Self-Adaptive Gradient-Based Thresholding Method for Coal Fire Detection Using ASTER Thermal Infrared Data, Part I: Methodology and Decadal Change Detection

Abstract

Coal 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 OPEN ACCESSRemote Sens. 2015, 7 6577 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)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011). Additionally, a decrease in the total coal fire area is consistent with the nationally sponsored fire suppression efforts during [2...

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

“…The use of ASTER TIR scenes acquired during different seasons precludes the use of a fixed threshold for segmenting temperature images. We previously addressed this limitation and proposed a method (SAGBT) that can be applied to multiple ASTER TIR scenes in a consistent and uniform way [8]. SAGBT defines coal fire areas as regions with temperature anomalies bounded by sharp decreases in LST, from central high temperature anomalies to low background temperature.…”

“…We observed that these extreme gradient lines maintain topological properties and connect high gradient pixels in the gradient image and surrounding high temperature areas in the temperature image. Masked within the fire risk area that is closed by the outer boundaries of the coal-bearing strata, an intermediate threshold was retrieved by reading an average temperature along the thinned extremely high gradient lines from the potential high temperature image (high temperature image showed in Figure 3a) [8]. In the fine-tuning process, the final threshold was selected as the mean value of multiple intermediate thresholds generated by eleven line sequences resulting from thinned extreme gradient images.…”

“…SAGBT is mainly based on the thermal spatial distribution of a TIR image itself. Figure 4 shows a coal fire map resulting from the method above and depicting coal seam outcrops and coalfield borders [8]. …”

“…The Self-Adaptive Gradient-Based Thresholding (SAGBT) method for coal fire detection proposed in our previous paper [8] was analyzed and validated. This satellite image-based method used ASTER thermal infrared images and relied on a coal fire risk area enclosed by the outer boundaries of coal-bearing strata to exclude false positives.…”

“…For instance, thermal infrared (TIR) images acquired by orbital sensors over multiple dates have been used effectively for coal fire anomaly detection [7]. In our previous work [8], we delineated coal fire areas by using TIR images acquired by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. The study proposed the Self-Adaptive Gradient-Based Thresholding method (SAGBT) and used the spatial variation of temperature (increases or decreases) over the landscape.…”