基于时序特征的玉麦轮作制耕地提取方法研究

Study on Extraction Method of Wheat-Maize Rotation System Based on Time Series Characteristics

[目的]该研究对小麦、玉米轮作制下耕地的面积与分布有重要意义。[方法]基于目视判别收集样本点和GEE平台,分析地物的NDVI、EVI等指数时序特征,开发特征识别算法提取2018年玉麦轮作区面积分布,同时与CART算法监督分类结果进行精度分析。[结果]与基于多光谱的监督分类相比,基于时序特征的识别算法准确率、精准率、F1 Score和Kappa系数分别提高了0.121、0.110、0.136和0.246。[结论]基于时序特征的特征识别算法可有效识别玉麦轮作区,该算法可为轮作区农业生产提供基础数据支持。

[Objective]It is important to study the area and distribution of cultivated land under wheat and maize rotation system.[Method]Based on the collection of sample points and GEE platform,we analyzed the NDVI,EVI and other index time series features of ground objects,developed feature recognition algorithm to extract the 2018 wheat-maize rotation area distribution,and conducted precision analysis with the supervised classification results of CART algorithm.[Result]Compared with the multi-spectral supervised classification,the accuracy,F1 Score and Kappa coefficient of the time-series feature recognition algorithm were improved by 0.121,0.110,0.136 and 0.246,respectively.[Conclusion]The feature recognition algorithm based on time series features could effectively identify wheat-maize rotation area,and the algorithm could provide basic data support for agricultural production in rotation area.