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首页> 外文期刊>Intelligent automation and soft computing >CROP DISCRIMINATION IN SHANDONG PROVINCE BASED ON PHENOLOGY ANALYSIS OF MULTI-YEAR TIME SERIES
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CROP DISCRIMINATION IN SHANDONG PROVINCE BASED ON PHENOLOGY ANALYSIS OF MULTI-YEAR TIME SERIES

机译:基于多年序列物候分析的山东省农作物品种识别

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摘要

Crop type identification plays an important role in extracting crop acreage, assessing crop growth and arable land productivity. In this study, the main crops (winter wheat, summer maize and cotton) of Shandong Province as research objects, and the SPOT_VGT normalized difference vegetation index (NDVI) remote sensing datasets from 1999 to 2011 covering Shandong Province were acquired. The NDVI characteristic curves of typical features were extracted by combining the SPOT_VGT NDVI time series datasets, the HJ-IB image and the phenological information. Moreover, the reasonable dynamic thresholds were settled, the non-cultivated land areas were removed and the crop patterns and the crop types were identified based on the annual NDVI variation and the phenological information of the typical features. The accuracy assessment was performed through the spatial contrast and quantitative description. The overall accuracy is 77.10% in the spatial accuracy assessment compared with standard land cover classification map, and the overall relative errors of winter wheat, summer maize and cotton are 25.52%, 25.97% and 7.11 % in the quantitative accuracy assessment compared with the statistical datasets. The results of research show that it is feasible to identify the crop planting patterns and crop types using the proposed classification method by combining the SPOT_VGT NDVI time series datasets with the phenological information.
机译:作物类型识别在提取作物面积,评估作物生长和耕地生产力方面发挥着重要作用。本研究以山东省的主要农作物(冬小麦,夏玉米和棉花)为研究对象,获取了山东省1999〜2011年SPOT_VGT归一化植被指数(NDVI)遥感数据集。通过结合SPOT_VGT NDVI时间序列数据集,HJ-IB图像和物候信息,提取典型特征的NDVI特征曲线。此外,根据每年的NDVI变化和典型特征的物候信息,确定了合理的动态阈值,去除了未耕地面积,并确定了作物类型和作物类型。通过空间对比和定量描述进行准确性评估。与标准土地覆被分类图相比,空间精度评估的总体精度为77.10%,而定量精度评估的冬小麦,夏季玉米和棉花的总体相对误差与统计值相比分别为25.52%,25.97%和7.11%。数据集。研究结果表明,通过将SPOT_VGT NDVI时间序列数据集与物候信息相结合,使用所提出的分类方法来识别作物种植模式和作物类型是可行的。

著录项

  • 来源
    《Intelligent automation and soft computing》 |2013年第4期|513-523|共11页
  • 作者单位

    Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China,Geographic Science and Institute of Surveying and Mapping, Liaoning Technical University, Fuxin 123000, China,Key Laboratory for Information Technologies in Agriculture, Ministry of Agriculture, Beijing 100097,China;

    Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China,Key Laboratory for Information Technologies in Agriculture, Ministry of Agriculture, Beijing 100097,China;

    Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China,Key Laboratory for Information Technologies in Agriculture, Ministry of Agriculture, Beijing 100097,China;

    Geographic Science and Institute of Surveying and Mapping, Liaoning Technical University, Fuxin 123000, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Crop; Phenology; Identification; SPOT_VGT NDVI Time Series; Multi-year; Shandong Province;

    机译:作物;物候学鉴定;SPOT_VGT NDVI时间序列;多年;山东省;

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