Temporal sequence method for fire spot detection using Himawari-8 geostationary meteorological satellite

Jie CHEN; Wei ZHENG; Cheng LIU; Shihao TANG

doi:10.11834/jrs.20219176

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Temporal sequence method for fire spot detection using Himawari-8 geostationary meteorological satellite
Vol. 25, Issue 10, Pages: 2095-2102(2021)
Published： 07 October 2021 ，
DOI： 10.11834/jrs.20219176

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陈洁，郑伟，刘诚，唐世浩.2021.Himawari-8静止气象卫星时序法火点探测.遥感学报，25（10）： 2095-2102

Chen J， Zheng W， Liu C and Tang S H. 2021. Temporal sequence method for fire spot detection using Himawari-8 geostationary meteorological satellite. National Remote Sensing Bulletin， 25（10）：2095-2102
陈洁，郑伟，刘诚，唐世浩.2021.Himawari-8静止气象卫星时序法火点探测.遥感学报，25（10）： 2095-2102 DOI： 10.11834/jrs.20219176.

Chen J， Zheng W， Liu C and Tang S H. 2021. Temporal sequence method for fire spot detection using Himawari-8 geostationary meteorological satellite. National Remote Sensing Bulletin， 25（10）：2095-2102 DOI： 10.11834/jrs.20219176.

摘要

随着新一代静止气象卫星的发射，高频次和高时效的观测特性对于火点探测具有独特优势。本文基于Himawari-8新一代静止气象卫星高频次观测特点，提出有利于火情初期火点判识的时序探测方法。与传统的极轨气象卫星遥感火情监测采用的上下文法不同，时序探测法判识火点的方法依据为探测像元亮温在观测时间上的差异。研究结果显示，在无云及无异常热源条件下，相邻时次中红外亮温差异较小，当前后时次亮温差达到3K时，可判识出火点，而上下文法的阈值均在6 K以上，时序法的火点判识阈值较上下文法明显降低，探测相应的亚像元火点面积减小一倍以上，从而提高了火情判识的灵敏度，实现火点早期发现。本文介绍了时序法火点判识方法，并以黑龙江桦川县的星地同步观测实验进行验证，研究表明，时序法较上下文法在初发火点探测灵敏度方面有明显优势，时序法和上下文法的结合可提高气象卫星对火情发展过程的监测能力。

Abstract

Wild fires

especially large-scale wild fires

in forests

grasslands and farmlands have a significant influence on crop productivity

atmospheric pollution

biodiversity

climate change and public health. In recent years

the increasing events of forest fires in China

Australia

and Amazon Rain Forests and grassland fires in Mongolia have caused a large number of causality. Due to its great influences

growing emphasis has been placed on the monitoring of wild fires based on remote sensing products

such as using MODIS

NOAA and other polar orbit meteorological satellites. With the launch of a new generation of geostationary meteorological satellites

the characteristics of high frequency and real-time observation have obvious advantages for fire spot detection. Based on the high frequency observation characteristics of Himawari-8 that a new generation geostationary meteorological satellite

the objectives of this paper proposes a temporal sequence detection method to extract the initial fire spot of fire behavior. This method of geostationary meteorological satellite will greatly improve the method of fire identification

give full play to the advantage of temporal sequential

and realize the early detection of fire by remote sensing.

The method of study for identifying fire points is based on the pixel temperature brightness difference in observation times and its rate

which is different from the conventional contextual method used in remote sensing fire monitoring of polar orbit meteorological satellites. According to the brightness temperature change value of detected pixel at the same position and different time

when the brightness temperature change value of the current and subsequent times exceeds the threshold

the pixel can be identified as a fire point. The change of observation methods has brought about a great improvement in monitoring sensitivity and timeliness in fire monitor.

The results showed that under the condition of cloud-free and no abnormal heat source

the mid-infrared bright temperature have little difference between the adjacent times. Generally

the brightness temperature change of minute interval is less than 0.5 K. When the bright temperature rate between the current time and later time reaches 3K

the fire spot can be identified

while the threshold of contextual method is above 6 K. Compared with the contextual method

the temporal sequential method reduces the threshold of recognition by half and increases the sensitivity by more than twice. The fire spot detection threshold of the temporal sequential method is significantly lower than that of the contextual method.

This paper introduces the method of temporal sequence for fire spot detection

and verifies it with the satellite and ground synchronous observation experiment in huachuan county

heilongjiang province. The conclusion demonstrated that this method is benefit to find early fire spot. If the threshold of contextual method is used alone

the early fire is difficult to be obtained

but the method of temporal sequence can make up for this defect. Temporal sequence method is used in the early stage of fire identification

context method is used in the middle stage

The combination of the temporal sequence method and the fluctuation of contextual can improve the meteorological satellite monitoring ability of fire development process.

关键词

遥感Himawari-8静止气象卫星时序法火点探测

Keywords

remote sensingHimawari-8geostationary meteorological satellitetemporal sequence methodfire detection

references

Boles S H and Verbyla D L. 2000. Comparison of three AVHRR-based fire detection algorithms for interior Alaska. Remote Sensing of Environment, 72(1): 1-16 [DOI: 10.1016/s0034-4257(99)00079-6http://dx.doi.org/10.1016/s0034-4257(99)00079-6]

Calle A, Casanova J L and Romo A. 2006. Fire detection and monitoring using MSG Spinning Enhanced Visible and Infrared Imager (SEVIRI) data. Journal of Geophysical Research: Biogeosciences, 111(G4): G04S06 [DOI: 10.1029/2005JG000116http://dx.doi.org/10.1029/2005JG000116]

Chen J, Zheng W and Liu C. 2017. Application of grassland fire monitoring based on Himawari-8 geostationary meteorological satellite data. Journal of Natural Disasters, 26(4): 197-204

陈洁, 郑伟, 刘诚. 2017. Himawari-8静止气象卫星草原火监测分析. 自然灾害学报, 26(4): 197-204 [DOI: 10.13577/j.jnd.2017.0423http://dx.doi.org/10.13577/j.jnd.2017.0423]

Giglio L, Descloitres J, Justice C O and Kaufman Y J. 2003. An enhanced contextual fire detection algorithm for MODIS. Remote Sensing of Environment, 87(2/3): 273-282 [DOI: 10.1016/s0034-4257(03)00184-6http://dx.doi.org/10.1016/s0034-4257(03)00184-6]

Guang X and Xu Z. 2017. Real-time wildfire detection and tracking in Australia using geostationary satellite: Himawari-8. Remote Sensing Letters, 8(11): 1052-1061. [DOI: 10.1080/2150704X.2017.1350303http://dx.doi.org/10.1080/2150704X.2017.1350303]

Hassini A, Benabdelouahed F, Benabadji N and Belbachir A H. 2009. Active fire monitoring with level 1.5 MSG satellite images. American Journal of Applied Sciences, 6(1): 157-166 [DOI: 10.3844/ajassp.2009.157.166http://dx.doi.org/10.3844/ajassp.2009.157.166]

Henry R L. 1948. The transmission of powder films in the infra-red. Journal of the Optical Society of America, 38(9): 775-789 [DOI: 10.1364/JOSA.38.000775http://dx.doi.org/10.1364/JOSA.38.000775]

Kahle A B and Rowan L C. 1980. Evaluation of multispectral middle infrared aircraft images for lithologic mapping in the East Tintic Mountains, Utah. Geology, 8(5): 234-239 [DOI: 10.1130/0091-7613(1980)8<234:EOMMIA>2.0.CO;2http://dx.doi.org/10.1130/0091-7613(1980)8<234:EOMMIA>2.0.CO;2]

Kaufman Y J, Justice C O, Flynn L P, Kendall J D, Prins E M, Giglio L, Ward D E, Menzel W P and Setzer A W. 1998. Potential global fire monitoring from EOS-MODIS. Journal of Geophysical Research: Atmospheres, 103(D24): 32215-32238 [DOI: 10.1029/98JD01644http://dx.doi.org/10.1029/98JD01644]

Li Y J, Zheng W, Chen J and Liu C. 2017. Fire Monitoring and application based on meteorological satellite. Aerospace Shanghai, 34(4): 62-72

李亚军, 郑伟, 陈洁, 刘诚. 2017. 气象卫星遥感火情监测应用. 上海航天, 34(4): 62-72 [DOI: 10.19328/j.cnki.1006-1630.2017.04.008http://dx.doi.org/10.19328/j.cnki.1006-1630.2017.04.008]

Liu C, Li Y J, Zhao C H, Yan H and Zhao H M. 2004. The method of evaluating sub-pixel size and temperature of fire spot in AVHRR data. Journal of Applied Meteorological Science, 15(3): 273-280

刘诚, 李亚君, 赵长海, 阎华, 赵洪淼. 2004. 气象卫星亚像元火点面积和亮温估算方法. 应用气象学报, 15(3): 273-280 [DOI: 10.3969/j.issn.1001-7313.2004.03.003http://dx.doi.org/10.3969/j.issn.1001-7313.2004.03.003]

Matson M and Schneider S R. 1984. Fire Detection Using the NOAA-Series Satellite. NOAA Technical Report NESDIS 7. NOAA

Meng X C, Liu H and Cheng J. 2019. Evaluation and characteristic research in diurnal surface temperature cycle in China using FY-2F data. Journal of Remote Sensing, 23(4): 570-581

孟翔晨, 刘昊, 程洁. 2019. 基于FY-2F数据的中国区域地表温度日变化模型评价及特征研究. 遥感学报, 23(4): 570-581 [DOI: 10.11834/jrs.20197330http://dx.doi.org/10.11834/jrs.20197330]

Miao T T and Shen R P. 2013. Automatic extraction algorithm of forest fire points from MODIS imagery based on background information. Science of Surveying and Mapping, 38(5): 49-50, 60

缪婷婷, 沈润平. 2013. 基于背景信息的MODIS林火自动提取算法. 测绘科学, 38(5): 49-50, 60 [DOI: 10.16251/j.cnki.1009-2307.2013.05.008http://dx.doi.org/10.16251/j.cnki.1009-2307.2013.05.008]

Rong Z G, Liu C, Sun H, Ma L J, Lu N M, Liu J J, Zhang Y X, Zhong S Q, Zhang Y, Zhang P, Zhang J S, Li Y J, Zhang X Q, Ma R S and Wang J H. 2007. Sensitivity experiment for fire detecting using satellites' data and new detection channel selection for fire remote sensing. Advances in Earth Science, 22(8): 866-871

戎志国, 刘诚, 孙涵, 马轮基, 卢乃锰, 刘京晶, 张玉香, 钟仕全, 张艳, 张鹏, 张甲珅, 李亚君, 张行清, 马瑞升, 王君华. 2007. 卫星火情探测灵敏度试验与火情遥感新探测通道选择. 地球科学进展, 22(8): 866-871 [DOI: 10.3321/j.issn:1001-8166.2007.08.013http://dx.doi.org/10.3321/j.issn:1001-8166.2007.08.013]

Schroeder W, Oliva P, Giglio L and Csiszar I A. 2014. The new VIIRS 375 m active fire detection data product: algorithm description and initial assessment. Remote Sensing of Environment, 143: 85-96 [DOI: 10.1016/j.rse.2013.12.008http://dx.doi.org/10.1016/j.rse.2013.12.008]

Wickramasinghe C H, Jones S, Reinke K and Wallace L. 2016. Development of a multi-spatial resolution approach to the surveillance of active fire lines using Himawari-8. Remote Sensing, 8(11): 932 [DOI: 10.3390/rs8110932http://dx.doi.org/10.3390/rs8110932]

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