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Remote Sens. 2015, 7(3), 2602-2626; doi:10.3390/rs70302602

Self-Adaptive Gradient-Based Thresholding Method for Coal Fire Detection Based on ASTER Data—Part 2, Validation and Sensitivity Analysis

1
School of Geosciences and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China
2
Center for Geospatial Research, Department of Geography, The University of Georgia, Athens, GA 30602, USA
3
Biospheric Sciences Laboratory, The National Aeronautics and Space Administration (NASA) Goddard Space Flight Center, Greenbelt, MD 20771, USA
4
Department of Statistics, The University of Georgia, Athens, GA 30602, USA
5
College of Resources and Environment, The University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Academic Editors: Richard Gloaguen and Prasad S. Thenkabail
Received: 9 July 2014 / Revised: 11 February 2015 / Accepted: 17 February 2015 / Published: 5 March 2015
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Abstract

The self-adaptive gradient-based thresholding (SAGBT) method is a simple non-interactive coal fire detection approach involving segmentation and a threshold identification algorithm that adapts to the spatial distribution of thermal features over a landscape. SAGBT detects coal fire using multispectral thermal images acquired by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. The method was detailed by our previous work “Self-Adaptive Gradient-Based Thresholding Method for Coal Fire Detection Based on ASTER Data—Part 1, Methodology”. The current study evaluates the performance of SAGBT and validates its results by using ASTER thermal infrared (TIR) images and ground temperature data collected at the Wuda coalfield (China) during satellite overpass. We further analyzed algorithm performance by using nighttime TIR images and images from different seasons. SAGBT-derived fires matched fire spots measured in the field with an average offset of 32.44 m and a matching rate of 70%–85%. Coal fire areas from TIR images generally agreed with coal-related anomalies from visible-near infrared (VNIR) images. Further, high-temperature pixels in the ASTER image matched observed coal fire areas, including the major extreme high-temperature regions derived from field samples. Finally, coal fires detected by daytime and by nighttime images were found to have similar spatial distributions, although fires differ in shape and size. Results included the stratification of our study site into two temperature groups (high and low temperature), using a fire boundary. We conclude that SAGBT can be successfully used for coal fire detection and analysis at our study site. View Full-Text
Keywords: thermal infrared remote sensing; spontaneous combustion of coal seam-validations; simultaneous field measurement; advanced spaceborne thermal emission and reflection radiometer (ASTER) thermal infrared remote sensing; spontaneous combustion of coal seam-validations; simultaneous field measurement; advanced spaceborne thermal emission and reflection radiometer (ASTER)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Du, X.; Bernardes, S.; Cao, D.; Jordan, T.R.; Yan, Z.; Yang, G.; Li, Z. Self-Adaptive Gradient-Based Thresholding Method for Coal Fire Detection Based on ASTER Data—Part 2, Validation and Sensitivity Analysis. Remote Sens. 2015, 7, 2602-2626.

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