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遥感图像生态土地分类法在农作物种植面积提取中的应用

Study on Ecoland Classification of Remote Sensing Image in Crop Estimation

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【作者】 张树文薄立群

【Author】 ZHANG Shu wen, BO Li qun (Changchun Institude of Geography, Chinses Academy of Sciences,Changchun, Jilin 130021)

【机构】 中国科学院长春地理研究所!吉林长春130021

【摘要】 农作物遥感估产技术系统的中心环节之一是农作物种植面积的信息提取。快速、准确地获取农作物有关信息是一个关键性技术问题。针对松辽平原玉米遥感估产地区的自然环境和经济条件及玉米生长发育特征 ,利用以TM图像为主的多种遥感信息源 ,结合地面波谱测试等数据 ,运用生态土地分类方法实现主要农作物种植面积信息提取

【Abstract】 This paper discusses a specific method to be used to extract arable areas efficiently and effectively. As a key step, the classification method of remote sensing image processing is often regarded as one of the most important technological systems in crop estimation. Considering the features of natural environment, economic conditions as well as growing procedure of corns, based on the characteristics of the TM data, author puts forward an ecoland classification method (also called geoprocessing method) by which precision classification would be conducted in the project. The ecoland classifacation method is basically divided into three steps: the first one is called subarea in which similar spectrum is put into one group, that is, there is almost the same ecologic environment in the area, combined with GIS data such as social and economic statistics data, in this way, better recognized land patterns; the second is an extraction stage, based on the sublayers, different patterns can be easily extracted by means of geodata stored in the database; the last one is considered as a merge and analysis part, different classified subareas would finally be merged together in the basis of the same coordinate, subsequentlly, a synthetic analysis on the merged result should be made.As a result, coordinating with field surveying data in the process of automatic classification, the classified image with geodata is produced by means of the TM data source and can be easily extracted out related information for the further applications.

【基金】 国防科工委应用项目!(批准号 :Y -#14 );中国科学院创新项目!(批准号 :KZCX1-Y -0 2 -1-0 2 )
  • 【文献出处】 地理科学 ,Scientia Geographica Sinica , 编辑部邮箱 ,2000年06期
  • 【分类号】S126
  • 【被引频次】32
  • 【下载频次】661
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