scholarly journals Underground Coal Fires Identification and Monitoring Using Time-Series InSAR With Persistent and Distributed Scatterers: A Case Study of Miquan Coal Fire Zone in Xinjiang, China

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 164492-164506 ◽  
Author(s):  
jinglong Liu ◽  
Yunjia Wang ◽  
Yi Li ◽  
Libo Dang ◽  
Xixi Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Coal Fire ◽  
Fire Zone ◽  
Coal Fires ◽  
Underground Coal Fires

scholarly journals Underground Coal Fire Detection and Monitoring Based on Landsat-8 and Sentinel-1 Data Sets in Miquan Fire Area, XinJiang

2021 ◽  
Vol 13 (6) ◽  
pp. 1141
Author(s):  
Jinglong Liu ◽  
Yunjia Wang ◽  
Shiyong Yan ◽  
Feng Zhao ◽  
Yi Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Identification Accuracy ◽  
Omission Error ◽  
Detection Accuracy ◽  
Landsat 8 ◽  
Commission Error ◽  
Thermal Anomalies ◽  
Coal Fire ◽  
Coal Fires ◽  
Underground Coal Fires

Underground coal fires have become a worldwide disaster, which brings serious environmental pollution and massive energy waste. Xinjiang is one of the regions that is seriously affected by the underground coal fires. After years of extinguishing, the underground coal fire areas in Xinjiang have not been significantly reduced yet. To extinguish underground coal fires, it is critical to identify and monitor them. Recently, remote sensing technologies have been showing great potential in coal fires’ identification and monitoring. The thermal infrared technology is usually used to detect thermal anomalies in coal fire areas, and the Differential Synthetic Aperture Radar Interferometry (DInSAR) technology for the detection of coal fires related to ground subsidence. However, non-coal fire thermal anomalies caused by ground objects with low specific heat capacity, and surface subsidence caused by mining and crustal activities have seriously affected the detection accuracy of coal fire areas. To improve coal fires’ detection accuracy by using remote sensing technologies, this study firstly obtains temperature, normalized difference vegetation index (NDVI), and subsidence information based on Landsat8 and Sentinel-1 data, respectively. Then, a multi-source information strength and weakness constraint method (SWCM) is proposed for coal fire identification and analysis. The results show that the proposed SWCM has the highest coal fire identification accuracy among the employed methods. Moreover, it can significantly reduce the commission and omission error caused by non-coal fire-related thermal anomalies and subsidence. Specifically, the commission error is reduced by 70.4% on average, and the omission error is reduced by 30.6%. Based on the results, the spatio-temporal change characteristics of the coal fire areas have been obtained. In addition, it is found that there is a significant negative correlation between the time-series temperature and the subsidence rate of the coal fire areas (R2 reaches 0.82), which indicates the feasibility of using both temperature and subsidence to identify and monitor underground coal fires.

scholarly journals Multi-Scale Coal Fire Detection Based on an Improved Active Contour Model from Landsat-8 Satellite and UAV Images

2021 ◽  
Vol 10 (7) ◽  
pp. 449
Author(s):  
Yanyan Gao ◽  
Ming Hao ◽  
Yunjia Wang ◽  
Libo Dang ◽  
Yuecheng Guo
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Fire Detection ◽  
False Alarms ◽  
Landsat 8 ◽  
Traditional Methods ◽  
Coal Fire ◽  
Contour Model ◽  
Coal Fires ◽  
Underground Coal Fires

Underground coal fires can increase surface temperature, cause surface cracks and collapse, and release poisonous and harmful gases, which significantly harm the ecological environment and humans. Traditional methods of extracting coal fires, such as global threshold, K-mean and active contour model, usually produce many false alarms. Therefore, this paper proposes an improved active contour model by introducing the distinguishing energies of coal fires and others into the traditional active contour model. Taking Urumqi, Xinjiang, China as the research area, coal fires are detected from Landsat-8 satellite and unmanned aerial vehicle (UAV) data. The results show that the proposed method can eliminate many false alarms compared with some traditional methods, and achieve detection of small-area coal fires by referring field survey data. More importantly, the results obtained from UAV data can help identify not only burning coal fires but also potential underground coal fires. This paper provides an efficient method for high-precision coal fire detection and strong technical support for reducing environmental pollution and coal energy use.

scholarly journals From technology transfer to participative design: a case study of pollution prevention in South African townships

2006 ◽  
Vol 17 (3) ◽  
pp. 58-64 ◽  
Author(s):  
W Van Niekerk
Keyword(s):  
Air Pollution ◽  
South African ◽  
Pollution Prevention ◽  
Common View ◽  
Coal Fire ◽  
Coal Fires ◽  
Participative Design ◽  
Problem Solving Process

Since the 1960’s several unsuccessful attempts were made to reduce air pollution in South African townships. These efforts targeted coal fires and included devolatalised coal, low-smoke stoves and electrification. All these efforts were more or less prescriptive in nature, technology driven and shared a common view of the role of technology. In this paper a different approach is described that had very good results. This approach starts at the coal user and their preferences and does not try to change their behaviour or attitude by means of education or coercive measures. This was done because after several years of involvement with coal and wood using communities, a good understanding was developed of the role of fire in the household and the context in which possible solutions must function. Also, the user of the technology is made a full partner in the problem solving process. This approach resulted in the adaptation of an alternative method to light a coal fire. The adapted method enjoys widespread acceptance and can reduce the contribution of coal fires to air pollution by 50%. Plans are under way by the South African Government to introduce communities nationwide to this method.

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