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

Remote. Sens. Pub Date : 2022-07-23 DOI:10.3390/rs14153532
D. Santos, J. Cardoso-Fernandes, A. Lima, A. Müller, M. Brönner, A. Teodoro
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引用次数: 24

Abstract

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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光谱分析改进随机森林输入和其他增强的基于集成树的算法,用于检测挪威tyfjord的NYF伟晶岩
伟晶岩是锂、稀土等关键元素的重要来源,对当前和未来的科技发展具有重要的战略经济意义。查明新的伟晶岩矿床是欧洲联盟(欧盟)为减少对非欧洲国家进口这些原材料的依赖而采取的一项战略。正是在这种背景下,GREENPEG项目成立了,这是一个欧盟项目,其主要目标是以环保的方式在欧洲发现新的伟晶岩矿床。遥感是一种非接触式勘探工具,可以在勘探活动的早期阶段确定感兴趣的勘探领域。已经开发了几种RS方法来鉴定Li-Cs-Ta (LCT)伟晶岩,但在本研究中,开发了一种新的方法来检测挪威Tysfjord地区的Nb-Y-F (NYF)伟晶岩。该方法基于光谱分析选择Sentinel 2卫星的波段,并将波段比(Band ratio)和主成分分析(Principal Component analysis, PCA)等RS方法作为随机森林(Random Forest, RF)和其他基于树的集成算法的输入,以提高分类精度。获得的结果令人鼓舞,该算法能够成功识别已知的伟晶岩区域,并确定了新的勘探兴趣位置。
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