Application Of Remote Sensing Data In Lithological Discrimination Of Gypsum In Tataouine Area, Southern Tunisia

The abstract focuses on the application of remote sensing data for lithological discrimination in the Tataouine area of southern Tunisia, with a specific emphasis on gypsum of the Mestaoua formation [1], that constitutes the 4
^th
reserve worldwide. It highlights the relevance of remote sensing data due to their spatial, spectral, and radiometric characteristics, which are crucial for accurate lithological mapping and mineral exploration. The research project is part of the larger REMINT project led by the National Center for Cartography and Remote Sensing.Various spectral and radiometric transformations, including band ratios, spectral indices, principal components analysis (PCA), and independent components analysis (ICA), were applied to ASTER data to enhance lithological discrimination and delineation between outcropping formations [2, 3, 4, 5, 6, 7, 8, 9] in the study area. The results of these methods were compared with existing geological and field investigations, demonstrating their effectiveness in mapping lithological units.Notably, band ratios derived from the image spectra were utilized in two RGB color composites, namely (4/2, 4+6/5, 7/6) and (4+6/5, 7/6, 8/7), successfully discriminating lithological units such as the gypsum of the Mestaoua formation, Aeolian sand covers, clay, and sandstone of the Techout formation.The research findings provide valuable insights into the lithological composition of the Tataouine area, particularly regarding gypsum. These outcomes significantly contribute to the field of lithological mapping and mineral exploration. Furthermore, they demonstrate the potential of remote sensing data for identifying and characterizing mineral resources in similar regions. These results are significant for proving the great potential of ASTER data, which was downloaded free from the USGS site, in lithological discrimination and for opening a new avenue for lithological mapping using remote sensing data.