Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2010, Vol. 22 Issue (3) : 69-75     DOI: 10.6046/gtzyyg.2010.03.15
Technology Application |
Research on Hyperspectral Quantitative Remote Sensing Detection of the Rujigou Coal Fire Area in Ningxia
 MAO Yao-Bao
Aerophotogrammetry & Remote Sensing of China Coal, Xi’an  710054, China
Download: PDF(998 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

 Research on the quantitative remote sensing detection of the Rujigou coal fire area in Ningxia by using the night-time airborne hyperspectral imagery is reported in this paper. Based on synchronous measurement of spectra and temperature,the author performed the spectral analysis and information extraction of indicated ground objects and thermal anomaly for defining the right thermal band for temperature simulation,simulation formula and minimum anomalous temperature,with the detection precision attaining the 1∶2 000 scale. The coal fire area was delineated and the combustion intensity was detected accurately. The author also analyzed the thermal diffusion regularity and the corresponding relations between the remotely sensed thermal anomaly and the underground coal fire anomaly,and achieved the goal that remote sensing quantitative detection results could be directly applied to the design of fire-fighting engineering.

 

Keywords Rocky desertification      Spectral charater      Supervised classification      Data conversion     
: 

 

 
  TP 79

 
Issue Date: 20 September 2010
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Cite this article:   
MAO Yao-Bao. Research on Hyperspectral Quantitative Remote Sensing Detection of the Rujigou Coal Fire Area in Ningxia[J]. REMOTE SENSING FOR LAND & RESOURCES,2010, 22(3): 69-75.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2010.03.15     OR     https://www.gtzyyg.com/EN/Y2010/V22/I3/69

[1]管海晏,Genderen J L V,谭永杰,等.中国北方煤田自燃环境调查与研究[M].北京:煤炭工业出版社,1998:3-23.

[2]毛耀保,彭文祥,万余庆,等.中国新疆煤层自燃环境动态探测信息系统的开发[J].国土资源遥感,1997(1):37-43.

[3]徐水师,谭克龙, 曹代勇,等.中国煤炭资源遥感调查评价理论与技术[M].北京:科学出版社,2009:31-42.

[4]万余庆,谭克龙,周日平,等.高光谱遥感应用研究[M]. 北京:科学出版社,2006:5-25.

[5]王晓鹏,万余庆,张光超,等.多源遥感技术在汝箕沟煤田火区动态探测中的应用[J].中国煤田地质,2005,17(5):28-31.

[6]张建民,管海晏,Rosema A. 煤田火区四层空间遥感探测方法研究[J].国土资源遥感,2004(4):50-53.

[7]谭克龙,周日平,万余庆,等.地下煤层燃烧的高光谱及高分辨率遥感探测方法[J].红外与毫米波学报,2007,26(5):349-252.

[1] WANG Lingyu, CHEN Quan, WU Yue, ZHOU Zhongfa, DAN Yusheng. Accurate recognition and extraction of karst abandoned land features based on cultivated land parcels and time series NDVI[J]. Remote Sensing for Land & Resources, 2020, 32(3): 23-31.
[2] Linlin WU, Yunlan GUAN, Jiawei LI, Chenxin YUAN, Rui LI. Assessment of Karst rocky desertification based on MODIS: Exemplified by Guizhou Province[J]. Remote Sensing for Land & Resources, 2019, 31(4): 235-242.
[3] Yongmin WANG, Xican LI, Linya TIAN, Bin JIA, Hui YANG. Comparison and analysis of estimation models of soil organic matter content established by hyperspectral on ground[J]. Remote Sensing for Land & Resources, 2019, 31(1): 110-116.
[4] Zhaohua LIU, Chunyan ZHANG. Dynamic monitoring and driving factors analysis of urban expansion in Kaifeng[J]. Remote Sensing for Land & Resources, 2018, 30(4): 193-199.
[5] Ting WANG, Jun PAN, Lijun JIANG, Lixin XING, Yifan YU, Pengju WANG. Topographic variable analysis and lithologic classification based on DEM[J]. Remote Sensing for Land & Resources, 2018, 30(2): 231-237.
[6] GUO Liqin, ZHAO Zhifang, DAI Qixue, LIANG Mingyue, FU Yixun, CHEN Bailian. Temporal and spatial evolution and genesis of rocky desertification based on RS and GIS in Wenshan Prefecture[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(s1): 106-113.
[7] HE Hao, SHEN Yonglin, LIU Xiuguo, MA Li. Spatial-spectral constrained graph-based semi-supervised classification for hyperspectral image[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(3): 31-36.
[8] CAO Changlei, ZHAO Xuelian, MEI Hongbo. Research on data conversion from MapGIS to shapefile[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(2): 193-197.
[9] LI Jiancun, TU Jienan, TONG Liqiang, GUO Zhaocheng. 20 year’s evolution features and influence factor analysis of rocky desertification in Guizhou[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(4): 133-137.
[10] LI Weina, YANG Jiansheng, LI Xiao, ZHANG Jilong, LI Shiwei. Extraction of urban impervious surface information from TM image[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(1): 66-70.
[11] CHENG Yang, CHEN Jian-ping, HUANGFU Jiang-yun, TONG Li-qiang. Quantitative Prediction of Karst Rocky Desertification Deterioration Based on RS and GIS: A Case Study of Typical Karst Rocky Desertification Area of Du’an County, Guangxi[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(3): 135-139.
[12] LI Xue-yuan, LI Cheng-zun, ZHAO Bo. The Method for Transformation from the Data File Based on ArcGIS Engine to the Shapefile[J]. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(3): 156-160.
[13] SONG Qi-Fan, WANG Shao-Jun, ZHANG Zhi, WANG Peng, AN Ping. A Water Information Extraction Method Based on WorldView II
Remote Sensing Image in Tungsten Ore Districts: A Case Study of of Dayu County in Jiangxi Province
[J]. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(2): 33-37.
[14] LI Na, ZHAO Hui-Jie. An Improved Independent Component Analysis Method for Unsupervised Classification of Hyperspectral Data  [J]. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(2): 70-74.
[15] LI Li, TONG Li-Qiang, LI Xiao-Hui. The Remote Sensing Information Extraction Method Based on Vegetation Coverage[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(2): 59-62.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech