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Using remote sensing data for geological mapping in semi-arid environment: a machine learning approach

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Abstract

The geological map encapsulates basic information that can be crucial in a multitude of fields such as landslide risk assessment, engineering projects, as well as petroleum and mineral resources studies. In addition, it is difficult, expensive and time-consuming to achieve it in complex and inaccessible lands. However, remote sensing data linking and the application of Machine Learning Algorithms (MLAs) can be interesting for geological mapping of large areas, especially in arid and semi-arid regions, where remote sensing provides a diversified and detailed spatial database and MLAs offer the possibility of effective and efficient classification of remotely sensed images. This article highlights the use of Aster spectral data in a comparative approach of the performance of six (MLAs) to better produce the geological map of a portion of the Aït Ahmane region. The results indicated an overall Accuracy and a kappa coefficient that exceeded 60% for the different models. Prioritizing the Regularized Discriminant Analysis (RDA) (Kappa = 83.5%) and Support Vector Machines (SVM) (Kappa = 81%) algorithms, they managed to classify the lithology on Aster images of the region. However, the classification of lithology using the RDA was slightly more accurate than the one obtained by SVM with 2.3%. From the results shown, we can conclude that the ability of RDA as a learning algorithm is the best for the geological mapping of our study site.

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References

  • Aboelkhair H, Yoshiki N, Yasushi W, Isao S (2010) Processing and interpretation of ASTER TIR data for mapping of rare-metal-enriched albite granitoids in the Central Eastern Desert of Egypt. J Afr Earth Sci 58(1):141–618

    Article  Google Scholar 

  • Abrams M (2000) The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER): data products for the high spatial resolution imager on NASA‘s Terra platform. Int J Rem Sens 21:847–859

    Article  Google Scholar 

  • Admou H, Razin PH, Egal E, Youbi N, Soulaimani A, Blein O, Chevremont PH, Gasquet D, Barbanson L, Bouabdelli M, ANZAR-CONSEIL (2013) Notice explicative, carte geol. Maroc (1/50 000). Feuille Aït Ahmane, Notes et Mémoires Serv. Géol. Maroc N°533bis, MEM/BRGM

  • Álvaro JJ, Pouclet A, Ezzouhairi H, Soulaimani A, Hafid E, Gil A, Fekkak A (2014) Early Neoproterozoic rift-related magmatism in the Anti-Atlas margin of the West African craton, Morocco. Precambrian Res 255:433–442. https://doi.org/10.1016/j.precamres.2014.10.008

    Article  Google Scholar 

  • Amer R, Kusky T, Ghulam A (2010) Lithological mapping in the Central Eastern Desert of Egypt using ASTER data. J Afr Earth Sci 56:75–82

    Article  Google Scholar 

  • Blein O, Baudin T, Chèvremont P, Soulaimani A, Admou H, Gasquet P, Gombert P (2014) Geochronological constraints on the polycyclic magmatism in the Bou Azzer-El Graara inlier (Central Anti-Atlas Morocco). J Afr Earth Sci 99:287–306. https://doi.org/10.1016/j.jafrearsci.2014.04.021

    Article  Google Scholar 

  • Boser B, Guyon I, Vapnik V (1992) A training algorithm for optimal margin classifiers, in: Proceedings of the Fifth Annual Workshop on Computational Learning Theory. ACM, Pittsburgh, pp 144–152

    Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  • Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and Regression Trees. Chapman & Hall, New York

    Google Scholar 

  • Bujlow T, Riaz T, Pedersen JM (2012) A method for classification of network traffic based on C5. 0 Machine Learning Algorithm. In Computing, networking and communications (ICNC), 2012 international conference on (pp. 237–241). IEEE, Jan 2012

  • Chen T, Guestrin C (2016) Xgboost: A Scalable Tree Boosting System. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, 785–94. ACM. https://doi.org/10.1145/2939672.2939785

  • Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37:35–46. https://doi.org/10.1016/0034-4257(91)90048-B

    Article  Google Scholar 

  • Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30

    Google Scholar 

  • El Alaoui El Fels A, Saidi MEM, Bouiji A, Benrhanem M (2021) Rainfall regionalization and variability of extreme precipitation using artificial neural networks: a case study from western central Morocco. J Water Clim Change 12(4):1107–1122. https://doi.org/10.2166/wcc.2020.217

  • El Hadi H, Simancas JF, Martínez-Poyatos D, Azor A, Tahiri A, Montero P, González-Lodeiro F (2010) Structural and geochronological constraints on the evolution of the Bou Azzer Neoproterozoic ophiolite (Anti-Atlas, Morocco). Precambrian Res 182(1–2):1–14. https://doi.org/10.1016/j.precamres.2010.06.011

    Article  Google Scholar 

  • Foody GM (2002) Status of land cover classification accuracy assessment. Remote Sens Environ 80:185–201

    Article  Google Scholar 

  • Fujisada H, Ono A (1994) Observational performance of ASTER instrument on EOS AM1 spacecraft. Adv Space Res 14:147–150. https://doi.org/10.1016/0273-1177(94)90207-0

    Article  Google Scholar 

  • Gabr S, Ghulam A, Kusky T (2010) Detecting areas of high-potential gold mineralization using ASTER data. Ore Geol Rev 38(1–2):59–69

    Article  Google Scholar 

  • Gad S, Kusky T (2007a) ASTER spectral ratioing for lithological mapping in the Arabian-Nubian shield, the Neoproterozoic Wadi Kid area, Sinai, Egypt. Gondwana Res 11:326–335

    Article  Google Scholar 

  • Gad S, Kusky T (2007b) ASTER spectral ratioing for lithological mapping in the Arabian Nubian shield, the Neoproterozoic Wadi Kid area, Sinai, Egypt. Gondwana Res 11:326–335

    Article  Google Scholar 

  • Garg A, Raghava GP (2008) A machine learning based method for the prediction of secretory proteins using amino acid composition, their order and similarity-search. In Silico Biol 8(2):129–140

    Google Scholar 

  • Ge W, Cheng Q, Tang Y, Jing L, Gao C (2018) Lithological Classification Using Sentinel-2A Data in the Shibanjing Ophiolite Complex in Inner Mongolia, China. Remote Sens 10:638

    Article  Google Scholar 

  • Gong P, Howarth PJ (1990) An assessment of some factors influencing multispectral land cover classification. Photogramm Eng Remote Sens 56(5):597–603

    Google Scholar 

  • Hassan SM, Sadek MF (2017) Geological mapping and spectral based classification of basement rocks using remote sensing data analysis: the Korbiai-Gerf nappe complex, South Eastern Desert, Egypt. J Afr Earth Sci 134:404–418

    Article  Google Scholar 

  • Hastie T, Tibshirani R, Friedman J (2008) Random forests, Springer series in statistics, pp 587–604. https://doi.org/10.1007/978-0-387-84858-7_15

    Book  Google Scholar 

  • Hewson RD, Cudahy TJ, Mizuhiko S, Ueda K, Mauger AJ (2005) Seamless geological map generation using ASTER in the Broken Hill-Curnamona province of Australia. Remote Sens Environ 99:159–172

    Article  Google Scholar 

  • Höskuldson A (1988) PLS regression methods. J Chemom 2:211–228

    Article  Google Scholar 

  • Høyer AS, Klint KES, Fiandaca G, Maurya PK, Christiansen AV, Balbarini N, Møller I (2018) Development of a high-resolution 3D geological model for landfll leachate risk assessment. Eng Geol 249:45–59

    Article  Google Scholar 

  • Hussain L, Aziz W, Kazmi ZH et al (2014) Classification of human faces and Non faces using Machine learning techniques. Int J Electron Electr Eng 2:116–123

    Article  Google Scholar 

  • Ielsch G, Cuney M, Buscail F, Rossi F, Leon A, Cushing ME (2016) Estimation and mapping of uranium content of geological units in France. J Environ Radioact. https://doi.org/10.1016/j.jenvrad.2016.05.022

  • Kalogirou SA (2001) Artificial neural networks in renewable energy systems applications: a review. Renew Sust Energ Rev 5:373–401

    Article  Google Scholar 

  • Kuhn M (2008) Building predictive models in R using the caret package. J Stat Softw, http://www.jstatsoft.org/v28/i05/

  • Kuhn M, Johnson K (2013) Factors that can affect model performance. In: Applied predictive modeling. Springer New York, New York, pp 521–546. https://doi.org/10.1007/978-1-4614-6849-3_20

    Chapter  Google Scholar 

  • Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33(1):159

    Article  Google Scholar 

  • Le, Xiang L, Zhang D, Dong C (2008) Characteristics of remote sensing emission spectra of composite igneous rocks. International workshop on education technology and training and international workshop on geoscience and remote sensing. IEEE

  • Lippitt CD, Rogan J, Li Z, Eastman JR, Jones TG (2008) Mapping Selective Logging in Mixed Deciduous Forest: A Comparison of Machine Learning Algorithms. Photogramm Eng Remote Sens 74(10):1201–1211. https://doi.org/10.14358/PERS.74.10.1201

    Article  Google Scholar 

  • Mohamed Abdi A (2019) Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data. GISci Remote Sens. https://doi.org/10.1080/15481603.2019.1650447

  • Mojid MA, Hossain ABMZ, Ashraf MA (2019) Artificial neural network model to predict transport parameters of reactive solutes from basic soil properties. Environ Pollut. https://doi.org/10.1016/j.envpol.2019.113355

  • Othman A, Gloaguen R (2014) Improving Lithological Mapping by SVM Classification of Spectral and Morphological Features: The Discovery of a New Chromite Body in the Mawat Ophiolite Complex (Kurdistan, NE Iraq). Remote Sens 6:6867–6896

    Article  Google Scholar 

  • Rajendran S, Nasir S, Kusky TM, Ghulam A, Gabr S, El-Ghali AKM (2013) Detection of hydrothermal mineralized zones associated with listwaenites in Central Oman using ASTER data. Ore Geol Rev 53:470–488

    Article  Google Scholar 

  • Regmi AD, Cui P, Dhital MR et al (2016) Rock fall hazard and risk assessment along Araniko Highway, Central Nepal Himalaya. Environ Earth Sci 75:1112. https://doi.org/10.1007/s12665-016-5905-x

    Article  Google Scholar 

  • Rieck K, Trinius P, Willems C, Holz T (2011) Automatic analysis of malware behavior using machine learning. J Comput Secur 19(4):639–668

    Article  Google Scholar 

  • Rogan J, Franklin J, Stow D, Miller J, Woodcock C, Roberts D (2008) Mapping Land-Cover Modifications over Large Areas: A Comparison of Machine Learning Algorithms. Remote Sens Environ 112(5):2272–2283. https://doi.org/10.1016/j.rse.2007.10.004

    Article  Google Scholar 

  • Stehman SV (1996) Estimating the Kappa coefficient and its variance under stratified random sampling. Photogramm Eng Remote Sens 62(4):401–407

    Google Scholar 

  • Tanya G, Surinder Singh K (2014) Comparison of classification techniques for intrusion detection dataset using WEKA. In IEEE international conference on recent advances and innovations in engineering (ICRAIE-2014), 9–11 May 2014, Jaipur, India

  • Tomy AM, Ahammed N, Subathraa MSP, Godson Asirvatham L (2016) Analysing the performance of a flat plate solar collector with silver/water nanofluid using artificial neural network. Proc Comput Sci 93:33–40

    Article  Google Scholar 

  • Vapnik VN (1998) Statistical learning theory. John Wiley & Sons Inc, Chichester

    Google Scholar 

  • Vaughan RG, Hook SJ, Calvin WM, Taranik JV (2005) Surface mineral mapping at Steamboat Springs, Nevada, USA, with multi-wavelength thermal infrared images. Remote Sens Environ 99(1–2):140–158

    Article  Google Scholar 

  • Xiong Y et al (2011) Lithological mapping of Bela ophiolite with remote-sensing data. Int J Remote Sens 32:4641–4658

    Article  Google Scholar 

  • Yamaguchi Y, Fulisada H, Kudoh M, Kawakami T, Tsu H, Kahle AB, Pniel M (1999) ASTER instrument characterization and operation scenario. Adv Space Res 23:1415–1424

    Article  Google Scholar 

  • Ye B, Tian S, Ge J, Sun Y (2017) Assessment of WorldView-3 data for lithological mapping. Remote Sens 9:1132

    Article  Google Scholar 

  • Yu L, Porwal A, Holden EJ, Dentith MC (2012) Towards automatic lithological classification from remote sensing data using support vector machines. Comput Geosci 45:229–239

    Article  Google Scholar 

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Authors and Affiliations

  1. Laboratoire de Géosciences et Environnement (LGSE), Département de Géologie, Faculté des Sciences et Techniques, Université Cadi Ayyad, BP 549, Marrakech, Morocco

    Abdelhafid El Alaoui El Fels & Mustapha El Ghorfi

  2. Mining Environment and Circular Economy (EMEC), Mohammed VI Polytechnic University, Lot 660, Hay Moulay Rachid, 43150, Ben Guerir, Morocco

    Mustapha El Ghorfi

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  1. Abdelhafid El Alaoui El Fels
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  2. Mustapha El Ghorfi
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Correspondence to Abdelhafid El Alaoui El Fels.

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Communicated by: H. Babaie

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El Fels, A.E.A., El Ghorfi, M. Using remote sensing data for geological mapping in semi-arid environment: a machine learning approach. Earth Sci Inform 15, 485–496 (2022). https://doi.org/10.1007/s12145-021-00744-w

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