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引用本文:

DOI:

10.11834/jrs.20209125

收稿日期:

2019-04-30

修改日期:

2019-10-13

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林火专栏 联合多源卫星遥感数据监测四川木里县森林火灾
饶月明, 王川, 黄华国
北京林业大学 省部共建森林培育与保护教育部重点实验室
摘要:

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,开展联合多源卫星遥感数据对小范围火灾联合监测的研究:(a)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;(b)使用Sentinel-2数据监测不同火烧区域光谱信息;(c)使用Sentinel-2数据提取dNBR,提出了基于OTSU算法分步骤确定不同程度火烧迹地与面积的方法;(d)建立Sentinel-1A极化比值PR(VV/VH)和NDVI之间关系,利用微波雷达突破云雨限制。结果表明:(a)高分四号联合IRS和PMS能够实时监测小范围火灾;(b)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);(c)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490nm-2200nm范围存在差异;(d)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;(e)火灾前后极化比值由6.6dB升高至10.8dB,NDVI与PR经线性回归,R2=0.58,验证R2=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 fire monitoring based on multi-sensor remote sensing techniques in Muli County of Sichuan Province
Abstract:

Abstract: Objective : Forest fires not only damage the stability of forest ecology seriously, but also threaten the safety of human life and property. It is difficult for traditional methods to detect fires in time due to its limited observation region and time window. Remote sensing can accurately detect fires in a larger area continuously. Besides, it improves the detection efficiency. At present, meteorological satellites and polar satellites are mainly used to detect forest fires. However, it is difficult for these satellites to detect small fire scars and deal with the influence of cloud and rain. In this study, multi-sensor remote sensing data were combined to monitor the forest fire spots and progress in Muli Tibetan Autonomous County of the Sichuan Province since March 30, 2019. Method : Firstly, we located the fire spots and time using the GF-4, a geostationary satellite of China. It has a multispectral camera (PMS), which has a high spatial (50 m) resolution. This satellite also carries a mid-infrared sensor (IRS,400m) which is sensitive to abnormal high temperatures. Secondly, we found the spectral difference between burning and unaffected forest stands using Sentinel-2. Thirdly, based on Sentinel-2, we classified the fire scars using OTSU algorithm to set the threshold of dNBR. In the end, we used SAR data from Sentinel-1A to relate NDVI to polarization ratio (PR). Result : Results show that: (1) The location of the fire spot can be determined accurately using IRS and PMS data from GF-4. (2) Combing the fire spot location and an NDVI sharp reduction (from 0.7 to 0.25), we confirmed the burning time to be March 30 using the PMS data from GF-4. (3) There is a significant difference in spectral curves among the burning forest stands, the unaffected areas and different type of burned areas in 490 nm and 2200 nm from Sentinel-2 data. (4) The total area of damaged fire scars was classified (41.56hm2), with an accuracy of 94.67% using the dNBR of 0.35 as the threshold from Sentinel-2 derived dNBR map. The lightly damaged fire scars were also classified (66.56 hm2, 90.94%). (5) The polarization ratio from Sentinel-1A data increased from 6.6dB to 10.8dB after the burning. NDVI linearly relates to the polarization ratio (R2=0.58 for fitting and 0.50 for verification). Conclusion : 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 hours. This research provides an efficient method which can ignore the influence of cloud and rain and other complex environment to monitor forest fires. If GF-4 could focus on monitoring this area, more details would be found to capture the progress of the fire. This study provides a reference for small fire identification after the fire occurrence. The research methods and results can provide technical supports for forest fire emergency.

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