Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    1998, Vol. 10 Issue (1) : 33-39     DOI: 10.6046/gtzyyg.1998.01.06
Applied Research |
FACTORS NEED TO BE THOUGHT ABOUT WHILE USING REMOTE SENSING IMAGES TO EXTRACT COAL FIRE INFORMATION
Wan Yuqing, Wu Junhu, Lei Xuewu
Aerophotogrammetry and Remote Sensing of China Coal, Xian 710054
Download: PDF(874 KB)  
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

Coal fire is a commonly existed disaster in the coal fields of northen China, it burns huge amount of coal resource, worsens environment. It is an important job to use remote sensing images to detect and monitor coal fires, deliver information for firefighting. Because of some restriction, we can’t get images with appropriate spectral resolution and the time of scanning we want. When analysing the images we have to take acount of the effects of image types, scanning data, DTM, climatic condition, character of rocks. The paper focuses on discussing the relationship between thermal radiant temperature and the factors mentioned above.

Keywords Quaternary      Remote Sensing      NDVI index      NDWI index      NDBI index      Feature Information     
Issue Date: 02 August 2011
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
ZHANG Wei
YANG Jin-Zhong
WANG Xiao-Hong
WU Dun-Wen
HU Yun-Lei
YAO Rong-Jun
Cite this article:   
ZHANG Wei,YANG Jin-Zhong,WANG Xiao-Hong, et al. FACTORS NEED TO BE THOUGHT ABOUT WHILE USING REMOTE SENSING IMAGES TO EXTRACT COAL FIRE INFORMATION[J]. REMOTE SENSING FOR LAND & RESOURCES, 1998, 10(1): 33-39.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.1998.01.06     OR     https://www.gtzyyg.com/EN/Y1998/V10/I1/33

[1] Mannstein H. Land Surface Energy Bardet, NATO ASISeries, 1994,124: 367-391

[2] Markham B L, Barker J L.Landsat MSS and TM Post Calibration Dynamic Ranges, Exoatmospheric Reflectances and at一Satellite Temperatures, EOSAT Landsat Technical Notes 1, Earth Observation Satellite Co. (Lanham, Maryland),August 1986.3-8

[3] Matron M, Dozier J. Identification of Subresoluti High Temperature Sources Using a Thermal IR Sensor,Phomgrammetric Engineering and Remote Sensing, 1981, 47:1311-1318

[4] Singh S M. Effect of Surface Wind Speed and Sensor View Zenith Angle Dependence of Emiseivity SST Retrieval form Thermal Infrared Data: ATSR, International Journal of Remote Sensing, 1994,15(13) :2615-2625

[5] Rees W G. Physical Principles of Remote Sensing, Cambridge University Press, ISI3N0 5213-5215 4, 1990

[6] Robert等著.汤定元等译.遥感手册.北京:国防工业出版社,1979

[7] 管海委.煤炭遥感应用.ACTA GEOLOGICAL SINICA. 1989, 2(3):254-260

[8] 刘培君.卫星遥感估侧土壤水分的一种方法.遥感学报.1997,1(2):

[9] 万余庆等.应用DTM改普煤层自姗的热红外探测.环境遥感,1996,11(4):

[10] 杨凯等.遥感图像处理原理与方法.北京:侧绘出版社,1988.63

[11] 张仁华.实脸遥感模型及地面基础.北京:科学出版社.1996.31,154,164,198

[12] 庄培仁.遥感技术及应用.北京:地质出版社,1986.18-20

[1] LI Weiguang, HOU Meiting. A review of reconstruction methods for remote-sensing-based time series data of vegetation and some examples[J]. Remote Sensing for Natural Resources, 2022, 34(1): 1-9.
[2] DING Bo, LI Wei, HU Ke. Inversion of total suspended matter concentration in Maowei Sea and its estuary, Southwest China using contemporaneous optical data and GF SAR data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 10-17.
[3] GAO Qi, WANG Yuzhen, FENG Chunhui, MA Ziqiang, LIU Weiyang, PENG Jie, JI Yanzhen. Remote sensing inversion of desert soil moisture based on improved spectral indices[J]. Remote Sensing for Natural Resources, 2022, 34(1): 142-150.
[4] ZHANG Qinrui, ZHAO Liangjun, LIN Guojun, WAN Honglin. Ecological environment assessment of three-river confluence in Yibin City using improved remote sensing ecological index[J]. Remote Sensing for Natural Resources, 2022, 34(1): 230-237.
[5] HE Peng, TONG Liqiang, GUO Zhaocheng, TU Jienan, WANG Genhou. A study on hidden risks of glacial lake outburst floods based on relief amplitude: A case study of eastern Shishapangma[J]. Remote Sensing for Natural Resources, 2022, 34(1): 257-264.
[6] LIU Wen, WANG Meng, SONG Ban, YU Tianbin, HUANG Xichao, JIANG Yu, SUN Yujiang. Surveys and chain structure study of potential hazards of ice avalanches based on optical remote sensing technology: A case study of southeast Tibet[J]. Remote Sensing for Natural Resources, 2022, 34(1): 265-276.
[7] WANG Qian, REN Guangli. Application of hyperspectral remote sensing data-based anomaly extraction in copper-gold prospecting in the Solake area in the Altyn metallogenic belt, Xinjiang[J]. Remote Sensing for Natural Resources, 2022, 34(1): 277-285.
[8] LYU Pin, XIONG Liyuan, XU Zhengqiang, ZHOU Xuecheng. FME-based method for attribute consistency checking of vector data of mines obtained from remote sensing monitoring[J]. Remote Sensing for Natural Resources, 2022, 34(1): 293-298.
[9] ZHANG Daming, ZHANG Xueyong, LI Lu, LIU Huayong. Remote sensing image segmentation based on Parzen window density estimation of super-pixels[J]. Remote Sensing for Natural Resources, 2022, 34(1): 53-60.
[10] XUE Bai, WANG Yizhe, LIU Shuhan, YUE Mingyu, WANG Yiying, ZHAO Shihu. Change detection of high-resolution remote sensing images based on Siamese network[J]. Remote Sensing for Natural Resources, 2022, 34(1): 61-66.
[11] SONG Renbo, ZHU Yuxin, GUO Renjie, ZHAO Pengfei, ZHAO Kexin, ZHU Jie, CHEN Ying. A method for 3D modeling of urban buildings based on multi-source data integration[J]. Remote Sensing for Natural Resources, 2022, 34(1): 93-105.
[12] YU Xinli, SONG Yan, YANG Miao, HUANG Lei, ZHANG Yanjie. Multi-model and multi-scale scene recognition of shipbuilding enterprises based on convolutional neural network with spatial constraints[J]. Remote Sensing for Natural Resources, 2021, 33(4): 72-81.
[13] LI Yikun, YANG Yang, YANG Shuwen, WANG Zihao. A change vector analysis in posterior probability space combined with fuzzy C-means clustering and a Bayesian network[J]. Remote Sensing for Natural Resources, 2021, 33(4): 82-88.
[14] AI Lu, SUN Shuyi, LI Shuguang, MA Hongzhang. Research progress on the cooperative inversion of soil moisture using optical and SAR remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(4): 10-18.
[15] LI Teya, SONG Yan, YU Xinli, ZHOU Yuanxiu. Monthly production estimation model for steel companies based on inversion of satellite thermal infrared temperature[J]. Remote Sensing for Natural Resources, 2021, 33(4): 121-129.
Viewed
Full text


Abstract

Cited

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