EGU22-4678, updated on 27 Mar 2022
https://doi.org/10.5194/egusphere-egu22-4678
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Lithology Mapping with Satellite, Fieldwork-based Spectral data, and Machine Learning: the case study of Beiras Group (Central Portugal)

João Pereira1, Alcides J.S.C. Pereira2, Artur Gil3, and Vasco M. Mantas4
João Pereira et al.
  • 1Department of Earth Sciences and Geosciences Centre, Faculty of Sciences and Technology, University of Coimbra, Portugal. joaopmpereira93@gmail.com; ORCID: 0000-0002-6163-7772
  • 2CITEUC – Centre for Earth and Space Research, Department of Earth Sciences, University of Coimbra, Portugal. apereira@dct.uc.pt; ORCID: 0000-0002-7392-2255
  • 3IVAR - Research Institute for Volcanology and Risks Assessment, University of the Azores, Ponta Delgada, Portugal. artur.jf.gil@uac.pt; ORCID: 0000-0003-4450-8167
  • 4CITEUC – Centre for Earth and Space Research and MARE – Marine and Environmental Sciences Centre, Department of Earth Sciences, University of Coimbra, Portugal. vasco.mantas@dct.uc.pt; ORCID: 0000-0001-9602-7715

The lack of cartography increases the problematic of poor knowledge of geological resources and land management in regions that could benefit greatly from this information. Remote sensing has been an invaluable mean of obtaining data to perform geological mapping objectively and with high scientific accuracy. In Portugal, there is a large gap of cartographic information at 1:50 000 scale throughout the territory, so this work intends to complement this problem through a set of techniques and methodologies applied to a study of a region of Grupo das Beiras.

Spectral databases serve as an initial tool for any methodology involving spectral analysis, namely for the development of cartography methods and quick characterization of rock samples.

To address these issues, a multispectral analysis of january and july 2015th scenes with low cloud cover and atmospheric corrections (level 2) was obtained from Landsat 8 (LS8). Certain statistical tests such as ANOVA and Tukey's were applied to both images to clearly know whether significant differences exist between lithologies.

For the hyperspectral analysis, two sampling campaigns were carried out with the collection of rock samples of metasediments and granites and soil. The analysis was performed in fresh samples, crushed samples (2 mm - 500 μm; 500 μm - 125μm; <125 μm) and soil samples demonstrating a significantly different spectral behavior among various particle sizes in the hyperspectral signatures between fresh and crushed samples. X-ray fluorescence (FRX) was used to obtain geochemical data of major elements to validate the spectral results obtained. As a result, there were identified correspondences between the obtained hyperspectral data and the databases as well in the literature meaning that the spectral signatures of this research are consistent with the studied samples.

The creation of machine learning models is an emerging tool for cartography in which LS8 reflectance data was used for this elaboration. In this work and for this context the models proved to be useful and successful for the image classification from algorithms assigned for this function.

How to cite: Pereira, J., Pereira, A. J. S. C., Gil, A., and Mantas, V. M.: Lithology Mapping with Satellite, Fieldwork-based Spectral data, and Machine Learning: the case study of Beiras Group (Central Portugal), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4678, https://doi.org/10.5194/egusphere-egu22-4678, 2022.

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