Abstract

Coal fire is a worldwide disaster that wastes massive energy and seriously pollutes the environment. Accurate acquisition of abnormal LST (land surface temperature) caused by underground coal fire is essential for coal fire monitoring and extinguishing. As a remote sensing technique, UAV (unmanned aerial vehicle) thermography can obtain LST images with very high spatial resolution and it has been used for coal fire monitoring. However, the accuracy of the UAV thermography obtained LST images (i.e., UAV LST images) has not yet been well studied, and the scale effect of UAV thermography for coal fire monitoring has not been discussed in previous studies. To this end, this study evaluates the accuracy of UAV LST images of coal fire areas based on the corresponding ground measurements. After that, the acquired UAV LST images are upscaled to different resolutions to simulate the LST images obtained at different observation scales. Finally, the local variance and Shannon entropy are employed to determine the optimal LST anomaly observation scale and coal fire area extraction scale. Baoan coalfield fire area, which is in Xinjiang province of China, is selected as the study area. The results show that the linear regression correlations R2 between UAV LST images and the LST values measured by thermal imaging camera and the infrared thermometer are both higher than 0.99. RMSE (Root Mean Square Error) between the thermal imaging camera LST measurements and that of UAV is 2.1 °C. When UAV LST images’ resolutions are better than 7.5 m, most of the LST anomalies can be detected, and the LST anomaly information loss is relatively small (less than 17%). The resolution of 4 m is the required lowest resolution to accurately extract the areas of coal fire. When the resolution is lower than 4 m, the high-temperature abnormal boundaries caused by coal fire are being blurred, making the extraction of coal fire combustion areas unreliable.

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