1229.full

ECONOMIC GEOLOGY AND THE

BULLETIN OF THE SOCIETY OF ECONOMIC GEOLOGISTS

VOL. 79 SEPTEMBER-OCTOBER, 1984 NO. 6

Use of Landsat Multispectral Scanner Data for Detection and Reconnaissance Mapping of Iron Oxide Staining

in Mineral Exploration, Central East Greenland KNUT CONRADSEN

Institute of Mathematical Statistics and Operations Research, (IMSOR), The Technical University of Denmark, DK-2800 Lyngby, Denmark

AND OLE HARPtZITH Nordisk Mineselskab A/S, Lers Parkalle 112, DK-2100 Copenhagen, Denmark

Abstract The investigated area in central east Greenland, covering a region of approximately 120,000

km 2, is situated in the high Arctic zone (7075 N). It is devoid of human activity such as mine operations, urban zones, etc., and has a low overall vegetation cover. The geology is varied and includes a complex mineralization pattern which ranges in time from Archcan to Tertiary and in type from small base metal vein occurrences with very minor or no visible alteration, to major stockwork molybdenum occurrences with very pronounced limonitic alteration even in this Arctic environment. Thus, central east Greenland is considered to represent an excellent test area for Landsat multispectral scanner data in order to detect and map color anomalous hydrothermal alteration associated with stockwork molybdenum mineralization as well as other types of iron oxide staining associated with sulfide mineralization in both metamorphic and volcanic terrain.

A known major limonitic altered zone associated with the Tertiary Malmbjerg molybdenum occurrence was selected as the test area. The color anomaly detection techniques developed at the Malmbjerg rust zone include the use of an interactive digital image processing system combined with computerized color plots (Applicon color plotter). In particular special ratio and factor analysis techniques proved to be very effective.

The best results with ratio techniques are obtained when band 4/band 5 is displayed as cyan (red), band 5/band 7 as magenta (green), and band 6/band 7 as yellow (blue). A characteristic pink to orange color corresponds to areas of limonitic rocks.

Using factor analysis, color composites displaying F2 as magenta (green), F3 as cyan (red), and F4 as yellow (blue) with a visually determined color stretch discriminates very effectively with yellow-orange to red-orange colors representing limonitic rocks. Furthermore, distinction between detected rust zones can be made by using different classification routines.

These techniques were used on a reconnaissance basis in the Tertiary igneous province and have located more than 50 significant rust zones. Furthermore, the developed ratio and factor analysis techniques were applied to reconnaissance mapping of Precambrian metamorphic and Devonian volcanic terrains covering more than 30,000 km 2 of ice-free area in a province of 500 km long by 300 km wide. This has resulted in the localization of 38 major (surface area > 0.5 km s) rust zones in Precambrian metamorphic terrain intruded by late Caledonian granites as well as several major color anomalous areas in Devonian volcanic terrain.

In general, the delineation from reconnaissance mapping of color anomalous (rusty) areas is believed to represent a regional low-cost method of identifying areas with a high potential for mineral deposits, in particular in a remote area like central East Greenland.

Introduction

THE results presented in this paper were obtained under a research project supported by the European

Economic Communities on the evaluation of the applicability of satellite remote sensing in mineral exploration, mainly in Arctic areas. The research comprises both a color anomaly investigation and a lin-

0361-0128/84/326/1229-1652.50 1229

1230 K. CONRADSEN AND O. HARPOTH

eament-circular feature analysis, but only the former is described here. A detailed description can be found in the two main reports (Conradsen et al., 1982a and b).

Landsat satellites have now been operating for more than ten years, and the last decade has seen a growing acceptance of satellite remote-sensing tech- nology in energy and mineral exploration which is documented in many reports, e.g., Sabins (1978), Ro- wan and Lathram (1980), Goetz and Rowan (1981), and Goetz et al. (1983).

The Landsat data available are partly photographic material and partly computer compatible tapes (CCT's). The photographic material can, of course, be used as in ordinary photogeological interpretation, but the main advantage of Landsat multispectral scanner (MSS) data is the possibility of digital ma- nipulation in order to enhance geologically interesting features such as iron oxide staining and lineaments.

In Arctic areas like east Greenland it is a great advantage to use Landsat data because the logistical problems of using conventional exploration methods are enormous and very costly. Thus, the availability of satellite imagery of expectedly increasing quality will be of great importance. However, there are also problems with such work in Arctic regions. First of all, the relatively low sun angle gives a lot of shadow, which is a problem in investigations of limonitic alteration but is an advantage in lineament analysis because edges are enhanced by the shadows. Second, the extensive snow cover, which persists for more than nine months each year can make it difficult to get good coverage of images.

Methods used in other parts of the world have often turned out to be useful in Greenland as well, but in order to extract all relevant information from satellite imagery, it was necessary to modify known techniques and develop new approaches. We shall briefly indicate the nature of these developments in this paper; for a detailed description the reader is again referred to the main reports.

Area Description

The investigated area in central east Greenland (approximately 120,000 km 2) is situated between 70 o and 75 N, and is bounded on the west by the Green- land ice cap and on the east by the Greenland Sea (Fig. 1).

The physiography of the area is dominated by mountainous terrain, varying from glacier-dissected Alpine regions with peaks nearing 3,000 m at sea level, to less rugged plateau mountains cut by deep U-shaped valleys, to relatively fiat low-lying areas of sedimentary rocks. Numerous long and deep fjords and valleys dissect the entire area. In general the glacially eroded land surface is very well exposed,

but the large low-lying areas of sedimentary rocks may exhibit extensive vegetation cover of the high Arctic type.

The climate is Arctic, with an average yearly pre- cipitation of 400 mm and average temperatures for July and January of 5C and -20C, respectively. The area is free from extensive snow cover from mid June to mid September.

The geologic setting of central east Greenland (Fig. 2) can be summarized as follows. The East Greenland fold belt occupies most of the investigated area. A considerable proportion of it comprises crystalline infracrustal gneisses, granites, and metamorphosed supracrustals, which comprise a number of metamorphic complexes, each with characteristic lithol- ogies and structures. Some of the complexes preserve both pre-Caledonian (Archean to late Proterozoic) and Caledonian orogenic features reflecting the composite origin of the East Greenland fold belt (Hen- riksen and Higgins, 1976).

The Archean-Proterozoic metamorphic complexes are flanked by late Proterozoic and lower Paleozoic sediments (Fig. 2) which comprise the 12,000-m-thick late Precambrian Eleonore Bay Group, the 700-m- thick Tillite Group, and the 3,000-m-thick Cambrian Ordovician limestone and dolomite sequence.

During the Caledonian orogeny, metamorphism and deformation was widespread but variable throughout the fold belt. Only the westernmost parts of the Eleonore Bay Group sediments have been weakly metamorphosed, while the Eleonore Bay Group and Cambrian-Ordovician sediments to the east were not affected by Caledonian metamorphism. Deformation, mainly expressed as broad fold structures, affected both the metasedimentary complexes and the weakly to nonmetamorphosed Eleonore Bay Group sediments. Furthermore, granitic intrusive activity was widespread during the Caledonian orogeny.

After the main phase of the Caledonian orogeny; a major northwest-southeast-trending molasse basin developed in the central part of the area (Fig. 2). Continental clastic sediments with a cumulative thickness of more than 7,000 m were deposited in the basin during the Middle and Upper Devonian. Associated acid volcanism is represented as lava flows and tuffs within this sequence. After several phases of post-Caledonian folding, thrusting, and uplift, continental molasse deposition continued in the Car- boniferous and Lower Permian with a cumulative sediment thickness of more than 4,000 m.

The late Permian Sea transgressed the area from the northeast, and 1 to 2 km of shallow marine and continental Upper Permian-Triassic sediments in- cluding carbonates, evaporites, and red beds were deposited in a north-south-trending basin (Birkelund and Perch-Nielsen, 1976). Sedimentation of mainly marine origin continued through the Jurassic and

LANDSAT DETECTION AND MAPPING, GREENLAND 1231

ANDREE LAND

YMERS {)

SUESS LAND

LYELLS LAND TRAILL

0

SOLSIDEN

MALMBJERG "'- MELLEM

MALMROS KLINT

JAMESON LAND

MILNE LAND

o 5o

KM

FIG. 1. Locality map, central east Greenland.

Cretaceous, ending with uplift at the beginning of the Tertiary. This Tertiary uplift was accompanied by the extrusion and intrusion of igneous rocks, comprising plateau basalts, basalt dikes and sills, and a line of alkaline intrusive centers (Fig. 2). A detailed description of the geology of central East Greenland is given in Hailer (1971) and Escher and Watt (1976).

Although only limited mineral exploration activity has been carried out, widespread mineralization is known from all over the area. The major mineralizing epochs presently known are given in Table 1.

Limonitic alteration associated with mineralization is known from a wide variety of occurrences, the most conspicuous being that associated with Tertiary stockwork-molybdenum mineralization. Other rust zones associated with mineralization include Archcan gold-uranium occurrences, minor disseminated sulfide mineralization of probable lower to middle Protero-

zoic age, pyrite-gold mineralization of late Caledonian age, and base metal veins of Paleozoic age.

Data Description and Standard Procedures Most of the data used in the present study originate

from Landsat satellites (L1-L3). These circle the earth at an altitude of about 920 km in a sun-synchronous orbit. The electromagnetic reflection from the earth's surface is registered in four wavelengths by a multispectral scanner. The wavelength intervals are: band 4, -0.5 to 0.6 #m; band 5, '0.6 to 0.7 #m; band 6 '0.7 to 0.8 #m; and band 7, '0.8 to 1.1 #m. One picture element (pixel) covers approximately 79 X 57 m, and a Landsat scene consists of 2,286 lines, each with 3,240 pixels. The scene thus covers approximately 185 X 185 km 2. For details on the Land- sat system, see, for instance, Sabins (1978) and its references.


::.:i 75* tl

GEOLOGICAL MAP OF CENTRAL EAST GREENLAND

Quaternary Ternary basalts (& alkah intrusions at c 72 N) Cretaceous

Jurassic

Permo-Tras (mainly marine)

Permo-Carb. t molasse-type sediments Devonian Cambrmn-Sduran

Late Precambrian & Eocambrian Eleonore Bay Group & Tillite Group Md-Precambran sediments

Gneiss, mgmattic and sedimentary complexes (Caledoman or older) ..'! Gneiss complexes (foreland) Fault / / ..... ,:i? :' '. :;::; ...... .: Thrust / /':" ;;::;

/ ..

...

:;?-.t: /

0 0 100km

Ilaston rland

Claverng (

Hold with Hope

72*

Jameson Land

)hcal Society 0

Vega Sund

Land

Land

72*

FIG. 2. Simplified geologic map of central east Greenland. (Source, the Geological Survey of Greenland.)

The area investigated is covered by nine Landsat scenes, but in all, 15 computer compatible tapes (CCT's) were used. The majority of the analyses are based on six tapes acquired from September 5, 1980, to September 9, 1980. These cover almost all of the area, and owing to the short period of time in which they were acquired, they are well suited for com- parisons of areas located in different scenes.

It is beyond the scope of this paper to give a thor- ough exposition of image-processing techniques used in the analyses of remotely sensed data. The reader

is referred to the rapidly growing literature on the subject. A good first reference is Sabins (1978), Siegal and Gillespie (1980), or the 1983 special issue of Economic Geology on remote sensing. A brief survey of methods actually used is given in this paper. For further details see Conradsen and Gunulf (1980) and Conradsen et al. (1982a and b).

The analysis of satellite images given in digital form involves large amounts of computing. The bulk of the computations were carried out at the Technical Uni- versity of Denmark on an IBM 3033 with a 6-Mbyte


TABLE 1. Major Mineralizing Epochs, Central East Greenland 1. Archcan

2. Upper Proterozoic

3. Caledonian orogeny

4. Upper Paleozoic

5. Triassic

6. Tertiary

Disseminated type--gold-uranium Stratiform--copper Vein type--tungsten-antimony-gold Vein type--base metal-silver Vein type--tin-tungsten Vein type--gold Skarn type--tungsten Vein type--base metal-silver-gold Strata-bound--base metal-silver-

barite Vein type--uranium Stratiform-strata-bound--base

metal-silver

Stockwork--molybdenum-tungsten Vein type--base metal-silver

core memory. Studies on smaller test areas were carried out on an interactive image-processing system with a 512 X 512 pixel color display (IDIMS with a DeAnza display). Color hard copies were produced with an Applicon ink color plotter.

The software used comprises standard image-processing software such as VICAR, developed at the Jet Propulsion Laboratory, and the software from the IDIMS system. Many of the statistical analyses and analyses of a numerical character have been performed using the library packages BMDP (Dixon and Brown, 1979), IMSL (International Mathematical and Statistical Libraries, 1980), and SAS (Statistical Anal- ysis System, 1982). Furthermore, a subroutine library consisting of more than 50 subroutines has been developed, mainly in FORTRAN IV. These routines comprise data-organizing routines, routines for classifying and transforming images, and color-plotting routines for ink jet plotters.

Before more detailed analyses were undertaken, most of the tapes were corrected for 6-line striping by a histogram equalization routine and rectified in order to fit the existing topographic and geologic maps (at scales of 1:250,000, 1:100,000, and 1:50,000) of the study area. Besides the geodetic rectification this also involved composition of mosaics and (indirectly) a resampling to other pixel sizes. For example, the pixel size used in the 1:50,000 images is approximately 50.8 X 50.8 m. Survey images have been produced of the entire area, partly as one-channel images with a very coarse color scale (called density slicing) and partly as three-channel false color composites with channel 4 as yellow, channel 5 as magenta, and channel 7 as cyan. The number of colors is mostly taken as 17 X 17 X 17. In most images a uniform stretch has been applied, i.e., each color interval holds the same percentage of pixels.

Color Anomaly Enhancement Techniques Investigations by Rowan et al. (1974), Ballew

(1977), Cole (1977), Vincent et al. (1977), Smith et al. (1978), Ashley et al. (1979), and Ashley and Abrams (1980) have shown that areas of limonitic rocks are distinguishable from areas comprising nonlimonitic rocks using ratio techniques of Landsat MSS data.

To test the applicability of the Landsat MSS data for detection of areas of limonitic rocks in east Green- land, a known major limonitic altered zone associated with the Tertiary Malmbjerg (Figs. 1 and 3) molybdenum occurrence was selected as a first test area. Different ratio and factor analysis techniques were then applied to the spectral data from the test area.

Ratio techniques Ratio techniques are a highly effective means of

minimizing brightness variations owing to the topographic slope and changes in albedo. Furthermore, limonitic altered rock has absorption bands around 0.8 to 1.1 ttm (Rowan et al., 1974, 1977), and therefore it can be advantageous to analyze ratios between the different bands instead of the bands themselves. It can be argued that one should make corrections for atmospheric scattering and absorption before forming the ratios. This, however, tends to decrease the signal-to-noise ratio and therefore reduces the spatial information (Rowan and Lathram, 1980). Fur- thermore, empirical investigations indicated no im- provement by doing the corrections, so it was decided to work on either ratios x/y of the original data or on ratios (x + 1)/(y + 1) in order to avoid division with 0. Since x/y = (x + 1)/(y + 1) + (x - y)/(y(y + 1)), we have x/y (x + 1)(y + 1) for the highly correlated MSS values, at least as long as y varies around a value > 0. For values closer to zero the approximation is of course worse, but in the study area this is very seldom the case. In the sequel the ratio between, e.g., bands 4 and 5 will be named Q4/5, etc.

Analyses of ratio color composites from, e.g., southern Nevada show that a very effective combi- nation for mapping limonitic altered rocks is to map the ratio Q4/5 as cyan, Q5/6 as yellow, and Q6/7 as magenta (Rowan et al., 1974). Limonitic altered rocks appear green in this color ratio composite image. This is also the case in the present study. However, many glaciers have a very similar image color which makes the technique useless in, for instance, a regional mapping of limonitic altered zones in Greenland. A study of the distributions of the different ratios showed that a possible alternative would be to map the ratios Q4/ 5, Q6/7, and Q5/7 as cyan, yellow, and magenta. In Figure 4, which shows the cumulative distribution of the ratios Q4/5, Q5/6, Q5/7, and Q6/7 for two rust populations (1 and 2) and for two rock populations


FIG. 3. Distribution of major rust zones in Tertiary alkaline intrusions. Outlined areas refer to areas shown in Figures 5 through 9.

(5 and 6), it is seen that Q5/7 gives a better discrimination between rust and rock than Q5/6. By using an empirically determined color stretch (Table 1), limonitic altered rocks turned out pink-red-orange (Fig. 6, below), whereas the rest of the scenes had very dark blue or greenish colors. This seemed to be true over the whole range where this ratio image was used in the regional mapping of anomalous color zones.

Factor analysis Another very useful enhancement technique is

factor score plots (principal component score plots). A major problem in "seeing" the alterations is the great change in intensity over the scenes (snow, rock, shadow, etc.) that easily dominates the more subtle spectral effects of the alterations. Since the first principal component in this study basically is an overall measurement of the intensity, an obvious way of en- hancing the "higher order" variations is to use only the second, third, and fourth principal component (F2, F3, F4) in false color composites. In the present study the principal components were based on pixels representing bed rock, and the first component ac- counted for 97.8 percent of the total variation. Map- ping F2 as magenta, F3 as cyan, and F4 as yellow, it was possible to find a color stretch in which iron oxide-stained areas turned out yellowish orange (Fig.

7, below) and the rest as very dark green. The stretch is given in Table 2.

Besides the Malmbjerg test area three other major rust zones (Fndal, Solsiden, and Vildthorn; Fig. 3) were tested. In order to discriminate between the different types of limonitic alterations, samples were drawn from five populations (four rust zones and red beds). The three first canonical variates CV1, CV2, and CV3 were computed and used as basic variables in color plots. Mapping CV1 as cyan, CV2 as magenta, and CV3 as yellow with the stretch given in Table 2 gave images that discriminated very well between the different color anomaly types (Fig. 8, below).

Classification Techniques Different classification techniques were tested for

the major rust zones (Malmbjerg, Fndal, Solsiden, and Vildthorn; Fig. 3). The main quality criterion was that rust pixels should be classified correctly and that ordinary rock pixels should not be misclassified as rust.

Several modifications of ordinary Bayesian and maximum likelihood classifiers had to be made. The nature of these was partly to augment the number of variables by adding linear as well as nonlinear functions of the MSS bands, and partly to utilize the hierarchical structure in the populations that were classified.


e4./

2

i i i o.so o. o. o.5

i

1. MalmbJerg ruit 2. Traiii ruit

t + )6. Tr III i i oo oe6 o q, oe 1o 11o 16 i '? i 154 ICl

cc i

Q 5/7

i i

.co .00 t .oe i, 6 1.2, i .52 ! ,o t .4e t .68 1.64 12?2 t

FIG 4. The cumulative distribution of the ratios Q4/5, Q5/6, Q5/7, and Q6/7 for two rust populations (1 and 2) and for two rock populations (5 and 6).

As we worked with several different training areas, it turned out that if the set of variables considered was the original four MSS channels, plus the ratios

between those plus the factor scores, then one of the original MSS bands (either 5 or 7), plus all the ratios (Q4/5, Q4/6, Q4/7, Q5/6, Q5/7, Q6/7), plus the three

T^BLE 2. The Limits for the Ratios (Q), Factor Scores (F), and Canonical Variates Used in the Rust Enhancement Plots Color scale division

Variable Lower limit Upper limit Lower limit Upper limit Color Q4/5 0.561 0.695 255 0 Cyan (red) Q6/7 1.226 1.692 255 0 Yellow (blue) Q5/7 -0.422 4.494 255 0 Magenta (green) F3 0.798 10.290 0 255 Cyan (red) F2 - 14.248 13.498 0 255 Magenta (green) F4 -10.216 48.301 0 255 Yellow (blue) CV1 1.4576 4.7118 255 0 Cyan (red) CV2 -6.5511 -1.0311 255 0 Magenta (green) CV3 -9.5556 12.0588 0 255 Yellow (blue)

] A linear stretch is used between the limits. The negative values in the ratios determine the stretch; they are not used in the mapping. The colors are for mixtures of pigments. In the color scale division 255 corresponds to full intensity and 0 to zero intensity


last factor scores would give the best discrimination. A main problem in the original classification was that in order to ensure that pixels known to be color anomalous were in fact classified as rust, the discrimination procedures had to be tuned in such a way that too many ordinary rock pixels were classified as rust. This made the classification maps almost useless. By augmenting the number of variables as described above it was possible to decrease the number of misclassified rock pixels from approximately 9 percent to approximately 5 percent in a small test area consisting of approximately 23,000 pixels. The test area was not used in the estimation of the discriminant functions. A more detailed discussion will be available (K. Conradsen and J. Gunulf, in prep.). If one further used the hierarchical structure in the data, these re- suits could be even further improved. Consider for instance the following groups: (1) rock, (2) rust, (3) shadow on rock, (4) shadow on ice, (5) snow, and (6) glacier. Then there is an obvious hierarchy, where the first level consists of the three groups rock-rust, shadow, and snow-glacier. The second level consists of the original six groups. By first classifying in level i with one scheme, and thereafter subdividing the three combined groups with three different schemes, it was possible to bring the number of misclassified rock pixels down to approximately 0.5 percent.

The procedures described above are only appli- cable when an exhaustive classification is wanted, i.e., when all pixels must be classified. In a regional analysis, however, it can be difficult to cover all types of pixels in the calibration set, and thus a great number of misclassified pixels must be expected. Therefore, it can be useful to have nonexhaustive procedures, that is, procedures where a pixel is classified as either belonging to one of the calibration populations or to some unspecified population.

Furthermore, the regional analyses in this study involve different types of color anomalies or rust zones. Therefore we have found procedures that discriminate between rust and rock pixels as well as between different rust types.

The main characteristic of the nonexhaustive procedures is that a pixel, in order to be classified, must lie within a certain distance from the calibration populations. If so, the pixel is classified either by means of a quadratic type of discriminant procedure or by a box classification. These procedures have also been adapted to utilize a hierarchical structure similar to what is described above. They compare very favorably with other methods.

Mapping and Analysis of Hydrothermal Alteration Zones in Tertiary Alkaline Intrusions

A northeast-trending belt of plutonic centers stretches from the Werner Bjerge in Scoresby Land

to the eastern part of Traill (Fig. 3). Within this belt four major areas of plutonic activity have been distinguished. The Tertiary plutonic rocks intruded Paleozoic and Mesozoic sediments and range in composition from pyroxenites and gabbros to alkali granites with intermediate felsic rocks as the most com- mon. The geology of the area is described in detail by Bearth (1959), Kapp (1960), and Noe-Nygaard (1976).

A stockwork (porphyry) molybdenite deposit, Malmbjerg (Figs. 1 and 3) containing 150 X 106 met- ric tons of ore grading 0.23 percent MoS2 is situated in the westernmost part of the Werner Bjerge alkaline complex. The orebody is associated with a multiple intrusive alkali granite stock. Widespread alteration is connected with the mineralizing events. The following alteration has been recognized: (1) manganese- oxide staining; (2) quartz-sericite-pyrite alteration; (3) epidotization; and (4) greisenization.

The most conspicuous hydrothermal alteration at Malmbjerg is a distinct color anomalous zone associated with the quartz-sericite-pyrite zone which cuts west-northwest across Malmbjerg north of the granite. Nearest to the orebody this zone is intensely ho- mogeneously altered with red and yellow iron oxide staining (predominantly stemming from goethite, limonite, and jarosite), whereas to the north away from the orebody the staining dies out along widely spaced meter-wide colored zones.

Similar color anomalous areas caused by iron oxide staining are widespread in the alkaline intrusions, and a possible stockwork molybdenite occurrence (Mel- lempas) has been recognized during recent exploration (A. Geyti, pers. commun., 1981). The Malm- bjerg mineralization has been described by Bearth (1959), Kirchner (1964), and Nielsen (1976).

On the basis of these conspicuously colored zones of hydrothermal alteration, some of which are ob- viously associated with molybdenum mineralization, analysis of Landsat MSS data from the belt of Tertiary intrusive activity was performed with the purpose of trying to distinguish areas with limonitic altered rocks.

Investigations dealing specifically with hydrothermal alteration of porphyry systems as described by Schmidt et al. (1975), Birnie and Dykstra (1978), Sabins (1978), and Abrams et al. (1983) have already proved to be successful in delineating known as well as unknown areas of hydrothermal limonitic alteration associated with copper mineralization.

In summary the following techniques were useful for distinguishing areas of hydrothermally altered limonitic rocks (Figs. 5-9).

1. Color composites (bands 4, 5, and 7). A few of the major, intense rust zones can be distinguished by a weak yellow-green color when displayed on the color display of the IDIMS system.


2. Ratio plots. This technique separates areas of limonitic and nonlimonitic rocks very well. The in- dividual ratios Q4/5, Q5/7, and Q6/7 distinguish clearly whereas the ratios Q4/6, Q4/7, and Q5/6 are not usable (Fig. 4). The best results are obtained when Q4/5 is displayed as cyan (red), Q5/7 as magenta (green), and Q6/7 as yellow (blue). A characteristic pink to orange color corresponds to areas of limonitic rocks. This is illustrated by Figure 6.

3. Variation images. Preliminary work by Con- radsen and Gunulf (1980) and Conradsen et al. (1982a) has shown that this technique can be used to some extent. Compared with other techniques, it is considered to be of minor importance.

4. Factor plots. Factor analysis is an effective method of detecting areas of limonitic rocks. In particular, factor 3 is a good discriminator, but factor 4 is also usable, especially when a VARIMAX rotated solution is used. Factor color composites representing F2 as magenta (green), F3 as cyan (red), and F4 as yellow (blue) with a visually determined color stretch discriminates very effectively with yellow-orange to red-orange colors representing limonitic rocks. Figure 7 illustrates this.

5. Canonical variables plots. This technique is also an effective method of delineating rusty areas. This is shown in Figure 8.

6. Classification plots. In an attempt to distinguish between the rust zones in the area, different classification routines, as described above, were established. The four rust zones tested, Malmbjerg, Fndal, Vildthorn, and Solsiden, are all shown in Figure 3. Furthermore, a test area at Malmros Klint in Fleming Fjord (Fig. 1), which comprises typical red-bed pixels, as well as a sixth group representing pixels of a rusty affinity were studied. As seen in Figure 9, where classified pixels using the box classification are over- laid on ordinary color composites, the Malmbjerg, Fndal, and Vildthorn rust zones are better defined or classified than the Solsiden rust zone, which probably is because the color anomaly at Solsiden is more inhomogeneous than the other rust zones.

By combining information from ratio, factor, canonical variables, and classification plots, reconnaissance mapping resulted in the identification of more than 50 major rust zones (classified as either of the four types) in the belt of Tertiary plutonic activity (Fig. 3). Most of these are located in the southeastern part of Traill which has only been explored to a minor degree.

An elaboration of the conditions and limitations of color anomaly interpretation from Landsat MSS data must consider the following points. First of all, one must be aware that iron oxide-stained rocks exposed on shadowed slopes will not be detected by any of the methods used. Second, a certain minimum size

of the area of exposed limonitic rocks is required because of the 80-m spatial resolution of Landsat (L1- L3). Furthermore, distinction between rust zones is only valid when the following are considered. First, only rust zones originating in the same scene (identical CCT) are immediately comparable. Furthermore, the location of the exposed rust zone's relation to the sun azimuth and the slope of the hill are conditions to be considered before possible similarities and differences in reflection patterns can be established.

The major rust zones detected in this study also show differences when the above conditions are considered. These differences are mainly caused by the varied size and intensity of limonitic altered areas. The two extremes are the Solsiden with a relatively small area of rather diffuse iron oxide staining and the Fndal representing a very large homogeneous area with intense limonitic staining. Thus a preliminary classification of all rust pixels into the six classes of the box classification routine (Fig. 9) is believed to be valuable in an exploration phase.

Reconnaissance Mapping of Color Anomalous Zones in Precambrian Metamorphic Terrain

As seen in Figure 2, a major part of central East Greenland is comprised of metamorphic complexes intruded by late orogenic granites. A detailed description of their geology is given in Hailer (1971) and Henriksen and Higgins (1976). A few rust zones, some of which are associated with sulfide mineralization, are known in the area. These include rust zones (or color anomalous areas) on Clavering (Fig. 1) which have been subjected to base metal and gold exploration, rust zones in the Flyverfjord region (Fig. 1) which are considered to be of potential for gold and uranium, and rust zones in the central metamorphic complexes of Andree Land, Suess Land, and LyeIls Land (Fig. 1) which are caused by minor sulfide mineralization.

The techniques used to identify color anomalous zones successfully in Tertiary alkaline intrusions were tested on Clavering where a few rust zones associated with base metal mineralization were already mapped.

The application of ratio, factor analysis, and classification techniques enabled the detection of most of the mapped rust zones and delineated several hith- erto unknown anomalies. However, the best known rust zone, Rustplateau, which covers an area more than 3 km long and several hundreds of meters wide could only be detected as a very small and inhomogeneous anomaly. This is probably because of the extensive vegetation cover on Rustplateau. On the basis of the results from this test area, reconnaissance mapping at a scale of 1:250,000 of color anomalous areas in all metamorphic terrains was performed using both ratio techniques and factor analysis. The total

12 3 8 K. CONRADSEN AND O. HARPOTH

FIG. 5. Simplified geologic maps of the study areas shown in Figures 6 through 9.

ice-free area covered with this reconnaisance mapping represents more than 30,000 km 2 in a province 500 km long and 300 km wide.

As seen in Figure 10, 38 major (surface area > 0.5 km 2) rust zones were detected with both ratio and factor analysis techniques. Furthermore, numerous rust zones of minor importance were inter- preted throughout the investigated area. Besides reconnaissance mapping, detailed mapping of selected rust zones was carried out using classification techniques.

Although a major part of the detected color anomalous zones is probably due to iron oxide staining originating in biotite weathering, some of the rust zones are known to be associated with sulfide mineralization and some are for geologic reasons believed to stem from weathering of iron sulfides. Thus, it must be concluded that reconnaissance mapping using

ratio techniques and factor analysis is an effective low-cost tool for delineating areas of high mineral potential in future exploration in East Greenland.

Other Color Anomaly Investigations

Other investigations comprise among other things reconnaissance mapping of color anomalous areas in Devonian rhyolitic rocks, analysis of strata-bound base metal mineralization associated with Upper Permian carbonates, and mapping of red-bed outcrops.

Although detected color anomalies in Devonian rhyolites do not represent clear exploration targets, as no connection between rust and mineralization has yet been established, it is believed that the information obtained could be of significance and use for future exploration of the acid volcanics in central east Greenland.


0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

I ! ! ! ! I' !. 11-1 17'i'i.'E-[ I--I

o 5

FIG. 6. Color ratio composite of the Traill O area (upper part), Malmbjerg area (lower left), and Vildthorn area (lower right). All areas are indicated in Figures 3 and 5. The color stretch is given in Table 9.. Mask images: 1 = water, 9. = snow, 3 = ice, and 5 = shadow on ice-snow Bright blue and bluish green colors = rock and vegetation. Pink to orange colors = rust zones.

1240 K. COHRADSEH AHD O. HARPOTH

Ln 0 Ln 0 fj-1 0 fj-1 0 Ln o

11.]i " ILllibel _1"'i I ..... i, I I L,'I 0 0 Ln 0 03 Z) .1

Ln 0 Ln0 fj'l 0 Ln 0 fj'l 0

L,-1 0 01 0 L,-1 0 0 m3a L. 03 ,) Ln 0 Ln 0 L,q 0 Ln 0 Ln 0

i !1 I I ,,I,11 Ii li1.1,11 i,,! I i I km 5

FIG. 7. Factor-color composite of the Traill O area (upper part), Malmbjerg area (lower left), and Vildthorn area (lower right). All areas are indicated in Figures 3 and 5. The color stretch is given in Table 1. Mask images: 1 = water, 2 = snow, 3 = ice, and 5 = shadow on ice-snow. Bright green-dark green colors = rock and vegetation. Orange to orange red colors = rust zones.


,.MALM.BJE; G j -

j' - \

VILDHeRN

km

5

FIG. 8. Canonical-variable color composite of the Traill D area (upper part), Malmbjerg area (lower left), and Vildthorn area (lower right). All areas are indicated in Figures 3 and 5. The color stretch is given in Table 1. Mask images: 1 = water, 2 = snow, 3 = ice, and 5 = shadow on ice-snow. Yellowish, greenish, and dark colors = rock and vegetation. Orange to bright red colors = rust zones.

1242 K. CONBADSEN AND O. HAP, POTH

-,,- FONDA

o $

FIG. 9. Box classification color composite of the Traill O area (upper part), Malmbjerg area (lower left), and Vildthorn area (lower right). All areas are indicated in Figures 3 and 5. Classified pixels are given a color as indicated in the mask image. 1 = Malmbjerg type, = Traill O type, 3 = Solsiden type, 4 = Vildthorn type, 5 = pixels with rusty affinity, and 9 = red-bed type.


FIG. 10. Distribution of detected color anomalous zones in the metamorphic terrain of central East Greenland.

Concerning the analysis of strata-bound mineralization, it can be concluded that, although the ex- tension of the mineralized areas is measured in ki- lometers and although the alteration--developed mainly as silicification and dolomitization with abun- dant rust--is pronounced, none of the computed en- hancements used is capable of distinguishing these areas from unmineralized ones. The main reason for this is probably the fact that, although the alteration is extensive and pronounced, it is also very inho- mogenous and thus difficult to distinguish with the resolution of the Landsat data.

Summary and Conclusions The use of special ratio and factor analysis tech-

niques combined with different classification routines on Landsat MSS data for detection and reconnaissance mapping of iron oxide-stained zones in remote areas with low vegetation cover seems to be an excellent low-cost method to delineate areas with a high potential for mineral deposits.

Unfortunately, Landsat satellites 1, 2, and 3 do not provide data which enable detection of zones of hydrothermal alteration which are associated with limonite. However, future Landsat systems with ad-

ditional spectral bands in the 1.6- and 2.2-ttm regions will enable detection of such alteration, as well as distinction between areas of hydrothermal alteration associated with limonite and limonitic rocks not associated with hydrothermal alteration.

Acknowledgments Part of this research was carried out while the first

author was visiting the Department of Statistics at Stanford University. He is grateful to Paul Switzer for making that visit possible. The visit was partly supported by the Danish Science Foundation and by the Otto Mnsteds Foundation, and the research was supported by the European Economic Communities, contract 112-79-MPPDK. Jan Gunulf and Gert Nils- son worked as computer scientists on the project, and their contributions were of great importance. Thanks are also due to John L. Pedersen for a critical reading of the manuscript. September 3, 1983; April 16, 1984

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