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基于MERSI数据的处理及旬植被指数生成方法 被引量:5

Method of Processing and Compositing 10 Days'Vegetation Indices Based on MERSI Data
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摘要 为了实现自动批量处理国产FY-3A/B MERSI光学影像数据,提取全国范围植被指数特征,为今后全国植被类型制图方法研究做准备,对MERSI数据经过物理量计算、几何校正、拼接、掩膜等处理、提取植被指数特征和水云标识数据、按旬生成最大植被指数时间序列影像和合成旬水云标识影像等方法进行了研究;利用IDL 7.1编程语言实现了自动批量处理全国范围MERSI数据,按旬生成时间序列250 m空间分辨率的植被指数数据的算法程序;并对基于以上方法生成的归一化植被指数(NDVI)和增强性植被指数(EVI)结果进行比较分析。结果表明:NDVI的旬影像比EVI旬影像植被指数值分布更均匀,能够更好地反映全国植被分布与物候变化特点。该研究采用程序算法实现对FY-3A MERSI光学影像数据的处理,操作简单易行,节省了大量人力和时间;且生成的结果可为今后按MERSI NDVI时间序列影像进行全国植被类型分布研究、监测植被长势、开展国产数据在林业中的应用等后续工作做准备。 In order to achieve automatic batch processing national FY-3A/B MERSI optical images,to extract the feature of vegetation index and to be preparation for vegetation types mapping methodology at national scale for future,the processes of physical quantity calculation,geometric correction,mosaic,mask and composite with time series maximum vegetation indices and flag images of 10 days based on MERSI L1B images had been studied.In addition,these processes have been integrated the program using IDL 7.1 language.The 10 days composed vegetation index with 250 meters spatial resolution,including Normal Difference Vegetation Index (NDVI) and Enhanced Vegetation Index(EVI) images had been generated by the method.At the same time,the differences between the NDVI and EVI had been analyzed.The results showed that the composed NDVI with 10 days ' continuous MERSI images were better than that of EVI in reflecting the distribution and climatic change of vegetation in China.The processing FY-3A MERSI optical images by program was not only save manpower and time,but also easy operation.In addition,this research was preparing for the future work such as studying the distribution of vegetation types,monitoring vegetation growth,developing applications in the forestry field using domestic satellite data and so on.
出处 《中国农学通报》 CSCD 2012年第10期63-68,共6页 Chinese Agricultural Science Bulletin
基金 国家卫星应用高技术产业化专项"国产遥感影像在林业灾害应急处理中的应用产业化示范工程"(发改办高技[2010]37号)
关键词 FY-3A MERSI 植被指数 时间序列 FY-3A MERSI vegetation index time-series
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