ABSTRACT The geology of the Central African Republic (CAR) is still disparate and only the work of Mestraud in 1964 which gives us a little detail on certain formations. Detailed studies have not yet been carried out in much of the country, particularly in the southern part of the Congo Craton because of the vegetation cover that is a handicap. Remote sensing is nowadays an indispensable tool to solve a problem. The analysis on this part of the country by the remote sensing methods such as NDVI (Normalized Difference Vegetation Index) and the Supervised classification shows very dense vegetation cover . We have masked the vegetation the water and the clouds. Therefore, we used only the opens area to detect hydrothermal alteration mineral anomalies using band ratios. To overcome this problem, the use of other images and methods are essential to enhance the mineralization of the Craton of Congo in CAR by remote sensing. İt would be desirable to use other images, and methods to explore with certainty these minerals. Keywords: CAR, Vegetation, NDVİ, Alteration Mineral, Congo Craton USE REMOTE SENSING FOR MINERAL EXPLORATION, IMPACT OF VEGETATION COVER ON THE LANDSAT 8 IMAGE: CASE OF THE SOUTHERN PART OF CENTRAL AFRICAN REPUBLIC 1. INTRODUCTION Cratons revealed significant mineralizations (Arnaud Fontaine et al., 2017) such as Iron, Cu, Ni, Au and U. The Central African Republic (CAR) is geologically characterized by mainly Congo Craton in the south and Pan-African units in the north. There is rich gold, diamond, uranium, iron mineralization in the basement rocks and recent deposits. We know that the best studied cratons such as Pilbara and Yilgarn (ANTHONY A. MOREY, May 2008; PHIL BIERWIRTH, 2002; T. J. Roache et al., 02 May 2014,) , the São Francisco in Brazil (J.S.F. Barbosaa, 2004) and the West African. It should be noted this research has been done on this part of the craton to show its mineralization in certain minerals. With the advent of remote sensing, which is nowadays a very important science in the exploration of mineral resources, we can finally reveal this challenge. This is why in this research we will study in a simple way how to detect to hydrothermal mineral alteration in dense vegetation covers using remote sensing. 2. MATERIAL AND METHOD The study area is located in the South and South-West of the Central African Republic. Landsat 8 images were acquired on the website earthexplorer .usgs.gov/. All these image were taken on a Path (181) and Row (57) on 2014-12-31 and 2018-01-08. Data was processed to correct radiometric correction and atmospheric correction, haze removal and cloud, vegetation and water masking. The method used for atmospheric correction is the FLAASH method (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes). After having made the correction to these images, we then calculate the NDVI (Normalized Difference Vegetation Index) NDVI = ((IR - R) / (IR + R)) And make a supervised classification (Spectral Angle Mapper) to follow the temporal evolution of the vegetation cover. Finally, an attempt was made to determine the hydrothermal Alteration minerals in opaen area using band ratios . Geomatica, Envi 5.3, Erdas 2014 and Arcgis 10.4.1 was used as software. Figure 1 : Localization and Geology of Study Area 3. GEOLOGY OF CONGO CRATON IN CAR The southern domain in Congo Craton in CAR) consists of (1) Archean and Paleoproterozoic micaschists and quartzites (Poidevin,1991). The intermediate domain consists of Archean gneisses, metabasites, Paleoproterozoic metasedimentary rocks and metasedimentary rocks and migmatites. A northern domain that does not belong to the Congo Craton. It has also corresponds to the western extension of the Pan-African chain in Cameroon, and it consists of granulites, orthogenesis, and Neoproterozoic granites (833 ± 66 Ma, Pin and Poidevin, 1987). Figure 2 : Mbomou Craton and Geology of study Area 4. RESULTS The Normalized Difference Vegetation Index (NDVI) is a standardized index allowing you to generate an image displaying greenness (relative biomass). The NDVI is preferred for global vegetation monitoring because it helps compensate for changing illumination conditions, surface slope, aspect, and other extraneous factors (Lillesand 2004). The negative values represent clouds, water, and snow, and values near zero represent rock and bare soil. The documented and default NDVI. We note that a slight variation of NDVI between 2014 and 2018. In 2014 NDVI varies from -1 to 0.489 and that of 2018 varies from -1 to 0.458. These values show that the vegetation cover remains dense during these periods (Figure 3). IR = pixel values from the infrared band R = pixel values from the red band Spectral Angle Mapper (SAM) is one of the physically-based spectral classification that uses an n-D angle to match pixels to reference spectra. The algorithm determines the spectral similarity between two spectra by calculating the angle between the spectra and treating them as vectors in a space with dimensionality equal to the number of bands. This technique is preferred in this study because it is relatively insensitive to illumination and albedo effects. we used an area of 25366 km2 to make the classification because of the quality of the image and the opens area (Figure 4). 5. ESSAY OF HYDROTHERMAL ALTERATION We used the band ratio method (Sabin 1997) to determine the different alteration minerals of the study area. Clay = SWIR1 / SWIR2 ; Ferrous = SWIR / NIR and İron oxide = RED / BLUE. We then have the composite band and slice density to determine the different annoyances in its minerals. By masking the water, clouds and vegetation and building(figure 6), we have detected some anomalies in ferrous, clay and iron oxide minerals on open area (Figure 7) 6. CONCLUSION In conclusion, we found dense vegetation cover in the study area. The NDVİ of 2014 (-1 - +0.489) and that of 2018 (-1 - +0.458). The supervised classification allowed us to notice a small percentage variation of water, land use, vegetation and soils and rock between 2014 and 2018. We have detected some anomalies in ferrous, clay and iron oxide minerals on open area. This is why we have to introduce other methods and the satellite image to study the geology of the Congo craton. Figure 4 : Supervised Classification of Study Area Figure 3 : NDVI of Study Area Figure 7 : Alteration mineralGeology of Study Area Mamadou TRAORE *, Tolga ÇAN, Senem TEKİN Çukurova University, Department of Geological Engineering, 01330 Balcalı, Adana, Turkey (matraba77@gmail.com) Figure 5 : Supervised Classification REFERENCE Allan H.Wilson and J.Adrian Versfeld. (May 1994). The early Archaean Nondweni greenstone belt, southern Kaapvaal Craton, South Africa, Part I. Stratigraphy, sedimentology, mineralization and depositional environment. Precambrian Research, 67, 243-276. ANTHONY A. MOREY. (May 2008). Bimodal Distribution of Gold in Pyrite and Arsenopyrite: Examples from the Archean Boorara and Bardoc Shear Systems, Yilgarn Craton, Western Australia. Economic Geology,, 103, pp. 599–614. Arnaud Fontaine et al. (2017). Geology of the world-class Kikia polyphase gold deposit : West African Craton, Burkına Faso. Journal of African Earth Sciences. Floyd F. Sabins. (20 April 1999,). Remote sensing for mineral exploration. Ore Geology Reviews 14 1999 157–183Ž . Geological Survey of Canada. (1989). Geology of Canada. Ottawa, Canada: Geological Survey of Canada. J.S.F. Barbosaa, P. S. (2004).Archean and Paleoproterozoic crust of the São Francisco Craton, Bahia, Brazil: geodynamic feature. Precambrian Research, 133 1–27. OUABEGO KOURTENE Mariane. (Novembre 2013). Contribution a l’etude de la chaine panafricaine des oubanguides en republique centrafricaine. These pour obtenir le grade de Docteur d’Aix-Marseille Universite, Faculté des Sciences et Techniques, Géosciences de l’Environnement. PHIL BIERWIRTH. ( 2002). Hyperspectral Mapping of Mineral Assemblages Associated with Gold Mineralization in the Central Pilbara, Western Australia. Economic Geology ,, Vol. 97, , pp. 819–826. Segal, D. "Theoretical Basis for Differentiation of Ferric-Iron Bearing Minerals, Using Landsat MSS Data." Proceedings of Symposium for Remote Sensing of Environment, 2nd Thematic Conference on Remote Sensing for Exploratory Geology, Fort Worth, TX (1982): 949-95 T. J. Roache et al. (02 May 2014,). Epidote–clinozoisite as a hyperspectral tool in exploration for Archean gold Australian Journal of Earth Sciences: An International Geoscience Journal of the Geological Society of Australia. Figure 6 : Masking vegetation,water and building