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R A D A R S A T G E O L O G Y H A N D B O O K

Reproduction of the contents of this Handbook is permissible and encouraged.

Cover: photograph courtesy of Dr. Fons Dekker of Dekker Remote Sensing; RADARSAT subscene ofVenezuela © Canadian Space Agency/Agence spatiale canadienne 1996. Data received by the Canada Centre forRemote Sensing. Processed and distributed by RADARSAT International.

T A B L E O F C O N T E N T S

page

Preface

Objectives VIHandbook Organization VIAcknowledgments VI

Chapter 1 Comparison of Satellite Imaging Systems

Comparison: Optical and Radar Data 1 - 1The RADARSAT Satellite 1 - 3

Chapter 2 The RADARSAT Satellite

Understanding Radar Imagery 2 - 1RADARSAT’s State-of-the-Art Features 2 - 4Unique Characteristics of SAR Data 2 - 8

Chapter 3 Visual Interpretation of RADARSAT Imagery

Structural Interpretations 3 - 1Lithologic Interpretations 3 - 8Geological Applications Guidelines 3 - 13

Chapter 4 Image Enhancement of RADARSAT Data

Hardcopy Products 4 - 1Digital Products 4 - 1

Chapter 5 Value-Added RADARSAT Products

Radar Imagery Manipulation 5 - 1Data Integration 5 - 6

Chapter 6 Summary

Summary 6 - 1

Reference Materials

Glossary G - 1References R - 1

List of Figures

FIGURE 1.1 Optical vs. Radar Data: Tropical Environment - Guyana 1 - 2FIGURE 2.1 Electromagnetic Spectrum 2 - 1FIGURE 2.2 Energy Interaction in a SAR System 2 - 2FIGURE 2.3 Surface Roughness from Varying Terrains 2 - 3FIGURE 2.4 SAR Operating Beam Modes 2 - 4FIGURE 2.5 RADARSAT Beam Positions 2 - 5FIGURE 2.6 RADARSAT Resolution Comparison: Island of Borneo 2 - 6FIGURE 2.7 Incidence Angle Comparison: Timmins, ON, Canada 2 - 8FIGURE 2.8 Effects of Terrain Relief on Viewing Geometry 2 - 10FIGURE 2.9 Terrain Relief Effects on SAR Imagery: Calgary, AB, Canada 2 - 11FIGURE 2.10 Radiometric Effects: Quito, Ecuador 2 - 12FIGURE 3.1 Lineament Extraction Using RADARSAT: Rio Arauca, Venezuela 3 - 2FIGURE 3.2 Lineament Detection in an Arid Environment: Death Valley, NV, USA 3 - 3FIGURE 3.3 Detection of Lineaments Using Varying Viewing Angles 3 - 4FIGURE 3.4 Scarp Slope Detection: Taipei, Taiwan 3 - 6FIGURE 3.5 Local Drainage Anomaly: Rio Arauca, Venezuela 3 - 7FIGURE 3.6 Local Drainage Anomaly: Venezuela 3 - 8FIGURE 3.7 Quaternary Lithology: Bathurst Island, NWT, Canada 3 - 9FIGURE 3.8 Volcanic Lithology: Kamchatka Peninsula, Russian Federation 3 - 10FIGURE 3.9 Treetop Geology in Tropical Regions: Kalimantan, Indonesia 3 - 12FIGURE 4.1 Unfiltered vs. Filtered RADARSAT Image 4 - 3FIGURE 4.2 Block Averaged and Decimated Images: Vancouver, BC, Canada 4 - 5FIGURE 5.1 Stereo Viewing with RADARSAT: Sarawak, Malaysia 5 - 3FIGURE 5.2 RADARSAT Anaglyph: Southern Highlands, Papua New Guinea 5 - 4FIGURE 5.3 RADARSAT Image Mosaic: Island of Borneo 5 - 5

FIGURE 5.4 DEM Created with RADARSAT Data: Papua New Guinea 5 - 7FIGURE 5.5 Integration of RADARSAT/Optical Data: Cape Breton Island, NS, Canada 5 - 8FIGURE 5.6 LANDSAT TM/RADARSAT Merge: Vancouver, BC, Canada 5 - 9FIGURE 5.7 IHS Transformation: Sudbury Basin, ON, Canada 5 - 10

List of Tables

TABLE 3.1 Geological Activities and RADARSAT Recommendations 3 - 13

A C K N O W L E D G E M E N T S

V I R A D A R S A T G E O L O G Y H A N D B O O K

O B J E C T I V E S

The RADARSAT Geology Handbook is intended to be used as a training and

reference guide for those geologists making the transition from the optical to the

radar environment, and for those geologists who are already using RADARSAT

data. It will also be a useful desktop reference for performing initial geological

interpretations with RADARSAT imagery. The examples presented in this

handbook clearly demonstrate RADARSAT’s powerful utility in the world of

hydrocarbon and mineral exploration.

H A N D B O O K O R G A N I Z A T I O N

The RADARSAT Geology Handbook is divided into six chapters. The first chapter

is designed to provide the geologist with a comparison of optical and radar data

products, and to introduce the RADARSAT satellite. The satellite is explained in

more detail in Chapter Two. The third chapter discusses the techniques used to

visually interpret geologic structures and lithologies on RADARSAT imagery. The

chapter provides examples from arctic, volcanic, arid and tropical environments and

shows a range of structural features. The geologic mapping capabilities offered by

radar technology are also presented. The fourth chapter discusses RADARSAT film

and digital data products, and the digital image processing techniques used to

enhance structural features and SAR image appearance. The fifth chapter explains

the RADARSAT value-added products and various data fusion techniques. This

section covers anaglyphs, DEMs and stereo pairs. A brief summary of the handbook

is provided in Chapter Six, and is followed by a glossary of terms and an extensive

reference list.

A C K N O W L E D G M E N T S

The completion of this handbook was made possible from input provided by the

staff at RADARSAT International (RSI), geologists at the Canada Centre for

Remote Sensing (CCRS), and Dr. Fons Dekker of Dekker Remote Sensing in

Calgary, Alberta, Canada. This handbook marks the end of a two-year joint

Canadian International Development Agency (CIDA) project between Venezuela

and Canada.

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COMPARISON OF SATELLITE IMAGING SYSTEMS

THE RADARSAT SATELLITE

VISUAL INTERPRETATION OF RADARSAT IMAGERY

IMAGE ENHANCEMENT OF RADARSAT DATA

VALUE-ADDED RADARSAT PRODUCTS

SUMMARY

REFERENCE MATERIALS

1

C O M P A R I S O N O F S A T E L L I T E I M A G I N G S Y S T E M S 1 - 1

With the rapid advancement of the technology age and the increased competition

to locate new natural resources deposits to meet the demands of the growing

global economy, geologists are considering new and effective exploration

techniques. There has been a steady movement from traditional analog

techniques to the use of more advanced digital imagery.

Satellite imagery has numerous advantages over its predecessor aerial photography

(Berger, 1994), providing cost-effective increased spatial coverage. Digital satellite

data can easily be manipulated and enhanced to highlight features of interest.

Data fusion with existing geologic data can be easily performed using the

appropriate software. The main trade-offs in using digital satellite data over

airborne data are limited resolution and limited choice of flight directions. This

challenge will be overcome with the launch of high resolution Smallsat satellites

in 1997 and 1998, with resolutions ranging from 1 to 5 metres.

C O M P A R I S O N : O P T I C A L A N D R A D A R D A T A

RADARSAT differs from optical sensors in the kind of data it acquires and how

the data is collected. Optical multispectral systems which include LANDSAT

TM and SPOT are referred to as passive systems, in that they rely on sunlight

reflected off the Earth to image the planet’s surface. Since data is collected at

frequencies roughly equivalent to the human eye, sensors are unable to collect

data in darkness or wherever conditions such as cloud cover, fog, dust, hail or

smoke prevail. RADARSAT by comparison, uses Synthetic Aperture Radar

(SAR), which sends its own microwave signals down to the Earth and processes

the signals that it receives back. As an active sensor, RADARSAT’s longer

wavelength is better suited for atmospheric penetration and can collect data

regardless of the Earth’s atmospheric conditions. This ability provides the user

with significant advantages when it comes to viewing under conditions that

preclude observations made by aircraft or optical satellites.

In the past few years, radar remote sensing has proven to be an effective tool for

the extraction of geological information, unrestricted by external illumination

conditions. SAR imagery is particularly suited for geological mapping primarily

in tropical regions, because of the geological structure, surficial bedrock and

lineaments information it provides. The radar backscatter qualities are directly

related to ground topography, dielectric properties, and surface roughness of

the terrain being imaged. In addition, radar can acquire multiple images which

can be used to provide stereoscopic viewing.

Figure 1.1 provides a comparison of optical and radar data collected for a

region over coastal Guyana. The LANDSAT TM image shows very little surface

expression and its clarity is hindered by the moderate cloud cover. The

RADARSAT image penetrates through the cloud cover and uses “treetop

geology” to express the underlying geologic features and structures.

FIGURE 1.1: Optical vs. radar data: tropical environment - Guyana

The two images compare acquisitions over an area in Guyana made by LANDSAT TM and

RADARSAT. Note the prominent geologic structure that is visible in the lower left corner of the

RADARSAT image, but remains undetected on the cloud-covered LANDSAT imagery. RADARSAT:

Wide beam position 1, acquired August 19, 1996. LANDSAT-5: acquired December 30, 1986 TM,

Bands 3, 4, 5. RADARSAT data © Canadian Space Agency/Agence spatiale canadienne 1996.

Received by the Canada Centre for Remote Sensing. Processed and distributed by RADARSAT

International. LANDSAT data © EOSAT.

1 - 2 R A D A R S A T G E O L O G Y H A N D B O O K

C O M P A R I S O N O F S A T E L L I T E I M A G I N G S Y S T E M S 1 - 3

T H E R A D A R S A T S A T E L L I T E

RADARSAT, launched November 4, 1995 was the result of a joint venture

between the Canadian Government, private industry and NASA. As Canada’s

first Earth observation satellite, and the world’s first operationally-oriented

radar sensor, RADARSAT is providing valuable information for use in monitoring

the world’s environmental and natural resources.

Addressing Canada’s first major need for a radar sensor, RADARSAT provides

effective surveillance of the Canadian Arctic, which is characterized by long

periods of darkness in the winter. Major shipping routes cross this vast region

which recently gained importance due to its significant mineral and petroleum

reserves. Another need met by RADARSAT is to monitor Canada’s coastline,

which is one of the longest in the world, and is perennially cloud-covered

and foggy.

RADARSAT was launched in a sun-synchronous, dawn-dusk orbit with a

24-day repeat cycle. It provides regular imaging opportunities as frequently as

daily above the Arctic, and every five days over equatorial latitudes.

For geologists who have been trained to work with optical images, a transition

must be made to understand the unique features of radar imagery and to

successfully utilize the radar data. An introductory discussion of the

RADARSAT satellite is provided in Chapter Two, followed by an overview of

radar image interpretation in Chapter Three.

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SUMMARY

REFERENCE MATERIALS

1

T H E R A D A R S A T S A T E L L I T E 2 - 1

RADARSAT differs from research-oriented radar sensors such as ERS and

JERS-1, given that RADARSAT is the first radar sensor totally dedicated to

operational applications and it offers a variety of beam modes to meet

requirements of the particular application at hand. Using a single frequency

C-Band (5.3 Ghz frequency or 5.6 cm wavelength), the RADARSAT SAR has

the ability to shape and steer its radar beam over a 500 kilometre range.

RADARSAT provides complete global coverage with the satellite’s orbit repeated

every 24 days. The Arctic is imaged daily, while equatorial areas achieve complete

coverage approximately every 5 days. The following sections discuss

RADARSAT’s unique features and the benefits these pose for geologists.

U N D E R S T A N D I N G R A D A R I M A G E R Y

What is a radar image?

Radar images are single frequency representations of the Earth, which highlight

changes in the terrain’s roughness, relief, and moisture levels. They are similar

to other types of Earth observation imagery in that they represent the reflectivity

portion of the electromagnetic spectrum (Figure 2.1). However, radar imagery

is derived from a portion of the light spectrum that human vision is unable to

detect. This special wavelength is capable of penetrating rain, cloud and haze, to

provide a continually clear view of the Earth.

FIGURE 2.1: Electromagnetic spectrum

OPTICAL RADAR

Gamma Rays X-Rays U.V. Rays Visible

Near and MidInfrared

Thermal Infrared Microwave TV/Radio

(cm) 10-10

10-9

10-8

10-7

10-6

10-5

10-4

10-3

10-2

10-1

1.0 10 100

FIGURE 2.2: Energy interaction in a SAR system

Why are radar images black and white?

Radar images are black and white not because they are fundamentally different

from other data sources (i.e LANDSAT or SPOT optical sensors), but because

they do not have a multispectral component necessary for false-colour formation.

LANDSAT TM is sensitive to the Earth’s reflectivity at seven different

wavelengths, hence the seven bands. Colour is achieved by combining any three

of the optical bands. RADARSAT contains only one spectral band, and thus

offers a unique dataset for the exploration geologist.

What do radar images consist of?

A radar image is the ratio of microwave energy transmitted to the Earth to the

energy which is reflected directly back to the sensor. The energy returning to

the sensor is called backscatter (see Figure 2.2). The backscatter of an imaged

area is dependent upon local topography, centimetre-scale roughness, and

dielectric properties, which are directly affected by moisture levels. Low



backscatter values are portrayed as dark image tones or grey levels which

approximate black, while high backscatter values are shown as light image tones

or grey levels approximating white.

FIGURE 2.3: Surface roughness from varying terrains

What sort of information is provided by radar images?

Radar imagery provides valuable information to a broad user community.

Geology, agriculture and landcover mapping are just a few of the applications

which benefit from the way in which a radar image portrays landcover types.

Although no two land units are the same, there are general rules which apply to

certain landcover classes. Water is usually dark- a product of specular reflection

and resulting weak return, while urban areas are always very bright due to the

presence of corner reflectors (see Figure 2.3). Everything else falling between

these two extremes is represented in various shades of grey. By interpreting the

various tones, textures and patterns on the image, the user can unlock information

pertaining to geologic structure and lithology.

R A D A R S A T ’ S S T A T E - O F - T H E - A R T F E A T U R E S

Range of Beam Modes

RADARSAT is equipped with seven beam modes, which offer image resolutions

ranging from 8 to 100 metres. RADARSAT is designed in such a way that its

beam can be steered at incidence angles ranging from 10-60 degrees. It offers

spatial coverage ranging from 50-500 kilometre swaths, and can be used for

mapping at scales of 1:1,000,000 to 1:50,000. These features are described in

Figures 2.4 and 2.5.

FIGURE 2.4: SAR operating beam modes



FIGURE 2.5: RADARSAT beam positions

BEAM MODE BEAM POSITION INCIDENCE APPROXIMATE NOMINALANGLE RANGE (º) RESOLUTION (m) AREA (km)

Fine F1 near 36.4 - 39.6 8 * 50 x 50F1 36.8 - 39.9F1 far 37.2 - 40.3F2 near 38.8 - 41.8F2 39.2 - 42.1F2 far 39.6 - 42.5F3 near 41.1 - 43.7F3 41.5 - 44.0F3 far 41.8 - 44.3F4 near 43.1 - 45.5F4 43.5 - 45.8F4 far 43.8 - 46.1F5 near 45.0 - 47.2F5 45.3 - 47.5F5 far 45.6 - 47.8

* tape recorded data may cover smaller area

Standard S1 20 - 27 25 100 x 100S2 24 - 31S3 30 - 37S4 34 - 40S5 36 - 42S6 41 - 46S7 45 - 49

Wide W1 20 - 31 30 165 x 165W2 31 - 39 150 x 150W3 39 - 45 150 x 150W3 (for tape recorded) 138 x 150

ScanSAR Narrow SN1 20 - 40 50 300 x 300SN2 31 - 46

ScanSAR Wide SW1 20 - 49 100 500 x 500

Extended High H1 49 - 52 25 75 x 75H2 50 - 53H3 52 - 55H4 54 - 57H5 56 - 58H6 57 - 59

Extended Low L1 10 - 23 35 170 x 170

In Figure 2.6, three RADARSAT beam modes are used to view the Island of

Borneo, supplying the user with both a regional and a site-specific view of the

newly discovered Busang gold deposit.

FIGURE 2.6: RADARSAT resolution comparison: Island of Borneo

ScanSAR Narrow: acquired August 5, 1996, area: 125 x 125 km, subscene. Standard beam position 6:

acquired September 29, 1996, area: 79 x 73.5 km, subscene. Fine beam position 5: acquired May 12,

1996, area: 10 x 16 km, subscene. RADARSAT data © Canadian Space Agency/Agence spatiale

canadienne 1996. Received by the Canada Centre for Remote Sensing. Processed and distributed by

RADARSAT International.


RADARSAT’s ScanSAR Narrow beam mode offers 50 m resolution and a 300

by 300 km coverage, which supports the identification of structural trends and

features. The Standard beam mode (25 metre resolution) helps optimize

mapping, geophysical and drilling programs. The Fine beam mode, (8 metre

resolution) is suitable for mapping at scales of 1:50,000.

Range of Viewing Geometries

RADARSAT’s side-looking geometry greatly enhances subtle topographic

features that aid in the interpretation of lineaments. RADARSAT offers 35

beam positions with a viewing angle range of 10 to 60 degrees. The effects of

using different viewing angles is seen in Figure 2.7 of Timmins, Ontario. The

RADARSAT Wide beam position 1 image, which uses a smaller (steeper) angle,

has less tonal variation and makes geologic features more difficult to discern.

The Wide beam position 2 image, with its larger (shallower) viewing angle,

clearly emphasizes the subtle geological structures and increases the

land/water contrast.



FIGURE 2.7: Incidence angle comparison: Timmins, ON, Canada

The shallower angle of the Wide beam position 2 image creates a larger artificial shadow, thus

highlighting subtle geological features more clearly than the steeper Wide beam position 1 image.

Wide beam position 1: acquired September 15, 1996, subscene. Wide beam position 2: acquired

August 12, 1996, subscene. Descending pass. RADARSAT data © Canadian Space Agency/Agence

spatiale canadienne 1996. Received by the Canada Centre for Remote Sensing (CCRS). Processed

and distributed by RADARSAT International. Enhancement and interpretation by CCRS.

U N I Q U E C H A R A C T E R I S T I C S O F S A R D A T A

Image Geometry

It is virtually impossible to represent an area on the Earth’s surface without

geometrical distortion. Within radar imagery, tall objects appear to lean towards


the radar sensor. The radar sensor measures the time-delay between transmission

and reception for each radar pulse. Since the radar pulse generally reflects off

the mountain top first, the mountain top is interpreted as being closer than the

mountain base. This phenomenon is called foreshortening (see Figure 2.8a).

When foreshortening becomes so extreme that an object “falls over”, resulting

in the loss of one side of the mountain, it is known as layover (see Figure 2.8b).

Shadows are also an inherent characteristic of most radar images, and occur

primarily on the leeward sides of mountains. Shadowing enhances lineaments,

joints, and faults, by highlighting changes in feature orientation. This concept is

explained in Figure 2.8c. The effects of terrain relief on SAR imagery is clearly seen

in Figure 2.9.

Image Radiometry

SAR images are monochromatic (black and white) and the relative

brightness of a pixel is directly related to the radar reflectivity of the ground

target it portrays. The radiometric values range between the two extremes:

completely dark and completely bright. Figure 2.10 illustrates how the radar

technology can affect the digital values used to represent two very similar areas.

Therefore, if an object reflects much of its incident radar energy back to the

sensor, it will have a relatively high digital value and be represented as a white

pixel. If an object does not reflect much energy back to the sensor, it will attain

a low digital value and be represented as a black pixel.

2 - 1 0 R A D A R S A T G E O L O G Y H A N D B O O K

FIGURE 2.8: Effects of terrain relief on viewing geometry

radarshadowArtist concepts: drawings not to scale.

C) ShadowShadow area not imaged

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A) Foreshortening1- SAR perceived distance2- Actual distance

B) Layover1- Top of mountain viewed before the bottom2- Actual ground distance

FIGURE 2.9: Terrain Relief Effects on SAR Imagery: Calgary, AB, Canada

The effects of layover, foreshortening and shadows are clearly illustrated in this mountainous

terrain. Standard beam position 1: acquired February 12, 1996, area: 20 x 20 km, subscene.

RADARSAT data © Canadian Space Agency/Agence spatiale canadienne 1996. Received by the

Canada Centre for Remote Sensing. Processed and distributed by RADARSAT International. Data

acquired during the commissioning phase and may not conform to system specifications.

T H E R A D A R S A T S A T E L L I T E 2 - 1 1

LookDirection


FIGURE 2.10: Radiometric Effects: Quito, Ecuador

The sensor-facing slope is extremely bright and the leeward slope is dark, despite the valley having

symmetrical slopes of similar land cover. Fine beam position 5: acquired February 4, 1996, descending

pass, subscene. RADARSAT data © Canadian Space Agency/Agence spatiale canadienne 1996.


International.

Proper interpretation of radar images demands a basic understanding of

elevation-related effects (such as layover and shadows) on the image’s geometry

and radiometric values.

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SUMMARY

REFERENCE MATERIALS

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V I S U A L I N T E R P R E T A T I O N O F R A D A R S A T I M A G E R Y 3 - 1

Radar technology has an inherent sensitivity to the detection of lineaments,

joints, faults and shear zones due to its side-looking configuration, and can

therefore provide precise information on the subtle changes in relief.

RADARSAT brightens the sensor-facing slopes of ridges while accentuating the

lee slopes with shadow leaving the interpreter with a clear view of the surface. It

is up to the interpreter to determine the origin of the surface undulations

whether it be geomorphologic or structural. It is the knowledge of the study

area, and the other vital datasets such as seismic, aeromagnetics, and gravity,

which enable the geologist to draw accurate conclusions. Radar-derived

geologic information is used in Quaternary mapping, mineral and hydrocarbon

exploration, and geologic hazard identification.

This chapter covers the interpretation of structural and lithological

information from a RADARSAT image by making specific reference to

lineaments, bedding plane attitude and drainage network analysis.

S T R U C T U R A L I N T E R P R E T A T I O N S

RADARSAT provides geologists with a unique and complementary data source

with respect to contact, structure, lineaments and landforms. The following

section provides examples of various structures that can be detected using

RADARSAT imagery.

Interpreting Exposed Structures

Lineaments

RADARSAT has an ability to highlight linear elements of an image which are

not near-parallel to the look direction. Not only does RADARSAT provide an

image which clearly shows lineaments, but it offers additional information to

allow the interpreter to determine its heritage. Lineaments may be

human-induced, geomorphological or structural in origin. RADARSAT provides

information to assist with the discrimination of various lineament features.


Pipelines, hydro corridors, railroads and roads are examples of human-induced

lineaments. Eskers, aligned sinkholes, and longitudinal dunes are geomorpho-

logic lineaments. Bedrock faulting produces a unique set of surface characteristics,

which in many cases provides information on fault type and relative age. Spatial

offsets of lithologic units and sharp breaks in topography may indicate the presence

of faults. RADARSAT imagery reveals lineaments previously undetected using

optical data. Lineaments detected from RADARSAT imagery are shown in

Figures 3.1 and 3.2.

FIGURE 3.1: Lineament extraction using RADARSAT: Rio Arauca, Venezuela

This example shows a series of parallel lineaments related to faulting in a tropical environment.




Normal Fault

Transfer Fault

Arauca Arch Direction

Thrust Fault


FIGURE 3.2: Lineament detection in an arid environment: Death Valley, NV, USA

One of the many lineaments in this arid enviroment is highlighted above. ScanSAR Narrow beam

position 2: acquired March 23, 1996, ascending pass, subscene. RADARSAT data © Canadian

Space Agency/Agence spatiale canadienne 1996. Received by the Canada Centre for Remote

Sensing. Processed and distributed by RADARSAT International. Data acquired during the

commissioning phase and may not conform to system specifications.

Look Direction Effects on Lineaments

Many natural features have a strong, preferred orientation, which is commonly

expressed as parallel linear features (Sabins, 1987). The success of detecting

certain directions of lineaments is related to the look direction. Linear geologic

features that are oriented at a normal or oblique angle to the radar look direction,

are enhanced by highlights and shadows (Sabins, 1987). As the lineaments

become aligned with the look direction, the subsequent reduction of backscatter

decreases the probability of lineament detection. The amount of weathering

along the lineament will also affect feature detection (see Figure 3.3).

FIGURE 3.3: Detection of lineaments using varying viewing angles

This diagram delineates the areas of lineament suppression, while outlining the optimal angles for

increased feature detection.



Note: If your lineaments trend within the zone of suppression, choose an image

from the opposite orbital direction (descending in this case) to ensure a “clear”

view of the lineaments in a given concession. Acquiring both ascending and

descending passes of a region of interest ensures complete detection of

lineaments.

Strike and Dip

Folds, domes and basins reveal tell-tale signs of their attitudes when they

deform under the forces of erosion. Drainage networks and unique erosion

patterns provide insight into the classification of scarp and dip slopes, and an

approximation of their orientation (i.e strike and dip). The general criteria for

determining the dip direction of inclined rock units is discussed in the following

section.

Scarp Slopes

Scarp slopes perpendicular to bedding are characterized by two easily identifiable

thematic characteristics on radar imagery. Firstly parallel topographic benches

reflect the presence of rock units with alternating resistivities. These appear as

light and dark bands on one side of a positive structure. Secondly stream

networks appear more dendritic on the scarp slope than those on the dip slopes.

Dip Slopes

Dip slopes parallel to bedding are characterized by triangular-shaped ridges

(flatirons), which generally point away from the dip direction (see Figure 3.4).

The drainage network has long branches and is less dendritic than in areas

associated with scarp slopes.

FIGURE 3.4: Scarp slope detection: Taipei, Taiwan

In this example, the striped scarp slope and the flatirons are clearly visible, differentiating them

from a Quaternary formation and providing concise structural information. Standard beam

position 4: acquired January 2, 1996, descending pass, subscene. RADARSAT data © Canadian




Highly deformed slopes benefit from stereoscopic coverage. With a three-

dimensional perspective, all of the available geologic information can be viewed

using the stereo pair.

Drainage Network Analysis

Radar is sensitive to drainage networks due to the dielectric differences between


water, moist soil and riparian vegetation, and erosion-induced topographic

variations. Structural information can be derived using interpretation techniques

based on tonal variations. Abrupt changes in drainage density can denote

lithologic and structural differences.

Local Drainage Anomalies

Flowing water develops unique drainage patterns as it accommodates changes

in surface conditions. Obscured and buried structures can cause streams and

rivers to develop unique drainage patterns referred to as “drainage anomalies”.

Anomalous drainage patterns may indicate the presence of faults or fractures,

but require ancillary data to reach a final conclusion. A few examples of these

anomalies are shown in Figures 3.5 and 3.6.

FIGURE 3.5: Local drainage anomaly: Rio Arauca, Venezuela

This example shows a river flowing through a fault cluster (refer to Figure 3.1) and the resulting

right angle turns in the river’s course. This feature indicates that there is resistant underlying

bedrock impeding the water’s flow path. Inset: Right-angle bends (Berger, 1994). Standard beam

position 5: acquired March 3, 1996, subscene. RADARSAT data © Canadian Space Agency/Agence

spatiale canadienne 1996. Received by the Canada Centre for Remote Sensing. Processed and

distributed by RADARSAT International. Data acquired during the commissioning phase and may

not conform to system specifications.


FIGURE 3.6: Local drainage anomaly: Venezuela

The braided drainage system can be representative of elevation changes, impeding dome

structures or changes in surface material density. Existing geologic datasets should be consulted

to confirm the basin’s evolution. The RADARSAT image clearly shows it as being anomalous. Inset:

Abnormal Divergence (Berger, 1994). Standard beam position 5: acquired March 3, 1996, subscene.




L I T H O L O G I C I N T E R P R E T A T I O N S

RADARSAT imagery provides information on the exposed bedrock, layers of

vegetation, and the overburden found in the area. The dominant parameter

determining how a given rock unit will appear in a radar image is its surface

texture. Rock units break down differentially, resulting in unique surface

roughness distinguishable on radar imagery due to contrasting backscatter. In

the next section, surficial materials are identified using RADARSAT imagery in

varying environments and terrains.

Quaternary Environments

Quaternary mapping in glaciated terrain involving the delineation of landforms


?

with the assessment of surficial material, has been successfully demonstrated

using radar alone or in combination with other data sources. Radar data provides

information on landform topography, from which many other Quaternary features

can be identified by their characteristic morphology. Figure 3.7 is a striking

representation of the surficial and structural geology in the arctic, characterized

by a remarkable pattern of folds. The sharp contact between rock types is

distinguished using tonal and textural differences.

FIGURE 3.7: Quaternary lithology: Bathurst Island, NWT, Canada

Figure 3.7 shows an image of an arid and arctic environment. The finer siltstone particles produce a

darker return, while the coarser limestone particles produce a brighter backscatter return attributed

to the increased surface roughness. These textures result from freeze/thaw processes acting upon

units of different resistivities, creating surface debris with differing particle sizes. Standard beam

position 7: acquired March 21, 1996, descending pass, subscene. RADARSAT data © Canadian


Sensing (CCRS). Processed and distributed by RADARSAT International. Data acquired during the

commissioning phase and may not conform to system specifications. Enhancement and

interpretation by CCRS.


Volcanic Environments

The ability to image rock units is a result of the side-looking configuration of

radar, which highlights topographic relief. Figure 3.8 shows an image collected

in a volcanic environment in which the surface texture controls the appearance

of the rock strata. Terrain units are identified by the unique textures, shapes,

and tones in the radar imagery, which in this case are affected by the age and

texture of the various lava flows, soil moisture, and the absence/presence

of snow.

FIGURE 3.8: Volcanic lithology: Kamchatka Peninsula, Russian Federation

The large volcano seen in the centre of the image is Mount Kluchevskaya Sopka, which has an

elevation of approximately 4,500 metres. Standard beam position 4: acquired April 1, 1996,

descending pass, subscene. RADARSAT data © Canadian Space Agency/Agence spatiale

canadienne 1996. Received by the Canada Centre for Remote Sensing. Processed and distributed

by RADARSAT International.


Forested, Tropical Environments

Unfortunately overburden is found in most of the world’s terrains (as is evident

in the examples above), and in those environments there is little surface or

canopy penetration by a radar signal. The collected lithologic information is

based on erosional patterns and structural inferences, in combination with

supporting datasets. In tropical regions, RADARSAT indirectly creates a

representation of the surface by imaging variations in the heights of treetops,

because regional and local geomorphology is mimicked in the tree canopy

(Figure 3.9). To the skilled interpreter, radar imagery creates an image as

though the forest cover is removed. Exploration geologists can then use this

information to detect small-scale geologic structures, erosional patterns, and

subtle topographic features.

V I S U A L I N T E R P R E T A T I O N O F R A D A R S A T I M A G E R Y 3 - 1 1


FIGURE 3.9: Treetop geology in tropical regions: Kalimantan, Indonesia

The “Treetop Geology” concept is illustrated in this RADARSAT image of a dense tropical forest in

Indonesia. An eroded, sedimentary dome structure is clearly evident. RADARSAT was also able to

detect this feature, despite the heavy forest cover and perennial cloud cover. Fine beam

position 4: acquired March 21, 1996, area: 21 x 21 km, subscene. RADARSAT data © Canadian





G E O L O G I C A L A P P L I C A T I O N S G U I D E L I N E S

TABLE 3.1: Geological Activities and RADARSAT Recommendations

GEOLOGICAL ACTIVITY RESPONSE AND RECOMMENDATIONS

Geological RADAR RESPONSE Structure Mapping Geological structures often have characteristic forms, which, if located

near the Earth’s surface, may be manifested topographically as the side-looking configuration of radar highlights relief.

RECOMMENDATIONSRADARSAT Beam Mode: All beam modes are suitable for geological structure mapping. The final beam mode selection is dependent on the areal coverage and the level of detail required. Generally, Fine and Standard beam modes are best suited for detailed geological structure mapping, while Wide and ScanSAR are better for basin-wide geological structure mapping.

RADARSAT Incidence Angle: Shallow incidence angles are ideal for enhancing subtle terrain features through shadowing. In areas of high relief, too much shadowing may occur with shallow incidence angles and therefore, intermediate incidence angles may be more suitable.

Look Direction: Orientation of geological structures relative to look direction should be considered.

When to Acquire RADARSAT Data: Acquire data when geological structure information is required, regardless of seasons. Avoid periods of heavy snowcover.

Lineament RADAR RESPONSEIdentification Lineaments, such as folds and faults may be manifested as topographic

relief. The ability to image lineaments is a result of the side-looking configuration of radar which highlights relief.

RECOMMENDATIONSRADARSAT Beam Mode: The beam mode chosen is dependent on the width of the lineaments, and on areal coverage and the level of detail required. Generally, Fine and Standard beam modes are best suited for detailed lineament identification, while Wide and ScanSAR are better for regional identification.



RADARSAT Incidence Angle: Shallow incidence angles are ideal for enhancing the subtle topographic relief of lineaments.

Look Direction: A look direction perpendicular to the direction oflineaments enhances their detectability. The acquisition of both ascending and descending passes maximizes the number of lineaments that can be identified.

When to Acquire RADARSAT Data: Acquire data when lineament information is required.

Seismic Zones RADAR RESPONSEIdentification Seismic zones are often characterized by the presence of faults which

may be manifested topographically. The ability to image seismic zones is a result of the side-looking configuration of radar, which highlights this topography.

RECOMMENDATIONSRADARSAT Beam Mode: The beam mode chosen is dependent on the width of the lineaments associated with seismic zones, and on areal coverage and the level of detail required. Generally, Fine and Standard beam modes are best suited for detailed seismic zones identification, while Wide and ScanSAR are better for regional seismic zone identification.

RADARSAT Incidence Angle: Shallow incidence angles are ideal forenhancing the subtle topographic relief of lineaments.

Look Direction: A look direction perpendicular to the direction of lineaments will enhance their detectability. The acquisition of both ascending and descending passes maximizes the number of lineaments that can be identified.

When to Acquire RADARSAT Data: Acquire data when lineamentinformation associated with seismic zones is required.

Landform Delineation RADAR RESPONSELandforms often have characteristic shapes which may be manifested as topographic relief. The ability to image landforms is a result of the side-looking configuration of radar, which highlights relief.

RECOMMENDATIONSRADARSAT Beam Mode: All beam modes are suitable for landform



delineation. The final beam mode selection is dependent on the areal coverage and the level of detail required. Generally, Fine and Standard beam modes are best suited for detailed landform delineation, while Wide and ScanSAR are better for regional landform delineation.

RADARSAT Incidence Angle: Shallow incidence angles are ideal for enhancing subtle terrain features through shadowing.

Look Direction: Orientation of land forms relative to look direction should be considered.

When to Acquire RADARSAT Data: Acquire data when landform information is required, regardless of season. Avoid periods of heavy snowcover.

Surficial Bedrock RADAR RESPONSEGeological Mapping Depending on the type of physical weathering, surficial bedrock may

characteristically fracture to produce fragment sizes, which are a function of elements such as rock fabric, texture and mineral composition. Individual rock units may break down differentially, resulting in unique backscatter.

RECOMMENDATIONSRADARSAT Beam Mode: All beam modes are suitable for surficialbedrock mapping. The final beam mode selection is dependent on the areal coverage and the level of detail required. Generally, Fine and Standard beam modes are best suited for detailed surficial bedrock mapping, while Wide and ScanSAR are better for regional surficial bedrock geological mapping.

RADARSAT Incidence Angle: The main parameter that may differentiate rock fragment size associated with surficial bedrock units is surface roughness. Shallow incidence angles maximize the contrast in backscatter resulting from variances in roughness.


When to Acquire RADARSAT Data: Acquire data when moisture levels are low in order that the backscatter be more closely correlated to surface roughness than to moisture content.


Surficial Material RADAR RESPONSEAssessment Non-vegetated, unconsolidated surficial material contains different

fragment sizes which may produce a characteristic soil roughness and soil moisture holding capability. Radar is sensitive to changes in roughness and moisture, and the result is contrasting backscatter between different surficial units.

RECOMMENDATIONSRADARSAT Beam Mode: All beam modes are suitable for the assessmentof surficial materials. The final beam mode selection is dependent on the areal coverage and the level of detail required. Generally, Fine and Standard beam modes are best suited for detailed surficial assessment, while Wide and ScanSAR are better for regional surficial material assessment.

RADARSAT Incidence Angle: If surficial material is assessed based on soil moisture, steep incidence angles are preferred to minimize thebackscatter associated with soil roughness. If surficial materials are assessed based on soil surface roughness, shallow incidence angles are better suited.


When to Acquire RADARSAT Data: If surficial material assessment is based on soil surface roughness, then acquire data when the moisture levels are low to ensure that the backscatter is more closely correlated to surface roughness than it is to moisture content.

Sedimentology RADAR RESPONSEMapping Unconsolidated sediments, such as those deposited by glaciers, are often

manifested as topographic relief. The ability to image sedimentological units is a result of the side-looking configuration of radar which highlights topographic relief. Sediments also have characteristic grain sizes with different moisture holding capacities, and they may produce a characteristic surface roughness. Radar is sensitive to changes in moisture and roughness, which results in contrasting backscatter between different sediments. Each consolidated type shows unique erosional patterns, including karsting in carbonates and bedding in clasticenvironments.




RECOMMENDATIONSRADARSAT Beam Mode: All beam modes are suitable for sedimentology mapping. The final beam mode selection is dependent on the areal coverage and the level of detail required. Generally, Fine and Standard beam modes are best suited for detailed sedimentology mapping, while Wide and ScanSAR are better for basin-wide sedimentology mapping.

RADARSAT Incidence Angle: If sedimentology mapping is carried out based on the delineation of topographic relief, shallow incidence angles are ideal for enhancing subtle terrain features. If sedimentology mapping is carried out based on soil moisture differences, steep incidence angles are preferred to minimize the backscatter associated with soil roughness. If sedimentology mapping is carried out based on the determination of surface roughness, shallow incidence angles are better as they maximize the contrast in surface roughness.

Look Direction: In areas of moderate to high relief, acquisition of both ascending and descending passes allows the true form of topographic features to be represented.

When to Acquire RADARSAT Data: If sedimentology mapping is carried out based on soil moisture or surface roughness, then the data should be acquired when the vegetation is at a minimum to avoid having the vegetation response to RADARSAT’s energy dominate the backscatter.

Landslide Hazard RADAR RESPONSEAssessment RADARSAT Beam Mode: Landslide hazard areas are defined when the

locations of past landslides are identified. Landslides change the landscape through the transportation of vegetation and soil, thus affected areas have different canopy and soil roughness than surrounding unaffected areas. Radar is sensitive to these variances in roughness, and produces contrasting backscatter between affected and unaffected areas.

RECOMMENDATIONSRADARSAT Beam Mode: Both Standard and Fine beam mode can be usedto obtain detailed information on individual landslides.

RADARSAT Incidence Angle: Shallow incidence angles will minimize geometric distortions associated with areas of moderate to high relief.


Look Direction: For moderate to high relief terrain, acquisitions of both ascending and descending passes maximizes the amount of landslide information.

When to Acquire RADARSAT Data: Acquire data when landslide information is required.

Coastal Erosion RADAR RESPONSEAssessment A smooth water surface is a specular reflector, which results in low

backscatter. The rougher surface of the land however, is a diffuse scatterer and produces relatively high amounts of backscatter. Evaluationof the change in backscatter over time allows the assessment of coastal erosion.

RECOMMENDATIONSRADARSAT Beam Mode: Depending on the level of detail required, all of RADARSAT’s beam modes are suitable for coastal erosion assessment.

RADARSAT Incidence Angle: Shallow incidence angles create the greatest contrast between water and land. At these angles there is specular reflection from the water surface while the roughness of the land surface is enhanced.

Look Direction: N/A

When to Acquire RADARSAT Data: Coastal erosion assessment deals with the delineation of the land/water boundary over time. Acquire data when the coastline needs to be monitored.


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I M A G E E N H A N C E M E N T O F R A D A R S A T D A T A 4 - 1

RADARSAT’s multiple beam modes and positions combined with the available

levels of processing provide geologists with an extensive selection of digital

products. To efficiently access the information contained within these products,

one must understand the digital characteristics and the implications of the

selected image processing procedures. Before the imagery is interpreted, the

RADARSAT image is calibrated to compensate for errors related to satellite

movement, the SAR antenna and ground reception. Calibration provides a

means of ensuring the consistency of data quality both within the image

(relative calibration) and from one image to another (absolute calibration).

Various digital image processing techniques used to analyze radar images are

discussed in the following sections.

H A R D C O P Y P R O D U C T S

Hardcopy products are often the most convenient to use. They are portable and

easy to handle in remote locations. These print and film products can also be

utilized as field maps and interpreted directly to extract application information.

Hardcopy RADARSAT products contain the actual image plus auxiliary

information.

D I G I T A L P R O D U C T S

Digital RADARSAT products can provide the geologist with greater flexibility

in how the data is manipulated and utilized. Digital interpretations are less

labour-intensive and less costly than visual interpretations. Digital 8-bit images

contain 256 grey levels, while the human eye can only distinguish approximately

16 to 32 distinct grey tones. Therefore, a visual interpretation alone may not be

sufficient to notice all the subtle radiometric differences in a radar image.

Speckle

SAR imagery is characterized by the presence of speckle, which is a “salt and

pepper” effect occurring throughout the radar image. The SAR sensor transmits

thousands of pulses for a given area on the ground defined as the resolution

cell. The pulses are reflected from many scattering points within the resolution

cell, but the motion of the satellite causes each received pulse to be phase-shifted.

When all the pulses are added vectorially, resolution cells within a homogeneous

ground region will have a different backscatter value. A seemingly

homogeneous surface area will have an irregular distribution of light and dark

pixels, producing a granular effect. The brightness variation is called speckle.

Speckle is a phenomenon of the technology used in the creation of the image

and is not related to the imaged target. There will always be high pixel

variability even if the landcover is uniform.

Speckle can be controlled at the initial image processing stage by data sampling.

Any attempts to rid the image of speckle will result in a loss of potentially

important information; therefore, one must carefully analyze the methodology

used, such that the information loss is minimized and the overall smoothing

effect increases image interpretability.

Speckle Removal

The most common form of speckle removal is filtering, which employs various

techniques of pixel averaging to smooth out the speckle. The extent of the

smoothing varies with the filtering technique and how it is implemented.

Filtering techniques are included in the radar modules of commercially available

image analysis software packages. Mean and median filters are examples of

non-adaptive filters which can be used to despeckle, and the Frost and Kuan are

examples of adaptive filters. Figure 4.1 is an example of a RADARSAT subscene

which has been filtered to remove the speckle. The sedimentary geological

feature in this remote area of Indonesia is more clearly emphasized when the

Kuan and/or Gamma filters are applied to remove speckle. These adaptive

filters smooth speckled areas while preserving point target detail. Additional

information regarding the digital manipulation of RADARSAT imagery can be

obtained from the RADARSAT Data Processing and Integration Handbook.



FIGURE 4.1: Unfiltered vs. filtered RADARSAT images

Fine beam position 4: acquired March 21, 1996, area: 3.5 x 5 km, subscene. RADARSAT data

© Canadian Space Agency/Agence spatiale canadienne 1996. Received by the Canada Centre for

Remote Sensing. Processed and distributed by RADARSAT International. Data acquired during the


Spatial Filtering Techniques

Decimation and Block Averaging

A radar pixel is rarely interpreted individually, but rather in combination with

surrounding pixel values creating shapes and patterns which provide the basis

for interpreting structural features. In some cases, it is desirable to have a

relatively simple procedure which is able to reduce the original image to a more

manageable file size. Decimation takes every nth pixel value to produce an

image with coarser resolution. The trade-off is a decreased file size. Decimation

results in a loss of information and discontinuous lines because neighbouring

values are not considered.

To retain more image information, the block average procedure is used. This

degrades the image both spatially and radiometrically, resulting in a much

smoother-looking image. The pixel values in the original image are modified on

the basis of the grey levels of the neighbouring pixels (Lillesand and Kiefer,

1994) to produce one mean value for the moving window. Moving

neighbourhoods or windows of 3 x 3 or 5 x 5 pixels are typically used in such

procedures. Although smoothing can produce a less granular texture, it creates

larger pixels and may give the appearance of softened edges in the image. A

RADARSAT image using the block averaging and decimation methods is shown

in Figure 4.2.



FIGURE 4.2: Block averaged and decimated Images: Vancouver, BC, Canada

The Block Averaged image shows more contrast between landcover classes and the land/water

boundaries than the decimated image. Block averaging provides increased delineation of linear

features given that neighbouring values are taken into consideration. Fine beam position 3:

acquired March 12, 1996, area: 16.5 x 16.5 km, subscene. RADARSAT data © Canadian Space

Agency/Agence spatiale canadienne 1996. Received by the Canada Centre for Remote Sensing.

Processed and distributed by RADARSAT International. Data acquired during the commissioning

phase and may not conform to system specifications.

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For some applications, a single RADARSAT image may be the only data you

require. For other applications, it may be useful to have several RADARSAT

images covering a large area or acquired at different times, or from different

look directions. Furthermore, you may want to integrate (or fuse) RADARSAT

data with other image products, geologic map data, or database information

associated with features on the image. This chapter discusses the various

techniques used to digitally integrate radar data.

R A D A R I M A G E R Y M A N I P U L A T I O N

Stereo Viewing

Stereo pairs are a helpful tool for mapping structures that are either highly

deformed or consisting of extremely low-angle dipping strata (Mahmood, et al.,

1996). RADARSAT images acquired using different beam positions contain

parallax in exactly the same manner as aerial photos. The common area between

the two images can be represented in three dimensions. Radar stereo pairs can

then be viewed using traditional photogrammetric techniques including use of a

stereoscope or glasses designed to show three dimensions. Stereo pairs can be

used to enhance the interpretability of geologic structures (see Figure 5.1), for

assessing the dip and strike of inclined strata, or for mapping highly-deformed

landforms.

Anaglyphs

Stereo pairs can be represented in anaglyph format, whereby the two images are

overlaid digitally: one in shades of red, and the other in shades of blue. When

viewed through proper glasses, the two images appear to create a three-dimen-

sional viewing environment. This effect is clearly demonstrated in the anaglyph

created of Papua New Guinea (see Figure 5.2).

Mosaics

Extensive coverage of a particular area can be obtained by overlapping parallel

strips into a mosaic. Mosaics are an inexpensive (in some cases less than

$0.02/km2 USD) and easy-to-use source of information for mapping large

V A L U E - A D D E D R A D A R S A T P R O D U C T S 5 - 1

areas. Figure 5.3 showing the Island of Borneo, is an example of a mosaic

created using 12 RADARSAT ScanSAR beam mode images. Many image

analysis packages offer the functionality to join RADARSAT images and adjust

the radiometric properties to produce such a seamless mosaic. An accurate

mosaic requires that the radar look direction be held constant; if not, the

shadows and highlights will be reversed in different parts of the mosaic.


FIGURE 5.1: Stereo viewing with RADARSAT: Sarawak, Malaysia

An example of a RADARSAT stereo pair created for an area of folded sediments. Standard beam

position 5 (left): acquired May 28, 1996. Standard beam position 6 (right): acquired June 3, 1996,

area: 22 x 72 km. RADARSAT data © Canadian Space Agency/Agence spatiale canadienne 1996.


International.


FIGURE 5.2: RADARSAT anaglyph: Southern Highlands, Papua New Guinea

Standard beam position 2: acquired September 6, 1996. Standard beam position 7: acquired

September 9, 1996. RADARSAT data © Canadian Space Agency/Agence spatiale canadienne 1996.


International.


FIGURE 5.3: RADARSAT image mosaic: Island of Borneo

Image mosaic comprised of 12 ScanSAR Narrow images. RADARSAT data © Canadian Space

Agency/Agence spatiale canadienne. Received by the Canada Centre for Remote Sensing.

Processed and distributed by RADARSAT International. Image mosaic by Resource GIS and

Imaging Ltd. (RGI).


DEM Creation Using RADARSAT Stereo Pairs

Stereo pairs can be used to derive digital elevation models (DEMs) for almost

any location in the world. DEMs are a representation of the surface elevation,

and provide the means of correcting the geometric distortion inherent in SAR

imagery. With these DEMs and the appropriate software, it is possible to create

three-dimensional perspectives which best suit the user’s viewing needs. The

accuracy of the derived DEM will depend on the use of an appropriate combination

of beam positions for a specific terrain relief. In mountainous regions, same side

stereo pairs can be collected in both ascending and descending orbits. This

allows areas excluded by shadows or layover to be filled in from an opposite

viewing direction. DEMs can be used to correct geometric distortions such as

foreshortening and layover.

RADARSAT-derived DEMs can now be created at levels of detail approximate

to 1:100,000 scale mapping standards. Figure 5.4 shows a DEM derived from a

RADARSAT stereo image pair created using Standard beam positions 2 and 7.

The resulting image can be imported into a GIS system as a base map, especially

in those areas where only limited or outdated maps are available.

D A T A I N T E G R A T I O N

Radar data is easily integrated with other datasets, thus creating an enhanced

interpretive mapping tool. The decision to integrate is made when the general

characteristics of a dataset are summarized. For example, although radar data

provides thematic value, its strongest value is related to structure.

Aeromagnetics and gravity are two perfect examples. These two datasets benefit

synergistically from the radar data, resulting in datasets which have more

thematic value than the sum of their original parts.

Figures 5.5 and 5.6 represent SAR images which have been digitally fused with a

multi-channel optical image to obtain increased geologic information. The

combined maps are very useful for the geologic mapping of structures and

lithology. The merged image retains most of the multispectral LANDSAT TM

information, while the RADARSAT image accentuates the terrain features.


RADARSAT data provides information on surface expression, while

aeromagnetic data shows sediment thickness. Radar data is commonly merged

with LANDSAT TM (see Figure 5.6), adding thematic information to help infer

information about the underlying soil, sediment and bedrock conditions.

FIGURE 5.4: DEM created with RADARSAT data: Papua New Guinea

The image was colourized according to elevation, and draped over a perspective view of the DEM.

Standard beam position 2: acquired August 29, 1996. Standard beam position 7: acquired August 26,

1996. RADARSAT data © Canadian Space Agency/Agence spatiale canadienne 1996. Received by

the Canada Centre for Remote Sensing. Processed and distributed by RADARSAT International.

DEM by Intermap Technologies Ltd.


FIGURE 5.5: Integration of RADARSAT/optical data: Cape Breton Island, NS, Canada

This merged image emphasizes key features enhanced by integrating radar and optical data. The

RADARSAT portion outlines subtle geological features and lineaments, including the Aspy Fault,

while the LANDSAT data clearly identifies vegetated and cleared areas. RADARSAT Standard beam

position 1: acquired November 28, 1995, subscene. LANDSAT: acquired September 14, 1990, TM,

bands 4, 5, 7. RADARSAT data © Canadian Space Agency/Agence spatiale canadienne 1995.

Received by the Canada Centre for Remote Sensing (CCRS). Processed and distributed by

RADARSAT International. Data acquired during the commissioning phase and may not conform to

system specifications. LANDSAT data received by CCRS and reproduced courtesy of EOSAT.

Enhancement and interpretation by CCRS.


FIGURE 5.6: LANDSAT TM/RADARSAT merge: Vancouver, BC, Canada

The RADARSAT Fine beam mode image provides textural information on the surrounding landcover,

while the LANDSAT TM image separates vegetated areas from urban areas. The final merged product

is better at identifying point targets such as oil storage tanks (centre of image). RADARSAT Fine

beam position 3: acquired March 3, 1996. LANDSAT: acquired September 1994, bands 1, 2, 3.


Canada Centre for Remote Sensing (CCRS). Processed and distributed by RADARSAT International

(RSI). Data acquired during the commissioning phase and may not conform to system specifications.

LANDSAT data received by CCRS and processed by RSI. Reproduced courtesy of EOSAT.

IHS (Intensity-Hue-Saturation) Transformation

Images can be fused using multi-channel image combination techniques. One


technique is known as an IHS (Intensity-Hue-Saturation) Transformation,

which is a common method used to fuse optical and radar images, and is

adequate for highlighting topographic features. Three channels of optical or

other data are represented as Red-Green-Blue (RGB) channels in a digital

image. Using an image analysis program, the RGB image is converted to an IHS

image. The intensity channel (I) is then replaced with the radar image, while the

optical data remains in the hue (H) and saturation (S) channels. The final image

retains most of the multispectral information, as well as accentuated terrain

features from the radar image (see Figure 5.7).

FIGURE 5.7: IHS transformation: Sudbury Basin, ON, Canada

The aeromagnetic data shows geologic units expressed from their respective magnetic signatures,

while RADARSAT offers structural and topographic information. RADARSAT Standard beam position

1: acquired June 4, 1996. RADARSAT data © Canadian Space Agency/Agence spatiale canadienne

1996. Received by the Canada Centre for Remote Sensing (CCRS). Processed and distributed by

RADARSAT International. Enhancement and interpretation by CCRS.


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S U M M A R Y 6 - 1

The imagery examples presented throughout this handbook have demonstrated

that RADARSAT imagery can support geological mapping based on image

tones, textures and patterns caused by topographic contrasts and surface

roughness (Mahmood, et al., 1996). RADARSAT has proven itself to be of

significant use for geological applications.

RADARSAT can help with the logistical planning of projects due to its ability to

collect data under inclement weather and in areas with dense tropical

vegetation. Radar can greatly improve the success of mineral prospecting and

petroleum extraction, by providing information on:

• regional seismic line coverage

• drill site access

• production facilities

• pipeline routing and access

• hydrocarbon reserves

In closing, RADARSAT imagery is only one source of data available to

geologists. By integrating RADARSAT data with other surface and subsurface

data, published geologic maps, and field checks, exploration geologists can

achieve optimal results in interpreting geologic structures.

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G L O S S A R Y G - 1

G L O S S A R Y

A

Active System: A remote sensing system that emits its own energy source to

illuminate the Earth’s terrain. RADARSAT is one example.

Anaglyph: A method of obtaining a three-dimensional image of topography

by viewing two adjoining images that have been assigned red and blue colours.

They can be viewed in three dimensions by means of special lenses: one being

red and the other blue.

Antenna: A device that transmits and receives microwave and radio energy in

radar systems.

B

Backscatter: The portion of the microwave energy scattered by the terrain

surface directly back towards the antenna.

Block Averaging: A spatial filtering technique which reduces the image size by

using a moving window to average neighbouring pixels to produce a final

brightness value.

C

Contact Print: A reproduction from a photographic negative in direct contact

with photosensitive paper.

Corner Reflector: A cavity formed by two or three smooth planar surfaces

intersecting at right angles. Energy entering a corner reflector is directed back

towards the source.

D

Decimation: A spatial filtering technique that samples every nth pixel value,

without considering the neighbouring pixel values to produce an image with a

decreased file size.

Dielectric Constant: A measure of the electrical properties of surface

materials, which influence the radar return.

Digital Elevation Models (DEMs): A stereo analysis technique whereby a

computer ingests information from a digitized stereo pair and produces a

geometrically corrected digital map with correlated elevation measurements.

Digital Image Processing: Computer manipulation of the digital number

(DN) values of an image.

Drainage Anomaly: Unique pattern of stream channels and valleys that

appears anomalous with respect to the surrounding drainage features. Usually

reflects local topographic and structural controls.

F

Far Range: The portion of the radar image furthest from the sensor flight

path.

Flatiron: A typical surface expression of a dissected cuesta. Expressed as a

triangularly shaped ridge with an apex that points away from the dip direction.

Foreshortening: A distortion on radar images that occurs when the emitted

wavefront strikes the top of a vertical feature and makes it appear to lean toward

the sensor.

G

Geometric Correction: Image processing procedure that corrects spatial

distortions in an image.

G - 2 R A D A R S A T G E O L O G Y H A N D B O O K


Grey Scale: A sequence of grey tones ranging from black to white.

Ground Control Point: A geographic feature of known location that is

recognizable on images and can be used to determine geometric corrections.

Ground Range: On radar images, the distance from the ground track to the

object.

H

Hue: Indicates the dominant wavelength of a colour. Hue is usually

represented as a value between 0 and 255.

I

Image: A pictorial representation acquired in any wavelength of the

electromagnetic spectrum.

Intensity-hue-saturation (IHS) Transformation: Any image enhancement

technique that separates intensity, hue, and saturation components from an

image and modifies them.

Incidence Angle: The angle formed between an imaginary line normal to the

surface and another connecting the antenna and the target.

Interpretation: The process in which a person or image processing software

extracts information from an image.

L

Layover Effect: An extreme case of foreshortening when the topographic

slope is equal to or larger than the incidence angle. The slope facing the radar

image disappears underneath the back slope.

Lineaments: Linear topographic or tonal feature on the terrain and on images

that may represent zones of structural weaknesses.

Look Direction: The direction chosen for a radar survey to take advantage of

radar shadows, which improve the appearance of features of interest.

M

Microwave: Electromagnetic wavelength of 1mm-1m (.3GHz to 300GHz).

Mosaic: Composite image made by piecing together individual images

covering adjacent areas.

N

Near Range: Refers to the portion of the radar image closest to the satellite

flight path.

O

Obscured Structure: A geological structure whose rock units are partially

covered by vegetation or soil, and must be analyzing its effect on surface

features.

Overlap: The extent to which adjacent images cover the same terrain,

expressed as a percentage.

P

Parallax: Displacement of a target position in an image caused by the shift in

the observation position.

Passive System: A remote sensing system that relies on naturally occurring

radiation or reflected light from the Earth’s surface to create an image.

LANDSAT TM and SPOT are examples.



Pixel: The smallest discrete area on an image (from PICTure ELement).

R

Radar: An acronym for RAdio Detection And Ranging. Radar is an active

form of remote sensing that operates in the microwave and radio wavelength

regions.

Radar Shadow: Dark signatures on a radar image representing no signal

return. A shadow extends in the far range direction from an object that intercepts

the radar beam.

Range Resolution: The spatial resolution in the range direction, which is

determined by the pulse length of the transmitted microwave energy.

Resolution: Ability to separate closely spaced objects on an image. Resolution

is commonly expressed as the most closely spaced line pairs per unit distance

that can be distinguished.

S

Scarp Slope: The short and steep slope of a cuesta. The scarp slope indicates

the anti-dip direction.

Shadow: See Radar Shadow.

Signature: A set of characteristics by which a material or an object may be

identified on an image or photograph.

Slant Range: An imaginary line running between the antenna and the target.

Speckle: Uncertainty associated with each pixel in the image of the scene,

producing a salt-and-pepper effect.

Stereo: Imaging technique of using parallax properties from two images of the

same area to extract elevation information.

Surface Roughness: The average vertical relief of small-scale irregularities of

the terrain surface.

Synthetic Aperture Radar (SAR): SAR systems use the motion of the satellite

and doppler frequency shift to electronically synthesize the large antenna necessary

for acquisition of high-resolution radar images.

T

Tone: Each distinguishable shade of grey from white to black on an image.

Topographic Inversion: The illusion that may occur on images with extensive

shadows. Ridges appear to be valleys, and valleys appear to ridges. The illusion

is corrected by orienting the image so that the shadows trend from the top

margin of the image to the bottom.

Treetop Geology: The ability to map surface topography indirectly by

examining variations in the heights of treetops. This is one of the major

advantages of radar over passive systems.


R E F E R E N C E M A T E R I A L S R - 1

R E F E R E N C E S

Journals/Books

Berger, Z., 1994. Satellite Hydrocarbon Exploration: Interpretation andIntegration Techniques. Springer-Verlag Publishers, 319 pp.

Brown, R.J., B. Brisco, et al., 1996. RADARSAT Applications: Review of

GlobeSAR Program. Canadian Journal of Remote Sensing, Vol 22, No. 4,

pp. 404-419.

CASI, 1993. Special Issue: RADARSAT. Canadian Journal of Remote Sensing,

Vol 19, No. 4, entire issue.

CASI, 1994. Special Issue on Radar Geology. Canadian Journal of RemoteSensing, Vol 20, No. 3, entire issue.

Chigne, N., F. Dekker, et al., 1996. Spaceborne Radar (ERS-1 and

RADARSAT-1) Tests for Hydrocarbon Exploration in Venezuela.

II AAPG/SVG International Congress and Exhibition, Caracas, Venezuela,

8-11 September, pp. A9-A10.

Dekker, F., and D. Nazarenko, 1994. Radar Offers Many Unique Benefits as an

Exploration Tool in Tropical Environments. Earth Observation Magazine, Vol 3, No. 5, May, pp. 26-29.

Dekker, F., 1994. A Comparison of Various Remote Sensing Tools, IncludingAirborne and Satellite SAR, for Hydrocarbon Exploration in Tropical RainForests: Projected Usefulness of RADARSAT For Geological Applications. Report

prepared for RADARSAT International, #93-110, 54 pp.

Evans, D.L., T.G Farr, et al., 1986. Multipolarization Radar Images for

Geologic Mapping and Vegetation Discrimination. IEEE Transactions onGeoscience and Remote Sensing, Vol GE-24, No. 2, pp. 246-257.

Harris, J.R., R. Murray, and T. Hirose, 1990. IHS Transform for the

Integration of Radar Imagery with Other Remotely Sensed Data.

Photogrammetric Engineering and Remote Sensing, Vol 56, No. 12, December,

pp. 1631-1641.

Lillesand, T.M, and R.W. Kiefer, 1994. Remote Sensing and ImageInterpretation. John Wiley and Sons, Inc., Toronto, ON, Canada, 750 pp.

Mahmood, A.S., Carboni et al., 1996. Potential Use of RADARSAT in

Geologic Remote Sensing. Eleventh Thematic Conference and Workshop onApplied Geologic Remote Sensing, Las Vegas, NV, 27-29 February, 1995,

pp. I475-I484.

Thompson, M.D., and B.J. Mercer, 1996. Digital Terrain Models. EarthObservation Magazine, March, pp. 22-26.

RADARSAT International, 1995. RADARSAT Illuminated: Your Guide toProducts and Services. Unpublished manual, 60 pp. (available from RADARSAT

International).

RADARSAT International, 1996. RADARSAT Data Processing andIntegration Handbook. Unpublished manual, 30 pp. (available from

RADARSAT International).

RADARSAT International, 1997. Hydrocarbon Exploration UsingRADARSAT: Looking for Oil and Gas in Venezuela. Unpublished

brochure (available from RADARSAT International).

Rossignol, S., and K.P. Corbley, 1996. Reconnaissance by Radar. CanadianMining Journal, Vol 117, No. 6, December, pp. 13-16.

Sabins, F.F. Jr., 1987. Remote Sensing: Principles and Interpretation.W.H. Freeman and Company, 3rd Ed., New York, 404 pp.

Singhroy, V.H., 1992. Radar Geology: Techniques and Results. Episodes, Vol 15

pp. 15-20.

R - 2 R A D A R S A T G E O L O G Y H A N D B O O K

R E F E R E N C E M A T E R I A L S R - 3

Websites

RADARSAT International (education) www.RADARSATinACTION.com

RADARSAT International (corporate) www.rsi.ca

Canada Centre for Remote Sensing www.ccrs.nrcan.gc.ca

Canadian Space Agency www.radarsat.space.gc.ca

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