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Detection of Coal Fire Dynamics and Propagation Direction from Multi-Temporal Nighttime Landsat SWIR and TIR Data: A Case Study on the Rujigou Coalfield, Northwest (NW) China

1
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
2
ICube, CNRS, Université de Strasbourg, Boulevard Sebastien Brant, BP10413, F-67412 Illkirch, France
3
Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
4
School of Geography, Beijing Normal University, Beijing 100875, China
5
Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2014, 6(2), 1234-1259; https://doi.org/10.3390/rs6021234
Received: 2 December 2013 / Revised: 24 December 2013 / Accepted: 15 January 2014 / Published: 29 January 2014
Coal fires are common and serious phenomena in most coal-producing countries in the world. Coal fires not only burn valuable non-renewable coal reserves but also severely affect the local and global environment. The Rujigou coalfield in Shizuishan City, Ningxia, NW China, is well known for being a storehouse of anthracite coal. This coalfield is also known for having more coal fires than most other coalfields in China. In this study, an attempt was made to study the dynamics of coal fires in the Rujigou coalfield, from 2001 to 2007, using multi-temporal nighttime Landsat data. The multi-temporal nighttime short wave infrared (SWIR) data sets based on a fixed thresholding technique were used to detect and monitor the surface coal fires and the nighttime enhanced thematic mapper (ETM+) thermal infrared (TIR) data sets, based on a dynamic thresholding technique, were used to identify the thermal anomalies related to subsurface coal fires. By validating the coal fires identified in the nighttime satellite data and the coal fires extracted from daytime satellite data with the coal fire map (CFM) manufactured by field survey, we found that the results from the daytime satellite data had higher omission and commission errors than the results from the nighttime satellite data. Then, two aspects of coal fire dynamics were analyzed: first, a quantitative analysis of the spatial changes in the extent of coal fires was conducted and the results showed that, from 2001 to 2007, the spatial extent of coal fires increased greatly to an annual average area of 0.167 km2; second, the spreading direction and propagation of coal fires was analyzed and predicted from 2001 to 2007, and these results showed that the coal fires generally spread towards the north or northeast, but also spread in some places toward the east. View Full-Text
Keywords: multi-temporal remote sensing; dynamics; coal fires; thresholding technique; TM; ETM+ multi-temporal remote sensing; dynamics; coal fires; thresholding technique; TM; ETM+
MDPI and ACS Style

Huo, H.; Jiang, X.; Song, X.; Li, Z.-L.; Ni, Z.; Gao, C. Detection of Coal Fire Dynamics and Propagation Direction from Multi-Temporal Nighttime Landsat SWIR and TIR Data: A Case Study on the Rujigou Coalfield, Northwest (NW) China. Remote Sens. 2014, 6, 1234-1259.

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