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

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

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

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

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

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

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

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 OUTLINES IEEE Internaional Geoscience and Remote Sensing Symposium- 29 july N. Mezned

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

3) Work positioning 10  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 2011 IEEE Internaional Geoscience and Remote Sensing Symposium- 29 july N. Mezned

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 OUTLINES IEEE Internaional Geoscience and Remote Sensing Symposium- 29 july N. Mezned

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

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 OUTLINES IEEE Internaional Geoscience and Remote Sensing Symposium- 29 july N. Mezned

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

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

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

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

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 OUTLINES IEEE Internaional Geoscience and Remote Sensing Symposium- 29 july N. Mezned

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

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

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) 2011 IEEE Internaional Geoscience and Remote Sensing Symposium- 29 july N. Mezned

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

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

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

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 OUTLINES IEEE Internaional Geoscience and Remote Sensing Symposium- 29 july N. Mezned

6) Conclusion and perspectives  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. Conclusion Perspectives  We plan for more campaign,  We propose to test the SMA approach for different mining sites IEEE Internaional Geoscience and Remote Sensing Symposium- 29 july N. Mezned

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