基于不同卫星传感器的森林火灾红外辐射归一化方法研究
|
摘要
不同卫星传感器识别森林火灾时存在时间差异及辐射差异,通过对AUQA亮温、NOAA-19地表温度、FY-3B亮温数据进行影响因素分析,确定大气、地形、地表温度为归一化参数,建立基于3个卫星传感器的亮温归一化模型。利用地面接收站2017年4月份影像数据,将归一化模型应用到森林火灾识别中,建立了一套适合湖南省的森林火灾阈值。研究结果表明:不同卫星传感器的红外辐射归一化模型能较好地消除辐射差异影响,弥补卫星传感器的时间差异性,提高不同卫星传感器对森林火灾监测的速度。
Different satellite sensors identify the time difference of forest fire and the difference of radiation. Through the analysis of the influence factors of AUQA brightness temperature, NOAA-19 surface temperature and FY-3 B brightness temperature data, the atmospheric, topographic and surface temperature are defined as normalization parameters, and the brightness temperature normalization model based on three satellite sensors is established. Using the image data of the ground receiving station in April 2017, the normalized model was applied to the forest fire identification, and a set of forest fire threshold was set up in Hunan province. The results show that the infrared radiation normalization model of different satellite sensors can better eliminate the influence of radiation difference, make up the time difference of satellite sensors and improve the speed of forest fire monitoring by different satellite sensors.
Different satellite sensors identify the time difference of forest fire and the difference of radiation. Through the analysis of the influence factors of AUQA brightness temperature, NOAA-19 surface temperature and FY-3 B brightness temperature data, the atmospheric, topographic and surface temperature are defined as normalization parameters, and the brightness temperature normalization model based on three satellite sensors is established. Using the image data of the ground receiving station in April 2017, the normalized model was applied to the forest fire identification, and a set of forest fire threshold was set up in Hunan province. The results show that the infrared radiation normalization model of different satellite sensors can better eliminate the influence of radiation difference, make up the time difference of satellite sensors and improve the speed of forest fire monitoring by different satellite sensors.
引文
[1]孙亚强.森林防火装备专业化建设问题的初步探讨[J].中国应急管理,2012(11):54-55.
[2]于淼,冯仲科,徐伟恒.基于测树枪的林火行为参数精准观测研究[J].中南林业科技大学学报,2016,36(5):68-72.
[3]张婕,张文煜,冯建东,等.基于亮温植被指数气溶胶光学厚度的MODIS火监测算法研究[J].遥感技术与应用,2016,31(5):886-892.
[4]郜风国.遥感图像处理中的多源遥感数据解析研究[D].北京:中国科学院研究生院,2011.
[5]苏海滨.多时相遥感图像相对辐射归一化与镶嵌[D].北京:中国科学院地理科学与资源研究所,2007.
[6]郭亚东,史舟.先进星载热发射和反射辐射仪(ASTER)的特点及应用[J].遥感技术与应用,2003,18(5):346-352.
[7]郭鹏,邹春辉,赵学斌.MODIS影像水体监测方法研究进展[J].气象科技,2012,40(6):869-873.
[8]魏然.多源遥感地表温度数据时空融合研究及应用[D].武汉:武汉大学,2016.
[9]张贵,龙朝夕,邓妮娜,等.森林火灾恢复力评价研究[J].中南林业科技大学学报,2015,35(9):9-12.
[10]陈述彭.遥感大辞典[J].北京:科学出版社,1990.
[11]赵永光.遥感观测数据要素匹配与转换方法研究[D].北京:中国科学院大学,2015.
[2]于淼,冯仲科,徐伟恒.基于测树枪的林火行为参数精准观测研究[J].中南林业科技大学学报,2016,36(5):68-72.
[3]张婕,张文煜,冯建东,等.基于亮温植被指数气溶胶光学厚度的MODIS火监测算法研究[J].遥感技术与应用,2016,31(5):886-892.
[4]郜风国.遥感图像处理中的多源遥感数据解析研究[D].北京:中国科学院研究生院,2011.
[5]苏海滨.多时相遥感图像相对辐射归一化与镶嵌[D].北京:中国科学院地理科学与资源研究所,2007.
[6]郭亚东,史舟.先进星载热发射和反射辐射仪(ASTER)的特点及应用[J].遥感技术与应用,2003,18(5):346-352.
[7]郭鹏,邹春辉,赵学斌.MODIS影像水体监测方法研究进展[J].气象科技,2012,40(6):869-873.
[8]魏然.多源遥感地表温度数据时空融合研究及应用[D].武汉:武汉大学,2016.
[9]张贵,龙朝夕,邓妮娜,等.森林火灾恢复力评价研究[J].中南林业科技大学学报,2015,35(9):9-12.
[10]陈述彭.遥感大辞典[J].北京:科学出版社,1990.
[11]赵永光.遥感观测数据要素匹配与转换方法研究[D].北京:中国科学院大学,2015.