Spectral Analysis to Improve Inputs to Random Forest and Other Boosted Ensemble Tree-Based Algorithms for Detecting NYF Pegmatites in Tysfjord, Norway

As an important source of lithium and rare earth elements (REE) and other critical elements, pegmatites are of great strategic economic interest for present and future technological development. Identifying new pegmatite deposits is a strategy adopted by the European Union (EU) to decrease its impor...

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Bibliographic Details
Published in:Remote Sensing
Main Authors: Douglas Santos, Joana Cardoso-Fernandes, Alexandre Lima, Axel Müller, Marco Brönner, Ana Cláudia Teodoro
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2022
Subjects:
rare earth elements
critical raw materials
band ratios
principal component analysis
remote sensing
Sentinel 2
Science
Q
Norway
The Sentinel
Tysfjord
Online Access:https://doi.org/10.3390/rs14153532
https://doaj.org/article/b2d43d40a6c744cc8592c3aec5bd9e31
Description
Summary:As an important source of lithium and rare earth elements (REE) and other critical elements, pegmatites are of great strategic economic interest for present and future technological development. Identifying new pegmatite deposits is a strategy adopted by the European Union (EU) to decrease its import dependence on non-European countries for these raw materials. It is in this context that the GREENPEG project was established, an EU project whose main objective is to identify new deposits of pegmatites in Europe in an environmentally friendly way. Remote sensing is a non-contact exploration tool that allows for identifying areas of interest for exploration at the early stage of exploration campaigns. Several RS methods have been developed to identify Li-Cs-Ta (LCT) pegmatites, but in this study, a new methodology was developed to detect Nb-Y-F (NYF) pegmatites in the Tysfjord area in Norway. This methodology is based on spectral analysis to select bands of the Sentinel 2 satellite and adapt RS methods, such as Band Ratios and Principal Component Analysis (PCA), to be used as input in the Random Forest (RF) and other tree-based ensemble algorithms to improve the classification accuracy. The results obtained are encouraging, and the algorithm was able to successfully identify the pegmatite areas already known and new locations of interest for exploration were also defined.

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