integrating landsat, geologic, and airborne gamma ray data

Integrating Landsat, Geologic, and Airborne Gamma Ray Data as an Aid to Surficial

Geology Mapping and Mineral Exploration in the Manitouwadge Area, Ontario*

I.M. Kettles, A.N. Rencz, and S.D. Bauke

Abstract For suq5cial mapping and mineral prospecting purposes, Landsat Thematic Mapper, airborne gamma ray spectrometry, and geological data were integrated for a 5000-km2 area near Manitouwadge, Ontario. Using characteristic Landsat data signatures for the main surficial units, the signatures for 6,250,000 30-by 30-m-pixel areas were evaluated, and each pixel area was assigned to a surficial unit category. When the predictive surficial geology map thus generated was compared to eight surficial geology units on a published suq5cial geology map, there were obvious visual similarities, with an overall pixel-by-pixel accuracy of 46 percent. Pixel areas with Landsat and gamma ray signatures comparable to those of training sites in base metal-enriched tills near the Manitouwadge area mines, commonly formed clusters or near-linear bands overlying Archean greenstone belts or in close contact with a carbonatite complex where Fe and Zn mineralization is known. Tests on these derivative pixel areas indicated that their distribution was controlled almost entriely by the gamma ray data signatures.

Introduction Over the last two decades, remoteIy sensed spectral reflectance (Landsat Thematic Mapper (TM)) and airborne gamma ray spectrometry data have been used as aids for mapping and mineral prospecting (Rencz et al., 1990; Rencz and Sangster, 1989; Rencz and Shilts, 1981; Harris et al., 1998; Reeves et al., 1997; Lipton, 1997; Shives et al., 1995). In Canada, 95 percent of which has been glaciated, successful surficial geology mapping using Landsat TM images is dependent on there being a consis- tent relationship between surface patterns or colors on the images and the different classes of glacial and related sediments. This implies generally a relationship between vegetation and sediment, although other factors such as drainage and topography affect their association (Ustin et al., 1999). Also, using the same rationale, Landsat TM data have been used to detect vegetation which characterizes areas with anomalously high or low concentrations of metallic elements (Horler et al., 1981). In this case, spectral geobotany is dependent upon the detection of differences in vegetation physiology caused by the

stress on vegetation related to anomalous element levels in the underlying surficial sediments. Vegetation growing in very metal-rich soils typically displays an altered spectral response within the wavelengths 500 and 800 nm (Horler et al., 1980). While the Landsat TM scanner acquires data from these wavelengths, the successful detection of any such vegetation is dependent on the size of the affected areas relative to the size of the pixel areas and band widths associated with the scanner.

Airborne gamma ray spectrometry data provide information about the distribution of potassium (K), equivalent ura- nium (eU), and equivalent thorium (eTh) in the upper 1 m of surficial materials (Shives et al., 1995). Knowledge of the distribution of surface materials is useful for interpreting gamma ray data because gamma radiation is attenuated by water and unconsolidated sediments (Schetselaar and Rencz, 1997). Ratio patterns of K, eU, and eTh in surficial materials can enhance subtle variations in the elemental concentrations caused by lithological variation or the alteration processes associated with mineralization.

In the present study, relationships between Landsat TM imagery, airborne gamma ray spectrometry, and geological data are investigated for surficial mapping and mineral prospecting purposes. The study area encompasses over 5000 kmz in the vicinity of Manitouwadge, in northwestern Ontario. Four copper-zinc-silver deposits have been mined in the Manitou- wadge greenstone belt, and the mineral potential is known or suspected to be high elsewhere within this greenstone belt, as well as in surrounding areas (Figures 1 and 2). This research has two main objectives: (1) to characterize the spectral and other signatures of different types of surficial materials in order to develop predictive surficial materials maps for poorly known areas in the study area; and (2) to characterize, similarly, metal- rich glacial sediments in areas of known base metal mineralization in bedrock and use these data to search for comparable areas.

Development of a methodology for mapping and mineral prospecting based on the use ofremotely sensed data is important for the Manitouwadge region. Similar to many other Pre- cambrian shield terrain areas in Canada, the area surrounding Manitouwadge is heavily forested, uninhabited, and poorly accessible. Except for a network of logging roads north and

*Geological Survey of Canada Contribution #1999068.

I. Kettles and A. Rencz are with the Geological Survey of Can- ada, 601 Booth Street, Ottawa, Ontario KIA OE8, Canada ([email protected]).

S. Bauke is with Northwood Geoscience, Ltd., 43 Auriga Dr., Nepean, Ontario K2E 7Y8, Canada.

PHOTOGRAMMETRIC ENGINEERING 81 REMOTE SENSING

Photogrammetric Engineering & Remote Sensing Vol. 66, No. 4, April 2000, pp. 437-445.

0099-1112/00/6504437$3.00/0 8 2000 American Society for Photogrammetry

and Remote Sensing

April 2000 437

Legend

Proterozoic InERleive Rocks

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Archean lnbwlve Rocks pg Qranodlorlte to gmlte 1 Dlorlte-rnonzonlte-granodlorlte suite 171 ~ t s b e e r i n g g r a n i t l c r o c k a

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lrontormation

-- -dVke ....-- Q6dOgM Contact

Faun c h a d - Railway

0 4 0 lrilonmtru

Figure 1. Bedrock map of Manitouwadge area, northwestern Ontario (after Ontario Geological Survey, 1991). Also marked are the outlines of the traditional surficial geology map area (heavy dashed line), Manitouwadge greenstone belt (A), Screiber-Hemlo greenstone belt (B), Killala Lake complex (C), and Port Cold- well complex (D). Inset Canada map shows location of the study area.

south of the town of Manitouwadge, the study area is accessible Nee, 1998). The most widespread surficial deposit is till. It only by aircraft on floats, a few naviagable waterways, and foot commonly forms a thin discontinuous veneer (0 to 1.5 m traverses. As a result, it is very difficult to carry out mapping thick), which fails to mask the topography of the underlying and mineral prospecting in the field. bedrock. In places, the till thickness is greater than 1.5 m and

may exceed 10 m. The till is partially derived from local Pre- Setting cambrian lithologies, but also contains variable concentrations The Manitouwadge study area has rugged topography and lies of Paleozoic limestones and dolostones, as well as Proterozoic in the southern boreal forest. The forest, parts of which have metasediments which were transported by glaciers from the been clear-cut for timber, is characterized by a mixture of white Hudson Bay Lowlands. (Kettles, 1993; Kettles, 1994). Where spruce, balsam fir, aspen, and jack pine, and in poorly drained thick, the till commonly contains high concentrations of fine- areas by black spruce. The area is underlain by Archean green- grained detritus derived from the Paleozoic carbonate lithol- stone belts and granitoid plutons of the Wawa subprovince of ogies. the Canadian Shield (Williams and Breaks, 1990; Zaleski and Glaciolacustrine deposits consisting of silt, fine sand, and Peterson, 1993; Ontario Geological Survey, 1991, Figure 1). clay are widespread and occur in low-lying areas. In some The Manitouwadge greenstone belt, comprising highly de- areas east of Manitouwadge there are outwash sands and gravel formed metavolcanic and metasedimentary rocks, hosts four deposits above the maximum levels of the glacial lake incur- known volcanogenic massive sulphide deposits, the largest of sion from the Lake Superior basin. Also present are glaciofluv- which is the Geco Cu-Zn-Ag deposit (Friesen et a]., 1982, Fig- ial sands and gravels which take the form of eskers, subaqueous ure 2). Part of the Schreiber-Hernlo greenstone belt underlies fans, kames, and kame terraces. There are minor alluvial sands, the southern part of the study region. South of the study area, gravels, and silts along major rivers and streams, and aeolian this belt is host to the Hemlo gold deposits. Also present are two dunes where outwash or glaciolacustrine sediments predomi- carbonatite-alkalic complexes, Killala Lake, west of Vein Lake, nate. Accumulations of organic materials in the form of bogs and Coldwell Complex in the southwestern part of the study and fens are widespread, particularly in areas underlain by area (Sage, 1991). The northern part of the study region is glaciolacustrine sediments. underlain by granites and gneisses of the Quetico subprovince (Williams, 1991). Dlgital Data Sets

All glacial sediments in the Manitouwadge area are Data for this study included 1:250,000-scale topographic and thought to have been deposited during the Late Wisconsinan, hydrographic coverage, and Landsat TM and airborne gamma predominantly from south-southwestward flowing ice (Bar- ray spectroscopy data (Table 1). Landsat la data for bands 1 to 7 nett, 1991; Kristjansson and Geddes, 1986; Kettles and Way were acquired for an image area of 2500 by 2500 pixels (pixel

438 Apri l .?OW PHOTOORAMM€rRIC ENGINEERING & REMOlE SENSING

Copper @pm) in Till Legend MIN MAX %TILE

2 6 18.5 A Mine Site r 7 10 49.7

11 17 72.6 B Big Narna

18 36 89.8 Training Sites 37 49 96.3 G Geco Training Sites 60 94 98.1 96 1596 100

n 4 5 7 0 2 0 0 0

metre8

Figure 2. Distribution of copper in <0.063mm fraction of till in the vicinity of the Manitouwadge greenstone belt. Also depicted are the mine sites, the locations of the Big Nama and Geco training sites, and selected units of underlying bedrock. Bedrock units are numbered from oldest to youngest as follows: 1-mafic to intermediate metavolcanic rock; 2-felsic to intermediate metavolcanic rock; and 3-metasedimentaly rocks. Iron formation is shown in black and faults as a long-dashed black line.

TABLE 1. CHARACTERISTICS OF LANDSAT (TM BANDS 1-7) AND AIRBORNE GAMMA RAY DATA SETS

Landsat Thematic Mapper Bands Thematic Mapper Electromagnetic Wavelength Pixel Size

Data Number Spectrum (@I (m)

1 blue (visible) 0.45-0.52 30 2 green (visible) 0.52-0.60 30 3 red (visible) 0.63-0.69 30 4 near-infrared 0.76-0.90 30 5 mid-infrared 1.55-1.75 30 6 thermal-infrared 10.40-12.50 120 7 mid-infrared 2.08-2.35 30

Gamma Ray Channels Airborne

Gamma Ray Energy Spectromety Major Window Distinctive

Data Emitters Measurement (MeV) Characteristic

K40 direct 1.36-1.56 0.012% of K indirect, via

daughter BiZ14 1.66-1.86 99.3% of U indirect, via

ThZ32 daughter TiZo8 2.41-2.81 100% of Th

size = 30 m by 30 m) and downloaded into a microcomputer- based image analysis system (PCI, 1997, Plate 1). The reconnaissance scale gamma ray data, collected with a flight-line spacing of 5 kilometers, were the only data of this type available for the entire study region. These data, based on 200- by 200-m grid cells, were acquired with 16-bit resolution but were scaled to 8-bit resolution. Data representing K, eU, and eTh were geographically registered with the Landsat data.

The 1:1,000,000-scale digital map of bedrock geology was used (Ontario Geological Survey, 1991), except near the Mani- touwadge greenstone belt area where it was modified after Zaleski and Peterson (1993) (Figure 1). Published surficial geology maps (1:50,000 scale) were digitized for the Manitou- wad e and Vein Lake areas (modified from Kristjansson and Ged dB es (1986) and Kettles and Way Nee (1998)) (Plate 2b). The digital bedrock and surficial geology maps were geographically registered to the Landsat image and resampled to the same 30- by 30-m-pixel areas which compose the Landsat image. Geo- chemical and lithologic information for till was obtained from Geological Survey of Canada Open Files 2616,2933, and 3562 (Kettles, 1993; Kettles, 1994; Kettles et al., 1998)

Methodology For the surficial mapping phase of the study, training sites were chosen in representative areas of the main surficial units-thin till, thick till, glaciofluvial and outwash sands and gravels; glaciolacustrine silts; and clays; bogs; fens; and rock outcrop. The small number of pixels (less than 85) in the training sites for three surficial units-fen, bog, outwash sands, and gravels- reflects the limited distrubution of these units in the study area. Training sites were chosen in areas where there were no obvious signs of forest clearing, As a result, many of the training sites are located in the pristine forest surrounding Vein Lake where logging is prohibited. For each training site, data for TM bands 1 to 7 were extracted and, near Vein Lake and the town of Manitouwadge, the surficial unit from the published surficial geology map was also captured.

For investigations of metal-rich glacial sediments, training sites were selected over the Manitouwadge greenstone belt near the mines where the tills are known to be enriched in metals, including Cu and Zn (Kettles et al., 1998, Figure 2). In addi- tion to the requirement of having high metal levels in till, the training sites were again selected in forested terrane. As a result, the choice of potential training sites near the mines was limited because the Manitouwadge area has been extensively logged. For purposes of this analysis, the training sites located southwest of the Big Nama Mine formed one group and those located south of the Geco Mine formed a second group (Figure 2). Landsat TM band data, 5-km gamma ray data, and the bedrock lithology data were extracted for each pixel area within the two groups of training sites.

The extracted data were analyzed statistically, using Stat- view software, to determine a characteristic range of data values (Figure 3; Table 2). Data values for each of the seven TM bands were captured for the surficial unit training sites, and both the TM bands and three gamma ray channels were extracted for each group of training sites for the metal-rich sediments near the mine sites (Figure 2; Table 2). Using these data signatures and a maximum-likelihood classifier procedure (PCI, 1997), the entire data set was searched, pixel-by-pixel, to identify pixels with comparable spectral signatures. After pixel areas were so classifed, the anomaly map (Plate I), based on the data signatures obtained for the Big Nama and Geco training sites, was produced in one step and the Landsat- derived surfical geology map (Plate 2a) in three steps, as described below.

The first step in producing the predictive surficial map involved selectively color-coding pixel areas with data signatures comparable to the main surficial units. Results of this first

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING 4pr i l2000 439

I I

440 April 2000 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

0- a;; 1 # = a t E

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80.1 1 a;:; # ; ; j t C

Figure 3. Box and whisker diagrams showing characteristic data signatures for main surficial geology units, based on the training site data. Surficial units from the traditional surficial geology map are shown on the x-axis. The notch in the middle of the box represents the median value, the box top and bottom, the 75th and 25th percentile values, and the limits of the solid lines extending from the box top and bottom, the 12.5th and 87.5th percentile values.

run showed that there were many areas of 50 kmz or more which remained mostly unclassified after treatment with the maximum-likelihood classifier. When the field mapping data and aerial photographs were examined, the unclassified areas were identified as dominantly the cutover areas surrounding Manit- ouwadge, some thin till areas in the northern part of the study region, and, at scattered locations, fens. New training sites were established and characteristic data signatures were obtained for these three additional terrain types and a second version of the surficial map was generated, using both the new and original data signatures. In the third step, the distribution of classified surficial units on the second version map were examined and evaluated, based on knowledge of the glacial history for the region (Barnett, 1991; Barnett et al., 1991). At this point, the deci- sion was made to combine, as one unit on the predictive map, pixel areas classified as sand and gravel from glaciofluvial and outwash sources because they are generally texturally and com- positionally similar. Also combined were the main organic units-bog and fen. The final surficial geology map was then produced, based on this simplified unit legend.

Predictive Suflcial Geology Map -- . The predictive surficial geology map is shown in Plate 2a. Cal- culations based on the map indicate that the most extensive units are thin till, thick till, and glaciolacustrine silt and clay, which make up 30 percent, 16 percent, and 15 percent of the image area (Table 3). Cutover areas are also a significant unit in this region, making up another 11 percent.

To test the reliability of the map, units on the published 1:50,000-scale surficial geology map for the Vein Lake-Mani- touwadge area (modified from Kettles and Way Nee (1998) and Kristjansson and Geddes (1986, Plate 2b)) were compared to the classified units on the Landsat-derived map (Plate 2a) for the same 1500-km2 area (Plate 2a). The former (Plate 2b) is referred to as the traditional map because it was generated using air photo interpretation and field mapping and the latter (Plate 2a) is referred to as the Landsat comparison map. When the overall distribution of surficial units on the traditional and Landsat comparison maps were inspected visually, general correspondance was noted between the major units, including rock outcrop, glaciolacustrine silt and clay, and thin till, in many areas. For example, on both maps rock outcrop is preva- lent to the east surrounding Manitouwadge and glaciolacustrine silts and clays are widespread southwest of Vein Lake.

For a more systematic comparison, the traditional and Lan- dsat comparison maps were subjected to an accuracy assessment. This was accomplished by constructing a contingency table or confusion matrix, based on a pixel-by-pixel comparison (Schowengerdt, 1983; Campbell, 1987) (Table 4). Although data for the entire Landsat image area were classified, an accuracy assessment was possible only where there was digital cov-

TABLE 2. STATISTICAL SUMMARY OF LANDSAT AND AIRBORNE GAMMA RAY DATA VALUES FOR TRAINING SITES IN METAL-ENRICHED DRIFT. MANITOUWADGE AREA - -

Landsat Thematic Mapper Bands Training Statistical

Gamma Ray Channels

Site Measures 1 2 3 4 5 6 7 K U T h

Big No. of Pixels Nama Mean

Median Lower L imi t Upper Limit Range

Geco No. of Pixels Mean Median Upper L imi t Lower Limit Range

PHOTOGRAMMRRIC ENGINEERING & REMOTE SENSING April 2000 441

TABLE 3. AREA COMPARISON OF TRADITIONAL AND LANOSAT-DERIVED SURFICIAL GEOLOGY MAPS, MANITOUWAOGE AREA

Landsat Map Traditional Map

Total Area Comparison Area Total Area Surficial

Unit No. of Pixels % Image No. of Pixels % Image No. of Pixels % Image

Rock 290173 4.7 150340 8.7 192233 11.1 Thin Till 1843527 29.5 493213 28.4 722915 41.7 Thick Till 1001300 16.0 239598 13.8 79583 4.6 SandIGravel 252765 4.0 64007 3.7 50461 2.9 Silt/Clay 915044 14.7 391555 22.6 551334 31.8 Organics 114767 1.8 31841 1.8 60936 3.5 Water 440115 7.0 76585 4.4 76585 4.4 Cutover 700104 11.2 238505 13.7 0 0.0 Unclassified 411729 6.6 49679 2.9 0 0.0 No Data 280476 4.5 0 0.0 0 0.0 Total 6250000 100.0 1735323 100.0 1735323 100.0

TABLE 4. CONFUSION MATRIX OF TRADITIONAL SURFICIAL GEOLOGY MAP AND LANOSAT COMPARISON MAP, MANITOUWADGE AREA

Landsaf Comparison Map

Traditional Map % Pixels Classified by Surficial Unit

Number Analysis Surficial of Thin Thick Sand/ Silt/

TVDR Unit Pixels Unclas. Rock Till Till Gravel Clay Organic Water Cutover -,r-

(il Cutover Areas Included

(ii) Cutover Areas Not Included

Rock Thin Till Thick Till SandIGravel Silt1 Clay Organic Water

Rock Thin Till Thick Till SandIGravel Silt/ Clay Organic Water

Accuracy Assessment

[i) Cutover Areas Included (ii) Cutover Areas Not Included --

Overall Accuracy (O/O) Kappa Coefficient Standard Deviation Cofidence Level 99% 95% 90%

erage for the published surficial geology maps (Plate 2). The comparison was undertaken in two ways. In the first analysis, all surficial units for the Landsat comparison map were compared with units on the traditional map, while, for the second analysis, areas classified as cutovers on the Landsat comparison map were masked out on both maps prior to analysis. On the traditional map, interpretations of surficial units were made in clear-cut areas, based on their field characteristics and their position in the landscape, despite the scanty vegetation cover.

The overall accuracy determined for the first analysis, which included cutovers, was 40 percent and the second, without cutovers, was 46 percent. The discussions that follow will focus on the latter analysis. The greatest accuracy was obtained for the three major units-thin till (48 percent), silt and clay (43 percent), and rock (46 percent)-and the least for organic deposits (8 percent). Kappa coefficients were also calculated for the two analyses-0.32 with cutovers and 0.29

without cutovers. The kappa is a measure of the difference between the observed agreement between the two maps and the agreement that might be contributed solely by chance matching of the two maps (Campbell, 1987). The kappa of 0.29 for the cutover-free analysis can be interpreted as meaning the classification has achieved an accuracy that is 29 percent better than would be expected from chance assignment of pixels to the different unit categories.

The discrepancies between the traditional and Landsat- derived maps may not be as pronounced as they initially seem. When the confusion matrix was examined, some trends in error patterns were noted. In many cases, pixel areas mapped as one surficial unit on the traditional map were classified as a closely related unit on the Landsat comparison map. For example, besides the 48 percent of pixel areas similarly classified as thin till on the Landsat map, another 2 1 percent of the thin till pixel areas were classified as thick till and 26 percent as rock outcrop.

442 April 2000 PHOTOGRAMMETRIC ENGINEERING 81 REMOTE SENSING

There are at least two explanations for the discrepancies between closely related units on the two types of maps. First, some surficial units, including rock, thin till, and thick till, form a continuum in nature, the boundaries between which are commonly indistinct, must be interpreted, and may be some- what arbitrarily drawn. Second, the map unit polygons on the traditional map, generated at the 1:50,000 scale, are relatively large and generalized. For example, within the area covered by a thin till polygon on the traditional 1:50,000-scale map, there are generally numerous small areas (less than 50 m by 50 m) of thick till and rock outcrop and even a small number of areas with less closely related deposits such as peat. In contrast, on the Landsat comparison map, the surficial cover of each 30- by 30-m area is evaluated. Consequently, the Landsat-derived map provides more information on small-scale changes in the distribution of surfical materials than does the 1:50,000-scale published map.

In other cases, the errors probably reflect similarities between the end members of different surficial units. Some types of surficial materials formed in different depositional environments may have similar textures, lithological composi- tions, and positions in the landscape, hence ensuring similar vegetation and reflectances on Landsat images. For example, 16 percent of the areas mapped as silt and clay on the traditional map are classified as thick till on the Landsat comparison map and 34 percent of the sand and gravel areas as thin till. On the basis of field mapping, it is known that where tills are thick, they are enriched generally in fine-grained Paleozoic carbonate detritus. Hence, in places, they may be similar in texture to glaciolacustrine silts and clays. In contrast, in many areas where the till cover is thin and discontinuous, it is composed mainly of large-sized clasts derived from the underlying or nearby Archean bedrock and, hence, has a more gravelly texture.

There is limited correspondance between the traditional and Landsat comparison maps for some of the minor surficial units. Only 16 percent of the sand and gravel pixel areas and 8 percent of the organic deposits are similarly identified. As described above, a high percentage of the sand and gravel pixel areas (34 percent) on the traditional map are classified as thin till on the Landsat map while 42 percent of the organic areas are classed as silt and clay. The latter is not surprising as, in parts of the study region, glaciolacustrine silts and clays which generally occupy low-lying poorly drained areas are overlain by a thin veneer of organic materials.

Also of note is the larger percentage of unclassified areas on the Landsat-derived map for the whole study area (7 percent) as compared to the Landsat comparison area (3 percent) (Table 3). The largest number of training sites for the study was established within the Landsat comparison area near Vein Lake because the region had not been logged. Away from the training sites, more pixel areas remain unclassified after treatment with the maximum-likelihood classifier. This phenomenon may reflect regional changes in sediment facies and composition caused by changes in the underlying bedrock lithologies or the transport and deposition patterns of glacial debris. The underlying bedrock forms part of two Canadian Shield subprovinces (Ontario Geological Survey, 1991) and, in parts of the study area, there are plumes of thick fine-grained till, thought to have formed by late glacial ice streams in the last continental ice sheet (Hicock et al., 1989). It is possible also that some differences may reflect changes in vegetation patterns caused by small climatic differences between the northern part and the southern part of the study area which lies in close proximity to Lake Superior.

those for the training sites in areas of metal-rich drift near Big Nama and Geco mines (Figure 2 and Plate 1). The Big Nama- style anomalies are composed of around 7200 of the 6,250,000 pixels in the total image (0.12 percent of the image) (Table 5) and some take the form of clusters in several parts of the study area. Near the Manitouwadge mines, there is a large cluster overlying granitoid, metasedimentary, and iron formation bedrock (A on Plate 1). In the southwestern part of the study area, two large clusters overlie gneisses adjacent to and west of the Schreiber-Hemlo greenstone belt (B and C). A group of pixel areas north of Lake Superior take the form of a linear band which lies along the northern edge of the Port Coldwell Com- plex (D). Elsewhere there is another cluster associated with car- bonitites, this one located on the western edge of the Port Cold- well complex (E). Carbonatites in some areas are known to be enriched in Fe and Zn (Ford et al., 1988), and there are numerous Cu showings along the northern edge of the Port Coldwell complex (Ontario Division of Mines, 1971).

The anomalies based on the Geco data signatures are composed of more than 11,000 pixels of the 6,250,000 pixels in the total image (0.18 percent of the image) (Table 5). The Geco-style anomalies generally take the form of linear bands and occur in several different parts of the study area. In some areas these bands overlie greenstone belts-one in the northwestern part of the Manitouwadge belt (F on Plate 1) and two over the Schrieber-Hemlo belt in the southwestern part of the study area (G and H). In the northern part of the study area, more than four of these linear clusters occur close together overlying Que- tico gneisses and granites (J).

To test and compare the significance of the Landsat and airborne gamma ray data signatures for the detection of the above anomalies, the integrated dataset for the entire study area was further analyzed by dividing it into its component parts (Table 5). Using the characteristic signatures of only one type of data (i.e., Landsat or gamma ray) at a time, the dataset was searched, pixel by pixel, to extract pixel areas with comparable signatures. Using the statistics for the Big Nama training sites, 20 percent of the image was extracted using only the Landsat data, 0.4 percent using only the gamma ray data, and only 0.1 percent using the two types. A similar pattern emerged for the Geco training sites. More than 20 percent of the image is extracted using only the Landsat data, 0.4 percent using only the gamma ray data, and 0.2 percent using the two data sets. These test results show that in both cases the gamma ray data signatures are almost the only control on limiting the classified data to anomalies.

There are a large number of pixel areas in the study area with Landsat data signatures similar to those for the two groups of training sites near the Manitouwadge mines. This indicates that, near mineralized outcrops in the Manitou- wadge greenstone belts, the changes in vegetation caused by the high levels of copper, zinc, and other elements in the soils are insufficient, in terms of their degree of influence and/or areal

TABLE 5. CLASSIFICATION OF TRAINING SITE PIXELS IN METAL-ENRICHED DRIFT NEAR MANITOUWADGE, BASED ON SUBSETS OF LANDSAT AND GAMMA RAY DATA

Image Pixels

Training Data Sets Number % Site Processed Total Classified Classsified

Big Nama All Data 6,250,000 7,311 0.1

Gamma Ray Data Only 6,250,000 24,121 0.4 Landsat Data Only 6,250,000 1,276,762 20.4

Geco Anomalies Based on Data Signatures for Metal-Rich Drlft All Data 6,250,000 11,182 0.2 Gamma Ray Data Only 6,250,000 17,403 0.3

The anomalies indicated on Plate 1 are pixel areas with both Landsat Data Only 6,250,000 3,853,866 61.7 Landsat and airborne gamma ray data signatures comparable to

PHOTOGRAMMETRlC ENGINEERING & REMOTE SENSING April 2000 443

extent, to make these areas spectrally distinct. Hence, in this region Landsat data were of little value as an aid for detecting areas with anomalous element levels in the underlying surficial sediments.

To further investigate the relationship between the gamma ray data and the derivative pixel areas, airborne gamma ray maps (not shown) were generated for the Port Coldwell complex area, using data collected at 5- and 1-kilometer flight line spacings (data at 1-kilometer spacings are available only for the southwestern part of the study area). When the 5-kilometer and 1-kilometer maps were compared, there were significant differences in the distribution patterns of K, eU, and eTh, which upon closer inspection seemed to reflect differences primarily in the spacing of the flight lines and number of gamma ray data measurements on the two maps. Some areas with relatively high or low concentrations of K, eU, and eTh, portrayed on the 1-kilometer map, were missed completely on the 5-kilometer map due to their irregular shape or small size. When the derivative pixel areabased on the Big Nama training site data (D from Plate 1) was plotted on the two maps and compared, it was possible to better isolate target anomalous areas with respect to the bedrock geology. In this case, the Big-Nama style anomaly takes the form of a northwest-southeast trending band, characterized by moderate levels of K, eU, and eTh, which lies along the edge but just north of a major high-level anomaly associated with the Port Coldwell complex on the two gamma ray maps.

Discussion and Conclusions It is possible to generate a predictive surficial geology map, based on Landsat data, which broadly defines the main surficial geology units in the study area. When the predictive map was compared visually to published surficial geology maps (1:50,000 scale), there were obvious visual similarities in the distribution of the seven map units. However, when the overall accuracy was calculated, pixel by pixel (1 pixel = 30 m by 30m), it seemed low-40 to 46 percent.

While the Landsat-derived map is no substitute for good field mapping, it has the most value in areas where the surficial geology is either very poorly or very well known. Where poorly known, it provides a preliminary reconnaissance tool. For example, in many parts of the study region, the main surficial unit is a cover of glaciolacustrine silt and clay. Because of the homogenizing processes associated with the deposition of these sediments, they are known to impede mineral exploration using surficial sampling methods (Kaszycki, 1989; McClenaghan, 1994). In this case, the southwestern part of the study area is poorly known but considered to have high mineral potential. The Landsat-derived map shows that in this region thin till and sand and gravel are dominant rather than silt and clay. This estimate of the surficial cover provides useful information for explorationists setting up sampling surveys or interpreting geochemical data for the southwestern region. Where the surficial geology is well known, the predictive map may prompt a reexamination of some unit interpretations on the published maps. Changes in the distribution patterns of some Landsat-derived map units may indicate regional-scale changes in sediment facies or composition, and, hence, provide further insights into glacial dispersal patterns, also important for mineral exploration and for determining glacial history.

Discrepancies in the classifications of units between the predictive and the published surficial geology maps were attributed to three main factors. First, many surficial units form a continuum in nature and, hence, class boundaries between units differ between the two maps. Second, the predictive and published maps were generated at different scales and thus carry different levels of detail. Third, there is some overlap in textural composition between the end-member categories of some surficial units deposited in different depositional environments which occupy similar positions in the landscape.

Hence, in some places different surficial units (e.g., very gravelly till and glaciofluvial gravels] may support similar types of vegetation and be characterized by similar TM data signatures.

In the present study, the maximum-likelihood classifier was used to evaluate each pixel in the image area and to assign it into one of the designated surficial unit categories. Using this method, the analyst should take care to select training sites which represent the range of variations for each unit. Alter- nately, and as our goal for future work, ideal pixel areas could be selected as training sites for each surficial unit, and methods such as mixing models could be used to determine the proba- bilities of classifying a pixel into each of the different desig- - - nated units.

Areas were delineated which have similar Landsat and airborne gamma ray data signatures to the training sites in metal- rich drift near the Big Nama and Geco mine sites at Manitou- wadge. When these Landsat and gamma ray data subsets were processed separately, results show that these derivative anomalies reflect almost exclusively the gamma ray data signatures. Many of these anomalies corresponded to geologic features and to the location of known mineral occurrences within the region. As such, they provide possible targets for further exploration work, although their significance is not well understood.

Also the "gamma ray" anomalies should be used only as broad exploration targets for the following reasons. First, the gamma ray data used for the study were collected along flight lines spaced 5 kilometers apart, and, hence, any derivative maps based on these data are generalized. When 5-kilometer gamma ray maps were compared with the 1-kilometer maps available only for the southwestern part of the study area, significant differences in the distribution patterns of K, eU, and eTh anomalies were noted. Second, the gamma ray data have undergone two stages of interpolation: (1) the filtering inherent in gridding the gamma ray data to produce the 200- by 200-m cells between the 5-km flight lines and (2) the filtering within the PC1 gridding process. Prior to initiating follow-up field work based on any anomalies generated using this methodology, careful consideration needs to be given to the locations of the flight lines used for gamma ray data collection relative to the locations of the training sites and the spectral anomalies

Acknowledgments The authors wish to thank Robert Shives for helpful discussions pertaining to the airborne gamma ray spectrometry data; Karen Turnbull for assisting with the Landsat-derived surficial geology map; Janice Aylsworth and three unknown reviewers for their comments on the manuscript; and T. Barry for carto- graphical assistance.

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444 April 2000 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

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(Received 04 January 1999; accepted 03 April 1999; revised 29 June 1999)

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