A time series decomposition approach to detect coal fires in parts of the Gondwana coalfields of India from VIIRS data

ABSTRACT

This paper proposes an algorithm to detect coal fires using time-series Land Surface Temperature (LST) maps generated from data acquired by a wide-swath Visible Infrared Imaging Radiometer Suite (VIIRS) satellite sensor. VIIRS provides remotely sensed thermal images daily during daytime and nighttime. The proposed algorithm primarily uses the trend component extracted at pixel level from time-series LST maps by using the Seasonal Trend decomposition based on Loess (STL) model. The proposed algorithm is suitable for detecting coal fires at the regional scale. The obtained results were validated with coal fire observations collected during a field survey.

KEYWORDS:

• Coal fire
• VIIRS
• land surface temperature
• time series decomposition
• Gondwana coalfields

Acknowledgement

This research was funded by the Indian Space Research Organization (ISRO) under the Earth Observation Application Mission (EOAM) project scheme. The authors would like to acknowledge Bharat Coking Coal Ltd. (a subsidiary of Coal India Ltd.) for field support.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/14498596.2023.2183431

Additional information

Funding

This work was supported by the Indian Space Research Organisation.