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熊勤学,黄敬峰.利用NDVI指数时序特征监测秋收作物种植面积[J].农业工程学报,2009,25(1):144-148.DOI:
利用NDVI指数时序特征监测秋收作物种植面积
投稿时间:2007-09-04  修订日期:2007-11-16
中文关键词:  神经网络,监督分类,MODIS,NDVI
基金项目:863项目“水稻长势监测与产量估算统计遥感模型研究”(2006AA12010104)子课题“基于WEB的水稻遥感估产系统的研究”
作者单位
熊勤学 1. 长江大学农学院荆州 434025 
黄敬峰 浙江大学农业遥感与信息技术应用研究所杭州 310229 
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中文摘要:通过对湖北省江陵县各种地物MODIS数据NDVI时序特征的分析,选取夏秋作物轮作期(5月下旬)和NDVI均值为标准,采用分层方法区分秋收作物(中稻、晚稻、棉花)区与其它区,然后利用BP神经网络法对三种作物进行监督分类,得出了三种作物种植的空间分布。并将结果与ETM数据的神经网络监督分类值作为标准值进行了误差分析,得出其平均误差率为13.6%,能准确反映江陵县秋收作物分布状况,该分层模式加BP神经网络监督分类方法适用于多种作物种植区的作物分类。
Xiong QinXue,Huang JingFeng.Estimation of autumn harvest crop planting area based on NDVI sequential characteristics[J].Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE),2009,25(1):144-148.DOI:
Estimation of autumn harvest crop planting area based on NDVI sequential characteristics
Author NameAffiliation
Xiong QinXue 1. College of Agriculture, Yangtzeu University, Jingzhou 434025, China 
Huang JingFeng 2. Institute of Remate Sensing 
Key words:neural networks, supervised classification, MODIS, NDVI
Abstract:NDVI sequential characteristics of various ground objects was analyzed which was on MODIS data of Jiangling County, Hubei Province. By the standards of crop rotation period’s data and NDVI mean value, spatial distributions of three kinds of crops were obtained using BP neural network supervised classification based on the hierarchical method for crop regions and non-crop regions distinguishing. Compared with standard results calculated with ETM data, the average error ratio of the research is 13.6%. Results indicated that the method can accurately reflect various crop distributions in Jiangling county and be applied for crops classification.
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