Self-Adaptive Gradient-Based Thresholding Method for Coal Fire Detection Using ASTER Thermal Infrared Data, Part I: Methodology and Decadal Change Detection
AbstractCoal fires that are induced by natural spontaneous combustion or result from human activities occurring on the surface and in underground coal seams destroy coal resources and cause serious environmental degradation. Thermal infrared image data, which directly measure surface temperature, can be an important tool to map coal fires over large areas. As the first of two parts introducing our coal fire detection method, this paper proposes a self-adaptive threshold-based approach for coal fire detection using ASTER thermal infrared data: the self-adaptive gradient-based thresholding method (SAGBT). This method is based on an assumption that the attenuation of temperature along the coal fire’s boundaries generates considerable numbers of spots with extremely high gradient values. The SAGBT method applied mathematical morphology thinning to skeletonize the potential high gradient buffers into the extremely high gradient lines, which provides a self-adaptive mechanism to generate thresholds according to the thermal spatial patterns of the images. The final threshold was defined as an average temperature value reading from the high temperature buffers (segmented by 1.0 σ from the mean) and along a sequence of extremely high gradient lines (thinned from the potential high gradient buffers and segmented within the lower bounds, ranging from 0.5 σ to 1.5 σ and with an upper bound of 3.2 σ, where σ is the standard deviation), marking the coal fire areas. The SAGBT method used the basic outer boundary of the coal-bearing strata to simply exclude false alarms. The intermediate thresholds reduced the coupling with the temperature and converged by changing the potential high gradient buffers. This simple approach can be economical and accurate in identifying coal fire areas. In addition, it allows for the identification of thresholds using multiple ASTER TIR scenes in a consistent and uniform manner, and supports long-term coal fire change analyses using historical images in local areas. This paper focuses on the introduction of the methodology. Furthermore, an improvement to SAGBT is proposed. In a subsequent paper, subtitled “Part 2, Validation and Sensitivity Analysis,” we address satellite-field simultaneous observations and report comparisons between the retrieved thermal anomalies and field measurements in different aspects to prove that the coal fires are separable by the SAGBT method. These comparisons allowed us to estimate the accuracy and biases of the SAGBT method. As an application of the SAGBT, a relationship between coal fires’ decadal variation and coal production was also examined. Our work documented a total area increase in the beginning of 2003, which correlates with increased mining activities and the rapid increase of energy consumption in China during the decade (2001–2011). Additionally, a decrease in the total coal fire area is consistent with the nationally sponsored fire suppression efforts during 2007–2008. It demonstrated the applicability of SAGBT method for long-term change detection with multi-temporal images. View Full-Text
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Du, X.; Cao, D.; Mishra, D.; Bernardes, S.; Jordan, T.R.; Madden, M. Self-Adaptive Gradient-Based Thresholding Method for Coal Fire Detection Using ASTER Thermal Infrared Data, Part I: Methodology and Decadal Change Detection. Remote Sens. 2015, 7, 6576-6610.
Du X, Cao D, Mishra D, Bernardes S, Jordan TR, Madden M. Self-Adaptive Gradient-Based Thresholding Method for Coal Fire Detection Using ASTER Thermal Infrared Data, Part I: Methodology and Decadal Change Detection. Remote Sensing. 2015; 7(6):6576-6610.Chicago/Turabian Style
Du, Xiaomin; Cao, Daiyong; Mishra, Deepak; Bernardes, Sergio; Jordan, Thomas R.; Madden, Marguerite. 2015. "Self-Adaptive Gradient-Based Thresholding Method for Coal Fire Detection Using ASTER Thermal Infrared Data, Part I: Methodology and Decadal Change Detection." Remote Sens. 7, no. 6: 6576-6610.