1. RME
TAILING MODELLED AND
MEASURED SPECTRUM FOR
MINE TAILING MAPPING IN
TUNISIAN SEMI-ARID CONTEXT
N. Mezned1,2, S. Abdeljaouad1 , M. R. Boussema3
1 RME/FST, 2Isepbg 3 LTSIRS/ENIT,
(Tunis, Tunisia) (Tunis, Tunisia) (Tunis, Tunisia)
2011 IEEE Internaional Geoscience and Remote
N. Mezned 1
Sensing Symposium- 29 july

2. Context
Mine tailing impact
Soils Vegetation Ecologic Systems Water quality
Ba/Fl Hammam Zriba mine site Pb/Zn Jebel Hallouf-Bouaouane mine Pb/Zn Jebel Ressas mine site Tunisia Pb/Zn Jebel Ressas mine site Tunisia
Tunisia site Tunisia
Human life
2011 IEEE Internaional Geoscience and
N. Mezned Remote Sensing Symposium- 29 july 2

3. Context
 North of Tunisia: several types of mine
(Pb, Zn, Fl, …, etc.)
 Mejerda river watershed: precious
source of water
a
Environment risks
a
a
2011 IEEE Internaional Geoscience and
N. Mezned Remote Sensing Symposium- 29 july 3

4. Context
 Necessity of mine tailing mapping
 Advantages: low costs
spatial coverage
Remote sensing: satellite data
2011 IEEE Internaional Geoscience and
N. Mezned Remote Sensing Symposium- 29 july 4

5. OUTLINES
1) Context
2) Study area and problematic
3) The used data
4) The proposed approach
5) Experimental results
6) Conclusion and perspectives
2011 IEEE Internaional Geoscience and
N. Mezned Remote Sensing Symposium- 29 july 5

6. 2) Study area and problematic
Mine tailings
Kassab Wady
Jebel Hallouf-Bouaouane
Mine
Mejerda River
N. Mezned 2011 IEEE Internaional Geoscience and
Remote Sensing Symposium- 29 july 6

7. 2) Study area and problematic
Jebel Hallouf-Bouaouane
 188 mille tonnes of metal (84 Pb et 64 Zn) in 1952
 Abandoned since 1986
Important quantity of tailing
Mine activity Environment impact
Terrain subsidence
N. Mezned 2011 IEEE Internaional Geoscience and
Remote Sensing Symposium- 29 july 7

8. OUTLINES
1) Context
2) Study area and problematic
3) Work positioning
4) The used data
5) The proposed approach
6) Experimental results
7) Conclusion and perspectives
N. Mezned 2011 IEEE Internaional Geoscience and 8
Remote Sensing Symposium- 29 july

9. 3) Work positioning
 Passive Remote sensed data
Multispectral data Hyperspectral data
(landsat TM, ETM+, ASTER, etc.) (Hyperion,HyMap, etc.)
Mine site mapping using HyMap
Mineral mapping using Landsat TM
(Taylor and Vukovic, 2001) and
data, (Zhang et al., 2007) Probe data (Staenz et al., 2003)
Mineral mapping using Landsat ETM+
data and field spectra measured with
+
ASD spectroradiometer , (Liu et al., 2003) field measured spectra
or spectra from publicly library
N. Mezned 2011 IEEE Internaional Geoscience and 9
Remote Sensing Symposium- 29 july

10. 3) Work positioning
 Passive Remote sensed data
 Problems :
• Mine tailing risks on environment and human health
 Objective:
 Mine tailing mapping using multispectral data
 Tailing modelled spectra with respect to the field truth
SMA overcome the luck of spectroradimeter
N. Mezned 2011 IEEE Internaional Geoscience and 10
Remote Sensing Symposium- 29 july

11. OUTLINES
1) Context
2) Study area and problematic
3) Work positioning
4) The used data
5) The proposed approach
6) Experimental results
7) Conclusion and perspectives
N. Mezned 2011 IEEE Internaional Geoscience and 11
Remote Sensing Symposium- 29 july

12. 3) The used data
 Multispectral data: Landsat ETM+
• 6 bands,
• 30 m,
Landsat ETM+  Publically library: JPL spectral data
(05/03/2000)
 Field campaign data: Mineral identification
and abundance estimation
• 18 samples/dyke = 54 tailing measurements,
N. Mezned 2011 IEEE Internaional Geoscience and 12
Remote Sensing Symposium- 29 july

13. OUTLINES
1) Context
2) Study area: Soil salinity, the problematic
3) The used data
4) Work positioning
5) The proposed approach
6) Experimental results
7) Conclusion and perspectives
N. Mezned 2011 IEEE Internaional Geoscience and 13
Remote Sensing Symposium- 29 july

14. 5) The proposed approach
Tailing modeling spectrum for ETM+
classification: spectral unmixing
ETM+ image
endmember
pre- Classification Validation
processing selection
N. Mezned 2011 IEEE Internaional Geoscience and 14
Remote Sensing Symposium- 29 july

15. 5) The proposed approach
Tailing modeling spectrum for ETM+
classification: spectral unmixing
ETM+ image
endmember
pre- Classification Validation
processing selection
Vegetation
Soils
Tailing component
spectrum?
Direct mean: Measured
by spectroradiometer
Indirect mean: Modelled
with respect of field truth
N. Mezned 2011 IEEE Internaional Geoscience and 15
Remote Sensing Symposium- 29 july

16. 5) The proposed approach
Tailing modeling spectrum for ETM+
classification: spectral unmixing
ETM+ image
endmember
pre- Classification Validation
processing selection
Linear spectral
unmixing
1. Vegetation
3 fraction maps 2. Soils
3. Mine tailings
Measured Modelled
spectrum spectrum
N. Mezned 2011 IEEE Internaional Geoscience and 16
Remote Sensing Symposium- 29 july

17. 5) The proposed approach
Tailing modeling spectrum for ETM+
classification: spectral unmixing
ETM+ image
endmember
pre- Classification Validation
processing selection
Comparison
RMS errors
N. Mezned 2011 IEEE Internaional Geoscience and 17
Remote Sensing Symposium- 29 july

18. OUTLINES
1) Context
2) Study area: Soil salinity, the problematic
3) The used data
4) Work positioning
5) The proposed approach
6) Experimental results
7) Conclusion and perspectives
N. Mezned 2011 IEEE Internaional Geoscience and 18
Remote Sensing Symposium- 29 july

19. 5) Experimental results
1. Tailing modelled spectrum: SMA
Hematite JPL library Kaolinite
Goethite
Pyrite
Re sampled spectra to Landsat
ETM+ band passes
Galena
linear combination Quartz
Calcite
Sphalerite
Tailing Modelled spectrum
N. Mezned 2011 IEEE Internaional Geoscience and
Remote Sensing Symposium- 29 july 19

20. 5) Experimental results
1. Tailing mlodelled spectrum: SMA
Sampling
18 samples for each dyke = 54 samples
Laboratory analysis
- X Ray Diffraction XRD
Identification
- Calcimetry
and
- Counting on polished sections % of minerals
N. Mezned 2011 IEEE Internaional Geoscience and 20
Remote Sensing Symposium- 29 july

21. 5) Experimental results
2. ETM+ Linear spectral unmixing
• We used both ASD measured and SMA modelled spectra in the
classification processes,
Mine tailing fraction maps generated from the ETM+ linear spectral
unmixing using: (a) the measured spectrum with ASD spectroradiometer
and (b) the modelled tailing spectrum and (c)
21
N. Mezned 2011 IEEE Internaional Geoscience and
Remote Sensing Symposium- 29 july

22. 5) Experimental results
2. Classification validation
99.6 % of pixels have an RMS errors:
< 2.6 10-5 using the modelled spectrum,
tailing map
< 1.9 10-5 using the measured spectrum.
< 3.3 10-5 using derived ETM+ spectrum
22
N. Mezned 2011 IEEE Internaional Geoscience and
Remote Sensing Symposium- 29 july

23. 5) Experimental results
2. Classification validation
99.6 % of pixels have an RMS errors:
< 2.6 10-5 using the modelled spectrum,
tailing map
< 1.9 10-5 using the measured spectrum.
< 3.3 10-5 using derived ETM+ spectrum
23
N. Mezned 2011 IEEE Internaional Geoscience and
Remote Sensing Symposium- 29 july

24. 5) Experimental results
2. Classification validation
99.6 % of pixels have an RMS errors:
< 2.6 10-5 using the modelled spectrum,
tailing map
< 1.9 10-5 using the measured spectrum.
< 3.3 10-5 using derived ETM+ spectrum
24
N. Mezned 2011 IEEE Internaional Geoscience and
Remote Sensing Symposium- 29 july

25. OUTLINES
1) Context
2) Study area: Soil salinity, the problematic
3) The used data
4) Work positioning
5) The proposed approach
6) Experimental results
7) Conclusion and perspectives
N. Mezned 2011 IEEE Internaional Geoscience and 25
Remote Sensing Symposium- 29 july

26. 6) Conclusion and perspectives
Conclusion
 The results comparison indicate that the modelled spectrum can even
better characterize the tailings in the case of semi-arid context,
 The SMA approach can be an optimal solution to replace the lack of the
spectroradiometer and can be applied successfully to multispectral data
analysis, particularly those acquired during previous periods.
Perspectives
We plan for more campaign,
 We propose to test the SMA approach for different mining sites.
N. Mezned 2011 IEEE Internaional Geoscience and 26
Remote Sensing Symposium- 29 july

27. Thanks for your
attention
N. Mezned 2011 IEEE Internaional Geoscience and 27
Remote Sensing Symposium- 29 july