农作物种植面积遥感估算的影响因素研究

Important factors affecting crop acreage estimation based on remote sensing image classification technique

针对不同的农作物种植结构区,研究影响遥感影像分类各因素与农作物种植面积估算精度的定性和定量关系是十分必要的。以Rapid Eye影像提取的早稻种植信息为研究对象,从农作物的种植成数、种植破碎度和地块形状指数3个角度进行了不同空间分辨率下各因素对农作物面积监测的影响研究。结果表明:随着农作物种植成数的降低,种植结构越来越破碎,种植地块趋于狭长分布,各分辨率下农作物面积估算精度均呈递减趋势;要达到85%以上的面积估算精度,当作物种植成数在50%以上时,可选取高于150 m分辨率的遥感数据;当作物种植较为破碎时,需要在提高影像空间分辨率的同时融入其他技术手段;当作物种植地块为狭长分布时,提高影像的空间分辨率并不能保证面积估算精度,必须通过其他技术手段达到精度要求;并最终得到了4种影响因素对面积估算精度的定量评估模型。研究结果为解决不同农作物种植结构区遥感数据的选择、面积估算精度的提高,以及在特定研究区和数据源条件下可达到的面积估算水平等问题提供了理论基础。

It is necessary and valuable to study the effect of influencing factors of crop classification on crop acreage estimation from both qualitative and quantitative points of view. Therefore, the authors analyzed the resolution effect on the acreage estimation accuracy by using RapidEye imagery. Spatial statistics methods and manifold accuracy evaluation indices were used respectively to analyze the data with different index statistics of crop proportion, crop fragmentation and shape. The results show that decreased crop proportion and increased crop fragmentation and shape index will lead to reducing regional accuracy under all resolutions. And in order to keep the accuracy higher than 85%, we can select any resolution higher than 150 m data when the crop proportion is higher than 50%, so as to achieve the accuracy requirements. As merely improving resolution cannot guarantee the crop acreage estimation accuracy when the crop land exhibits long and narrow distribution, other technology must be adopted in this case. Finally the quantitative influence model of the four factors for crop acreage estimation accuracy is built. The results of this paper would provide academic reference for resolving the problem of data selection and accuracy improvement in crop acreage estimation by remote sensing.