基于HJ-1 CCD的县域农作物种植结构提取和时空分析(英文稿)

作物分类和时空变化监测信息可以为农业管理提供依据，多年作物种植结构图反映了作物种植方式的变化，对经济和社会分析起着重要作用。然而，用于绘制作物分布图的卫星影像不能同时具有高时间高空间分辨率，在提取作物种类复杂多样地区的种植结构图时，往往难以提供足够的作物生长周期内影像。该研究提出了一种既经济又高效的解决方案，即利用重复周期短的环境一号CCD（HuanJing-1 Charge-Coupled Device，HJ-1 CCD）图像和免费Landsat-8图像来提取中国监利县的作物种植区时空变化图。根据NDVI时间序列曲线定义了不同作物生育期物候指标例如归一化植被指数（Normalized Difference Vegetation Index，NDVI）的最大值、日期和天数等，用于作物分类。为了获取物候指标的阈值，首先从15m Landsat-8影像中提取典型种植区，然后利用典型种植区作物生长阶段NDVI时间序列曲线，得到物候指标中的NDVI阈值和时间阈值，再根据这些阈值制定了分类规则，并获得了2009-2016年作物分布图。根据多年主要作物分布图，分析不同作物的土地利用变化。最后利用高空间分辨率卫星图像和监利县统计年鉴中的作物面积数据对作物分类结果进行精度评估。与高空间分辨率图像相比，平均分类精度为84%，与统计作物面积数据相比，分类精度达到81.60%。结果表明，该研究为在像监利县这样复杂地区进行常规的作物分布制图提供了一种可行的分类方法。通过对夏收作物的时空动态变化分析可以发现，油菜农业机械化水平低、劳动力成本高，导致愿意种植油菜的农民较少。对于秋收作物，政府设定了中稻最低收购价标准，大大降低了农民种植中稻的风险，对农民种植秋收作物具有指导作用。

Extraction and spatio-temporal analysis of county-level crop planting patterns based on HJ-1 CCD

Abstract: Crop-type classification and spatio-temporal change detection support rational agricultural management; perennial crop maps reflect changes in crop planting patterns and are useful for economic and social analysis. The satellite images used for mapping crops, however, are not at high spatial and high temporal resolution. They do not provide sufficient data for all stages of growth when mapping spatial distributions of crops in areas with a great variety of agricultural practices and products. A cost-effective solution using short repeat cycle Huan Jing (HJ)-1 Charge-Coupled Device (CCD) imagery and the freely available Landsat-8 imagery was proposed to produce annual crop maps reflecting spatio-temporal changes in planting areas in Jianli County, China. Phenological metrics such as maximum Normalized Difference Vegetation Index (NDVI) values, dates, and the number of days in the growth stages of the different crops were defined from time-series NDVI curves and used for crop classification. Typical planting areas were extracted from the 15 m pan-sharpened Landsat-8 images. The NDVI and time thresholds for phenological metrics were obtained from the NDVI time series curves during the crop growth stage of typical planting areas. Classification rules were established to create crop maps from 2009 to 2016 and applied land-use changes for different crops based on multi-year crop classification maps reflecting the distribution of dominant crops. The high-spatial-resolution China satellite images and crop area data from Jianli statistical yearbook were used to perform an accurate assessment. The average classification accuracy rate was 84% when compared with the high-spatial-resolution imagery, and the classification area matched up to 81.60% with the statistical crop area data. These results indicated that this method provided a possible means for classification permitting regular mapping of crop distributions in complex areas like Jianli County. By the spatio-temporal analysis of summer-harvest crops, it could be found that fewer farmers were willing to plant oilseed rape because of the high labor cost caused by this crop''s low agricultural mechanization level. For autumn-harvest crops, the government set the standard of the lowest purchase price for the middle-season rice, which greatly reduced the risk of planting the middle-season rice for farmers. This might guide farmers'' decisions and lead to small changes in the middle-season rice area. Meteorological data indicated that it had continuous rainfall and waterlogging in the summer of 2016, which led to the reduction of the middle-season rice area, and especially the big reduction of cotton area.