地球信息科学学报 ›› 2011, Vol. 13 ›› Issue (2): 273-280.doi: 10.3724/SP.J.1047.2011.00273

• 遥感技术与应用 • 上一篇    下一篇

基于NDVI时序数据的水稻种植面积遥感监测分析——以江苏省为例

苗翠翠1,3, 江南2, 彭世揆3, 吕恒2, 李扬3, 张瑜3, 王妮3, 李军4   

  1. 1. 无锡市自来水总公司,无锡 214031;
    2. 南京师范大学地理科学学院,南京 210097;
    3. 南京林业大学森林资源与环境学院,南京 210037;
    4. 无锡市城乡给排水设计院,无锡 214031
  • 收稿日期:2010-10-02 修回日期:2010-12-22 出版日期:2011-04-25 发布日期:2011-04-25
  • 通讯作者: 江南(1961-),男,教授,博士生导师。 主要从事国土资源遥感与地理信息系统开发研究。 E-mail:njiang@njnu.edu.cn E-mail:njiang@njnu.edu.cn
  • 基金资助:

    国家高技术研究发展专项"国家粮食主产区粮食作物种植面积遥感测量与估产业务系统" (2006AA120101)。

Extraction of Paddy Land Area Based on NDVI Time-series Data: Taking Jiangsu Province as an Example

MIAO Cuicui1,3, JIANG Nan2, PENG Shikui3, LV Heng2, LI Yang3, ZHANG Yu3, WANG Ni3, LI Jun4   

  1. 1. Wuxi Water Corporation, Wuxi 214031, China;
    2. School of Geography Science, Nanjing Normal University, Nanjing 210097, China;
    3. College of Forest Resources and Environment, Nanjing Forestry University, Nanjing 210037, China;
    4. Wuxi City & Country Water-Supply & Drainage Engineering Design Instrtute, Wuxi 214031, China
  • Received:2010-10-02 Revised:2010-12-22 Online:2011-04-25 Published:2011-04-25

摘要: MODIS植被指数时间序列产品能够连续反映植被的覆盖情况,是农作物遥感测量的重要数据源。本文选取江苏省为研究区,利用2008年23个时相的MODIS NDVI数据,采用S-G滤波法进行时间序列的重构,提高NDVI时间序列信息的真实性。另结合农作物物候历、种植结构、地面调查样本等辅助资料,将水稻植被指数时间序列曲线参量化为水稻物候生长期的关键值——生长周期的起始时间、生长幅度、生长长度以及生长过程的NDVI最大值。最后,利用这些关键值确定分类规则,采取决策树分类器,建立区域水稻种植面积提取模型,总体提取精度为87.5%,其表明MODIS植被指数时序数据及本文研究方法在农作物信息提取中的有效性。

关键词: NDVI时间序列, S-G滤波, 生长周期关键值, 决策树分类

Abstract: As a kind of crucial data which was able to reflect the continuous coverage of vegetation on the earth surface, the long time-series MODIS NDVI products have already become a reliable data source used in remote sensing for crop measurement. In this paper, a long time-series and high-resolution (250m) MODIS data in 2008 was used to extract the area of the paddy land in Jiangsu Province, China. In the beginning, an annual time-series NDVI dataset of the study area was built up by pre-processing,and a reconstruction of time-series based on Savitzky-Golay filter provided this study excellent images with the better visual effect and NDVI temporal profile. Considering the paddy phenological calendar, the identifiers of paddy were parameterized as starting time, range, extent, and maximum of NDVI during the growth period. In the end, through comparing the phenological key values, a reasonable classification rule was generated based on the thresholds of phenological key values, and a decision tree classifier was constructed to extract Paddy land area, whose precision was 87.5%. It turned out the feasibilities of long time-series MODIS NDVI data and our classification strategy adapted to the extraction of crop land area.

Key words: NDVI time-series, S-G filter, phenology key value, decision tree classifier.