Evaluation of EnMAP Hyperspectral Data for the Identification of Placers in the Rias Baixas Region (Spain)

Critical Raw Materials are crucial to achieve the European Unions (EU) goals of a climate-neutral economy by 2050. The high supply risk led the EU to prioritise domestic mineral exploration. This study, part of the S34I - SECURE AND SUSTAINABLE SUPPLY OF RAW MATERIALS project, utilised remote-sensing-based methods to identify and map heavy-mineral (HM) placer deposits in the Ria de Vigo, located in Galicia, Spain. Documented since the 70s, the sands of the Vigo beaches contain placers rich in Ti, Sn, Li, Rare Earth Elements (REE), Au, Fe and Cu. Mineral mapping was performed using hyperspectral EnMAP data. Band ratios were applied to identify possible mineralisation areas. Additionally, spectral unmixing was performed through the Mixture Tuned Matched Filtering (MTMF) workflow, included in ENVI 6.0 software, and two classification maps were obtained: one utilising the USGS spectral library and the other employing an HM concentrate spectral library. Band ratios were able to distinguish possible areas of hydrothermal alteration. MTMF classifications mapped most HM known to occur in the Ria, namely sillimanite, garnet, tourmaline, ilmenite, rutile, and monazite, were identified. This first approach will allow the selection of areas of interest for field validation and verification. The results will also be confronted with existing geological data. (c) 2025 by SCITEPRESS - Science and Technology Publications, Lda.

Authors

Institutions

Publication Details

Published
2025-01-01
DOI
https://doi.org/10.5220/0013496200003935
Primary Topic
Nutritional Studies and Diet
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Evaluation of EnMAP Hyperspectral Data for the Identification of Placers in the Rias Baixas Region (Spain)

Joana Cardoso-Fernandes, Beatriz Araújo, ALEXANDRE ANTÔNIO DE LIMA JUNIOR, Ana Cláudia Teodoro et al.
Nutritional Studies and Diet
article

Evaluation of EnMAP Hyperspectral Data for the Identification of Placers in the Rias Baixas Region (Spain)

Joana Cardoso-Fernandes, Beatriz Araújo, ALEXANDRE ANTÔNIO DE LIMA JUNIOR, Ana Cláudia Teodoro, Antônio Azzalini, Morgana Carvalho, Javier González
article en

Abstract

Critical Raw Materials are crucial to achieve the European Unions (EU) goals of a climate-neutral economy by 2050. The high supply risk led the EU to prioritise domestic mineral exploration. This study, part of the S34I - SECURE AND SUSTAINABLE SUPPLY OF RAW MATERIALS project, utilised remote-sensing-based methods to identify and map heavy-mineral (HM) placer deposits in the Ria de Vigo, located in Galicia, Spain. Documented since the 70s, the sands of the Vigo beaches contain placers rich in Ti, Sn, Li, Rare Earth Elements (REE), Au, Fe and Cu. Mineral mapping was performed using hyperspectral EnMAP data. Band ratios were applied to identify possible mineralisation areas. Additionally, spectral unmixing was performed through the Mixture Tuned Matched Filtering (MTMF) workflow, included in ENVI 6.0 software, and two classification maps were obtained: one utilising the USGS spectral library and the other employing an HM concentrate spectral library. Band ratios were able to distinguish possible areas of hydrothermal alteration. MTMF classifications mapped most HM known to occur in the Ria, namely sillimanite, garnet, tourmaline, ilmenite, rutile, and monazite, were identified. This first approach will allow the selection of areas of interest for field validation and verification. The results will also be confronted with existing geological data. (c) 2025 by SCITEPRESS - Science and Technology Publications, Lda.

Universidade do Porto (PT), Instituto Geológico y Minero de España (ES)
Climate action
Openalex Percentile: Top 90%
Nutritional Studies and Diet
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.