ASTER Multispectral Imagery for Spectral Unmixing based Mine Tailing

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					      ASTER Multispectral Imagery for Spectral
    Unmixing based Mine Tailing cartography in the
                  North of Tunisia
                        Nouha Mezned1 , Sˆ adi Abdeljaoued1 and Mohamed Rached Boussema2
                                         a
                                    1
                                                                          e
                                      Laboratoire des Ressources Min´ rales et Environnement,
                                                           e
                                                     Facult´ des Sciences de Tunis,
                                                                      e e
                                    Campus Universitaire du Belv´ d` re, 2092 Tunis, TUNISIA
                                                    Email: mezned nouha@yahoo.fr
                        2
                                            ee e                   e                  ` ee
                            Laboratoire de T´ l´ d´ tection et Syst` me d’Information a R´ f´ rences Spatiales
                                               ´                        e
                                               Ecole Nationale d’Ing´ nieurs de Tunis,
                                                                e e
                                                 1002 le Belv´ d` re Tunis, TUNISIA



   Abstract— In the North of Tunisia, Hammam Zriba region            to detect, to cartography and to follow mine tailing change
represents an important mine activity zone, it is among the          and impact on the environment. Remote sensing technologies
most important in the Maghreb . The mine tailings cause the          are an excellent tool for mine tailing environment change
environment degradation, so it had polluted the atmosphere, the
soils, the vegetation, the water quality and the ecosystem of the    following and impact detection.
Hammam ouady (Dry River) which is discharged directly in the            In this study, we are interested in the cartography of mine
Miliane River. The Miliane River represents the second important     tailings around the test site and particularly to generate a de-
resource of potable water, after Medjerda River. In this study, we   tailed spatially map. This work is based on the remote sensed
propose to use the ASTER VNIR and SWIR Surface Reflectance,           data analysis and its comparison with the local (relatively to
and TIR data for mine tailing cartography. Classification of the
SWIR product based on partial spectral unmixing is performing        the considered image) terrain truth. It’s beneficial for the local
using image derived endmembers identified as a mineral groups.        government to supervise the mineral environmental changes
Unmixing results coincide with XRD laboratory analysis of the        and be aware of the pollution status dynamically.
field samples. Moreover, the given results show that ASTER               We propose to use the multispectral Advanced Spaceborn
multispectral image is more precise for certain mineral detection    Thermal Emission And Reflection Radiometer ASTER Level
than Landsat ETM+ image.
                                                                     2B Visible Near Infra Red (VNIR) and Short Waves Infra
                      I. I NTRODUCTION                               Red (SWIR) data acquired on March 2001. This product
                                                                     present respectively a 15m and 30m spatial resolution, and
   In the north-Oriental of Tunisia, the Jurassic block zone         a 4 bands and 6 spectral bands spectral resolution image.
represents the Tunisian fluoric province (F-Ba-Zn-Pb) with            Moreover, we used Thermal Infra Red (TIR) products to test
12th deposits. This province includes J. Ressas exploited for        result complementarities.
lead and zinc, Hammam Zriba mine exploited for fluorite and              The classification of the multispectral image was performed
Barite, etc. Particularly, Hammam Zriba region (36◦ 20’N and         by the partial spectral unmixing method using the image
10◦ 13.5’E) represents an important mine activity zone, it is        derived endmembers compared to mineral library spectra. This
among the most important in the Maghreb with 5 milions tons          will allow us to bring more explication and interpretation
of ore with 15-35 percent CaF2 [1]. Moreover, it is character-       of mineral spatial distribution and to provide information on
ized by a touristique activity manifested by the thermal station     locating tailing zones.
and an interesting agriculture productivity. Thus, it constitues
an important economic activity province [2].                                          II. T EST S ITE DESCRIPTION
   In this site, the abandoned Hammam Zriba mine leaving                Hammam Zriba constitues an important economic activity
behind substantial quantities of finely-divided mineral waste or      region by its touristique activity manifested by the thermal
tailings. The mine tailings cause the environment degradation,       station, an interesting agriculture productivity and above all
so it had polluted the atmosphere, the soils, the vegetation,        an important mine activity zone [2]. The fluorspar mining
the water quality and the ecosystem of the Hammam ouady              industry of Hammam Zriba mine (thitonic-campanien contact)
(Dry River) which is discharged directly in the Miliane River.       in the north-oriental Tunisia is among the most important in
The Miliane River represents the second important resource of        the Maghreb with 5 milions tons of ore with 15-35 percent
potable water, after Medjerda River. Thus, tailings constitute a     of CaF2. The mineralization is constituted by these different
threat for the environment and particularly, the human health        minerals: barytine - Celestine with 40 to 45 percent, fluorite
(tailings cause silicosis disease). So, it became necessary          with 15 to 35 percent and quartz with 10 to 40 percent and
                                                                                                                       (a)




                                                                                                                       (b)
                                     (a)
Fig. 1.    Map showing the location of the Hammam Zriba mine 3D till
representation mapped with a ASTER VNIR imagery (60x100 pixels). The
white numerous indicate location of the mine tailings (1), thermal station (2),
El Hamam Ouady (3) and Meliane river (4).



blend, galena and pyrite with 5 to 15 percent and calcite with
2 to 10 percent [1].                                                                                                   (c)
   The mine is located near El Hamam Ouady (36◦ 20’N                              Fig. 2.     XRD analysis results. The d-spacing value: 3.406 indicates the
10◦ 12’E) (Figure 1) which discharge directly in the Meliane                      presence of Barite, 3.034 indicates the presence of Calcite, 3.15 indicates
                                                                                  the presence of Fluorite, 2.96 indicates the presence of Galena, 6.59 indicates
river, the second important river in Tunisia after Medjerda                       the presence of Hemimorphite, 3.46 indicates the presence of Mizzonite, 3.34
river. The resulting mine tailings , in particularly the residues                 indicates the presence of Quartz and 2.74 indicates the presence of Smithsonite
of lead, zinc and above all fluor and barium cause an impact
which is more visible on the human (cause silicosis disease),
faune and flore health. The exceeding of fluorite and barite                        B. ASTER data preprocessing
due to several years of fluorite barite-melting activities threat
human life and let vegetation and ecosystem suffering.                               The Japanese Advanced Spaceborne Thermal Emission and
                                                                                  Reflection Radiometer ASTER, one of the principal instru-
                                                                                  ments on Terra, was launched in December 1999 to estabilish
    III. S PECTRAL DATA AND F IELD DATA C OLLECTION                               a spaceborne capability for high spatial, multispectral visible-
                                                                                  shortwave infrared and thermal infrared remote sensing data
A. Field data collection and analysis
                                                                                  mapping of the Earth environment [3]. It covers the approxi-
   Tailings samples were collected at deposit location, con-                      mately 0.55 to 11.3 µm range, 4 VNIR bands, 6 SWIR bands,
sidered as the pollution source, through out the study site                       and 5 (TIR) bands with respectively 15m, 30m, and 90m
in February 2007. This choice of the campagne date was                            spatial resolution.
fundamental for the study to transmet and to preserve the                            We have used on-demand product ASTER Surface Re-
tailing state since the data acquisition period.                                  flectance which contains surface reflectance for each of the
   The samples cover different tailings areas within deposits to                  nine VNIR, SWIR and TIR bands. The Level 2B surface
include tailings of various mineral compositions and oxidation                    reflectance data set contains surface reflectance after apply-
stages (6 radials for each dyke). Each sample was gathered                        ing the atmospheric corrections to observed radiances. The
from the top of the tailing surface (first 3-4cm). We try by this                  accurate atmospheric correction removes effects of changes
restrict sampling to have an homogenous and a significative                        in satellite-sun geometry and atmospheric conditions and
sample for each dyke.                                                             improves surface type classification. Accurate calibration of
   The analysis using X Ray Diffraction XRD method (Figure                        ASTER band 9 however is considered problematic because
2) was conducted to reveal the mineral composition of these                       of the uncertainty of water vapor estimates for atmospheric
samples as listed below: Barite, Calcite, Fluorite, Galena,                       correction. So, ASTER Band 9 reflectance can be too high
Hemimorphite, Mizzonite and Smithsonite with different rel-                       that some mineral maybe overestimated [8].
ative concentration. This result confirms the band ratio com-                         The complete processing of the ASTER Surface Reflectance
bination analysis.                                                                data is illustrated by the flowchart on figure 3. It consists on
                          Fig. 3.   Flowchart illustrated the complete processing of the ASTER Surface Reflectance data.



three main steps. The first step is the geometrical registration,          respectively B13/B14 and B12/B13 Ratio image [4]. Carbon-
so as to have the same geodetic reference. The second one con-            ates have a unique spectral feature. They have low emissivity
cerns the image re-enhancement and the image transformation               in ASTER band 14 spectral region, and high emissivity in
by the band combination for easier image interpretation. In the           ASTER band 10 to 13 spectral region. The SiO2 minerals
last step, we make a principal component analysis (PCA) for               have another unique spectral property in TIR. They have lower
optimal endmembers selection needed for the partial spectral              emissivity in ASTER band 10 and band 12 spectral regions
unmixing method.                                                          than in band 11. [5]. The resulting index maps are shown
   The registration to the relief map is performed using the              in figure 5. Figure (a) show the repartition of the carbonate
Digital Elevation Model (DEM), so that the precision of the               minerals, particularly calcite mineral as proved by the XRD
registration is less than half-pixel. The band combination                analysis. This repartition indicates that this mineral existing
has been also tested to provide best image interpretation                 within deposits but not very highly significant (with a relative
using false-color composite image. The ASTER SWIR band                    important concentration). Whilst, quartz mineral (Figure (b))
combination were successfully applied for geologic [9] and                is less important than calcite within mine tailings. This is not
lithologic [4] interpretation. A general picture of the test site         a surprising result since the principal minerals are Barite and
is shown in Figure 4, combining SWIR bands 4,6 and 8 in                   fluorite.
Red Green Blue RGB and processed to increase the color
                                                                                IV. U NMIXING BASED M ULTISPECTRAL ASTER
saturation. In deed, Red-pink areas mark mostly the presence
                                                                                                 C ARTOGRAPHY
of kaolinite and/or alunite. As we can see, these minerals are
absent in the tailing deposit.                                            A. Mineral spectral library
   Moreover, the spectral indices resulting from the orthogonal              Based on the XRD analysis, different spectra was selected
transformation of the ASTER (SWIR) bands are used for                     from USGS [6] or JPL Spectral library [7] and have been
discrimination and mapping of surface rock types particularly,            resampled to demonstrate major spectral signatures of minerals
clays (such as Alunite, Kaolinite, Montmorillonite etc.). The             in SWIR. The mineral spectra which shown in figure 6 was
ASTER TIR bands can also used for the spectral indices                    used to the image derived endmembers identification. In deed,
generation. The resulting spectral index images are useful for            a first three endmembers were chosen from the ASTER SWIR
lithologic mapping and were easy to interpret geologically.               image window (60x100 pixels) using the scatter plots in the
We proposed to use ASTER TIR bands to mapping quartz                      first three principal components [10], which contain 99.64
and carbonate minerals.                                                   percent of the variability in the data. The mine tailing map
   The carbonate and the SiO2 index can be achieved using                 resulting from the classification based on the three image
                                                                         (a)
       Fig. 4.   SWIR bands 4,6 and 8 in Red Green Blue RGB color composite. The given white arrow indicates the mine tailing deposit location.




                                                   (a)                                         (b)
Fig. 5. Images showing the spectral indices resulting from B13/B14 and B12/B13 band Ratio combination for respectively the carbonate and the quartz
minerals analysis. The given red arrow and red line indicates the mine tailing deposit location.



derived endmembers was used for masking undesired zones                        Filtering to find the abundances of endmembers. For this
and highlight only the mine tailing interesting area. Thus, a                  technique, not all of the endmembers in the image need to
second three endmembers were chosen from the tailing area                      be known. It maximizes the response of the known endmem-
masked image and illustrated in figure 7. Compared to the                       ber and suppresses the response of the composite unknown
resampled library mineral spectra, only Calcite, smithsonite,                  background, thus matching the known signature. It provides a
galena and quartz minerals are identified and characterized by                  rapid means of detecting specific materials based on matches
an absorption feature in SWIR bands near 2.336 µm for calcite                  to library or image endmember spectra and does not require
and smithsonite, 2.167 µm and 2.262 µm for galena and quartz                   knowledge of all the endmembers within an image scene [8].
respectively. These minerals can be classified as a groups.                     So, results can be obtained in an area without any detailed
The class 1 groups quartz, calcite, smithsonite and galena.                    prior knowledge of the geology of the area. In other sides, this
The second class groups calcite, smithsonite and galena with                   technique may find some false positives for rare materials.
the gross shape curve in the mean ASTER image spectra                             The results of the matched filtering as shown by the figure
correspond to the significant spectral absorption of calcite                    appear as a series of gray-scale images, one for each selected
and smithsonite in band 8 centered in 2.334 µm . This same                     endmember. It provide a means of estimating relative degree
mineral composition belong also to Class 3 but the gross shape                 of match to the reference spectrum and approximate sub-pixel
curve in the mean ASTER image spectra correspond to the                        abundance. The figure 7 shows three maps for the different
absorption of galena in band 5 centered in 2.167 µm. It will be                classes: class1, class 2 and class 3. The maximum of the three
not possible to map particular minerals using ASTER imagery.                   classes is detectable in the tailing deposits area (indicates by
However, it is possible to differentiate between mentioned                     a white color) with different relative abundance. Black color
mineral groups. This is a significant improvement over Landsat                  indicates the absence of materials.
Enhancement Thematic Mapper plus (ETM+) data [11].
                                                                                                       V. C ONCLUSION
B. Mine tailing cartography
                                                                                  In this paper, a mine tailing cartography in the Hammam
   Several recent results have shown that spectral analysis of                 Zriba mine was achieved using the ASTER VNIR and SWIR
airborn imaging spectrometer and satellite data can provide                    reflectance surface and TIR data. False-color composite image
useful mineralogical and geochemical information for geologi-                  was used to get an overview about the area mineral compo-
cal mapping and exploration. In addition, the image processing                 sition. Band rationing is also applied in an attempt to map
techniques developed to facilitate surface compositional map-                  distribution of SiO2 and carbonates minerals. Moreover, the
ping using spectral unmixing techniques are now more robust                    classification was performed with partially spectral unmixing
[12]. Our investigation builds on the existing knowledge of                    method based on image derived endmembers. The unmixing
surface mineral identification techniques, particularly partial                 results has shown that the ASTER multispectral data is more
spectral unmixing method with reference to spectral library.                   precise than Landsat ETM+ data on mine tailing cartography.
In deed, we used as spectral mapping technique the Matched                     Furthermore, multispectral ASTER image cartography results
                   (a)                                   (a’)                                 (b)                                   (b’)




                   (c)                                   (c’)                                 (d)                                   (d’)




                   (e)                                   (e’)                                  (f)                                  (f’)




                   (g)                                  (g’)                                  (h)                                   (h’)
Fig. 6. Mineral library spectra and their correspondant resampled spectra to ASTER SWIR bandpasses: barite (a) and (a’), calcite (b) and (b’), fluorite (c)
and (c’), galena (d) and (d’), hemimorphite (e) and (e’), mizzonite (f) and (f’), quartz (g) and (g’) and smithsonite (h) and (h’).



show a complementary with laboratory analysis. However, it                      [5] Ninomiya, Yoshiki, Mapping quartz, carbonate minerals, and mafic-
could be important to make comparison of two different mine                         ultramafic rocks using remotely sensed multispectral thermal infrared
                                                                                    ASTER data, SPIE, SPIE Vol. 4710, pp. 191-202, 2002.
type results.                                                                   [6] USGS,             Digital Spectral library 05 Website adress:
                                                                                    http://speclab.cr.usgs.gov/spectral.lib05/spectral-lib04.html.
                      VI. ACKNOWLEDGMENT                                        [7] C. I. Grove, S. J. Hook and E. D. Paylor II, Laboratory Reflectance
                                                                                    Spectra of 160 Minerals, 0.4 to 2.5 Micrometers JPL Publication 92-2,
  The authors would like to thank the NASA Land Processes                           1992.
Distributed Active Archive Center and the User Services                         [8] P. Junek, Geological mapping in the Cheleken Peninsula, Turkministan
                                                                                    area using Advanced Spaceborne Thermal Emission and Reflection Ra-
USGS Earth Resources Observation and Science (EROS) for                             diometer (ASTER) data, ISPRS, 2004.
providing numerous remotely sensed data.                                        [9] M. Abrams and S.J. Hook, Simulated Aster Data for Geologic Studies,
                                                                                    IEEE TGARS, Vol. 33, No. 3, 1995.
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                                               (a)




                                               (b)




                                               (c)
Fig. 7. Class fraction maps derived from the partial spectral unmixing of ASTER SWIR image using the image derived endmembers spectra. Bright tones
in the fraction maps represent high fractions and dark tones represent low fractions. The given red arrows indicate the mine tailing deposit location.