地球信息科学学报

地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (5): 708-717.doi: 10.3724/SP.J.1047.2016.00708

• 遥感大数据协同计算综合应用 • 上一篇    下一篇

多星数据协同的地块尺度作物分类与面积估算方法研究

黄启厅1,2(), 覃泽林3, 曾志康3   

  1. 1. 中国科学院遥感与数字地球研究所 遥感科学国家重点实验室,北京 100101
    2. 中国科学院大学,北京 100049
    3. 广西农业科学院农业科技信息研究所,南宁 310001;
  • 收稿日期:2015-12-16 修回日期:2016-03-31 出版日期:2016-05-10 发布日期:2016-05-10
  • 作者简介:

    作者简介：黄启厅（1983-）,男,广西南宁人,博士生,研究方向为遥感信息提取。E-mail:huangqiting830112@163.com

  • 基金资助:
    国家高技术研究发展计划项目（2015AA123901）;广西科学研究与技术开发计划项目（14125008-1-6）;高分辨率对地观测系统重大专项（03-Y30B06-9001-13/15-01）;国家自然科学基金项目（41301473）

Study on the Crop Classification and Planting Area Estimation at Land Parcel Scale Using Multi-sources Satellite Data

HUANG Qiting1,2,*(), QIN Zelin3, ZENG Zhikang3   

  1. 1. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
    2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China
    3. Scientific and Technological Information Research Institute, Guangxi Academy of Agricultural Sciences, Nanning, 530007, China;
  • Received:2015-12-16 Revised:2016-03-31 Online:2016-05-10 Published:2016-05-10
  • Contact: HUANG Qiting

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摘要：

为了解决多云雨地区遥感数据时空覆盖缺失的问题,以满足对地块尺度作物种植信息日益迫切的应用需求,本文在遥感图谱认知理论框架下发展了一种基于多星数据协同的地块尺度作物识别与面积估算方法。首先,基于米级高分辨率影像提取农田地块对象;其次,通过对多源中分辨率时序影像的有效化处理和指数计算,获取“碎片化”的高时空覆盖有效数据,并以地块对象为单元构建时间序列;然后,在时序分析基础上,建立多维特征空间,结合作物生长物候特征,构建决策树模型进行作物分类识别与面积计算;最后,以湖南省宁远县为研究区开展了水稻种植信息的提取实验。结果表明：本文方法可在农田地块尺度下实现不同水稻类型的准确识别及其种植面积的精细提取,早、中、晚稻的用户精度分别可达94.33%、90.76%和95.95%,总体分类精度为92.51%,Kappa系数为0.90;早、中、晚稻面积提取精度分别为93.37%、91.23%和95.42%。试验结果证明了本文方法的有效性,为其他作物种植信息的精细提取提供了借鉴。

关键词: 遥感, 图谱认知理论, 作物识别, 地块尺度

Abstract:

To reduce the missing of remotely sensed data in the spatio-temporal coverage of the cloudy/rainy region and to further meet the urgent need for crop planting information at farmland parcel scale, a method of crop type identification and planting area estimation at parcel scale was developed in this paper by synergistically utilizing the multi-sources satellite imagery,with the support of remote sensing Tupu recognization theory. This method consists of three steps: firstly, based on the high resolution imagery, the objects of farmland parcel with exact boundary were extracted. Secondly, with the effective-data processing technology and the spectral indices calculation based on the multi-temporal medium resolution imagery, the fragmentary effective data was acquired and the time-series data for each object was further obtained. Finally, by constructing a multi-dimensional feature space with the help of time series analysis incorporating the crops’phenological feature, the crop types and their corresponding planting areas were mapped using the Decision Tree classifier. This method had been tested in Ningyuan county, Hunan Province, China. The results showed that, this method can precisely map the different rice types and corresponding planting areas at the farmland parcel scale. The user accuracy of the three rice types, i.e., the early double-season, single-season and late double-season rice,was 94.33%, 90.76 and 95.95%, respectively, and the overall accuracy was 92.51% with a Kappa coefficient of 0.90. The derived area accuracy of these three rice types also reached 93.37%, 91.23% and 95.42%,respectively. This experiment illustrated the effectiveness and usefulness of the proposed method and also provided a salutary lesson for the finely planting information extraction of other crops.

Key words: remote sensing, Tupu recognization theory, crop identification, land parcel scale