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<p>Monitoring the long-term evolution of open-pit mining spoil dumps is challenging, requiring the decoupling of short-term material deposition from long-term ecological succession. This study presents an automated framework for spoil dump mapping by fusing a novel Bare Coal Index (BCI), the LandTrendr time-series algorithm, and machine learning. Using the 1986–2021 Landsat record, the BCI leverages near-infrared (NIR) and shortwave infrared (SWIR) bands to quantify coal exposure, while LandTrendr tracks vegetation disturbance and recovery from kernel NDVI (kNDVI) trajectories. These spectral and temporal features were integrated with topographic data using a Random Forest machine learning classifier to distinguish internal from external spoil dumps, with boundaries refined by morphological operations. The BCI mapped bare coal with 92% accuracy, significantly outperforming baseline indices. The final fused model achieved 86% accuracy (F1-score = 0.84) in mapping spoil dump typologies, a 22% relative improvement over a disturbance-only approach. This work delivers a robust, scalable framework that fuses multiple data streams for the comprehensive mapping and monitoring of mining landscapes, supporting adaptive environmental management.</p>

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Fusing LandTrendr BCI and machine learning for spoil dump mapping

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月17日

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  • Xu Zhen,
  • Zhijian Yu,
  • YuMing Shi,
  • Yiyuan Zhao

摘要

Monitoring the long-term evolution of open-pit mining spoil dumps is challenging, requiring the decoupling of short-term material deposition from long-term ecological succession. This study presents an automated framework for spoil dump mapping by fusing a novel Bare Coal Index (BCI), the LandTrendr time-series algorithm, and machine learning. Using the 1986–2021 Landsat record, the BCI leverages near-infrared (NIR) and shortwave infrared (SWIR) bands to quantify coal exposure, while LandTrendr tracks vegetation disturbance and recovery from kernel NDVI (kNDVI) trajectories. These spectral and temporal features were integrated with topographic data using a Random Forest machine learning classifier to distinguish internal from external spoil dumps, with boundaries refined by morphological operations. The BCI mapped bare coal with 92% accuracy, significantly outperforming baseline indices. The final fused model achieved 86% accuracy (F1-score = 0.84) in mapping spoil dump typologies, a 22% relative improvement over a disturbance-only approach. This work delivers a robust, scalable framework that fuses multiple data streams for the comprehensive mapping and monitoring of mining landscapes, supporting adaptive environmental management.