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无人机热红外遥感技术在中国神木市火鸡图煤矿煤火识别中的应用。

Application of unmanned aerial vehicle (UAV) thermal infrared remote sensing to identify coal fires in the Huojitu coal mine in Shenmu city, China.

作者信息

He Xiaoyuan, Yang Xingke, Luo Zheng, Guan Tao

机构信息

School of Earth Science and Resources, Chang'an University, Xi'an, 710054, China.

Aerial Photogrammetry and Remote Sensing Bureau of China National Administration of Coal Geology, Xi'an, 710199, China.

出版信息

Sci Rep. 2020 Aug 17;10(1):13895. doi: 10.1038/s41598-020-70964-5.

DOI:10.1038/s41598-020-70964-5
PMID:32807894
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7431847/
Abstract

China is a major coal-producing country that consumes large amounts of coal every year. Due to the existence of many small coal kilns using backward mining methods, numerous worked-out areas have been formed. The coal mines were abandoned with no mitigation, so air penetrates into the roadways and contacts the coal seams; as a result, the residual coal seams spontaneously ignite to form coal fires. These coal fires have burned millions of tons of valuable coal resources and caused serious environmental problems. To implement fire suppression more effectively, coal fire detection is a key technology. In this paper, thermal infrared remote sensing from unmanned aerial vehicle combined with a surface survey is used to identify the range of coal fires in the Huojitu coal mine in Shenmu city. The scopes and locations of the fire zones are preliminarily delineated, which provides an accurate basis for the development of fire suppression projects.

摘要

中国是煤炭生产大国,每年煤炭消耗量巨大。由于存在许多采用落后开采方法的小煤窑,形成了众多采空区。煤矿被废弃且未采取治理措施,空气进入巷道并与煤层接触,导致残留煤层自燃形成煤火。这些煤火烧毁了数百万吨宝贵的煤炭资源,造成了严重的环境问题。为了更有效地实施灭火工作,煤火探测是一项关键技术。本文利用无人机热红外遥感结合地面勘查,确定神木市火鸡图煤矿煤火的范围。初步划定了火区的范围和位置,为灭火工程的开展提供了准确依据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c9e/7431847/ef045351756c/41598_2020_70964_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c9e/7431847/85b9f1e9e1ac/41598_2020_70964_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c9e/7431847/61c21dc321c6/41598_2020_70964_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c9e/7431847/f122dc538348/41598_2020_70964_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c9e/7431847/251bc5938032/41598_2020_70964_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c9e/7431847/7ab922823117/41598_2020_70964_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c9e/7431847/5f9fdd0cbbc0/41598_2020_70964_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c9e/7431847/ef045351756c/41598_2020_70964_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c9e/7431847/85b9f1e9e1ac/41598_2020_70964_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c9e/7431847/61c21dc321c6/41598_2020_70964_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c9e/7431847/f122dc538348/41598_2020_70964_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c9e/7431847/251bc5938032/41598_2020_70964_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c9e/7431847/7ab922823117/41598_2020_70964_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c9e/7431847/5f9fdd0cbbc0/41598_2020_70964_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c9e/7431847/ef045351756c/41598_2020_70964_Fig7_HTML.jpg

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