One of the major problems faced by the coal mining industry is the problem associated with the exposed coal. There are a lot of environmental, operational, and social concerns around the exposed coal issue.
Apart from polluting the air and water, the exposed coal also contributes to land degradation and the loss of vegetation.
The coal dust generated from the exposed coal area can affect the health of nearby residents. Further, there are safety risks associated with the hazardous conditions around the exposed coal. These include the risk of fire.
Reclamation and remediation of the exposed coal areas can be a costly affair for the industry. This necessitates the focus being shifted more towards reducing and preventing the spread of exposed coal areas.
While the main ways to stop exposed coal areas from spreading are to minimize overburden removal and use selective mining techniques, remote monitoring of mining sites with advanced technologies like drones and satellite imagery can help spot possible problems early.
Many remote-sensing satellites revolving around the earth are mounted with multispectral cameras that take pictures of the earth’s surface as they pass over it.
The multispectral cameras are capable of sensing the earth’s reflected radiation in various wavelength bands, such as visible, near-infrared (NIR), and shortwave-infrared (SWIR) bands.
The imagery data thus captured is then sent to the ground receiving stations to be processed further.
Geospatial analysts utilize this rich information for various applications. They process the satellite imagery data so as to detect and measure the extent of multiple surface features, or land cover classes. Each land cover class exhibits a unique spectral signature, as manifested in the satellite imagery data.
For discriminating exposed coal from other land cover classes such as vegetation and water, bands such as SWIR and NIR are prominently utilized.
This is because coal often exhibits high reflectance in the SWIR band and comparatively low reflectance in the NIR band. Color and texture features derived from the visible bands are also utilized for discriminating between the exposed coal areas.
Though this kind of discrimination is often difficult because of atmospheric and shadow effects in the satellite images, as well as because of the constraints imposed by the satellite sensor’s limited spatial resolution. Furthermore, accurate analysis also requires calibration based on ground truth.
Many methods have been developed by scientists to identify exposed coal areas from satellite images.
These methods include object-based analysis, the use of machine learning algorithms, and the calculation of indices from the provided satellite data.
In one very recent study (cited below), NASA’s Landsat satellite imagery was utilized to derive an index for the mapping of exposed coal. This index automates the process of detecting exposed coal in the satellite images.
If this index is applied to the satellite images taken from various time periods, it can map the changes in the extent of exposed coal areas happening on the ground from time to time.
Thus, satellite image-based analysis is helpful in monitoring exposed coal. This can become a main component in the preventive strategy adopted for limiting the spread of the exposed coal.
Geospatial One Pvt. Ltd. (https://geospatial.one) has expertise in dealing with any such satellite image analysis. To find out more about this subject, feel free to get in touch.
Reference:
Yang, Z., He, T., Zhang, J. and Zhao, Y., 2024. A novel index for exposed coal mapping using Landsat imagery. Ecological Indicators, 166, p.112395.
