Document Type : Original Article

Authors

1 Ph.D. Student in Dep. of Earth Sciences, Faculty of Sciences, Shiraz University

2 Prof. of Dep. of Earth Sciences, Faculty of Sciences, Shiraz University

Abstract

Twin Sentinel-2 MSI sensors are spatially like the Landsat-8 OLI super spectral instrument, aiming to additional data continuity for land surface monitoring were launched by European Space Agency. In this paper, the potential of these data was evaluated for discrimination of lithological units and alterations in the Esfordi phosphate deposit area and was compared with OLI and fused OLI data. Decorrelation stretch method was used for enhancing the lithological units of the study area, and all of the 3 datasets acceptably discriminated the rock units. Among these, MSI data could produce the lithological map with high resolution and highest level of reality owing to its high spatial resolution. For statistical comparison, Support Vector Machine and Random Forest methods were applied on datasets for classification of the lithological units and their accuracy was assessed using confusion matrices. Furthermore, the corresponding band ratios to which were defined for Landsat-5 TM, were applied on datasets for detecting the altered areas. Then the areas of each highlighted alteration zones were estimated for comparison. Furthermore, the scatterplots of band ratio images were prepared. MSI dataset revealed the highest overall accuracy and Kappa coefficient in Support Vector Machine and Random Forest classification. Also, the results of band ratioing showed that MSI and fused OLI data have the most correlation and similarities. This study demonstrated that MSI data are more optimal than OLI data for lithological and alteration mapping. Also, using fused OLI data in dates which there is no MSI data acquisition or for producing seamless geological maps in continental scale besides to MSI data, is efficient.

Keywords

سازمان زمین‌شناسی کشور، برگة شمارة 7153، نقشة 1:100,000 اسفوردی.
صالحی، ط.، هاشمی تنگستانی، م.، 1397، ارزیابی داده‌های سنجندة تصویربردار چندطیفی ماهوارة سنتینل‌-2 در بارزسازی زون‌های دگرسان کانسارهای مس پورفیری، مطالعة موردی: شمال‌شرقی اصفهان، اولین همایش ملی انجمن سنجش از دور زمین‌شناختی ایران، 23-21 آذر 1397، دانشگاه تحصیلات تکمیلی صنعتی و فنّاوری پیشرفتة کرمان.
کابلی‌زاده، م.، رنگزن، ک.، محمدی، ش.، 1397، کاربرد تلفیق تصاویر ماهواره‌ای لندست‌ 8 و سنتینل‌ 2 در پایش محیطی، سنجش از دور و سامانة اطلاعات جغرافیایی در منابع طبیعی، سال نهم، شمارة 3، صص. 71-53.
هاشمی تنگستانی، م.، شایگان‌پور، س.، 1398، تحلیل طیفی و آشکارسازی واحدهای سنگی کمپلکس سوریان، شمال‌شرق فارس با استفاده از داده‌های تصاویر ماهواره‌ای استر و سنتینل‌-2، سنجش از دور و GIS ایران، سال یازدهم، شمارة 2، صص. 78-63.
Bahrami, Y., Hassani, H. & Maghsoudi, A., 2018, Investigating the Capabilities of Multispectral Remote Sensors Data to Map Alteration Zones in the Abhar Area, NW Iran, Geosystem Engineering, 24(1), PP. 18-30, Retrieved from https://doi.org/ 10.1080/12269328.2018.1557083.
 
Breiman, L., 2001, Random Forests, Mach. Learn, 45, PP. 5-32.
De Boissieu, F., Sevin, B., Cudahy, T., Mangeas, M., Chevrel, S., Ong, C., Rodger, A., Maurizot, P., Laukamp, C. & Lau, I., 2018, Regolith-Geology Mapping with Support Vector Machine: A Case Study over Weathered Ni-Bearing Peridotites, New Caledonia, Int. J. Appl. Earth Obs. Geoinf., 64, PP. 377-385.
de Kok, R., Wężyk, P., Papież, M. & Migo, L., 2017, Applications of Sentinel-2 Data for Agriculture and Forest Monitoring Using the Absolute Difference (ZABUD) Index Derived from the AgroEye Software (ESA), Proc. SPIE 10421, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIX, 104211A (2 November 2017, Event: SPIE Remote Sensing, 2017, Warsaw, Poland).
Fal, S., Maanan, M., Baidder, L. & Rhinane, H., 2019, The Contribution of Sentinel-2 Satellite Images for Geological Mapping in the South of Tafilalet Basin (Eastern Anti-Atlas, Morocco), The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-4/W12, 2019 5th International Conference on Geoinformation Science – Geo Advances 2018, 10–11 October 2018, Casablanca, Morocco.
Forouzan, M. & Arfania, R., 2018, Integration of the Bands of ASTER, OLI, MSI Remote Sensing Sensors for Detection of Hydrothermal Alterations in Southwestern Area of the Ardestan, Isfahan Province, Central Iran, The Egyptian Journal of Remote Sensing and Space Sciences, In press, corrected proof, Available online 10 December 2018.
Ge, W., Cheng, Q., Jing, L., Armenakis, C. & Ding, H., 2018b, Lithological Discrimination Using ASTER and Sentinel-2A in the Shibanjing Ophiolite Complex of Beishan Orogenic in Inner Mongolia, China, Advances in Space Research., 62, PP. 1702-1716.
 
Ge, W., Cheng, Q., Tang, Y., Jing, L. & Gao, C., 2018a, Lithological Classification Using Sentinel-2A Data in the Shibanjing Ophiolite Complex in Inner Mongolia, China, Remote Sens., 10(4), P. 638.
Iurist, N., Stătescu, F. & Lateș, I., 2016, Analysis of Land Cover and Land Use Changes Using Sentinel-2 Images, PESD, 10(2), PP. 161-172.
Jami, M., Dunlop, A.C. & Cohen, D.R., 2007, Fluid Inclusion and Stable Isotope Study of the Esfordi Apatite-Magnetite Deposit, Central Iran, Economic Geology, 102, PP. 1111-1128.
Kadavi, P.R & Lee, C.W., 2018, Land Cover Classification Analysis of Volcanic Island in Aleutian Arc Using an Artificial Neural Network (ANN) and a Support Vector Machine (SVM) from Landsat Imagery, Geosciences Journal, 22(4), PP. 653-665.
Karaoui, I., Abdelghani, B., Arioua, A., Hssaisoune, M., Sabri, E.M., Ait Ouhamchich, K.Elhamdouni, D.El Amrani, I.Wafae, N., 2019, Evaluating the Potential of Sentinel-2 Satellite Images for Water Quality Characterization of Artificial Reservoirs: The Bin El Ouidane Reservoir Case Study (Morocco), Meteorology Hydrology and Water Management, 7(1).
Lefebvre, A., Sannier, C. & Corpetti, T., 2016, Monitoring Urban Areas with Sentinel-2A Data: Application to the Update of the Copernicus High Resolution Layer Imperviousness Degree, Remote Sensing, 8(606).
Lowe, B. & Kulkarni, A., 2015, Multispectral Image Analysis Using Random Forest, International Journal on Soft Computing, 6, PP. 1-14.
Sabins, F.F., 1999, Remote Sensing for Mineral Exploration, Ore Geol. Rev., 14, PP. 157-183.
Salehi, S., Mielke, C., Brogaard Pedersen, C. & Dalsenni Olsen, S., 2019, Comparison of ASTER and Sentinel-2 Spaceborne Datasets for Geological Mapping: A Case Study from North-East Greenland, Geological Survey of Denmark and Greenland Bulletin, 43.
Talukdar, S., Singha, P., Mahato, S., Shahfahad, Pal, S., Liou, Y.A. & Rahman, A., 2020, Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations-A Review, Remote Sens., 12, P. 1135.
van der Meer, F.D., van der Werff, H.M.A. & van Ruitenbeek, F.J.A., 2014, Potential of ESA’s Sentinel-2 for Geological Applications, Remote Sens. Environ., 148, PP. 124-133.
van der Werff, H.M.A. & van der Meer, F.D., 2015, Sentinel-2 for Mapping Iron Absorption Feature Parameters, Remote Sens., 7, PP. 12635-12653.
van der Werff, H.M.A. & van der Meer, F.D., 2016, Sentinel-2A MSI and Landsat 8 OLI Provide Data Continuity for Geological Remote Sensing, Remote Sens., 8, P. 883.Vapnik, V.N., 1995, The Nature of Statistical Learning Theory, 2nd edition, Springer-Verlag, New York.
Wahi, M., Taj-Eddine, K. & Laftouhi, N., 2013, ASTER VNIR & SWIR Band Enhancement for Lithological Mapping – A Case Study of the Azegour Area (Western High Atlas, Morocco), Journal of Environment and Earth Science., 3, PP. 33-45.
Wang, Q., Blackburn, G.A., Onojeghuo, A.O., Dash, J., Zhou, L., Zhang, Y. & Atkinson, P.M., 2017, Fusion of Landsat 8 OLI and Sentinel-2 MSI Data, IEEE Transactions on Geoscience and Remote Sensing, 55, P. 7.
Yang, X., 2011, Parameterizing Support Vector Machines for Land Cover Classification, Photogramm. Eng. Remote Sens., 77, PP. 27-38.