A1 Refereed original research article in a scientific journal
Machine Learning Algorithms for Acid Mine Drainage Mapping Using Sentinel-2 and Worldview-3
Authors: Farahnakian, Fahimeh; Luodes, Nike; Karlsson, Teemu
Publisher: MDPI
Publishing place: BASEL
Publication year: 2024
Journal: Remote Sensing
Journal name in source: REMOTE SENSING
Journal acronym: REMOTE SENS-BASEL
Article number: 4680
Volume: 16
Issue: 24
Number of pages: 17
eISSN: 2072-4292
DOI: https://doi.org/10.3390/rs16244680
Web address : https://doi.org/10.3390/rs16244680
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/478106500
Acid Mine Drainage (AMD) presents significant environmental challenges, particularly in regions with extensive mining activities. Effective monitoring and mapping of AMD are crucial for mitigating its detrimental impacts on ecosystems and water quality. This study investigates the application of Machine Learning (ML) algorithms to map AMD by fusing multispectral imagery from Sentinel-2 with high-resolution imagery from WorldView-3. We applied three widely used ML models-Random Forest (RF), K-Nearest Neighbor (KNN), and Multilayer Perceptron (MLP)-to address both classification and regression tasks. The classification models aimed to distinguish between AMD and non-AMD samples, while the regression models provided quantitative pH mapping. Our experiments were conducted on three lakes in the Outokumpu mining area in Finland, which are affected by mine waste and acidic drainage. Our results indicate that combining Sentinel-2 and WorldView-3 data significantly enhances the accuracy of AMD detection. This combined approach leverages the strengths of both datasets, providing a more robust and precise assessment of AMD impacts.
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Funding information in the publication:
This work is part of the Secure and Sustainable Supply of raw materials for EU Industry (S34I) project, n.101091616, funded by European Health and Digital Executive Agency (HADEA).
