王 堃, 顾晓鹤, 程耀东, 张竟成, 王慧芳, 齐 迹. 基于变化向量分析的玉米收获期遥感监测[J]. 农业工程学报, 2011, 27(2): 180-186.
    引用本文: 王 堃, 顾晓鹤, 程耀东, 张竟成, 王慧芳, 齐 迹. 基于变化向量分析的玉米收获期遥感监测[J]. 农业工程学报, 2011, 27(2): 180-186.
    Wang Kun, Gu Xiaohe, Cheng Yaodong, Zhang Jingchen, Wang Huifang, Qi Ji. Remote sensor monitoring for harvest time of summer maize based on change vector analysis[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2011, 27(2): 180-186.
    Citation: Wang Kun, Gu Xiaohe, Cheng Yaodong, Zhang Jingchen, Wang Huifang, Qi Ji. Remote sensor monitoring for harvest time of summer maize based on change vector analysis[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2011, 27(2): 180-186.

    基于变化向量分析的玉米收获期遥感监测

    Remote sensor monitoring for harvest time of summer maize based on change vector analysis

    • 摘要: 现有的作物收获期遥感监测的基本思路是利用NDVI曲线形态变化与作物形态的显著变化之间的响应关系,推测作物的收获期,并且仅对整个区域的平均收获期进行监测。该研究引进变化向量分析理论,以兖州市夏玉米为研究对象,以2009年覆盖研究区玉米收获季的三期环境小卫星B星座CCD(HJ-CCD)影像的NDVI信息为主要数据源,研究多时相NDVI的变化大小和变化过程与收获期的关系,进而构建基于变化向量分析的玉米收获期遥感监测模型,对研究区内玉米种植地块的收获期进行实时监测。通过野外实测样本对兖州玉米收获期遥感监测结果进行精度验证,精度可达89.65%,玉米收获期在空间呈南至北的分布,与兖州农业局提供的玉米收获期空间分布信息基本一致。研究表明,变化向量分析方法通过定量分析收获季节内NDVI的变化过程,有效地反映出玉米收获规律,为作物收获期遥感监测提供了一种新的研究思路。

       

      Abstract: At present, the basic idea of the study for monitoring crop harvest time is to predict the harvest time of crop by analyzing the relationship between the shape variety of NDVI curve and the significant change of crop morphology. However, this approach only considers the relationship between the change in the size of NDVI and harvest time, and only monitors the average harvest time in the whole region. Based on change vector analysis, choosing the summer maize in Yanzhou city as the study object and using the NDVI from HJ-CCD imagery covering maize harvest season in the research region in 2009 as the main data source, the study investigated the relationship between the change in the size and process of NDVI and the harvest time, and instantaneously monitored the harvest time of the summer maize fields. The precision verification for the result of remote sensing monitoring of harvest time was carried out through the survey samples, which the accuracy reached 89.65%. The spatial distribution of harvest time of maize was from the south to the north, which was basically consistent with the information offered by the Agricultural Bureau of Yanzhou city. The results show that change vector analysis as a new method through analyzing the change process of NDVI is effective for monitoring the harvest time of crop.

       

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