基于结构规模的冬小麦种植面积遥感抽样估算

在种植结构复杂地区,由于受到混合像元和同期作物的影响,传统的以规模为分层标志进行冬小麦种植面积遥感估算难以保证抽样效率和精度。该文综合考虑混合像元、同期作物的影响,构建了结构规模指标进行冬小麦种植面积遥感抽样估算。采用TM和QuickBird为研究数据,设计不同的抽样方案估算冬小麦的种植面积,计算标准误差、准确度和变异系数衡量估算精度,与传统简单随机、规模指标分层抽样进行对比分析,验证本文方法的有效性。试验结果表明,以结构规模指标分层抽样的反推结果在各项指标上均明显优于传统简单随机、规模指标分层抽样方式,尤其在小样本量时,标准误差降低2.0×105m2,准确度提升了1%。该研究结果为在大范围种植结构复杂地区进行冬小麦种植面积遥感估算的改进提供了试验依据。

Winter wheat area estimation based on structure and scale using remote sensing

Because of the effects of mixed pixels and plants with the same spectral character as winter wheat, the traditional way of sampling which is stratified by planting scale can not guarantee the estimation accuracy when estimating wheat areas with complicated planting structure by remote sensing. To solve this problem, a comprehensive structural index in sampling was defined which considered the two effects. The experimental data were the TM and QuickBird images of the same area acquired at nearly the same time. The area of winter wheat was estimated in different sampling methods and the standard error, degree of accuracy as well as coefficient of variation of the result were calculated to compare among random sampling, stratified sampling stratified by planting scale and stratified sampling stratified by structure. The results shows that no matter which estimate method is taken or how many samples are chosen, taking planting structure into account can always improve the quality of samples and raise the accuracy, especially when the sample size is small (on which occasion standard error is reduced by 2.0×105 m2 and degree of accuracy is increased by 1%). In this way, this research provided theoretical basis for monitoring the area of winter wheat by using remote sensing images in large scale.