为进一步提高基于遥感影像森林(人工林)植被信息提取的工作效率,基于Google Earth Engine(GEE)平台,以Landsat8 OIL影像为实验数据,利用监督分类、支持向量机、最大熵模型、随机森林以及根据试验区实际构建的决策树分类方法对试验区桉树人工林种植面积进行提取,并对各方法进行了比较,在此基础上利用决策树法提取了广西地区桉树种植面积,并利用无人机影像与Google Earth Pro历史影像对实验结果进行了验证。实验过程及结果表明:利用GEE平台可以高效快速地提取遥感植被信息。在以上5种方法中,决策树分类方法取得最好的效果,其试验区桉树提取总体精度与Kappa系数分别达到0.82,0.85;同时,利用决策树提取的广西桉树种植面积与统计资料的面积统计结果具有较好的一致性。说明构建的决策树分类方法对大区域、复杂山区植被覆盖信息的快速提取具有参考意义。

In order to further improve the efficiency of forest(plantation) vegetation extraction based on remote sensing image,this paper takes Landsat8 OIL as experimental data on the Google Earth Engine(GEE) platform,and uses supervised classification,support vector machine,maximum entropy model,random forest and decision tree classification based on the actual construction of the experimental area.Line extraction and comparison of various methods are made.On this basis,the area of Eucalyptus in Guangxi was extracted by decision tree,and the experimental results were validated by Unmanned aerial vehicle image and Google Earth Pro historical image.The experimental process and results show that remote sensing vegetation information can be extracted efficiently and quickly by using GEE.Among the five methods in this paper,the decision tree classification method achieves the best results.The overall accuracy and Kappa coefficients of Eucalyptus extraction in the experimental area are 0.82 and 0.85,respectively.At the same time,the area of Eucalyptus extracted by decision tree in Guangxi is in good agreement with the statistical data,which shows that the decision tree classification method constructed in this paper has a good consistency with the results of large area.Rapid extraction of vegetation cover information in complex mountainous areas is of reference significance.