基于GF-1/2影像数据的烟草种植区信息遥感监测

Remote Sensing Monitoring of Tobacco Growing Areas Based on GF-1/2 Image Data

为解决我国南方山区自然烟田地块较小、空间分布破碎且与其他农作物混杂,遥感调查精度低、受多云雨天影响大的难题,利用我国高分遥感卫星,开展多源、多时相遥感数据与面向对象分类相结合的烟草种植区提取方法,以毕节市七星关区大河乡为试验区,构建面向对象分类过程,并同基于像元的最大似然(ML)、神经网络(NN)和支持向量机(SVM)等方法进行对比。结果表明,面向对象方法精度最优,其次为ML、SVM以及NN,Kappa系数分别为0.948、0.936、0.930和0.905;此外,面向对象方法提取的烟田地块形状相对完整,有效避免了“椒盐现象”,视觉效果明显优于基于像元方式。面向对象的分类方法结合高分遥感星座可以准确地提取我国南方烟草种植区分布信息,从而有助于烟草的宏观管理、调控与决策。

Tobacco lands in the southern mountainous areas of China are mostly characterized with small patches and fragmentedly mixed with other croplands.The local cloudy and rainy weather also hinders capturing land cover by optical remote sensing and thereafter affects remote sensing investigation.To solve the above issues of tobacco land recognization,we proposed a tobacco growing area extraction approach by utilizing the nature of multiple scale object-based classification method and taking full advantage of the Gaofen multi-source and multi-temporal remote sensing imagery.The tobacco land recognization experiment was conducted in Dahe town,Qixingguan District,Bijie,and the object-based classification was compared with the pixel-based maximum likelihood(ML),neural network(NN),and support vector machine(SVM).The results showed that the accuracy of the object-based method was the best,followed by ML,SVM,and NN,with the Kappa coefficients of 0.948,0.936,0.930,and 0.905respectively.In addition,the shape of tobacco patches extracted by the object-based method were relatively complete,which effectively avoided the"salt and pepper phenomenon",and the visual effect was significantly better than that of the pixel-based method.The object-based classification method combined with Gaofen remote sensing constellation can accurately extract the distribution of tobacco planting areas in southern China,which is helpful to the macro-management,regulation,and decision-making.