Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List

Full text release has been delayed at the author's request until August 04, 2025

ETD Abstract Container

Abstract Header

Mapping and volume estimation of waste coal in abandoned mine lands using remote sensing and geospatial techniques

Dhakal, Sandeep
http://orcid.org/0000-0002-2689-3359
http://rave.ohiolink.edu/etdc/view?acc_num=osu1721323434699912

Abstract Details

2024, Master of Science, Ohio State University, Food, Agricultural and Biological Engineering.
Waste coal in abandoned mine lands poses significant environmental challenges, affecting nearby communities, rivers, and streams. Effective management of these piles is essential due to concerns such as acid mine drainage, soil and water contamination, coal fires, and methane emissions. Various strategies have been proposed for managing waste coal, including potential utilization for rare earth element recovery, soil amendment, construction aggregates, and energy generation. However, the implementation of these strategies remains uncertain due to the lack of precise location and volume data on waste coal piles. Traditional methods for gathering these data rely on field visits and Global Navigation Satellite System surveying, which are costly and labor-intensive. Advances in satellite technologies and the availability of digital elevation models (DEMs) offer an opportunity to estimate waste coal volume on a regional scale in a timely and cost-effective manner. Thus, the objective of this thesis was to develop a robust data analytical framework to locate and estimate the volume of waste coal piles on a regional scale, using the Muskingum River Basin (MRB) in Ohio as the study area. Initially, a prototype was developed to determine the most effective machine learning (ML) model to map waste coal piles in a historical coal mine site within the MRB. While all four ML models effectively identified dominant classes such as Grassland and Forest, the Random Forest (RF) model demonstrated superior performance in classifying the more complex waste coal class, with a precision of 86.15% and recall of 76.71%. Subsequently, the greatest disturbance and reclamation mapping of these waste coal piles were conducted using the LandTrendr algorithm to distinguish waste coal piles in abandoned mine lands from those in active mining areas. Moreover, this study utilized publicly available elevation models to estimate waste coal volume in the MRB. However, since historical terrain models are generally not available to facilitate regional-scale volume calculations, the accuracy of kriging-interpolated Digital Terrain Models (DTMs) for volume estimation was assessed using a coal storage facility within the MRB as a case study. The accuracy of kriging-interpolated DTM was compared against those generated using Structure-from-Motion (SfM) photogrammetry and Light Detection and Ranging (LiDAR). The Mean Absolute Percentage Error (MAPE) values indicated that the volume estimated with kriging-interpolated DTM was off by 6.66% and 8.40% compared to LiDAR and SfM-photogrammetry, respectively. Additionally, the effect of spatial resolution of elevation data on volume estimates was also explored. At a 10 m spatial resolution, the MAPE of the volume estimates with the kriging-interpolated DTM increased to 11.50% compared to LiDAR. Despite the variability in statistical error metrics with coarser resolution elevation data, such resolutions might still be suitable for regional-scale studies that demand higher computational resources. A regional-scale study was then conducted using publicly available Sentinel-2 imagery, DEMs, and thematic maps such as the cropland layer, global human settlement layer, and transportation layer. The Gray Level Co-occurrence Matrix algorithm was used to generate 17 distinct texture metrics to improve the classifier performance. The integration of texture metrics with spectral bands improved the performance of the object-based Support Vector Machine (SVM) in identifying waste coal, with precision increasing from 50% to 71.8%. The developed framework identified approximately 3.79 million m3 of waste coal distributed across 18.28 km2 in the MRB in Ohio. The developed framework is a crucial step toward understanding the distribution and availability of waste coal across the United States. It provides valuable insights that can guide the selection of appropriate reclamation approaches to address environmental concerns associated with waste coal piles.
Ajay Shah (Advisor)
Sami Khanal (Advisor)
Tarunjit Singh Butalia (Committee Member)
137 p.
Artificial Intelligence; Engineering; Geographic Information Science; Remote Sensing; Sustainability
Remote Sensing; Machine Learning; LandTrendr; Waste Coal; Abandoned Mine Lands; Stockpile Volume; UAS; SfM; LiDAR; Kriging; Photogrammetry

Recommended Citations

Citations

  • Dhakal, S. (2024). Mapping and volume estimation of waste coal in abandoned mine lands using remote sensing and geospatial techniques [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1721323434699912

    APA Style (7th edition)

  • Dhakal, Sandeep. Mapping and volume estimation of waste coal in abandoned mine lands using remote sensing and geospatial techniques. 2024. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1721323434699912.

    MLA Style (8th edition)

  • Dhakal, Sandeep. "Mapping and volume estimation of waste coal in abandoned mine lands using remote sensing and geospatial techniques." Master's thesis, Ohio State University, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=osu1721323434699912

    Chicago Manual of Style (17th edition)

osu1721323434699912
© 2024, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.