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Detection of naturally heavy-metal-poisoned areas by LANDSAT-1 digital data
Journal o f Geochemical Exploration, 8 ( 1 9 7 7 ) 4 5 7 - - 4 7 1
457
© Elsevier Scientific P u b l i s h i n g C o m p a n y , A m s t e r d a m - - P r i n t e d in T h e N e t h e r l a n d s
DETECTION LANDSAT-1
OF NATURALLY DIGITAL DATA
HEAVY-METAL-POISONED
AREAS
BY
B. B @ L V I K E N ' , F. H O N E Y 2 , S.R. L E V I N E 3 , R.J.P. L Y O N 3 a n d A. P R E L A T '
' Geological Survey o f Norway, N-7001 Trondheim (Norway) 2 Commonwealth Scientific and Industrial Research Organization, Wembley, W.A. (Australia) 3Stanford R e m o t e Sensing Laboratory, Department o f Applied Earth Sciences, Stanford University, Stanford, Calif. 94305 (U.S.A.) (Revised version a c c e p t e d M a r c h 1, 1 9 7 7 )
ABSTRACT B~lviken, B., H o n e y , F., LevJne, S.R., L y o n , R.J.P. a n d Prelat, A., 1 9 7 7 . D e t e c t i o n o f n a t u r a l l y h e a v y - m e t a l - p o i s o n e d areas b y L A N D S A T - 1 digital data. J. G e o c h e m . Ex-. plor., 8: 4 5 7 - - 4 7 1 . N a t u r a l p o i s o n i n g o f soil a n d v e g e t a t i o n d u e t o h e a v y m e t a l s o r i g i n a t i n g f r o m s u l p h i d e d e p o s i t s in t h e b e d r o c k has b e e n d e m o n s t r a t e d at several l o c a t i o n s in N o r w a y . A t s o m e o f these, t h e f e a t u r e s o f p o i s o n i n g are r a t h e r w i d e s p r e a d , suggesting t h a t t h e r e c o g n i t i o n o f s u c h n a t u r a l l y p o i s o n e d areas b y m e a n s o f satellite imagery a n d satellite digital tapes m i g h t b e possible. A n o c c u r r e n c e o f n a t u r a l Cu p o i s o n i n g n e a r Karasjok, n o r t h e r n N o r w a y , was selected as a t r a i n i n g area. L A N D S A T - 1 c o m p u t e r c o m p a t i b l e t a p e s were used t o p r e p a r e enh a n c e d c o l o u r r a t i o images a n d t o calculate r a t i o s o f c a l i b r a t e d channel r e f l e c t a n c e s . Ground r e f l e c t a n c e m e a s u r e m e n t s were also carried o u t using r a d i o m e t e r s w i t h b a n d passes e q u i v a l e n t t o t h o s e o n t h e L A N D S A T m u l t i s p e c t r a l s c a n n e r s y s t e m . C o m p a r e d w i t h t h e s u r r o u n d i n g s t h e L A N D S A T d a t a over t h e t r a i n i n g area s h o w e d u n i q u e spectral characteristics. T h e s e are also i n d i c a t e d o n t h e e n h a n c e d r a t i o images, a n d t h e digital d a t a c a n be u t i l i z e d in s u p e r v i s e d d i s c r i m i n a n t analysis a n d u n s u p e r v i s e d "clust e r " analysis. In p a r t i c u l a r , t h e area o f t h e n a t u r a l Cu p o i s o n i n g s h o w s l o w values in t h e b a n d 7 / b a n d 5 r e f l e c t a n c e ratio. A t p r e s e n t , a detailed, precise n u m e r i c a l c o r r e l a t i o n bet w e e n t h e g r o u n d a n d satellite r e f l e c t a n c e d a t a is n o t possible d u e t o d i f f e r e n c e s in resol u t i o n , a l t h o u g h spatial c o r r e l a t i o n o f s p e c t r a l t r e n d s c a n b e established. It a p p e a r s t h a t f e a t u r e s o f n a t u r a l h e a v y m e t a l p o i s o n i n g can be l o c a t e d w i t h t h e 0 . 4 5 - h e c t a r e r e s o l u t i o n of t h e L A N D S A T m u l t i s p e c t r a l s c a n n e r s y s t e m . T h e search for s u c h f e a t u r e s m i g h t t h e r e f o r e b e used as a t o o l in m i n e r a l e x p l o r a t i o n . T h e c u r r e n t resol u t i o n l i m i t a t i o n c a n be o v e r c o m e b y utilizing s c a n n e r s at aircraft altitudes.
458
B. BOLVIKEN ET AL.
INTRODUCTION In Norway, several occurrences of natural heavy-metal-poisoned soil and vegetation have been found (L~g et al., 1970; Lag and B¢lviken, 1974). It seems that such natural poisoning is a fairly c o m m o n feature at locations where sulphide mineralization occurs in bedrock. This type of poisoning is not caused by man; it is the result of natural processes by which heavy metals are extracted from crystalline mineralization in bedrock and overburden, transported downslope in groundwater solution, and reprecipitated in humus-rich softs where the heavy-metal-containing solutions emerge at the surface. Some of the heavy metals are strongly b o u n d to humus, and humusrich soils may, by this process, accumulate heavy metals from the moving groundwater. The soil gradually becomes restrictive for the natural plant growth. As a consequence, the rate of humus production is reduced. If the breakdown of humus occurs faster than the supply of new organic matter, the soil will become increasingly rich in heavy metals, and in some cases eventually become toxic to plants. in some cases eventually become toxic to plants. and stunted growth within areas of normal growth. At more advanced stages of poisoning, the vegetation may be dying, deficient or absent, so that patches of barren soil appear. The sizes of patches of poisoning may range from 10 .2 to 103 m 2 . Several patches may together form irregular patterns over quite large areas. The dry matter of samples of the topsoil of such patches might contain thousands of parts per million of heavy metal, sometimes as much as 10% Pb and 5% Cu. The heavy metals causing the poisoning originate some distance uphill from the poisoned areas. This distance might vary from a few up to hundreds of metres. Having originally established the indications of heavy metal poisoning, the p h e n o m e n o n may be easily recognized in the field. Since the metal source might be sulphides or other heavy metal mineralization in the bedrock, the search for areas of natural heavy metal poisoning can be used as a prospecting method. Obviously, in areas of glacial terrain, looking for patches of natural heavy metal poisoning might sometimes be more efficient than hunting for mineralized boulders, which normally are of much smaller size than poisoned patches. Moreover, patches of natural heavy metal poisoning might occur at the surface even though no mineralized outcrops or boulders are visible. Observations of poisoning as a prospecting m e t h o d has already been used with some success in northern Norway (A. Bj~brlykke, personal communication). The question arises: could areas of natural heavy metal poisoning be detected by reflectance measurements obtained from satellites or other remote platforms? If so, one would have a potential tool for regional mineral exploration. A cooperative research programme between the Geological Survey of Norway and the Stanford R e m o t e Sensing Laboratory has been started, which aims at answering this question. Initially, we have studied the possibil-
DETECTION OF METAL POISONING USING LANDSAT DATA
459
ities for using LANDSAT-1 images and digital data to detect areas naturally poisoned by heavy metals. Currently, the use of data from digitized multichannel air photographs are being investigated. We anticipate that future use of airborne scanners for detecting areas of poisoning is a realistic possibility. The present paper is a report on the first results of this research programme. LANDSAT-1 data from two test areas with known occurrences of natural heavy metal poisoning were investigated to see if the reflectances of the poisoned patches were different from those of the surrounding normal areas. The two test areas are (1) the Snertingdal area, southern Norway (Pb mineralization), and (2) the Karasjok area, northern Norway (Cu mineralization) (Fig. 1).
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Fig. 1. L o c a t i o n s o f s e l e c t e d o c c u r r e n c e s o f naturally h e a v y - m e t a l - p o i s o n e d areas in Norway. Snertingdal: Pb p o i s o n i n g ; Karasjok: Cu poisoning.
Although occurring over a fairly large area (approximately 10 hectares), the individual patches of Pb poisoning at Snertingdal are rather small, in most cases of the order of 10 m 2 or less (L~g and B¢lviken, 1974). It soon became clear, therefore, that the resolution of the LANDSAT-1 data (unit picture element or pixel approximately 60 m X 80 m) is n o t adequate for the detection of this type of poisoning. Consequently, the Snertingdal area is not considered here, but we hope to analyze the data from this locality later after having done more detailed aircraft and ground reflection measurements. At Karasjok, the poisoning is more widespread. In the following an ac-
460
B. B C L V I K E N ET AL.
count is given about the Karasjok area. After a short description of the locality, the main satellite data are presented followed by some conclusions which can be drawn from these data. The investigation was done in collaboration with the Sydvaranger Mining Company which holds the mining rights in the area. DESCRIPTION OF KARASJOK SURVEY AREA
The area is south of the village of Karasjok (Fig. 1) about 300 m above sea level in a dry and rather cool region (annum precipitation ca. 300 mm, mean temperature -2.6°C). The mineralization consists of disseminated chalcopyrite and pyrite in a nearly flat-lying muscovite- and amphibole-bearing gneiss. Above the gneiss, there is a concordant black schist rich in carbon and pyrrhotite. Both the gneiss and the schist are weathered down to approximately 0.5 m (B. Rosholt, personal communication). The superficial deposits generally consist of till, mostly fine sand, a few metres thick. In the mineralized area, however, a thickness of 0.5--1 m is common. The predominant tree species are birch (t~etula pubescens) and some pine (Pinus sylvestris). The ground-cover vegetation is comprised mainly of bilberry (Vaccinium myrtillus) and cowberry (V. vitis-idaea). Lag and B~blviken (1974) observed patterns of natural poisoning in the field and these could be verified by high Cu contents in soil and vegetation samples (Table I). The poisoned areas may be recognized (see Figs. 2 and 3) as: (1) Open areas in the otherwise dense birch forest. These vary in size and form from small patches to areas several thousands of square metres in extent, often with long axes normal to the map contours. TABLE I
Ash, heavy metals and p H in dry matter (105 ° C) of soil;ash and heavy metals in dry matter of vegetation from poisoned areas at a Cu occurrence Karasjok, northern Norway. Each sample for analysis is a composite of at least 10 subsamples spread evenly at the type of area being sampled (after L~g and B4blviken, 1974)
Soil s a m p l e s , d e p t h 2--5 c m Soil s a m p l e s , d e p t h 2 0 - - 2 5 c m Betula pubescens, leaves Betula, first-year twig
Betula, second-year twig Deschampsia flexuosa Festuca ovina Juncus trifidus Viscaria alpina
N
Ash (%)
Cd (ppm)
Cu (ppm)
Ni (ppm)
Pb (ppm)
Zn (ppm)
pH
9 5 4 4 4 2 3 1 1
85.1 96.6 1.9 1.2 1.5 5.8 3.9 3.5 5.7
0.6 0.9 trace trace trace 0.2 trace 0.5 0.3
7400 3000 7 5 12 271 334 69 895
32 38 6 1 4 7 5 9 10
18 20 4 2 3 3 4 3 3
36 58 102 116 178 42 97 112 63
5.0 5.1
Figures i n d i c a t e a r i t h m e t i c m e a n s o f N samples.
DETECTION OF METAL POISONING USING LANDSAT DATA
461
Fig. 2. N a t u r a l l y c o p p e r - p o i s o n e d area in b i r c h f o r e s t (Betula pubescens w i t h s o m e Pinus sylvestris), Karasjok, n o r t h e r n N o r w a y . Dark area in t h e m i d d l e r i g h t - h a n d side o f t h e picture is b a r r e n soil. G r o u n d - c o v e r v e g e t a t i o n o n t h e left h a n d side consists m a i n l y o f Juncus trifidus, Festuca ovina, Deschampsia flexuosa a n d Viscaria alpina.
Fig. 3. N a t u r a l l y c o p p e r - p o i s o n e d area a p p e a r i n g as o p e n space in t h e o t h e r w i s e d e n s e b i r c h f o r e s t (Betula pubescens w i t h s o m e Pinus sylvestris), Karasjok, n o r t h e r n N o r w a y . T h e n o r m a l g r o u n d - c o v e r v e g e t a t i o n in t h e forest is d o m i n a t e d b y Vaccinium myrtillus a n d V. vitis-idaea while Juncus trifidus, Festuca ovina a n d Deschampsia flexuosa prevail in t h e o p e n space w h e r e Cu c o n c e n t r a t i o n s in t h e soil is high (see T a b l e I). This area can be r e c o g n i z e d o n t h e aerial p h o t o g r a p h (Fig. 4).
462
B. BOLVIKEN ET AL+
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Fig. 4. Aerial p h o t o g r a p h o f an area w i t h occurrence o f natural copper p o i s o n i n g o f soil and vegetation, Karasjok, northern N o r w a y , Coordinates s h o w the L A N D S A T - 1 grid. T y p e areas indicated w i t h numbers: 1 = a n o m a l y ; 2 = a n o m a l y E; 3 = a n o m a l y W; 4 = birch 1; 5 = birch 2; 6 = birch NS; 7 = birch EW; 8 = ellipse W; 9 = grass 1; 10 = grass 2; 11 = grass 3. Differing orientations were used to s t u d y L A N D S A T - 1 sampling bias, ( 2 ) Through the ground.cover vegetation, where Juncus trifidus, Festuca ovina, Deschathpsia flexuosa and V i s c ~ alpina predominate instead of the normal vegetation which is rich in various herb species and Vaccinium myrtillus and V. vitis-idaea. (3) As barren areas up to several tens of square metres in extent at places where the water issues at the surface. Some of these features can also be seen on the black and white aerial
DETECTION OF METAL POISONING USING LANDSAT DATA
463
photographs. However, the main area of Cu poisoning is hard to distinguish from the normal waterlogged bogs in the area (Fig. 4). Measurements of the reflectances of the vegetation and soils using 4-channel EXOTECH radiometers (with LANDSAT equivalent bandpasses) con° firmed that sufficiently large differences existed for discrimination of the poisoned areas using the LANDSAT digital imagery data if the areas were greater than 0.45 hectare. COMPUTER PROCESSING OF S A T E L L I T E DATA
T w o numerical classification techniques were applied to the LANDSAT-1 reflectance data from the survey area to evaluate the results and use them as a tool for future detection of poisoned areas. These methods were: (1) Stepwise discriminant analysis. The BMD07M stepwise discriminant c o m p u t e r program which is part of a series of bio-medical statistical programs compiled by the UCLA Health Service Computing Facility, was used to calculate discriminant functions among the different t y p e areas and to plot the pixels of each type area as a function of canonical variables. For a description of the computational procedure to obtain the discriminant functions and canonical variables see Dixon (1970). (2) Pattern recognition. The cluster analysis program used for the analysis of the LANDSAT data is part of the Stanford R e m o t e Sensing Laboratories STANSORT package, running on a DEC-10 system. The STANSORT system has been described in detail previously (Honey, 1975). Some of the features of these programs used in this analysis were: (a) density slicing -- selection of portion of the digital range of any of the four LANDSAT channels to enable enhancement of specific target materials; (b) ratioing -- inter-channel ratios, followed b y density slicing for selective enhancement; (c) conversion of data to relative reflectance values, using predetermined atmospheric contribution parameters (Lyon et al., 1975); and (d) cluster analysis. The cluster analysis algorithm provides the most powerful technique in searching for anomalous areas. The sequence of operations is: {1) Normalization of the data to reduce the total radiance in the four channels to a constant value. This has the effect of reducing the variability due to topography. (2) Cycling through the data, generating new "standard" signature ranges when the current pixel does n o t fit into the standards previously generated, or classifying it with the appropriate symbol when it does fit one of those generated earlier. The limit of classes in this system is twenty-six -- if this number is exceeded the tolerance (width of the standard signature "gate") is increased and the data recycled. (3) Presentation of the classification on a screen and/or line printer. The algorithm uses a rapid, non-statistical approach to the classification, merely comparing shapes of signatures. It can also be termed a parallelepiped classifier, b u t it is n o t true "clustering".
464
B. BOLVIKEN ET AL.
S ATELLI TE IMAGERY AND DIGITAL DATA ANALYSIS
Examples of black and white satellite images of the Karasjok survey area are shown in Fig. 5 (channel 5) and Fig. 6 (ratio channel 7/5). Location of the satellite grid on the ground was achieved by the use of photographic enlargements of channel 5 and 6 images. By identifying pixel-size lakes on these images it was possible to superimpose the satellite grid on the aerial photograph (Fig. 4). Based on previous knowledge of the area (Lag and B~lviken, 1974)~ interpretation of aerial photography, helicopter reconnaissance and ground reflectance measurements, eleven type areas were selected for further study. These areas are indicated (areas 1--11) in Fig. 4. Area 1 is known to be anomalous, with Cu concentrations of up to 0.5% in the soil. On the aerial photograph this area appears to be a bog, b u t in fact it is an open space in the birch forest which is partly wet and partly dry. In some places the higher plants of the ground cover vegetation are absent
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Fig. 5. Channel 5 L A N D S A T - 1 image from an area with natural Cu poisoning at Karasjok, northern Norway. The poisoned area (in the middle of the north arrow) cannot be identified in the image. Characteristic elliptical features seem to be apparent, which m a y possibly have some connection with mineralization (see also Fig. 6). Satellite prints were obtained before field checking commenced.
DETECTION OF METAL POISONING USING LANDSAT DATA
465
Fig. 6. Black and white image reflectance ratio channel 7/5 of LANDSAT-1 digital data from an area with natural Cu poisoning at Karasjok, northern Norway. The poisoned area (in the middle of the arrow) cannot be'identified, but characteristic elliptical features apparently have some connection with mineralization (see also Fig. 5). Satellite prints were obtained before field checking commenced.
a n d t h e soil a p p e a r s b a r r e n . Where higher p l a n t s are p r e s e n t , Juncus trifidus, Festuca ovina, Deschampsia flexuosa a n d Viscaria alpina p r e d o m i n a t e at t h e e x p e n s e o f Vaccinium myrtillus, V. vitis-idaea a n d t h e n o r m a l h e r b species. Areas 2 and 3 are a d j a c e n t t o a r e a 1; a r e a 2 on t h e e a s t e r n side, a n d a r e a 3 o n t h e w e s t e r n side. A r e a 2 a n d 3 h a v e fairly n o r m a l g r o u n d - c o v e r v e g e t a t i o n (Vaccinium sp. w i t h h e r b s ) a n d s o m e birch. Areas 4--7 are t y p i c a l f o r t h e n o r m a l f o r e s t o f t h e a r e a c o n s i s t i n g o f birch (Betula pubescens) w i t h s c a t t e r e d p i n e (Pinus sylvestris). T h e g r o u n d - c o v e r v e g e t a t i o n is d o m i n a t e d b y Vaccinium myrtillus and V. vitis-idaea a n d various herbs. Area 8 is c h o s e n b e c a u s e it f o r m s a p a r t o f an elliptical s t r u c t u r e on t h e satellite images. Areas 9--11 are r a t h e r w e t o p e n spaces in t h e o t h e r w i s e dense birch forest, w i t h n o r m a l g r o u n d c o v e r v e g e t a t i o n o f v a r i o u s grasses. T h e s e areas can be easily c o n f u s e d on t h e aerial p h o t o g r a p h w i t h areas o f natural Cu p o i s o n i n g . T h e m e a n s a n d s t a n d a r d d e v i a t i o n s o f t h e r e f l e c t a n c e s a n d r e f l e c t a n c e ra-
466
B. BOLVIKEN ET AL.
tios were c o m p u t e d for each type area, and the results are shown in Table III and plotted in Fig. 7. Some observations on these results are: (1) The copper-poisoned areas cannot be recognized visually on the channel 5 satellite images but were easily seen using channel 7/channel 5 (Figs. 5 and 6). However, various curvilinear and linear features seem to be present on the images. At present, it is not known if these features may have any connection with the Cu mineralization, but experience from other places (Saunders et al., 1973) indicates that this possibly could be the case. (2) The reflectance ratios seem to be appropriate parameters for distinguishing areas of natural Cu poisoning from other type areas. The 7/5 ratio is lower for area 1 than for any other type area studied. It is interesting to note that the grass areas (9--11) (which on the aerial photograph can easily be misinterpreted as areas of natural Cu poisoning) show very high values for the 7/5 reflectance ratio. Moreover, grass areas are unique in showing higher values for the 6/5 ratio than for the 7/4 ratio. There is no evidence to indiTABLE II Values used to convert LANDSAT-1 brightness numerics to reflectance data, Karasjok, northern Norway. Calculations at dark target reflectance are based on own measurements and data taken from Romanova (1964). The bright target reflectance was obtained at Karasjok. (Methodology of Lyon et al., 1975) N Dark target radiance (18) Bright target radiance (14) Dark target reflectance (Romanova, 1964) Bright target reflectance (6) (measured at Karasjok)
CH4
CH5
CH6
CH7
16.24-+0.66 31.79-+5.78 6.0
9.57-+0.72 31.71-+7.64 3.0
6.76-+0.83 30.14-+7.36 1.5
0.88-+0.03 12.85-+3.87 1.0
22.6+0.68
26.0-+0.52
31.5-+0.63
32.8-+1.31
N = number of measurements; CH = channel numbers.
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Fig. 7. Plot of means and standard deviations (see Table III) of LANDSAT-1 reflectance ratios channels 7/4 and 7/5 for selected type areas shown in Fig. 4, Karasjok, northern Norway.
DETECTION OF METAL POISONING USING LANDSAT DATA
467
T A B L E III R e f l e c t a n c e a n d r e f l e c t a n c e ratios. L A N D S A T - 1 d a t a f r o m an area o f n a t u r a l Cu p o i s o n i n g near Karasjok, n o r t h e r n N o r w a y . F o r l o c a t i o n s o f t h e various t y p e areas, see Fig. 4. T h e 7 / 4 a n d 7 / 5 values are p l o t t e d in Fig. 7 T y p e area
Reflectance
R e f l e c t a n c e ratios
4*
5
6
7
7/6
7/5
7/4
6/5
6/4
5/4
.~ S V
10.97 1.26 0.11
9.27 0.90 0.10
30.82 1.32 0.04
40.17 2.65 0.07
1.30 0.07 0.95
4.33 0.66 0.15
3.69 0.42 0.11
3.36 0.34 0.10
2.83 0.21 0.07
0.89 0.15 0.17
2. A n o m a l y E .~ N=9 S V
10.79 1.05 0.10
8.13 0.79 0.10
30.64 1.62 0.05
40.64 1.82 0.04
1.33 0.09 0.07
5.22 0.53 0.10
4.54 0.82 0.18
3.94 0.51 0.13
2.93 0.32 0.11
0.77 0.11 0.14
3. A n o m a l y W .~ N=9 S V
9.67 0.50 0.05
7.70 0.55 0.07
31.4 1.93 0.06
41.0 1.89 0 05
1.34 0.05 0.04
5.90 0.56 0.09
4.43 0.32 0.07
4.41 0.35 0.08
3.31 0.25 0.08
0.76 0.05 0.07
4. Birch 1 N = 10
.~ S V
9.91 0.59 0.06
7.67 0.86 0.11
28.93 2.16 9.07
39.69 1.86 0.05
1.39 0.14 0.10
5.26 0.69 0.13
4.13 0.41 0.10
3.84 0.62 0.16
2.98 0.27 0.09
0.82 0.10 0.12
5. Birch 2 N=10
.~ S V
9.40 0.52 0.O6
6.92 0.71 0.10
32.39 1.77 0.05
45.26 2.86 0.06
1.40 0.09 0.06
6.47 0.71 0.11
4.86 0.47 0.10
4.68 0.48 0.10
3.47 0.28 0.08
0.77 0.09 0.12
6. Birch NS N = 10
-~ S V
9.40 0.52 0.06
7.40 0.57 0.08
28.93 1.67 0.06
38.90 2.08 0.05
1.36 0.08 0.06
5.57 0.58 0.10
4.16 0.22 0.05
4.15 0.41 0.10
3.09 0.23 0.07
0.77 0.09 0.12
7. Birch EW N = 10
X S V
9.60 0.52 0.05
6.93 0.33 0.08
27.63 2.41 0.09
38.12 1.51 0.04
1.38 0.10 0.07
5.50 0.33 0.06
3.97 0.20 0.05
3.99 0.36 0.09
2.88 0.22 0.07
0.72 0.05 0.07
8. Ellipse W N = 19
~" S V
10.24 0.58 O.O6
7.78 0.77 0.10
30.44 1.43 0.05
41.73 2.14 0.05
1.37 0.07 0.05
5.36 0.16 0.03
4.08 0.20 0.05
3.91 0.12 0.03
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5.91 0.89 0.15
3.85 0.53 0.14
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1. A n o m a l y N=9
N = n u m b e r of pixels ; X" = a r i t h m e t i c m e a n ; S = s t a n d a r d d e v i a t i o n o f individual o b s e r v a t i o n s ; V = coefficient of variation *Channel numbers
468
B. BOLVIKEN ET AL
cate t h a t factors other than the copper poisoning cause the low 7/5 reflection ratio (elsewhere typically indicative of a low biomass). Waterlogging could n o t be the only reason because (a) the poisoned areas are n o t especially wet, and (b) bog areas have other reflectance characteristics than the poisoned area. (3) The resolution of the LANDSAT-1 system is restricted by the pixel size of approximately 60 m × 80 m, therefore only patterns with this minim u m size can be distinguished on the ground. Area 1 consists of 9 pixels, the majority of which o represent copper-poisoned areas. observed during ear. . . . lier ground surveys (Lag and B~blvlken, 1974). Possible poisoned areas within the other type areas are too small to be detected from space. The results of the previously described classification and clustering procedures were: (1) Stepwise discriminant analysis. Five of the eleven type areas outlined above (see Table III and Fig. 4) were selected to test if the anomaly area 1 could be separated from the four others; grass (type area 9), grass (type area 10), birch (type area 2) and an area adjacent to the anomaly (type area 3). Fig. 8 shows a plot of the results. The coordinates are canonical variables which represent numerical transformation of the LANDSAT-1 reflectance data given in Table II. In the plot, the area I anomaly is separated as a differ-
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Fig. 8. Plot o f five different types of control areas (1, 2, 3, 9, 10, shown in Fig. 4) as a function o f the two best canonical variables calculated by stepwise discrimin~mt analysis. The values o f the canonical variables represent numerical transformations o f the LANDSAT-1 reflectance data given in Table II. Solid dots show the classification domains and are not data points ( E N = 47).
DETECTION OF METAL POISONING USING LANDSAT DATA
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Fig. 9. Results o f u n s u p e r v i s e d p a t t e r n r e c o g n i t i o n o f L A N D S A T - 1 r e f l e c t a n c e d a t a f r o m an area w i t h n a t u r a l Cu p o i s o n i n g ; K a r a s j o k , n o r t h e r n N o r w a y . T y p e area ( 1 ) a n o m a l y is used as a t r a i n i n g set (see Fig. 4). S y m b o l C = p o i s o n e d , Cu.
470
B. BOLVIKEN ET AL
ent group except for one pixel classified as belonging to the area 3, and one classified as birch (area 2). These two pixels are located near the centre of the area 1 anomaly (see Fig. 4). The a m o u n t of birch present on them, apparently, is sufficient to alter the spectral reflectance in a way that they seem to be more similar to the area 3 and area 2 groups than to the area 1 anomaly group. (2) Pattern recognition. The pixels located in area 1 (Fig. 4) were selected as a training group and the STANSORT pattern recognition technique was used to search for similar areas. Fig. 9 shows the results of the pattern recognition of the survey area (800 hectares, 2000 pixels). The pixels with the "C"s are classified as potential poisoned areas. The most promising area is located southeast of area 1. The area will be investigated on subsequent ground surveys. The " C " s north and west of area 1 are probably errors caused by the classification procedure employed.
CONCLUSIONS
An area of natural Cu poisoning associated with a Cu deposit at Karasjok, northern Norway, can be characterized in satellite data primarily by the low values of the 7/5 channels reflectance ratio. Normal grass areas, which on the aerial photographs appear to be similar to the poisoned areas, show high values for the 7/5 channels reflectance ratio. Heavy metal poisoning seems to be a c o m m o n feature near Norwegian sulphide deposits. Recording of multispectral reflectance from remote platforms, therefore, appears to be a promising regional exploration m e t h o d for disseminated, shallow Cu deposits in this part of Norway. Numerical classification techniques, such as pattern recognition and stepwise discriminant analysis proved to be adequate tools for distinguishing a copper-poisoned area from normal areas. These techniques could be applied to satellite data for routine searching for new poisoned areas. Characteristic elliptical features appear in the channel 5 and 6 images and are more accentuated in the reflectance ratio colour images (in particular 6/5). These elliptical features might have some relation to the mineralization, a possibility which should be further investigated. The spatial resolution of the LANDSAT-1 data is approximately 0.45 hectare which is n o t sufficient to identify all known patches of natural Cu poisoning at Karasjok or the occurrences of natural Pb poisoning found at other locations in Norway. This resolution problem may be overcome by using reflectance data obtained from digitized multispectral airphotography or airborne scanners. Multispectral aerial photography has been obtained for these areas and is being digitized and analyzed by computer in an a t t e m p t to overcome the resolution limitations of LANDSAT-1.
DETECTION OF METAL POISONING USING LANDSAT DATA
471
ACKNOWLEDGEMENTS
The authors wish to express their gratitude to the Sydvaranger Mining company for the information and assistance offered to perform this study. Special thanks are due to Mr. Bernt R~bsholt, exploration geologist of the Sydvaranger Mining Company, who provided the geological information. Mr. R~bsholt found the mineralization at Karasjok on 1968 during the follow up of a geochemical anomaly obtained in a stream sediment survey suggested by Professor J.A.W. Bugge and conducted by the Geological Survey of Norway 1967 as contract work for the Sydvaranger Mining Company.
REFERENCES Dixon, W.J. (Editor), 1970. Biomedical Computer Programs. Publications in Automatic Computations No. 2. University of California Press, Los Angeles, Calif., 773 pp. Honey, F., 1975. STANSORT: a fully interactive tape reading program for ERTS II, philosophy and operational usage (with users manual). Stanford Remote Sensing Lab. Rep., No. 75-13, 30 pp. Honey, F.R., Prelat, A.E. and Lyon, R.J.P., 1974. STANSORT: Stanford remote sensing laboratory pattern recognition and classification system. Proc. 9th Int. Syrup. on Remote Sensing of Environment, Ann Arbor, Mich., 9 pp. L~g, J. and B~blviken, B., 1974. Some naturally heavy-metal poisoned areas of interest in prospecting, soil chemistry and geomedicine. Nor. Geol. Unders., 304 : 73--96. L~,g, J., Hvatum, O.~. and B~blviken, B., 1970. An occurrence of naturally lead poisoned soil at Kastat near Gjovik, Norway. Nor. Geol. Unders., 266: 141--159. Lyon, R.J.P., Honey, F.R. and Ballew, G., 1975. A comparison of observed and modelpredicted atmospheric perturbations on target radiances measured by ERTS. Proc. Inst. Electr. Electron. Eng. Syrup. on Application of Remote Sensing and Digital Imagery to Mineral Exploration, Houston, Texas, 6 pp. Levine, S.R., 1975. An interactive program for producing computer-enhanced ERTS images (IMAGE), II. Methodology and development. Stanford Remote Sensing Lab. Rep., No. 75-4. Romanova, M.A., 1964. Air survey of sand deposits by spectral luminance. Laboratory of Aeromethods of the Academy of Sciences of the USSR, State Scientific and Technical Press for Literature on Petroleum and Solid Fuels, Leningrad. Saunders, D.F., Thomas, G.E., Kinsman, F.E. and Beatty, D.F., 1973. ERTS-1 imagery use in reconnaissance prospecting-evaluation of the commercial utility of ERTS-1 imagery in structural reconnaissance for minerals and petroleum. Type III Final Report to NASA. U.S. Department of Commerce, Natl. Tech. Inf. Serv., E74-10345.
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© Elsevier Scientific P u b l i s h i n g C o m p a n y , A m s t e r d a m - - P r i n t e d in T h e N e t h e r l a n d s
DETECTION LANDSAT-1
OF NATURALLY DIGITAL DATA
HEAVY-METAL-POISONED
AREAS
BY
B. B @ L V I K E N ' , F. H O N E Y 2 , S.R. L E V I N E 3 , R.J.P. L Y O N 3 a n d A. P R E L A T '
' Geological Survey o f Norway, N-7001 Trondheim (Norway) 2 Commonwealth Scientific and Industrial Research Organization, Wembley, W.A. (Australia) 3Stanford R e m o t e Sensing Laboratory, Department o f Applied Earth Sciences, Stanford University, Stanford, Calif. 94305 (U.S.A.) (Revised version a c c e p t e d M a r c h 1, 1 9 7 7 )
ABSTRACT B~lviken, B., H o n e y , F., LevJne, S.R., L y o n , R.J.P. a n d Prelat, A., 1 9 7 7 . D e t e c t i o n o f n a t u r a l l y h e a v y - m e t a l - p o i s o n e d areas b y L A N D S A T - 1 digital data. J. G e o c h e m . Ex-. plor., 8: 4 5 7 - - 4 7 1 . N a t u r a l p o i s o n i n g o f soil a n d v e g e t a t i o n d u e t o h e a v y m e t a l s o r i g i n a t i n g f r o m s u l p h i d e d e p o s i t s in t h e b e d r o c k has b e e n d e m o n s t r a t e d at several l o c a t i o n s in N o r w a y . A t s o m e o f these, t h e f e a t u r e s o f p o i s o n i n g are r a t h e r w i d e s p r e a d , suggesting t h a t t h e r e c o g n i t i o n o f s u c h n a t u r a l l y p o i s o n e d areas b y m e a n s o f satellite imagery a n d satellite digital tapes m i g h t b e possible. A n o c c u r r e n c e o f n a t u r a l Cu p o i s o n i n g n e a r Karasjok, n o r t h e r n N o r w a y , was selected as a t r a i n i n g area. L A N D S A T - 1 c o m p u t e r c o m p a t i b l e t a p e s were used t o p r e p a r e enh a n c e d c o l o u r r a t i o images a n d t o calculate r a t i o s o f c a l i b r a t e d channel r e f l e c t a n c e s . Ground r e f l e c t a n c e m e a s u r e m e n t s were also carried o u t using r a d i o m e t e r s w i t h b a n d passes e q u i v a l e n t t o t h o s e o n t h e L A N D S A T m u l t i s p e c t r a l s c a n n e r s y s t e m . C o m p a r e d w i t h t h e s u r r o u n d i n g s t h e L A N D S A T d a t a over t h e t r a i n i n g area s h o w e d u n i q u e spectral characteristics. T h e s e are also i n d i c a t e d o n t h e e n h a n c e d r a t i o images, a n d t h e digital d a t a c a n be u t i l i z e d in s u p e r v i s e d d i s c r i m i n a n t analysis a n d u n s u p e r v i s e d "clust e r " analysis. In p a r t i c u l a r , t h e area o f t h e n a t u r a l Cu p o i s o n i n g s h o w s l o w values in t h e b a n d 7 / b a n d 5 r e f l e c t a n c e ratio. A t p r e s e n t , a detailed, precise n u m e r i c a l c o r r e l a t i o n bet w e e n t h e g r o u n d a n d satellite r e f l e c t a n c e d a t a is n o t possible d u e t o d i f f e r e n c e s in resol u t i o n , a l t h o u g h spatial c o r r e l a t i o n o f s p e c t r a l t r e n d s c a n b e established. It a p p e a r s t h a t f e a t u r e s o f n a t u r a l h e a v y m e t a l p o i s o n i n g can be l o c a t e d w i t h t h e 0 . 4 5 - h e c t a r e r e s o l u t i o n of t h e L A N D S A T m u l t i s p e c t r a l s c a n n e r s y s t e m . T h e search for s u c h f e a t u r e s m i g h t t h e r e f o r e b e used as a t o o l in m i n e r a l e x p l o r a t i o n . T h e c u r r e n t resol u t i o n l i m i t a t i o n c a n be o v e r c o m e b y utilizing s c a n n e r s at aircraft altitudes.
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INTRODUCTION In Norway, several occurrences of natural heavy-metal-poisoned soil and vegetation have been found (L~g et al., 1970; Lag and B¢lviken, 1974). It seems that such natural poisoning is a fairly c o m m o n feature at locations where sulphide mineralization occurs in bedrock. This type of poisoning is not caused by man; it is the result of natural processes by which heavy metals are extracted from crystalline mineralization in bedrock and overburden, transported downslope in groundwater solution, and reprecipitated in humus-rich softs where the heavy-metal-containing solutions emerge at the surface. Some of the heavy metals are strongly b o u n d to humus, and humusrich soils may, by this process, accumulate heavy metals from the moving groundwater. The soil gradually becomes restrictive for the natural plant growth. As a consequence, the rate of humus production is reduced. If the breakdown of humus occurs faster than the supply of new organic matter, the soil will become increasingly rich in heavy metals, and in some cases eventually become toxic to plants. in some cases eventually become toxic to plants. and stunted growth within areas of normal growth. At more advanced stages of poisoning, the vegetation may be dying, deficient or absent, so that patches of barren soil appear. The sizes of patches of poisoning may range from 10 .2 to 103 m 2 . Several patches may together form irregular patterns over quite large areas. The dry matter of samples of the topsoil of such patches might contain thousands of parts per million of heavy metal, sometimes as much as 10% Pb and 5% Cu. The heavy metals causing the poisoning originate some distance uphill from the poisoned areas. This distance might vary from a few up to hundreds of metres. Having originally established the indications of heavy metal poisoning, the p h e n o m e n o n may be easily recognized in the field. Since the metal source might be sulphides or other heavy metal mineralization in the bedrock, the search for areas of natural heavy metal poisoning can be used as a prospecting method. Obviously, in areas of glacial terrain, looking for patches of natural heavy metal poisoning might sometimes be more efficient than hunting for mineralized boulders, which normally are of much smaller size than poisoned patches. Moreover, patches of natural heavy metal poisoning might occur at the surface even though no mineralized outcrops or boulders are visible. Observations of poisoning as a prospecting m e t h o d has already been used with some success in northern Norway (A. Bj~brlykke, personal communication). The question arises: could areas of natural heavy metal poisoning be detected by reflectance measurements obtained from satellites or other remote platforms? If so, one would have a potential tool for regional mineral exploration. A cooperative research programme between the Geological Survey of Norway and the Stanford R e m o t e Sensing Laboratory has been started, which aims at answering this question. Initially, we have studied the possibil-
DETECTION OF METAL POISONING USING LANDSAT DATA
459
ities for using LANDSAT-1 images and digital data to detect areas naturally poisoned by heavy metals. Currently, the use of data from digitized multichannel air photographs are being investigated. We anticipate that future use of airborne scanners for detecting areas of poisoning is a realistic possibility. The present paper is a report on the first results of this research programme. LANDSAT-1 data from two test areas with known occurrences of natural heavy metal poisoning were investigated to see if the reflectances of the poisoned patches were different from those of the surrounding normal areas. The two test areas are (1) the Snertingdal area, southern Norway (Pb mineralization), and (2) the Karasjok area, northern Norway (Cu mineralization) (Fig. 1).
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Although occurring over a fairly large area (approximately 10 hectares), the individual patches of Pb poisoning at Snertingdal are rather small, in most cases of the order of 10 m 2 or less (L~g and B¢lviken, 1974). It soon became clear, therefore, that the resolution of the LANDSAT-1 data (unit picture element or pixel approximately 60 m X 80 m) is n o t adequate for the detection of this type of poisoning. Consequently, the Snertingdal area is not considered here, but we hope to analyze the data from this locality later after having done more detailed aircraft and ground reflection measurements. At Karasjok, the poisoning is more widespread. In the following an ac-
460
B. B C L V I K E N ET AL.
count is given about the Karasjok area. After a short description of the locality, the main satellite data are presented followed by some conclusions which can be drawn from these data. The investigation was done in collaboration with the Sydvaranger Mining Company which holds the mining rights in the area. DESCRIPTION OF KARASJOK SURVEY AREA
The area is south of the village of Karasjok (Fig. 1) about 300 m above sea level in a dry and rather cool region (annum precipitation ca. 300 mm, mean temperature -2.6°C). The mineralization consists of disseminated chalcopyrite and pyrite in a nearly flat-lying muscovite- and amphibole-bearing gneiss. Above the gneiss, there is a concordant black schist rich in carbon and pyrrhotite. Both the gneiss and the schist are weathered down to approximately 0.5 m (B. Rosholt, personal communication). The superficial deposits generally consist of till, mostly fine sand, a few metres thick. In the mineralized area, however, a thickness of 0.5--1 m is common. The predominant tree species are birch (t~etula pubescens) and some pine (Pinus sylvestris). The ground-cover vegetation is comprised mainly of bilberry (Vaccinium myrtillus) and cowberry (V. vitis-idaea). Lag and B~blviken (1974) observed patterns of natural poisoning in the field and these could be verified by high Cu contents in soil and vegetation samples (Table I). The poisoned areas may be recognized (see Figs. 2 and 3) as: (1) Open areas in the otherwise dense birch forest. These vary in size and form from small patches to areas several thousands of square metres in extent, often with long axes normal to the map contours. TABLE I
Ash, heavy metals and p H in dry matter (105 ° C) of soil;ash and heavy metals in dry matter of vegetation from poisoned areas at a Cu occurrence Karasjok, northern Norway. Each sample for analysis is a composite of at least 10 subsamples spread evenly at the type of area being sampled (after L~g and B4blviken, 1974)
Soil s a m p l e s , d e p t h 2--5 c m Soil s a m p l e s , d e p t h 2 0 - - 2 5 c m Betula pubescens, leaves Betula, first-year twig
Betula, second-year twig Deschampsia flexuosa Festuca ovina Juncus trifidus Viscaria alpina
N
Ash (%)
Cd (ppm)
Cu (ppm)
Ni (ppm)
Pb (ppm)
Zn (ppm)
pH
9 5 4 4 4 2 3 1 1
85.1 96.6 1.9 1.2 1.5 5.8 3.9 3.5 5.7
0.6 0.9 trace trace trace 0.2 trace 0.5 0.3
7400 3000 7 5 12 271 334 69 895
32 38 6 1 4 7 5 9 10
18 20 4 2 3 3 4 3 3
36 58 102 116 178 42 97 112 63
5.0 5.1
Figures i n d i c a t e a r i t h m e t i c m e a n s o f N samples.
DETECTION OF METAL POISONING USING LANDSAT DATA
461
Fig. 2. N a t u r a l l y c o p p e r - p o i s o n e d area in b i r c h f o r e s t (Betula pubescens w i t h s o m e Pinus sylvestris), Karasjok, n o r t h e r n N o r w a y . Dark area in t h e m i d d l e r i g h t - h a n d side o f t h e picture is b a r r e n soil. G r o u n d - c o v e r v e g e t a t i o n o n t h e left h a n d side consists m a i n l y o f Juncus trifidus, Festuca ovina, Deschampsia flexuosa a n d Viscaria alpina.
Fig. 3. N a t u r a l l y c o p p e r - p o i s o n e d area a p p e a r i n g as o p e n space in t h e o t h e r w i s e d e n s e b i r c h f o r e s t (Betula pubescens w i t h s o m e Pinus sylvestris), Karasjok, n o r t h e r n N o r w a y . T h e n o r m a l g r o u n d - c o v e r v e g e t a t i o n in t h e forest is d o m i n a t e d b y Vaccinium myrtillus a n d V. vitis-idaea while Juncus trifidus, Festuca ovina a n d Deschampsia flexuosa prevail in t h e o p e n space w h e r e Cu c o n c e n t r a t i o n s in t h e soil is high (see T a b l e I). This area can be r e c o g n i z e d o n t h e aerial p h o t o g r a p h (Fig. 4).
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B. BOLVIKEN ET AL+
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Fig. 4. Aerial p h o t o g r a p h o f an area w i t h occurrence o f natural copper p o i s o n i n g o f soil and vegetation, Karasjok, northern N o r w a y , Coordinates s h o w the L A N D S A T - 1 grid. T y p e areas indicated w i t h numbers: 1 = a n o m a l y ; 2 = a n o m a l y E; 3 = a n o m a l y W; 4 = birch 1; 5 = birch 2; 6 = birch NS; 7 = birch EW; 8 = ellipse W; 9 = grass 1; 10 = grass 2; 11 = grass 3. Differing orientations were used to s t u d y L A N D S A T - 1 sampling bias, ( 2 ) Through the ground.cover vegetation, where Juncus trifidus, Festuca ovina, Deschathpsia flexuosa and V i s c ~ alpina predominate instead of the normal vegetation which is rich in various herb species and Vaccinium myrtillus and V. vitis-idaea. (3) As barren areas up to several tens of square metres in extent at places where the water issues at the surface. Some of these features can also be seen on the black and white aerial
DETECTION OF METAL POISONING USING LANDSAT DATA
463
photographs. However, the main area of Cu poisoning is hard to distinguish from the normal waterlogged bogs in the area (Fig. 4). Measurements of the reflectances of the vegetation and soils using 4-channel EXOTECH radiometers (with LANDSAT equivalent bandpasses) con° firmed that sufficiently large differences existed for discrimination of the poisoned areas using the LANDSAT digital imagery data if the areas were greater than 0.45 hectare. COMPUTER PROCESSING OF S A T E L L I T E DATA
T w o numerical classification techniques were applied to the LANDSAT-1 reflectance data from the survey area to evaluate the results and use them as a tool for future detection of poisoned areas. These methods were: (1) Stepwise discriminant analysis. The BMD07M stepwise discriminant c o m p u t e r program which is part of a series of bio-medical statistical programs compiled by the UCLA Health Service Computing Facility, was used to calculate discriminant functions among the different t y p e areas and to plot the pixels of each type area as a function of canonical variables. For a description of the computational procedure to obtain the discriminant functions and canonical variables see Dixon (1970). (2) Pattern recognition. The cluster analysis program used for the analysis of the LANDSAT data is part of the Stanford R e m o t e Sensing Laboratories STANSORT package, running on a DEC-10 system. The STANSORT system has been described in detail previously (Honey, 1975). Some of the features of these programs used in this analysis were: (a) density slicing -- selection of portion of the digital range of any of the four LANDSAT channels to enable enhancement of specific target materials; (b) ratioing -- inter-channel ratios, followed b y density slicing for selective enhancement; (c) conversion of data to relative reflectance values, using predetermined atmospheric contribution parameters (Lyon et al., 1975); and (d) cluster analysis. The cluster analysis algorithm provides the most powerful technique in searching for anomalous areas. The sequence of operations is: {1) Normalization of the data to reduce the total radiance in the four channels to a constant value. This has the effect of reducing the variability due to topography. (2) Cycling through the data, generating new "standard" signature ranges when the current pixel does n o t fit into the standards previously generated, or classifying it with the appropriate symbol when it does fit one of those generated earlier. The limit of classes in this system is twenty-six -- if this number is exceeded the tolerance (width of the standard signature "gate") is increased and the data recycled. (3) Presentation of the classification on a screen and/or line printer. The algorithm uses a rapid, non-statistical approach to the classification, merely comparing shapes of signatures. It can also be termed a parallelepiped classifier, b u t it is n o t true "clustering".
464
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S ATELLI TE IMAGERY AND DIGITAL DATA ANALYSIS
Examples of black and white satellite images of the Karasjok survey area are shown in Fig. 5 (channel 5) and Fig. 6 (ratio channel 7/5). Location of the satellite grid on the ground was achieved by the use of photographic enlargements of channel 5 and 6 images. By identifying pixel-size lakes on these images it was possible to superimpose the satellite grid on the aerial photograph (Fig. 4). Based on previous knowledge of the area (Lag and B~lviken, 1974)~ interpretation of aerial photography, helicopter reconnaissance and ground reflectance measurements, eleven type areas were selected for further study. These areas are indicated (areas 1--11) in Fig. 4. Area 1 is known to be anomalous, with Cu concentrations of up to 0.5% in the soil. On the aerial photograph this area appears to be a bog, b u t in fact it is an open space in the birch forest which is partly wet and partly dry. In some places the higher plants of the ground cover vegetation are absent
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Fig. 5. Channel 5 L A N D S A T - 1 image from an area with natural Cu poisoning at Karasjok, northern Norway. The poisoned area (in the middle of the north arrow) cannot be identified in the image. Characteristic elliptical features seem to be apparent, which m a y possibly have some connection with mineralization (see also Fig. 6). Satellite prints were obtained before field checking commenced.
DETECTION OF METAL POISONING USING LANDSAT DATA
465
Fig. 6. Black and white image reflectance ratio channel 7/5 of LANDSAT-1 digital data from an area with natural Cu poisoning at Karasjok, northern Norway. The poisoned area (in the middle of the arrow) cannot be'identified, but characteristic elliptical features apparently have some connection with mineralization (see also Fig. 5). Satellite prints were obtained before field checking commenced.
a n d t h e soil a p p e a r s b a r r e n . Where higher p l a n t s are p r e s e n t , Juncus trifidus, Festuca ovina, Deschampsia flexuosa a n d Viscaria alpina p r e d o m i n a t e at t h e e x p e n s e o f Vaccinium myrtillus, V. vitis-idaea a n d t h e n o r m a l h e r b species. Areas 2 and 3 are a d j a c e n t t o a r e a 1; a r e a 2 on t h e e a s t e r n side, a n d a r e a 3 o n t h e w e s t e r n side. A r e a 2 a n d 3 h a v e fairly n o r m a l g r o u n d - c o v e r v e g e t a t i o n (Vaccinium sp. w i t h h e r b s ) a n d s o m e birch. Areas 4--7 are t y p i c a l f o r t h e n o r m a l f o r e s t o f t h e a r e a c o n s i s t i n g o f birch (Betula pubescens) w i t h s c a t t e r e d p i n e (Pinus sylvestris). T h e g r o u n d - c o v e r v e g e t a t i o n is d o m i n a t e d b y Vaccinium myrtillus and V. vitis-idaea a n d various herbs. Area 8 is c h o s e n b e c a u s e it f o r m s a p a r t o f an elliptical s t r u c t u r e on t h e satellite images. Areas 9--11 are r a t h e r w e t o p e n spaces in t h e o t h e r w i s e dense birch forest, w i t h n o r m a l g r o u n d c o v e r v e g e t a t i o n o f v a r i o u s grasses. T h e s e areas can be easily c o n f u s e d on t h e aerial p h o t o g r a p h w i t h areas o f natural Cu p o i s o n i n g . T h e m e a n s a n d s t a n d a r d d e v i a t i o n s o f t h e r e f l e c t a n c e s a n d r e f l e c t a n c e ra-
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tios were c o m p u t e d for each type area, and the results are shown in Table III and plotted in Fig. 7. Some observations on these results are: (1) The copper-poisoned areas cannot be recognized visually on the channel 5 satellite images but were easily seen using channel 7/channel 5 (Figs. 5 and 6). However, various curvilinear and linear features seem to be present on the images. At present, it is not known if these features may have any connection with the Cu mineralization, but experience from other places (Saunders et al., 1973) indicates that this possibly could be the case. (2) The reflectance ratios seem to be appropriate parameters for distinguishing areas of natural Cu poisoning from other type areas. The 7/5 ratio is lower for area 1 than for any other type area studied. It is interesting to note that the grass areas (9--11) (which on the aerial photograph can easily be misinterpreted as areas of natural Cu poisoning) show very high values for the 7/5 reflectance ratio. Moreover, grass areas are unique in showing higher values for the 6/5 ratio than for the 7/4 ratio. There is no evidence to indiTABLE II Values used to convert LANDSAT-1 brightness numerics to reflectance data, Karasjok, northern Norway. Calculations at dark target reflectance are based on own measurements and data taken from Romanova (1964). The bright target reflectance was obtained at Karasjok. (Methodology of Lyon et al., 1975) N Dark target radiance (18) Bright target radiance (14) Dark target reflectance (Romanova, 1964) Bright target reflectance (6) (measured at Karasjok)
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CH5
CH6
CH7
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9.57-+0.72 31.71-+7.64 3.0
6.76-+0.83 30.14-+7.36 1.5
0.88-+0.03 12.85-+3.87 1.0
22.6+0.68
26.0-+0.52
31.5-+0.63
32.8-+1.31
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DETECTION OF METAL POISONING USING LANDSAT DATA
467
T A B L E III R e f l e c t a n c e a n d r e f l e c t a n c e ratios. L A N D S A T - 1 d a t a f r o m an area o f n a t u r a l Cu p o i s o n i n g near Karasjok, n o r t h e r n N o r w a y . F o r l o c a t i o n s o f t h e various t y p e areas, see Fig. 4. T h e 7 / 4 a n d 7 / 5 values are p l o t t e d in Fig. 7 T y p e area
Reflectance
R e f l e c t a n c e ratios
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5
6
7
7/6
7/5
7/4
6/5
6/4
5/4
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10.97 1.26 0.11
9.27 0.90 0.10
30.82 1.32 0.04
40.17 2.65 0.07
1.30 0.07 0.95
4.33 0.66 0.15
3.69 0.42 0.11
3.36 0.34 0.10
2.83 0.21 0.07
0.89 0.15 0.17
2. A n o m a l y E .~ N=9 S V
10.79 1.05 0.10
8.13 0.79 0.10
30.64 1.62 0.05
40.64 1.82 0.04
1.33 0.09 0.07
5.22 0.53 0.10
4.54 0.82 0.18
3.94 0.51 0.13
2.93 0.32 0.11
0.77 0.11 0.14
3. A n o m a l y W .~ N=9 S V
9.67 0.50 0.05
7.70 0.55 0.07
31.4 1.93 0.06
41.0 1.89 0 05
1.34 0.05 0.04
5.90 0.56 0.09
4.43 0.32 0.07
4.41 0.35 0.08
3.31 0.25 0.08
0.76 0.05 0.07
4. Birch 1 N = 10
.~ S V
9.91 0.59 0.06
7.67 0.86 0.11
28.93 2.16 9.07
39.69 1.86 0.05
1.39 0.14 0.10
5.26 0.69 0.13
4.13 0.41 0.10
3.84 0.62 0.16
2.98 0.27 0.09
0.82 0.10 0.12
5. Birch 2 N=10
.~ S V
9.40 0.52 0.O6
6.92 0.71 0.10
32.39 1.77 0.05
45.26 2.86 0.06
1.40 0.09 0.06
6.47 0.71 0.11
4.86 0.47 0.10
4.68 0.48 0.10
3.47 0.28 0.08
0.77 0.09 0.12
6. Birch NS N = 10
-~ S V
9.40 0.52 0.06
7.40 0.57 0.08
28.93 1.67 0.06
38.90 2.08 0.05
1.36 0.08 0.06
5.57 0.58 0.10
4.16 0.22 0.05
4.15 0.41 0.10
3.09 0.23 0.07
0.77 0.09 0.12
7. Birch EW N = 10
X S V
9.60 0.52 0.05
6.93 0.33 0.08
27.63 2.41 0.09
38.12 1.51 0.04
1.38 0.10 0.07
5.50 0.33 0.06
3.97 0.20 0.05
3.99 0.36 0.09
2.88 0.22 0.07
0.72 0.05 0.07
8. Ellipse W N = 19
~" S V
10.24 0.58 O.O6
7.78 0.77 0.10
30.44 1.43 0.05
41.73 2.14 0.05
1.37 0.07 0.05
5.36 0.16 0.03
4.08 0.20 0.05
3.91 0.12 0.03
2.97 0.15 0.05
0.76 0.05 0.08
9. Grass 1 N = 10
~" S V
10.02 0.66 0.07
6.63 1.13 0.17
38.47 4.23 0.11
50.73 6.42 0.13
1.31 0.08 0.06
7.80 1.24 0.16
5.06 0.73 0.14
5.91 0.89 0.15
3.85 0.53 0.14
0.70 0.17 0.24
10. G r a s s 2 N = 10
X S V
9.11 0.33 0.04
5.27 1.00 0.19
35.36 2.07 0.06
48.61 2.83 0.06
1.37 0.05 9.04
9.66 1.87 0.19
5.40 0.32 0.06
6.94 1.26 0.18
3.91 0.27 0.07
0.79 0.12 0.15
11. Grass 3 N=6
.~ S V
9.83 0.41 0.O4
6.60 0.00 0.00
44.23 2.28 0.05
55.15 2.16 0.04
1.23 0.05 0.04
8.33 0.33 0.04
5.60 0.41 0.07
6.70 0.36 0.05
4.47 0.37 0.08
0.70 0.00 0.00
1. A n o m a l y N=9
N = n u m b e r of pixels ; X" = a r i t h m e t i c m e a n ; S = s t a n d a r d d e v i a t i o n o f individual o b s e r v a t i o n s ; V = coefficient of variation *Channel numbers
468
B. BOLVIKEN ET AL
cate t h a t factors other than the copper poisoning cause the low 7/5 reflection ratio (elsewhere typically indicative of a low biomass). Waterlogging could n o t be the only reason because (a) the poisoned areas are n o t especially wet, and (b) bog areas have other reflectance characteristics than the poisoned area. (3) The resolution of the LANDSAT-1 system is restricted by the pixel size of approximately 60 m × 80 m, therefore only patterns with this minim u m size can be distinguished on the ground. Area 1 consists of 9 pixels, the majority of which o represent copper-poisoned areas. observed during ear. . . . lier ground surveys (Lag and B~blvlken, 1974). Possible poisoned areas within the other type areas are too small to be detected from space. The results of the previously described classification and clustering procedures were: (1) Stepwise discriminant analysis. Five of the eleven type areas outlined above (see Table III and Fig. 4) were selected to test if the anomaly area 1 could be separated from the four others; grass (type area 9), grass (type area 10), birch (type area 2) and an area adjacent to the anomaly (type area 3). Fig. 8 shows a plot of the results. The coordinates are canonical variables which represent numerical transformation of the LANDSAT-1 reflectance data given in Table II. In the plot, the area I anomaly is separated as a differ-
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470
B. BOLVIKEN ET AL
ent group except for one pixel classified as belonging to the area 3, and one classified as birch (area 2). These two pixels are located near the centre of the area 1 anomaly (see Fig. 4). The a m o u n t of birch present on them, apparently, is sufficient to alter the spectral reflectance in a way that they seem to be more similar to the area 3 and area 2 groups than to the area 1 anomaly group. (2) Pattern recognition. The pixels located in area 1 (Fig. 4) were selected as a training group and the STANSORT pattern recognition technique was used to search for similar areas. Fig. 9 shows the results of the pattern recognition of the survey area (800 hectares, 2000 pixels). The pixels with the "C"s are classified as potential poisoned areas. The most promising area is located southeast of area 1. The area will be investigated on subsequent ground surveys. The " C " s north and west of area 1 are probably errors caused by the classification procedure employed.
CONCLUSIONS
An area of natural Cu poisoning associated with a Cu deposit at Karasjok, northern Norway, can be characterized in satellite data primarily by the low values of the 7/5 channels reflectance ratio. Normal grass areas, which on the aerial photographs appear to be similar to the poisoned areas, show high values for the 7/5 channels reflectance ratio. Heavy metal poisoning seems to be a c o m m o n feature near Norwegian sulphide deposits. Recording of multispectral reflectance from remote platforms, therefore, appears to be a promising regional exploration m e t h o d for disseminated, shallow Cu deposits in this part of Norway. Numerical classification techniques, such as pattern recognition and stepwise discriminant analysis proved to be adequate tools for distinguishing a copper-poisoned area from normal areas. These techniques could be applied to satellite data for routine searching for new poisoned areas. Characteristic elliptical features appear in the channel 5 and 6 images and are more accentuated in the reflectance ratio colour images (in particular 6/5). These elliptical features might have some relation to the mineralization, a possibility which should be further investigated. The spatial resolution of the LANDSAT-1 data is approximately 0.45 hectare which is n o t sufficient to identify all known patches of natural Cu poisoning at Karasjok or the occurrences of natural Pb poisoning found at other locations in Norway. This resolution problem may be overcome by using reflectance data obtained from digitized multispectral airphotography or airborne scanners. Multispectral aerial photography has been obtained for these areas and is being digitized and analyzed by computer in an a t t e m p t to overcome the resolution limitations of LANDSAT-1.
DETECTION OF METAL POISONING USING LANDSAT DATA
471
ACKNOWLEDGEMENTS
The authors wish to express their gratitude to the Sydvaranger Mining company for the information and assistance offered to perform this study. Special thanks are due to Mr. Bernt R~bsholt, exploration geologist of the Sydvaranger Mining Company, who provided the geological information. Mr. R~bsholt found the mineralization at Karasjok on 1968 during the follow up of a geochemical anomaly obtained in a stream sediment survey suggested by Professor J.A.W. Bugge and conducted by the Geological Survey of Norway 1967 as contract work for the Sydvaranger Mining Company.
REFERENCES Dixon, W.J. (Editor), 1970. Biomedical Computer Programs. Publications in Automatic Computations No. 2. University of California Press, Los Angeles, Calif., 773 pp. Honey, F., 1975. STANSORT: a fully interactive tape reading program for ERTS II, philosophy and operational usage (with users manual). Stanford Remote Sensing Lab. Rep., No. 75-13, 30 pp. Honey, F.R., Prelat, A.E. and Lyon, R.J.P., 1974. STANSORT: Stanford remote sensing laboratory pattern recognition and classification system. Proc. 9th Int. Syrup. on Remote Sensing of Environment, Ann Arbor, Mich., 9 pp. L~g, J. and B~blviken, B., 1974. Some naturally heavy-metal poisoned areas of interest in prospecting, soil chemistry and geomedicine. Nor. Geol. Unders., 304 : 73--96. L~,g, J., Hvatum, O.~. and B~blviken, B., 1970. An occurrence of naturally lead poisoned soil at Kastat near Gjovik, Norway. Nor. Geol. Unders., 266: 141--159. Lyon, R.J.P., Honey, F.R. and Ballew, G., 1975. A comparison of observed and modelpredicted atmospheric perturbations on target radiances measured by ERTS. Proc. Inst. Electr. Electron. Eng. Syrup. on Application of Remote Sensing and Digital Imagery to Mineral Exploration, Houston, Texas, 6 pp. Levine, S.R., 1975. An interactive program for producing computer-enhanced ERTS images (IMAGE), II. Methodology and development. Stanford Remote Sensing Lab. Rep., No. 75-4. Romanova, M.A., 1964. Air survey of sand deposits by spectral luminance. Laboratory of Aeromethods of the Academy of Sciences of the USSR, State Scientific and Technical Press for Literature on Petroleum and Solid Fuels, Leningrad. Saunders, D.F., Thomas, G.E., Kinsman, F.E. and Beatty, D.F., 1973. ERTS-1 imagery use in reconnaissance prospecting-evaluation of the commercial utility of ERTS-1 imagery in structural reconnaissance for minerals and petroleum. Type III Final Report to NASA. U.S. Department of Commerce, Natl. Tech. Inf. Serv., E74-10345.