Teledetección de depósitos minerales ocultos mediante imágenes multiespectrales ASTER. Una revisión
[EN] The identification of mineral deposits lacking surface expression represents one of the major challenges in geological exploration. This study presents a systematic literature review, based on explicit selection criteria, on the use of the ASTER (Advanced Spaceborne Thermal Emission and Reflect...
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | español |
| OAI Identifier: | oai:dnet:riunet______::87d08aee096fc943d1745b5dd7ae2db0 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/235336 |
| Access Level: | acceso abierto |
| Palabra clave: | ASTER Hydrothermal alteration Concealed mineral deposits Systematic review PRISMA Alteración hidrotermal Depósitos minerales ocultos Revisión sistemática |
| Sumario: | [EN] The identification of mineral deposits lacking surface expression represents one of the major challenges in geological exploration. This study presents a systematic literature review, based on explicit selection criteria, on the use of the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) sensor for detecting hydrothermal alterations associated with concealed mineralization. Both traditional spectral techniques -such as band ratios and Directed Principal Component Analysis (DPCA)- and approaches integrating geochemical and structural information through machine learning algorithms are examined. The results indicate that, although ASTER remains highly relevant due to its VNIR-SWIR-TIR spectral configuration and open-access availability, there is still a lack of standardized protocols and comparative frameworks enabling the systematic integration of spectral, geochemical, and structural datasets. Recent literature shows a transition from purely spectral approaches toward multi-source schemes supported by supervised models and deep learning architectures, which tend to improve the delineation of prospective zones in areas with significant surface cover. Overall, the review highlights that the true potential of ASTER lies not in its isolated application, but in its integration within reproducible, mineral system oriented workflows aimed at reducing uncertainty in the exploration of concealed mineral deposits. |
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