基于GF-1卫星影像的中国冬小麦制图研究

旨在实现冬小麦面积自动化提取，本文提出一种基于冬小麦NDVI加权指数(WNDVI)的分类算法。将影像分割成标准的小区域，从而构建冬小麦分类单元，采用自适应方法确定冬小麦种植区和非种植区WNDVI分割阈值。以2013年10月至2014年5月期间的GF-1卫星WFV影像为例，构建了全国14个省（市、区）的1180个分类单元的WNDVI，实现了2014年全国主产区冬小麦种植区的空间分布图；采用14233个样本对结果进行验证精度，总体精度达到了90.6%。本方法自动化程度高，结果稳定，适合大范围冬小麦面积监测业务化运行。

National-scale Mapping of Winter Wheat in China Using GF-1 Imagery

To realize the automatically extraction of winter wheat, a classification algorithm based on weighted NDVI index (WNDVI) is proposed to construct the winter wheat classification units. Based on the WNDVI, the division thresholds between winter wheat area and other areas were determined by an adaptive method. Taking GF-1/WFV images from October 2013 to May 2014 as an example, WNDVI from 1180 classification units of 14 provinces (city and regions) in China was created. Spatial distribution maps of major winter wheat production areas were produced in 2014. The accuracy verification of the results was conducted with 14233 samples, and the overall accuracy reached 90.6%. The proposed method is highly automated, its results are stable, and it is suitable for large-scale monitoring of winter wheat areas.