摘要
为快速、准确地在遥感图像上提取各种农作物类型信息,满足国家农情遥感监测系统的要求,以2002年北京地区主要秋季作物提取为例,利用T erra/M OD IS数据,采用波谱分析的方法,建立一种基于遥感影像全覆盖的秋季作物类型自动提取方法,实现主要秋季作物遥感自动识别。首先根据研究区秋季作物的波谱特性和生物学特性,选取了红波段、蓝波段、近红外波段和中短波红外波段作为秋季作物类型提取的工作波段;同时,还利用由这4个波段构建的陆表水分指数和增强型指标指数作为遥感特征参量。其次根据研究区农作物物候历特征,提取了2002年4月到9月共7个时相的M OD IS数据。最后,采用分层决策树方法提取研究区主要秋季作物类型,并进行面积统计。为了验证其精度,与国家农业部农业统计数据进行比较,结果其精度达到86%以上。这表明,仅利用M OD IS自身光谱信息,即可较为准确地提取秋季作物类型信息,精度基本能满足了大尺度农情遥感监测的要求,可以为农业决策部门提供信息服务。
For meeting the demand for large-scale agricultural monitoring system with remote sensing technology, extracting crop information on the remote sensing image must be rapidly, precisely and reliably conducted. In this paper, the fall crop identification with Terra/MODIS was taken as an example in Beijing of China. Applying spectral analysis and time series characteristics, the decision tree algorithm was put forward, which can extract the main fall crops effectively and easily. Firstly, according to the spectral and biological characteristics of the fall crops, the spectral reflectances of MODIS were analyzed. One of red, blue, NIR and ESWIR band was selected as working band. Secondly, land surface water index（LSWI）, which is defined by NIR and ESWIR, enhanced vegetation index（EVI）, which is defined by Red, NIR and Blue bands were used as characteristic parameters to improve the precision. Finally, the decision tree algorithm was used for the fall crop identification. To verify the result, the extracting results were compared with the statistical result of State Statistics Bureau. The precision reaches 86%. This shows that it can obviously improve the crop identification accuracy with the decision tree algorithm and can be good enough to meet the operational method for agricultural condition monitoring with remote sensing and information service system at national-level.
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2006年第9期128-132,共5页
Transactions of the Chinese Society of Agricultural Engineering
基金
863计划(2003AA131020)
中国科学院知识创新工程重大项目(KZCX1-SW-01-02)资助
