Surface coal mine area monitoring using multi-temporal high-resolution satellite imagery

Date

2011-04-01

Author

Demirel, Nuray
Duzgun, H. Sebnem

Metadata

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Surface mining activities, exploitation of ore and stripping and dumping overburden, cause changes on the land cover and land use of the mine area. Sustainable mining requires continuous monitoring of these changes to identify the long-term impacts of mining on environment and land cover to provide essential safety measures. In this sense, digital image classification provides a powerful tool to obtain a rigorous data and hence diminishes the essence of time-consuming and costly field measurements. There are various image classification techniques, serving different features for different purposes, and the Support Vector Machine (SVM) classification method based on statistical machine learning theory stands out to be an effective and accurate image classification technique among them.

Subject Keywords

Fuel Technology, Economic Geology, Stratigraphy, Geology

URI

https://hdl.handle.net/11511/46633

Journal

INTERNATIONAL JOURNAL OF COAL GEOLOGY

DOI

https://doi.org/10.1016/j.coal.2010.11.010

Collections

Department of Mining Engineering, Article

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Citation Formats

N. Demirel and H. S. Duzgun, “Surface coal mine area monitoring using multi-temporal high-resolution satellite imagery,” INTERNATIONAL JOURNAL OF COAL GEOLOGY, pp. 3–11, 2011, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/46633.