文章摘要
基于相邻轨道图像的冬小麦面积提取及长势分析
Extraction of Winter Wheat Area and Growth Analysis Based on Remote Sensing Imagery of Adjacent Tracks
投稿时间:2016-01-13  
DOI:10.13254/j.jare.2016.0016
中文关键词: 遥感  冬小麦  面积  长势
英文关键词: remote sensing  winter wheat  area  growing situations
基金项目:“十二五”国家科技支撑计划项目(2015BAD23B0202,2013BAD05B06-5);国家自然科学基金(41271235)
作者单位E-mail
林芬 山东农业大学资源与环境学院, 山东 泰安 271018  
赵庚星 山东农业大学资源与环境学院, 山东 泰安 271018 zhaogx@sdau.edu.cn 
常春艳 山东农业大学资源与环境学院, 山东 泰安 271018  
王卓然 山东农业大学资源与环境学院, 山东 泰安 271018  
李慧 山东农业大学资源与环境学院, 山东 泰安 271018  
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中文摘要:
      冬小麦是我国北方主要农作物之一,及时掌握冬小麦面积信息及长势情况,能够快速地为农业生产管理者以及财政部门提供决策依据,有利于小麦增产、提高农民收入。本文以山东省滨州、东营市为研究区,通过主成分分析、监督及非监督分类结合的方法提取ETM+遥感影像的冬小麦信息,以SPSS聚类分析法估测滨州市冬小麦长势,用距离加权法构建相邻轨道图像的植被长势分级模型并估测东营市的冬小麦长势。结果显示:小麦提取平均精度约为93.79%,冬小麦分布呈现“西多东少,南多北少”的特征,一般小麦分布较多的地区长势也较好。基于重叠区距离加权法构建的植被长势分级模型,能够在一定程度上消除相邻轨道遥感图像的时间差异,实现大区域的植被长势分析。
英文摘要:
      Winter wheat is one of the most valuable crops in Northern China, so getting a good knowledge of real-time information of its area and growing situation can help the manager of agricultural production and financial departments to make better decisions, meanwhile it can also increase the output capacity and farmers' income. In this paper, Binzhou City and Dongying City of Shandong Province were taken as the research areas. We extracted the information of winter wheat from ETM+ remote sensing image based on a combined method of principal component analysis, supervised and unsupervised classification. The growing situation of winter wheat in Binzhou was estimated through clustering analysis in SPSS, and winter wheat growing situation in Dongying was predicted by building vegetation growing situation hierarchical model in adjacent tracks using the distance-weighted method. The results showed that the mean extracting precision was 93.79%. There was a clear tendency of its distribution with characteristics of concentrated in the west and in the south other than that in the east and in the north. Also the regions where the wheat was concentrately distributed had better growth in general. We found that the vegetation growing situation hierarchical model built with distance-weighted method in the overlapping areas could eliminate the time differences between two remote sensing images in adjacent tracks to some extent, and it was beneficial for winter wheat growth analysis in large-scale regions.
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