE

An empirical experiment was conducted by performing an unsupervised classification of image 

1563-23530 using the maximum-likelihood classification algorithm developed by the Purdue University 

Laboratory for Applications of Remote Sensing. Unsupervised classification simply separates the pixels 

into statistically distinct classes without selection 

by the interpreter. Figure 21 shows the results of
INTEGRATED TERRAIN MAPPING WITH DIGITAL LANDSAT IMAGES

FIGURE 20.—Classification of the alluvial valley of Cooper Creek, Landsat scene 1365-23570, July 25, 1973. Band 5 is shown 

in black and white. Four themes developed by multispectral classification are shown in color for the alluvial valley of 

Cooper Creek and adjacent uplands. Areas are automatically computed from theme pixel counts. Unmapped areas are 

shown by the background image.
CONCLUSIONS 31

LEGEND AREA 

Hectares

Bright vegetation 18, ZOO ha

Swamps I, 000 ha

Dark vegetation 51. 500 ha

Dry vegetation 85. ZOO ha

that classification. To minimize computing time and 

cost, the image was sampled by using every 23rd line 

and every 32nd column, thereby selecting 10,000 

evenly spaced pixels in the 2340 line by 3240 column 

scene. Only 0.136 percent of the scene was used and 

therefore each pixel represented approximately 330 

hectares. The sole classification instruction given was 

to classify the sampled scene into 20 clusters on the 

basis of statistical similarities. The resulting 20 

clusters were combined into 12 groups. Thus, figure 21 shows 20 symbols but only 12 colors.

The unsupervised classified image was not geometrically corrected, but it does represent the major 

land systems present in the area in its patterns. 

Such a classification method might be applicable as 

a first step in analysis of an area. Modified clustering 

techniques might also provide equally good and possibly more accurate results, but were not explored 

in this study.

CONCLUSIONS

The major conclusion of the research reported 

here is that the integrated mapping of land by computer processing of Landsat images is feasible in 

situations where the dominant reflectance of the 

land is characteristic of terrain attributes of importance for the purpose of the map at the time the 

image is taken. Mapping of discrete classes with 

single attributes is more difficult and more prone to 

error than is the mapping of integrated classes.

Digital classification of the Queensland Landsat 

images into integrated units produced a map that 

is not identical to the published land systems map, 

but which resembles it in large part and is as useful. 

The classification of the land into homogeneous units 

based on the statistical distribution of brightness 

values provides a first map product by delineating 

areas of known attributes and radiance. The second 

map product, which includes the interpretive boundaries and the names and descriptions of the unit, 

provides an integrated map which can be used as 

a guide for field checking and sampling.

Interactive digital processing has proven to be 

highly useful because the terrain classifications displayed can be readily checked to see if they are 

"photointerpretively reasonable;" as well as statistically precise.

The analyses reported here were done by sampling 

pixels for large areas. In the future, it would be 

most practical to use an interactive system to analyze sample areas using every pixel in the area, developing the means and covariance matrices for 

each theme, and then using a statistically powerful 

but less interactive system for maximum likelihood 

classification of an entire Landsat scene using every 

pixel. Such a process would use the advantages of 

human interaction plus the statistical and computing advantages of a larger computer system and 

would maximize the advantages of each system for 

the analysis.
32 INTEGRATED TERRAIN MAPPING WITH DIGITAL LANDSAT IMAGES

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