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  1. AU="Libo Dang"
  2. AU="Sun, Haoqi"
  3. AU="Jie Lin"
  4. AU="Jiang Huang" AU="Jiang Huang"
  5. AU="Yongliang Zhang"
  6. AU="Ernest, C Steven"
  7. AU="Axel Haferkamp"
  8. AU="Ciocan, Alexandra"

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  1. Artikel ; Online: Multi-Scale Coal Fire Detection Based on an Improved Active Contour Model from Landsat-8 Satellite and UAV Images

    Yanyan Gao / Ming Hao / Yunjia Wang / Libo Dang / Yuecheng Guo

    ISPRS International Journal of Geo-Information, Vol 10, Iss 449, p

    2021  Band 449

    Abstract: 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 ... ...

    Abstract 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.
    Schlagwörter coal fires ; Landsat-8 ; UAV ; multi-scale ; active contour model ; Geography (General) ; G1-922
    Thema/Rubrik (Code) 670
    Sprache Englisch
    Erscheinungsdatum 2021-06-01T00:00:00Z
    Verlag MDPI AG
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Artikel ; Online: Accuracy assessment and scale effect investigation of UAV thermography for underground coal fire surface temperature monitoring

    Gang Yuan / Yunjia Wang / Feng Zhao / Teng Wang / Leixin Zhang / Ming Hao / Shiyong Yan / Libo Dang / Bin Peng

    International Journal of Applied Earth Observations and Geoinformation, Vol 102, Iss , Pp 102426- (2021)

    2021  

    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. ... ...

    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.
    Schlagwörter UAV thermography ; Underground coal fire ; Land surface temperature ; Accuracy assessment ; Scale effect ; Physical geography ; GB3-5030 ; Environmental sciences ; GE1-350
    Thema/Rubrik (Code) 690
    Sprache Englisch
    Erscheinungsdatum 2021-10-01T00:00:00Z
    Verlag Elsevier
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  3. Artikel ; Online: Underground Coal Fire Detection and Monitoring Based on Landsat-8 and Sentinel-1 Data Sets in Miquan Fire Area, XinJiang

    Jinglong Liu / Yunjia Wang / Shiyong Yan / Feng Zhao / Yi Li / Libo Dang / Xixi Liu / Yaqin Shao / Bin Peng

    Remote Sensing, Vol 13, Iss 1141, p

    2021  Band 1141

    Abstract: 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 ... ...

    Abstract 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 (R 2 reaches 0.82), which ...
    Schlagwörter underground coal fire recognition ; thermal infrared technology ; DS-InSAR ; multi-source remote sensing data ; fire identification ; Science ; Q
    Thema/Rubrik (Code) 690
    Sprache Englisch
    Erscheinungsdatum 2021-03-01T00:00:00Z
    Verlag MDPI AG
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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