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当前位置: 首页 > 文章 > 柴达木地区枸杞种植区遥感提取方法对比研究 中国农学通报 2020 (17) 134-143
Position: Home > Articles > Extraction Methods of Wolfberry Plantation in Qaidam Region: A Comparative Study Chinese Agricultural Science Bulletin 2020 (17) 134-143

柴达木地区枸杞种植区遥感提取方法对比研究

作  者：
雷春苗;肖建设;史飞飞;郭英香;赵金龙;郑玲
单  位：
关键词：
柴达木;枸杞;随机森林;Softmax;支持向量机;BP神经网络
摘  要：
枸杞作为柴达木地区特色经济作物之一,利用高分辨率遥感影像开展枸杞种植区识别与提取,有利于政府和农业部门开展市场调控和作物精细化管理。以柴达木典型枸杞种植区诺木洪农场为例,利用随机森林、Softmax、支持向量机、BP神经网络和最大似然5种分类器开展农场内不同生长年限枸杞种植区精细化提取,并对结果进行精度验证。结果表明:采用随机森林的分类效果最佳,其总体分类精度达到93.8%,Kappa系数为0.93,采用Softmax、支持向量机和BP神经网络方法也均获得了较高的分类精度,其总体分类精度均达到了86.6%～87.6%,Kappa系数达到0.84～0.86,而最大似然法分类效果最差,其总体分类精度仅为76.9%,Kappa系数为0.73。通过实验利用国产高分辨率卫星结合较优的分类器能够实现包括枸杞等小宗特色经济作物种植区域和种植结构的精细化识别与监测。
译  名：
Extraction Methods of Wolfberry Plantation in Qaidam Region: A Comparative Study
作  者：
Lei Chunmiao;Xiao Jianshe;Shi Feifei;Guo Yingxiang;Zhao Jinlong;Zheng Ling;Qinghai Meteorological Service Center;Key Laboratory of Disaster Prevention and Mitigation of Qinghai Province;Institute of Qinghai Meteorological Science Research;Key Laboratory for Meteorological Disaster Monitoring and Early Warning and Risk Management of Characteristic Agriculture in Arid Regions;Ningxia Key Lab of Meteorological Disaster Prevention and Reduction;
关键词：
Qaidam;;wolfberry;;random forest;;Softmax;;SVM;;BPNN
摘  要：
In this study, we use high-resolution remote sensing images to identify and extract the planting areas of wolfberry, a special economic crop in Qaidam, to provide evidence for market regulation and fine crop management. Taking Numuhong Farm, a typical wolfberry plantation area in the region as the research area,and using five classifiers of random forest, Softmax, support vector machine, BP neural network and maximum likelihood, we conducted the refined extraction of wolfberry plantation areas with different growth years, and verified the accuracy of the classification results. The results show that the classification effect using the random forest method is the best, with an overall classification accuracy of 93.8% and a Kappa of 0.93.Softmax, support vector and BP neural network methods have also achieved high classification accuracy, and their overall classification accuracy is 86.6%～87.6%, and the Kappa coefficient is 0.84～0.86. The maximum likelihood method has the worst classification effect, its overall classification accuracy is only 76.9%, and the Kappa coefficient is 0.73. Therefore, the use of domestic high-resolution satellites combined with a better classification method can realize the fine identification and monitoring of the planting areas and structures of minor characteristic cash crops such as Chinese wolfberry.

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