Using optical imagery data for lithological mapping of composite volcanoes in high arid puna plateau. Tuzgle volcano case study
Geological mapping of volcanic areas is important to unravel the evolution of the volcanism with important implication for the assessment of the geologic hazard and the exploration and exploitation of energy and mineral resources. Volcanic mapping in Puna plateau is a challenging task due to inacces...
| Autores: | , , , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2017 |
| País: | Argentina |
| Recursos: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/67157 |
| Acesso em linha: | http://hdl.handle.net/11336/67157 |
| Access Level: | acceso abierto |
| Palavra-chave: | VOLCANIC MAPPING OPTICAL REMOTE SENSING TUZGLE VOLCANO PUNA PLATEAU https://purl.org/becyt/ford/1.5 https://purl.org/becyt/ford/1 |
| Resumo: | Geological mapping of volcanic areas is important to unravel the evolution of the volcanism with important implication for the assessment of the geologic hazard and the exploration and exploitation of energy and mineral resources. Volcanic mapping in Puna plateau is a challenging task due to inaccessibility, extreme climatic conditions, high altitude above sea level and dangerous issues like minefields, so an effective and validated methodology for mapping isfundamental to ensure the quality of the final results for the end-users. A practical mapping methodology using optical imagery data tested in Cerro Tuzgle because of its well-known stratigraphy is presented. Some of the most common and well known processing techniques (false color combinations, band ratios and principal component analysis) were applied to multispectral data (Landsat 7, Landsat 8 and ASTER). Supervised classification and an assessment of the classification were produced for every image created. The quantitativeaccuracy of the classification maps resulting from different data sets was assessed by comparing the classification with ground truth data extracted from the geological map by means of a confusion matrix and related statistics. From the validation over several processing techniques, we found that supervised classification over PCA in multispectral data was the best methodologyfor lithological mapping in volcanic areas. Comparing the three multispectral sensors used in this work, we achieve better results and more accuracy with ASTER images. Low costs, data availability and broad swath of the multispectral data make these images valuable for lithological mapping in arid volcanic regions. This time- and cost-effective methodology is adequate tocomposite volcano preliminary mapping, but the correct reconstruction of the stratigraphy of a volcano always requires field survey. |
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