华北平原2001-2011年冬小麦播种面积变化遥感监测

为及时、准确地获取华北平原冬小麦时空分布信息,构建多源遥感监测系统,基于MODIS EVI时间序列数据和两景TM影像,建立华北平原冬小麦时序波谱曲线库,并结合农作物物候历制订统一规则,在此基础上,重建华北平原2001-2011年冬小麦播种面积时空变化过程。结果表明:1)多源遥感监测系统提取华北平原2001-2011年冬小麦信息,在栅格尺度上获得了稳定的较高分类精度,平均为76.36%;在县域行政单元尺度上,2011年的冬小麦遥感监测面积与统计数据的耦合度也较高(决定系数为0.89,均方根误差为1.29×104 hm2);2)华北平原2001-2011年的冬小麦播种面积呈持续上升趋势,2011年比2001年增加了156.05×104 hm2(14.96%);3)冬小麦播种面积大致呈"南增北减"的时空变化格局:平原中南部的鲁西南平原、胶莱平原、豫东平原和皖北平原冬小麦种植面积扩张趋势显著;而北部的京津冀地区冬小麦面积明显收缩。该研究旨在为华北平原调整农业种植结构、制订粮食安全策略及优化水资源管理提供数据支持,也可为大范围、长时间尺度的作物播种面积时空变化遥感监测提供方法借鉴。

Remote sensing monitoring of changes in winter wheat area in North China Plain from 2001 to 2011

Abstract: The North China Plain is the major producing area for winter wheat in China. It is also amongst the global hotspots in terms of severe water scarcity. The monitoring and mapping of winter wheat in an efficient and accurate manner is of great significance for effective policy management on both agriculture and water resources in the North China Plain. Remote sensing technology has been proved to be a practical and feasible method for agricultural crop area extraction in previous researches. In this study, a multi-source remote sensing monitoring system was built, in order to quickly and accurately extract the spatial and temporal information of winter wheat in the North China Plain. Specifically, a spectral curve library for winter wheat was constructed, using MODIS EVI time-series data and two TM images in 2011; and the uniform rules were formulated for the identification of winter wheat, with the combination of phenology calendar in each province in the North China Plain. The spatial and temporal changes of areas planting with winter wheat in 2001-2011 in the North China Plain were extracted using the above uniform rules, and its patterns were analyzed at the grid level, the county level and the province level, respectively. The results showed that: 1) The classification accuracy of winter wheat extracted by the multi-source remote sensing monitoring system was good at the grid scale, and the overall accuracies were stable in 2001-2011, with the values ranging between 74.80% in 2001 and 78.14% in 2011; at the county level, area planting with winter wheat extracted by the multi-source remote sensing monitoring system in 2011 agreed well with the statistics, with the determination coefficient R2 of 0.89 and the root mean square error RMSE of 1.29×104 hm2; the error between the above two data sets was 11.31% in 2011. 2) Area planting with winter wheat in the North China Plain continued to increase in 2001-2011, and it was 156.05×104 hm2 (14.96%) more in 2011 than that in 2001; in 2001-2006, areas planting with winter wheat all increased in four provinces including Shandong, Henan, Jiangsu and Anhui, and it continued to increase in Shandong and Henan in 2006-2011, while weak reductions occurred in Jiangsu and Anhui at the same time; in comparison, areas planting with winter wheat all represented significant decline in volatility in 2001-2011 in Beijing, Tianjin and Hebei. 3) There was an obvious "increase in the south and decrease in the north" pattern for spatial and temporal changes of areas planting with winter wheat in 2001-2011 across the North China Plain; the spatial expansions of winter wheat were mainly spotted in the middle and southern part of the North China Plain, including the Southwest Shandong Plain, the Jiaolai Plain, the East Henan Plain and the North Anhui Plain, while shrinkages of winter wheat were mainly found in the northern part of the North China Plain, including the plain areas of Beijing, Tianjin and Hebei. The spatial pattern of changes in areas planting with winter wheat could be attributed to the spatial heterogeneity of water resources, and it was also likely to be associated with the increasing opportunity cost of agricultural labor forces, the benefit changes of winter wheat and the agricultural subsidy policy. However, the major driving forces and their driving mechanisms deserved deep analysis. This study can provide useful information for decision makings on crop planting structure adjustment, food security guarantee and water management optimization, and can also be used as a reference for large-scale and long-time monitoring of spatial and temporal information of crop planting areas with remote sensing data as the main data source.