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Crop classification and acreage estimation in North Korea using phenology features

机译:利用物候特征分析朝鲜的作物分类和播种面积

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摘要

In North Korea, reliable and timely information on crop acreage and spatial distribution is hard to obtain. In this study, we developed a fast and robust method to estimate crop acreage in North Korea using time-series normalized difference vegetation index (NDVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data. We proposed a method to identify crop type based on NDVI phenology features using data collected in other areas with similar agri-environmental conditions to mitigate the shortage of ground truth data. Eventually the classification map (MODIScrop) was assessed using the Food and Agriculture Organization (FAO) statistical data and high-resolution crop classification maps derived from one Landsat scene (LScrop). The Pareto boundary method was used to assess the accuracy and crop distribution of the MODIScrop maps. Results showed that acreage derived from the MODIScrop maps was generally consistent with that reported in the FAO data (a relative error <4.1% for rice and <6.1% for maize, and <9.0% for soybean except for in 2004, 2008, and 2009) and the maps derived from the LScrop (a relative error about 5% in 2013, and 7% in 2008 and 2014). The classification accuracy reached 74.4%, 69.8%, and 73.1% of the areas covered by the Landsat images in 2008, 2013, and 2014, respectively. This indicates that features derived from NDVI profiles were able to characterize major crops, and the approaches developed in this study are feasible for crop mapping and acreage estimation in regions with limited ground truth data.
机译:在朝鲜,很难获得有关作物种植面积和空间分布的可靠,及时的信息。在这项研究中,我们开发了一种快速有效的方法,可以使用由中等分辨率成像光谱仪(MODIS)数据得出的时间序列归一化差异植被指数(NDVI)估算朝鲜的农作物种植面积。我们提出了一种基于NDVI物候特征来识别农作物类型的方法,该方法使用在其他农业环境条件相似的地区收集的数据来缓解地面真实数据的短缺。最终,使用粮食及农业组织(FAO)统计数据和从一个Landsat场景(LScrop)得出的高分辨率作物分类图对分类图(MODIScrop)进行了评估。 Pareto边界方法用于评估MODIScrop地图的准确性和作物分布。结果显示,从MODIScrop地图得出的种植面积与粮农组织数据中所报告的基本一致(相对误差,大米<4.1%,玉米<6.1%,大豆<9.0%,2004、2008和2009年除外, )和根据LScrop绘制的地图(相对误差在2013年约为5%,在2008年和2014年约为7%)。在2008年,2013年和2014年,Landsat影像所覆盖区域的分类准确率分别达到74.4%,69.8%和73.1%。这表明从NDVI剖面获得的特征能够表征主要农作物,并且本研究开发的方法可用于在地面实测数据有限的地区进行农作物测绘和面积估算。

著录项

  • 来源
    《GIScience & remote sensing》 |2017年第3期|381-406|共26页
  • 作者单位

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China|Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China|Agr & Agri Food Canada, Sci & Technol Branch, Ottawa Res & Dev Ctr, Ottawa, ON K1A 0C6, Canada;

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China;

    Agr & Agri Food Canada, Sci & Technol Branch, Ottawa Res & Dev Ctr, Ottawa, ON K1A 0C6, Canada;

    Agr & Agri Food Canada, Sci & Technol Branch, Ottawa Res & Dev Ctr, Ottawa, ON K1A 0C6, Canada;

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China;

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China|Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China;

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China|Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China;

    Agr & Agri Food Canada, Sci & Technol Branch, Ottawa Res & Dev Ctr, Ottawa, ON K1A 0C6, Canada;

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

    crop classification; acreage estimation; MODIS NDVI; phenology; North Korea;

    机译:作物分类;收割估计;MODIS NDVI;物候学;朝鲜;

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