热带地理 ›› 2016, Vol. 36 ›› Issue (6): 976-984.doi: 10.13284/j.cnki.rddl.002892

• 论文 • 上一篇    下一篇

基于 NDVI 物候特征的华南地区冬种马铃薯遥感提取方法

黄思宇1,2,3,陈水森2,李 丹2,刘 尉1,2,3,王重洋1,2,3,姜 浩2   

  1. (1.中国科学院 广州地球化学研究所,广州 510640;2.a.广东省地理空间信息技术与应用公共实验室;b.广东省遥感与 GIS 应用重点实验室;c.广州地理研究所,广州 510070;3.中国科学院大学,北京 100049)
  • 收稿日期:2016-02-24 出版日期:2016-11-05 发布日期:2016-11-05
  • 通讯作者: 陈水森(1965―),男,江西人,研究员,博士生导师,主要从事环境遥感与 GIS 模拟研究,(E-mail)css@gdas.ac.cn
  • 作者简介:黄思宇(1990―),女,广州人,硕士研究生,研究方向为环境遥感,(E-mail)siyu_huang@hotmail.com
  • 基金资助:

    广东省科技计划项目(2012A020200018、2013B020501006、2016A020210060)

Remote Sensing Method Based on Multi-temporal NDVI Phenological Characters for Winter Potato Planting Area in South China

HUANG Siyu1,2,3,CHEN Shuisen2,LI Dan2,LIU Wei1,2,3,WANG Chongyang1,2,3,JIANG Hao2   

  1. (1.Guangzhou Institute of Geochemistry,Chinese Academy of Sciences,Guangzhou 510640,China;2.a.Guangdong Open Laboratory of Geospatial Information Technology and Application;b.Guangdong Key Laboratory of Remote Sensing and Geographical Information System Technology Application; c.Guangzhou Institute of Geography,Guangzhou 510070,China;3.University of Chinese Academy of Sciences,Beijing 100049,China)
  • Received:2016-02-24 Online:2016-11-05 Published:2016-11-05

摘要:

马铃薯是华南地区的特色冬种农作物,其地块的“早稻―晚稻―冬种马铃薯”三季种植模式具有特有的植被指数时间序列曲线特征。利用这一特征,提出一种基于 NDVI 时间序列数据和 SAM 的冬种马铃薯种植面积提取方法。以广东省惠州市稔平半岛为研究区,冬种马铃薯面积为研究对象,采用 2011 年 HJ-1 A/B CCD 遥感数据为主要数据,计算每一景影像的 NDVI 后以时间为坐标轴排列成 NDVI 时间序列数据集,在此提取冬种马铃薯种植区的 NDVI 时间序列参考曲线,使用光谱角度匹配(SAM)方法,计算每个像元的 NDVI 时间序列曲线与 NDVI时间序列参考曲线的光谱夹角值,根据 Rule 图像的统计参数确定夹角阈值,达到快速有效地在遥感影像上提取冬种马铃薯对应像元的目的。结果表明:研究区总体提取精度为 82.70%,重点种植区域提取精度为 93.75%,可见基于 NDVI 物候特征的 SAM 方法能够有效提取研究区冬种马铃薯的种植面积。

关键词: 冬种马铃薯, 种植面积, 光谱角度匹配, NDVI 特征曲线, 物候, 遥感

Abstract:

Vegetation index of each pixel in remote sensing image reflects the growth state of vegetation covered by pixels. If the vegetation index images obtained from all remote-sensing images in one year in the same area are lined up, the dynamic change of vegetation index time series can mirror the phenological rules in this area. Potato is a special kind of winter-planted crop in South China, and relevant parcels show peculiar curve characters of vegetation index time series in planting pattern–“early season rice–late season rice–winter-planted potato”. Taking Renping Peninsula in Huizhou City, Guangdong Province, as research area and the planting area of winter potato as research object, the paper makes use of those characters and employs HJ image data in 2011 as the primary data, with totaling 14 scenes’ HJ-1 A/B CCD remote sensing image data being used. After calculating the NDVI (Normalized Difference Vegetation Index) of images in each scene, NDVI time serials data set is obtained. Corresponding NDVI time serials curve of 26 training sample points is achieved for averaging, thus getting NDVI time serials character reference curve. Moreover, SAM (Spectral Angle Mapping) is used to calculate the value of spectral angle between NDVI time series curve of each pixel in the NDVI time serial data set and NDVI time series character reference curve, obtaining the image of spectral angle value. The pixel value in this image is the value of spectral angle between NDVI time serials curve of each pixel in the NDVI time serial data set and character reference curve. The 2-time standard deviation, 2.5-time standard deviation and 3-time standard deviation of the image of spectral angle value are taken as the threshold value. The spectral angle value of each pixel in the image of spectral angle value and the threshold value are compared, and if the spectral angle value is less than the threshold value, this pixel is classified as a target object, so as to separate the target pixel from pixelscovered by other types of land on the remote sensing images, for the purpose of rapidly and effectively extracting the planting area of winter potato. According to the comparative analysis, it is found that the result extracted by taking 2.5-time standard deviation as the threshold value fits best the actual planting conditions, thereby serving as the final extraction result. Research findings show that the overall extraction accuracy is 82.70% in the research area and 93.75% in key planting areas. This method, as one of the typical applications of homemade optical satellite to agriculture in South China, can extract the winter potato area effectively and will lay a firm foundation for accurately and rapidly monitoring other agricultural information (such as winter-fallowed cultivation) in South China where there is much cloud, rain and broken land.

Key words: winter potato, planting area, SAM, NDVI time series curves, phenology, remote sensing