Measuring Tailings Storage Facility Bathymetry Using Sentinel-2 and Landsat-8/9 Multispectral Imagery and Machine Learning

• Published in: International archives of the photogrammetry, remote sensing and spatial information sciences. remote sensing and spatial information sciences., 2024-09, Vol.XLVIII-M-4-2024, p.63-69
• Main Authors: Stringari, Caio E., Engelbrecht, Jeanine, Eaton, Brett
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial neural networks; Bathymeters; Bathymetric surveys; Bathymetry; Chemical damage; Earth observations (from space); Landsat; Machine learning; Mine drainage; Mine tailings; Mine wastes; Mine waters; Mining accidents & safety; Monitoring; Remote sensing; Satellite imagery; Satellite observation; Satellites; Storage facilities; Tailings; Water damage; Water discharge; Water management
• ISSN: 2194-9034; 1682-1750; 2194-9034
• DOI: 10.5194/isprs-archives-XLVIII-M-4-2024-63-2024

Tailings, a byproduct of mining, consist of fine sediment particles suspended in water that are stored in tailings storage facilities (TSFs). The discharge of untreated TSF water into the environment is typically prohibited due to its contact with mine tailings and processing chemicals. TSF failures have caused damage to communities and the environment, prompting calls for better management practices and advanced monitoring tools. For operational mine water management, boat-based bathymetric surveys have been used. However, these technologies have limitations, especially when the surveying of large facilities is required. Advances in remote sensing, particularly satellite-based earth observation (SBEO), offer cost-effective solutions for monitoring TSFs. This study explores the use of machine learning models, including XGBoost and Convolutional Neural Networks (CNNs), applied to Sentinel-2 and Landsat-8/9 data to estimate TSF bathymetry. Surveyed bathymetry datasets were used for model training, testing, and results validation. The results of the experiments revealed that high-accuracy bathymetric estimates could be obtained with mean absolute errors between 6 and 12 cm depending on the source of the data (i.e. Sentinel-2 or Landsat-8/9) and the model used (XGBoost vs CNN). Limitations include mixed pixel effects on the pond-beach interface and lower accuracies obtained in shallow areas, notably when XGBoost is used. This research underscores the potential of using satellite data and machine learning for TSF bathymetric monitoring, with implications for enhancing environmental and safety standards in mining operations.