结合植被覆盖度指数的土地覆盖分类方法研究

基于Landsat 8 OLI反射率数据，结合定量遥感反演植被覆盖度（fractional vegetation cover, FVC）提取的植被物候特征数据，对比了神经网络、支持向量机和随机森林3种土地覆盖分类方法．结果表明:随机森林分类方法具有较好的结果，反射率结合植被特征数据的分类方法的总体精度为85.52%，Kappa系数为0.8212，比仅用反射率的土地覆盖分类总体精度提高了3.45百分点，Kappa系数提高0.0429；植被覆盖度提取的植被特征数据能有效改善耕地、草地和裸地的制图精度和用户精度，对林地与水体的用户精度分别提高了7.79百分点与1.81百分点，灌木与人造地表的制图精度分别提升了7.69百分点与0.59百分点．整体来看，结合植被覆盖度及其派生植被特征进行土地覆盖信息的提取，在简单易行的同时，为提高分类精度提供了有效支持． 

Land cover classification combined with fractional vegetation cover

Land cover is closely related to local ecological environment．Remote sensing technology can quickly and accurately extract ground feature information, and plays an important role in land cover classification．Singular classification data source, mixed pixels, few quantitative remote sensing products, all leave plenty room for further improvements in existing land cover classification methods, and in the accuracy of present classifications．Landsat 8 OLI reflectance data were combined with vegetation phenological feature data (extracted by quantitative remote sensing inversion of Fractional Vegetation Cover - FVC), and the existing three land cover classification methods of neural network, support vector machine and random forest were compared．The random forest classification method showed good results．The overall accuracy of the classification method combining reflectance with vegetation feature data is 85.52%, and the Kappa coefficient is 0.8212, 3.45pct higher than the overall accuracy of land cover classification using reflectance alone, and the Kappa coefficient is increased by 0.0429．The vegetation feature data extracted by vegetation coverage can effectively improve mapping accuracy and user accuracy of cultivated land, grassland and bare land．User accuracy of woodland and water bodies was found to have increased by 7.79pct and 1.81pct respectively．Mapping accuracy of shrubs and artificial ground was found to have increased by 7.69pct and 0.59pct, respectively．Overall, extraction of land cover information combined with vegetation coverage and derived vegetation characteristics provides effective support for improving classification accuracy while being simple and easy to implement．