文献类型：期刊文献

中文题名：多源卫星遥感影像的林火监测

英文题名：Research on Forest Fire Monitoring Based on Multi-Source Satellite Remote Sensing Images

作者：尹俊玥[1] 何瑞瑞[2] 赵凤君[3] 叶江霞[1]

第一作者：尹俊玥

通信作者：Ye, JX[1];Zhao, FJ[2]

机构：[1]西南林业大学林学院,云南昆明650224;[2]巴音郭楞蒙古自治州林业和草原局,新疆巴州“三北”防护林建设管理办公室,新疆巴音郭楞841009;[3]中国林业科学研究院森林生态环境与自然保护研究所,国家林业和草原局森林保护学重点实验室,北京100091

年份：2023

卷号：43

期号：3

起止页码：917-926

中文期刊名：光谱学与光谱分析

外文期刊名：Spectroscopy and Spectral Analysis

收录：CSTPCD;;EI(收录号：20232314203708);Scopus;WOS:【SCI-EXPANDED(收录号:WOS:000968678100037)】；北大核心:【北大核心2020】；CSCD:【CSCD2023_2024】；PubMed;

基金：国家“十三五”重点研发计划项目课题(2020YFC1511601);国家自然科学基金项目(32071778)资助。

语种：中文

中文关键词：卫星遥感;GF-6 WFV;林火监测;FY-3D MERSI;烟雾特征

外文关键词：Satellite remote sensing;GF-6 WFV;Forest fire monitoring;FY-3D MERSI;Smoke characteristics

分类号：S762.2

摘要：目前遥感林火监测主要侧重极轨卫星火点探测精度,而基于多源遥感影像进行火点、烟雾特征等综合火场信息遥感监测识别研究较少。以云南省安宁市2020年5月9日森林火灾为研究对象,基于高分六号卫星宽幅(GF-6 WFV)数据和风云三号D极轨气象卫星中分辨率光谱仪(FY-3D MERSI)数据进行火场烟雾、火烧迹地提取及火点判识。首先根据GF-6 WFV影像,选取6种光谱特征指数,以最大似然法、支持向量机分类法及随机森林分类法,识别火场烟雾和火烧迹地,并进行精度评价;然后,基于1 km的FY-3D MERSI中红外通道数据,对潜在火点识别算法进行改进,根据FY-3C VIRR和MODIS火点探测基本原理,利用动态阈值和上下文检测法识别火点,再结合250m分辨率的远红外通道优化识别结果。最后结合两种数据提取的烟雾、火点及火烧迹地信息,探讨分析GF-6 WFV与FY-3D MERSI对于林火的监测能力。结果表明:通过5种特征指数及GF-6 WVF数据的8个波段,能有效识别出烟雾及火烧迹地,3种分类方法中随机森林分类效果最佳,总体分类精度和Kappa系数为97.20%和0.955;改进后的FY-3D MERSI数据火点识别算法,能有效提高火点识别的准确率;将中红外通道与远红外通道相结合探测火点,能使火点识别能力由千米级提高至百米级;综合GF-6 WFV及FY-3D MERSI数据可有效提取火场的烟雾、火烧迹地及火点信息。利用多源数据,可多方位进行林火监测预警,对于提高卫星遥感林火监测能力具有重要意义。
At present,remote sensing forest fire monitoring mainly focuses on the accuracy of fire point detection by polar-orbiting satellites.At the same time,there is less research on remote sensing monitoring and identification of fire points,smoke characteristics and other comprehensive fire information based on multi-source remote sensing images.The forest fire of May 9,2020,in Anning City,Yunnan Province,was studied based on the Gaofen-6 wide-field(GF-6 WFV)data and the FY-3D polar-orbiting meteorological satellite medium-resolution spectrometer(FY-3D MERSI)data for smoke,burned areas extraction and fire point identification.Firstly,Based on GF-6 WFV data,six spectral feature indices were selected to identify fire smoke and fire trails by maximum likelihood,support vector machine,and random forest classification methods,and evaluated for accuracy.Then,Based on the 1 km mid-infrared channel data of FY-3D MERSI,the potential fire point identification algorithm is improved,and the basic principles of FY-3C VIRR and MODIS fire point detection are combined with dynamic threshold and context detection method to identify fire points.Then the identification results are optimized by combining the far-infrared channel with 250 m resolution.Finally,the information on smoke,fire points and fire trails extracted from the two kinds of data were combined to explore and analyze the monitoring capability of GF-6 WFV and FY-3D MERSI for forest fires.The results show that the smoke and burned areas can be effectively identified by five feature indices and eight bands of GF-6 WVF data,and the random forest classification is the most effective among the three classification methods,with an overall classification accuracy and Kappa coefficient of 97.20%and 0.955.The improved fire point recognition algorithm for FY-3D MERSI data can effectively improve the recognition accuracy of fire points.Combining the mid-infrared-and far-infrared channels to detect fires can improve the fire detection accuracy from kilometer to 100 meter level.The combined GF-6 and FY-3D MERSI data can effectively extract smoke,burned areas and fire point information from the fire site,and the use of multi-source data can carry out forest fire monitoring and early warning in multiple directions,which is of great significance to improve the capacity of satellite remote sensing forest fire monitoring.