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.
Highlights
Subsurface and surface coal fires have been widely linked to pollution and loss of coal resources [1], making the identification and mapping of burning coal areas a critical component in understanding coal fire contributions to environmental degradation and to the economy
The use of ASTER thermal infrared (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]
To test the accuracy of coal fire delineation by self-adaptive gradient-based thresholding (SAGBT), we analyzed 256 temperature measurements collected at our sampling blocks
Summary
Subsurface and surface coal fires have been widely linked to pollution and loss of coal resources [1], making the identification and mapping of burning coal areas a critical component in understanding coal fire contributions to environmental degradation and to the economy. The occurrence and spatial distribution of coal fires are a function of coal properties, including microstructure, chemical constituents, and minerals (e.g., particle sizes and surface areas, rank, petrography, and pyrites) [2]. Spontaneous Combustion of Coal Seams (SCCS) is promoted by three main coal properties (high sulfur content, high thickness and low metamorphic degree) [3]. Environmental conditions play an important role in the distribution of SCCS, including atmospheric, geological and mining conditions (e.g., temperature, moisture, barometric pressure, oxygen concentration, bacteria, coal seams and surrounding strata, as well as mining operational methods, among others) [3]. This study delineates subsurface and surface coal fires and assumes that coal fire distribution is not spatially continuous due to the heterogeneous nature of coal fire properties and environmental conditions, including the distribution of oxygen conductive tunnels. Our assumptions are consistent with previous work by [4], who observed attenuation to cold ground within two meters of the edge of fire regions
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