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纸质出版日期： 2020-05-07 ，
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饶月明,王川,黄华国.2020.联合多源遥感数据监测四川木里县森林火灾.遥感学报,24(5): 559-570
Rao Y M,Wang C and Huang H G. 2020. Forest fire monitoring based on multisensor remote sensing techniques in Muli County, Sichuan Province. Journal of Remote Sensing(Chinese), 24(5): 559-570
森林火灾既严重影响森林生态系统的稳定，还威胁到人类生命财产安全。传统监测森林火灾方法，覆盖范围小，难以及时监测小面积火灾。遥感卫星能大范围精确监测火情，提高了监测方法的时效性，但使用单一卫星数据源很容易受到云雨等客观环境因素影响，降低监测的时效性。本文以四川木里藏族自治县“330森林火灾”区域为对象，开展多源卫星遥感数据对小范围火灾联合监测的研究。首先，充分挖掘高分四号高时空分辨率和中红外火烧敏感波段优势，联合烟幕、温度和植被指数时序变化确定火烧时间与位置；然后，使用Sentinel-2数据监测不同火烧区域光谱信息；接着，使用Sentinel-2数据提取dNBR(differenced Normalized Burn Ratio)，提出了基于最大类间方差算法（OTSU）分步骤确定不同程度火烧迹地与面积的方法；最后，建立Sentinel-1A极化比值PR(Polarization Ratio)和NDVI之间关系，利用微波雷达突破云雨限制。结果表明：(1)高分四号联合IRS(InfraRed Scanner)和PMS(Panchromatic Multispectral Sensor）能够实时监测小范围火灾；(2)根据火点位置，确定火灾蔓延期间NDVI下降（由0.7降低至0.25），确定起火时间(3月30日)；(3)火灾区域与未受灾区，以及不同类型火烧迹地之间的光谱在490—2200 nm范围存在差异；(4)基于OTSU算法自动确定阈值，确定林地损失面积41.56公顷（dNBR=0.35），精度达94.67%，提取林地过火未损失面积66.56公顷（dNBR=0.10），精度达90.94%，林地损失区域基本符合实际调查结果；(5)火灾前后极化比值由6.6 dB升高至10.8 dB，NDVI与PR经线性回归，
R
2
=0.58，验证
R
2
=0.50。联合多源卫星监测森林火灾，能提高森林火灾监测的时效性，避免了云雨等复杂环境的影响。研究成果能为小火点的及时识别和灾害评估提供参考，其应用可为林火应急响应提供技术支撑。
Forest fires seriously damage the stability of forest ecology and threaten the safety of human lives and properties. Traditional methods cannot detect fire disasters in time because of their limited observation region and time window. Remote sensing can accurately and continuously detect fire disasters in a large area with improved detection efficiency. Meteorological and polar satellites are mainly used to detect forest fires. However
these satellites cannot detect small fire scars and deal with the influence of cloud and rain. In this study
multisensor remote sensing data were combined to monitor the forest fire spots and progress in Muli Tibetan Autonomous County of Sichuan Province since March 30
2019.First
we located the fire spots and their time of occurrence using the Chinese Gaofen-4 (GF-4) satellite. This geostationary satellite has a PMS (Panchromatic Multispectral ) camera with a high spatial (50 m) resolution and a mid-infrared sensor (IRS
400 m) that is sensitive to abnormally high temperatures. Second
we computed the spectral difference between burning and unaffected forest stands using Sentinel-2. Third we classified the fire scars on the basis of Sentinel-2 using the OTSU algorithm to set the threshold of differenced Normalized Burn Ratio (dNBR). Finally
we used the synthetic aperture radar data from Sentinel-1A to relate Normalized Difference Vegetation Index (NDVI) to Polarization Ratio (PR).
The results show that: (1) The location of fire spots can be accurately determined using the IRS and PMS data from GF-4. (2) The burning time is March 30 using the PMS data from GF-4 by combining the fire spot location and the sharp reduction in NDVI (from 0.7 to 0.25). (3) A difference is observed in the spectral curves of among the burning forest stands
unaffected areas
and different types of burned areas of Sentinel-2 data at 490 and 2200 nm. (4) The total area of damaged fire scars is 41.56 hm
2
with 94.67% accuracy using the dNBR of 0.35 as the threshold from Sentinel-2 derived dNBR map. Lightly damaged fire scars are also classified (66.56 hm
2
90.94%). (5) The PR from Sentinel-1A data increases from 6.6 dB to 10.8 dB after burning. NDVI is linearly related to PR (
R
2
=0.58 for fitting and 0.50 for verification).
The above results agree well with the fire spot location and area from local field reports
and the burning time error is less than 12 h. This study provides an efficient method that can ignore the influence of cloud and rain and other complex environments to monitor forest fires. Many details can be obtained to capture the fire progress when GF-4 is used in monitoring. This study provides a reference for the identification of small fire disasters after their occurrences. The research methods and results can provide technical support for forest fire emergency.
森林火灾监测高分四号卫星合成孔径雷达多源遥感数据火灾蔓延
forest fires monitoringGF-4SARmulti-sensor remote sensing datafire spreading
Chuvieco E, Englefield P, Trishchenko A P and Luo Y . 2008. Generation of long time series of burn area maps of the boreal forest from NOAA–AVHRR composite data. Remote Sensing of Environment, 112(5): 2381-2396 [DOI: 10.1016/j.rse.2007.11.007http://dx.doi.org/10.1016/j.rse.2007.11.007 ]
De Santis A and Chuvieco E . 2007. Burn severity estimation from remotely sensed data: Performance of simulation versus empirical models. Remote Sensing of Environment, 108(4): 422-435 [DOI:10.1016/j.rse.2006.11.022http://dx.doi.org/10.1016/j.rse.2006.11.022 ]
Fernandez-Carrillo A, McCaw L and Tanase M A . 2019. Estimating prescribed fire impacts and post-fire tree survival in eucalyptus forests of Western Australia with L-band SAR data. Remote Sensing of Environment, 224: 133-144 [DOI: 10.1016/j.rse.2019.02.005http://dx.doi.org/10.1016/j.rse.2019.02.005 ]
García M J L and Caselles V . 1991. Mapping burns and natural reforestation using Thematic Mapper data. Geocarto International, 6(1): 31-37 [DOI: 10.1080/10106049109354290http://dx.doi.org/10.1080/10106049109354290 ]
Gong D P, Li B Y and Liu X D . 2018. Comparative analysis of burn index adaptability when evaluating grassland fire severity. Acta Ecologica Sinica, 38(7): 2434-2441
宫大鹏, 李炳怡, 刘晓东 . 2018. 草原火烧严重度燃烧指数的适用性比较分析. 生态学报, 38(7): 2434-2441 [DOI: 10.5846 /stxb201703290545http://dx.doi.org/10.5846 /stxb201703290545 ]
He Q J and Liu C . 2008. Improved algorithm of self-adaptive fire detection for MODIS data. Journal of Remote Sensing, 12(3): 448-453
何全军, 刘诚 . 2008. MODIS数据自适应火点检测的改进算法. 遥感学报, 12(3): 448-453 [DOI: 10.3321/j.issn:1007-4619.2008.03.010http://dx.doi.org/10.3321/j.issn:1007-4619.2008.03.010 ]
Ji W, Tao Y, Zhao D A, Yang J and Ding S H . 2014. Iterative threshold segmentation of apple branch images based on CLAHE. Transactions of the Chinese Society for Agricultural Machinery, 45(4): 69-75
姬伟, 陶云, 赵德安, 杨俊, 丁世宏 . 2014. 基于CLAHE的苹果树树枝迭代阈值分割方法研究. 农业机械学报, 45(4): 69-75 [DOI: 10.6041/j.issn.1000-1298.2014.04.011http://dx.doi.org/10.6041/j.issn.1000-1298.2014.04.011 ]
Key C H and Benson N C . 2006. Landscape Assessment (LA) sampling and analysis methods//FIREMON: Fire Effects Monitoring and Inventory System. Ogden, UT: USDA Forest Service, Rocky Mountain Research Station
Lin Z Y, Chen F, Niu Z, Li B, Yu B, Jia H C and Zhang M M . 2018. An active fire detection algorithm based on multi-temporal FengYun-3C VIRR data. Remote Sensing of Environment, 211: 376-387 [DOI: 10.1016/j.rse.2018.04.027http://dx.doi.org/10.1016/j.rse.2018.04.027 ]
Matthew M W, Adler-Golden S M, Berk A, Felde G, Anderson G P, Gorodetzky D, Paswaters S and Shippert M . 2002. Atmospheric correction of spectral imagery: evaluation of the FLAASH algorithm with AVIRIS data//Applied Imagery Pattern Recognition Workshop. Washington, DC, USA: IEEE: 157-163 [DOI: 10.1109/AIPR.2002.1182270http://dx.doi.org/10.1109/AIPR.2002.1182270]
Meng R, Wu J, Zhao F, Cook B D, Hanavan R P and Serbin S P . 2018. Measuring short-term post-fire forest recovery across a burn severity gradient in a mixed pine-oak forest using multi-sensor remote sensing techniques. Remote Sensing of Environment, 210: 282-296 [DOI: 10.1016/j.rse.2018.03.019http://dx.doi.org/10.1016/j.rse.2018.03.019 ]
Otsu N . 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1): 62-66 [DOI:10.1109/TSMC.1979.4310076http://dx.doi.org/10.1109/TSMC.1979.4310076 ]
Paloscia S, Pettinato S, Santi E, Notarnicola C, Pasolli L and Reppucci A . 2013. Soil moisture mapping using Sentinel-1 images: algorithm and preliminary validation. Remote Sensing of Environment, 134: 234-248 [DOI: 10.1016/j.rse.2013.02.027http://dx.doi.org/10.1016/j.rse.2013.02.027 ]
Peterson D, Wang J, Ichoku C, Hyer E and Ambrosia V . 2013. A sub-pixel-based calculation of fire radiative power from MODIS observations: 1: algorithm development and initial assessment. Remote Sensing of Environment, 129: 262-279 [DOI: 10.1016/j.rse.2012.10.036http://dx.doi.org/10.1016/j.rse.2012.10.036 ]
Sun Y H, Qin Q M, Ren H Z and Zhang T Y . 2017. Consistency analysis of surface reflectance and NDVI between GF-4/PMS and GF-1/WFV. Transactions of the Chinese Society of Agricultural Engineering, 33(9): 167-173
孙元亨, 秦其明, 任华忠, 张添源 . 2017. GF-4/PMS与GF-1/WFV两种传感器地表反射率及NDVI一致性分析. 农业工程学报, 33(9): 167-173 [DOI: 10.11975/j.issn.1002-6819.2017.09.021http://dx.doi.org/10.11975/j.issn.1002-6819.2017.09.021 ]
Tanase M A, Santoro M, De la Riva J, Perez-Cabello F and Le Toan T . 2010. Sensitivity of X-, C-, and L-Band SAR backscatter to burn severity in Mediterranean pine forests. IEEE Transactions on Geoscience and Remote Sensing, 48(10): 3663-3675 [DOI: 109/tgrs.2010.2049653http://dx.doi.org/109/tgrs.2010.2049653 ]
Tang S H, Qiu H and Ma G . 2016. Review on progress of the Fengyun meteorological satellite. Journal of Remote Sensing, 20(5): 842-849
唐世浩, 邱红, 马刚 . 2016. 风云气象卫星主要技术进展. 遥感学报, 20(5): 842-849 [DOI: 10.11834/jrs.20166232http://dx.doi.org/10.11834/jrs.20166232 ]
Tucker C J . 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2): 127-150 [DOI: 10.1016/0034-4257(79)90013-0http://dx.doi.org/10.1016/0034-4257(79)90013-0 ]
Veraverbeke S, Dennison P, Gitas I, Hulley G, Kalashnikova O, Katagis T, Kuai L, Meng R, Roberts D and Stavros N . 2018. Hyperspectral remote sensing of fire: state-of-the-art and future perspectives. Remote Sensing of Environment, 216: 105-121 [DOI: 10.1016/j.rse.2018.06.020http://dx.doi.org/10.1016/j.rse.2018.06.020 ]
Wickramasinghe C, 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 ]
Wooster M J, Xu W and Nightingale T . 2012. Sentinel-3 SLSTR active fire detection and FRP product: pre-launch algorithm development and performance evaluation using MODIS and ASTER datasets. Remote Sensing of Environment, 120: 236-254 [DOI: 10.1016/j.rse.2011.09.033http://dx.doi.org/10.1016/j.rse.2011.09.033 ]
Yao J Y, Raffuse S M, Brauer M, Williamson G J, Bowman D M J S, Johnston F H and Henderson S B . 2018. Predicting the minimum height of forest fire smoke within the atmosphere using machine learning and data from the CALIPSO satellite. Remote Sensing of Environment, 206: 98-106 [DOI: 10.1016/j.rse.2017.12.027http://dx.doi.org/10.1016/j.rse.2017.12.027 ]
Zhao B, Zhao W J, Pan J, Li J C and Hu D Y . 2010. The application of NOAA-AVHRR to forest fire detection in eastern Jilin Province. Remote Sensing for Land and Resources, 22(1): 77-80
赵彬, 赵文吉, 潘军, 李家存, 胡德勇 . 2010. NOAA-AVHRR数据在吉林省东部林火信息提取中的应用. 国土资源遥感, 22(1): 77-80 [DOI: 10.6046/gtzyyg.2010.01.14http://dx.doi.org/10.6046/gtzyyg.2010.01.14 ]
Zheng H N, Wen G Y and Cai Y X . 1999. Forest fire catastrophe and its threshold. Fire Safety Science, 8(3): 1-5
郑焕能, 温广玉, 柴一新 . 1999. 林火灾变阈值. 火灾科学, 8(3): 1-5
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