Application of remote sensing techniques in lithology identification in Almeria

Research Article
Open access

Application of remote sensing techniques in lithology identification in Almeria

Hejun Chen 1*
  • 1 Faculty of Science and Engineering, University of Nottingham, Nottingham UK    
  • *corresponding author Sgyhc2@nottingham.ac.uk
Published on 21 July 2023 | https://doi.org/10.54254/2755-2721/7/20230469
ACE Vol.7
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-61-4
ISBN (Online): 978-1-915371-62-1

Abstract

Remote sensing is emerging as an important method of information extraction in the field of lithology identification as a technology that can provide a cost-effective way of detecting and monitoring the physical characteristics of an area. This paper uses remote sensing techniques for lithology identification in Almeria, discusses the flaws and applicability of remote sensing techniques, and proposes a combined remote sensing approach. The lithological results identified by remote sensing indicate that northern Almeria is dominated by metamorphic and carbonate rock and the southern part is predominantly mafic rocks and felsi rocks. The identification results in Almeria from remote sensing imagery synthesised using Landsat-8 multiple band sets differ from actual lithology distribution. It shows the limitations of remote sensing techniques for lithology identification. Based on the limitations of remote sensing techniques demonstrated in the case study, this paper discusses how remote sensing improves the identification and analysis of lithology in other research cases by means of technical improvements combined with other techniques.

Keywords:

Lithology identification, Remote Sensing Techniques, Almeria

Chen,H. (2023). Application of remote sensing techniques in lithology identification in Almeria. Applied and Computational Engineering,7,247-256.
Export citation

References

[1]. Gani, N.D.S. and Abdelsalam, M.G. 2006 Remote sensing analysis of the gorge of the Nile, Ethiopia with emphasis on dejen–gohatsion region, Journal of African Earth Sciences, 44(2), 135–150. Available at: https://doi.org/10.1016/j.jafrearsci.2005.10.007.

[2]. Chou, X., Fu, B. and Zheng, J. 1996 Thermal infrared multispectral remote sensing detection of sedimentary rock information and evaluation of its effectiveness, Remote Sensing Technology and Applications, 7–13.

[3]. JIn, H., Tong, Q. and Zheng, L. 1994 Imaging spectroscopy and thermal infrared multispectral Geological Mapping Research by Imaging Spectroscopy and Thermal Infrared Multispectral Techniques, Environmental Remote Sensing, 138–144.

[4]. Mars, J.C. 2002 Geologic mapping of the Sierra San José mountain range, Mexico using advanced spaceborne thermal emission and reflection radiometer (ASTER) data: a remote sensing tool to assist geologic mapping in the field, (2002 Denver Annual Meeting (October 27-30, 2002)). Available at: https://gsa.confex.com/gsa/2002AM/webprogram/Paper41355.html (Accessed: October 14, 2022).

[5]. Rowan, L.C. and Mars, J.C. 2003 Lithologic mapping in the mountain pass, California area using advanced spaceborne thermal emission and reflection radiometer (ASTER) data, Remote Sensing of Environment, 84(3), 350–366. Available at: https://doi.org/10.1016/s0034-4257(02)00127-x.

[6]. Huang, Y.I., Li, P. and Li, Z. 2003 Geostatistics-based image texturing in application to lithology classification, Remote Sensing of Land Resources, 45–49.

[7]. Li, P. 2004 Lithology classification using ASTER images and geostatistical textures Classification, Mineral rock, 116–120.

[8]. Zhao, J., Yang, S. and Chen, H. 2004 Fractal texture-based rock identification method for remote sensing images Fractal Texture, Remote Sensing Information Theory Research, 2–4.

[9]. Jiang, P. and Shi, S. 1995 The fBm texture classification model and its application to lithology identification and its application in lithology recognition, Environmental remote sensing, 38–44.

[10]. Ma, C., Ma, J. and Han, X. 2002 Application of multi-source data to extract Lithological information in high vegetation cover areas: an example from the Qianyang region, Hunan, Geological Sciences, 365–371.

[11]. Zhang, W.L. 2005 Trends in remote sensing anthill identification - integration of remote sensing and aerial radiological information, Mineral and Rock Geochemistry Bulletin, pp. 88–91.

[12]. Koopmans, B.N. 1988 Third Airborne Imaging Spectrometer Workshop, Photogrammetria, 42(4), 181–183. Available at: https://doi.org/10.1016/0031-8663(88)90054-3.

[13]. Kruse, F.A. et al. 1993 The Spectral Image Processing System (sips)-interactive visualization and analysis of Imaging Spectrometer Data, AIP Conference Proceedings [Preprint]. Available at: https://doi.org/10.1063/1.44433.

[14]. Rowan, L.C., Simpson, C.J. and Mars, J.C. 2004 Hyperspectral analysis of the ultramafic complex and adjacent lithologies at mordor, NT, Australia, Remote Sensing of Environment, 91(3-4), 419–431. Available at: https://doi.org/10.1016/j.rse.2004.04.007.

[15]. GREEN A A, BERMAN M,SWTTZER B,et al. 1988 A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Transaction on Geoscience and Rermote Sensing,26(1):65-74. Available at: https://doi.org/10.1109/36.3001


Cite this article

Chen,H. (2023). Application of remote sensing techniques in lithology identification in Almeria. Applied and Computational Engineering,7,247-256.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 3rd International Conference on Materials Chemistry and Environmental Engineering (CONF-MCEE 2023), Part II

ISBN:978-1-915371-61-4(Print) / 978-1-915371-62-1(Online)
Editor:Ioannis Spanopoulos, Niaz Ahmed, Sajjad Seifi Mofarah
Conference website: https://www.confmcee.org/
Conference date: 18 March 2023
Series: Applied and Computational Engineering
Volume number: Vol.7
ISSN:2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. Gani, N.D.S. and Abdelsalam, M.G. 2006 Remote sensing analysis of the gorge of the Nile, Ethiopia with emphasis on dejen–gohatsion region, Journal of African Earth Sciences, 44(2), 135–150. Available at: https://doi.org/10.1016/j.jafrearsci.2005.10.007.

[2]. Chou, X., Fu, B. and Zheng, J. 1996 Thermal infrared multispectral remote sensing detection of sedimentary rock information and evaluation of its effectiveness, Remote Sensing Technology and Applications, 7–13.

[3]. JIn, H., Tong, Q. and Zheng, L. 1994 Imaging spectroscopy and thermal infrared multispectral Geological Mapping Research by Imaging Spectroscopy and Thermal Infrared Multispectral Techniques, Environmental Remote Sensing, 138–144.

[4]. Mars, J.C. 2002 Geologic mapping of the Sierra San José mountain range, Mexico using advanced spaceborne thermal emission and reflection radiometer (ASTER) data: a remote sensing tool to assist geologic mapping in the field, (2002 Denver Annual Meeting (October 27-30, 2002)). Available at: https://gsa.confex.com/gsa/2002AM/webprogram/Paper41355.html (Accessed: October 14, 2022).

[5]. Rowan, L.C. and Mars, J.C. 2003 Lithologic mapping in the mountain pass, California area using advanced spaceborne thermal emission and reflection radiometer (ASTER) data, Remote Sensing of Environment, 84(3), 350–366. Available at: https://doi.org/10.1016/s0034-4257(02)00127-x.

[6]. Huang, Y.I., Li, P. and Li, Z. 2003 Geostatistics-based image texturing in application to lithology classification, Remote Sensing of Land Resources, 45–49.

[7]. Li, P. 2004 Lithology classification using ASTER images and geostatistical textures Classification, Mineral rock, 116–120.

[8]. Zhao, J., Yang, S. and Chen, H. 2004 Fractal texture-based rock identification method for remote sensing images Fractal Texture, Remote Sensing Information Theory Research, 2–4.

[9]. Jiang, P. and Shi, S. 1995 The fBm texture classification model and its application to lithology identification and its application in lithology recognition, Environmental remote sensing, 38–44.

[10]. Ma, C., Ma, J. and Han, X. 2002 Application of multi-source data to extract Lithological information in high vegetation cover areas: an example from the Qianyang region, Hunan, Geological Sciences, 365–371.

[11]. Zhang, W.L. 2005 Trends in remote sensing anthill identification - integration of remote sensing and aerial radiological information, Mineral and Rock Geochemistry Bulletin, pp. 88–91.

[12]. Koopmans, B.N. 1988 Third Airborne Imaging Spectrometer Workshop, Photogrammetria, 42(4), 181–183. Available at: https://doi.org/10.1016/0031-8663(88)90054-3.

[13]. Kruse, F.A. et al. 1993 The Spectral Image Processing System (sips)-interactive visualization and analysis of Imaging Spectrometer Data, AIP Conference Proceedings [Preprint]. Available at: https://doi.org/10.1063/1.44433.

[14]. Rowan, L.C., Simpson, C.J. and Mars, J.C. 2004 Hyperspectral analysis of the ultramafic complex and adjacent lithologies at mordor, NT, Australia, Remote Sensing of Environment, 91(3-4), 419–431. Available at: https://doi.org/10.1016/j.rse.2004.04.007.

[15]. GREEN A A, BERMAN M,SWTTZER B,et al. 1988 A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Transaction on Geoscience and Rermote Sensing,26(1):65-74. Available at: https://doi.org/10.1109/36.3001