Spatio-Temporal Change Detection of North Antelope Rochelle and Black Thunder Coal Fields of US Using Multi-temporal Remote Sensing Satellite Data
Opencast mining activities including mineral exploration, extraction, waste dumping and stockpiling affect the nearby environment and land cover. To minimize the negative impacts of mining on the environment, it is highly recommended to adopt sustainable mining practices along with proper and regular monitoring of mining activities. In this research study, 32 years of Landsat satellite data from 1984 to 2016 was used to map the spatiotemporal changes in coal mining activities of the World’s largest North Antelope Rochelle and Black Thunder coal fields of the United States of America. Digital image processing of satellite images was done and the Maximum Likelihood Classification algorithm was applied to quantify the land cover changes in the region. The most appropriate band selection was done for the identification of coal seams through satellite images. The results showed that the Shortwave Infrared-2, Near Infrared and Green bands of Landsat satellite, highlighted as Red, Green, and Blue respectively are proven to be very effective in the identification of areas of coal mining. The classification results showed that since 1984, coal mining has been expanded by 74,000 ha with an increase of more than 87% in thirty-two years. Whereas almost 94% decrease in the vegetation cover of the region was also observed during the same period of time. The methodology adopted in this research study is efficient, time-saving and inexpensive to monitor the detailed surface mining activities over a large period of time, and is applicable to other mining regions, as well.
KeywordsRemote sensing Sustainable mining Coal mining Change detection
The work presented here is a Ph.D. research study in the School of Mining Engineering at the University of the Witwatersrand, Johannesburg, South Africa.
- 7.Arcadia Publishing: Evolution of the Coal Industry in America, 28 July 2017. https://www.arcadiapublishing.com/Navigation/Community/Arcadia-and-THP-Blog/October-2017/%E2%80%8BEvolution-of-the-Coal-Industry-in-America
- 13.Gilmore, S., Saleem, A., Dewan, A.: Effectiveness of DOS (Dark-Object Subtraction) method and water index techniques to map wetlands in a rapidly urbanising megacity with Landsat 8 data. In: Research@ Locate 2015, pp. 100–108 (2015)Google Scholar
- 14.Cabral, A.I., Silva, S., Silva, P.C., Vanneschi, L., Vasconcelos, M.J.: Burned area estimations derived from Landsat ETM+ and OLI data: comparing genetic programming with maximum likelihood and classification and regression trees. ISPRS J. Photogramm. Remote Sens. 142, 94–105 (2018)CrossRefGoogle Scholar
- 17.Singh Bramhe, V., Kumar Ghosh, S., Kumar Garg, P.: Extraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using Support Vector Machine method. Geocarto Int. 1–18 (2019)Google Scholar