基于无人机遥感影像和面向对象技术的荒漠草原植被分类OA北大核心CSTPCD

Exploring suitable remote sensing classification methods for desert grassland vegetation and clarifying the types of plant species and their distribution in desert grassland areas can improve the ability of fine biodiversity monitoring in desert grassland,which is of great significance for the protection and management of desert grassland as well as for the sustainable development of ecosystems within the landscape. This research studied the typical vegetation of Stipa breviflora,Convolvulus tragacanthoides,Salsola laricifolia,Ephedra rhytidosperma in the desert grassland of the floodplain fan at the eastern foothill of Helan Mountains. We used remote sensing images from unmanned aerial vehicles (UAVs),processed by object-oriented classification and regression tree (CART),K-nearest neighbor (KNN),random forest (RF) and support vector machine (SVM) classification methods,combined with the feature selection algorithm to optimize the image features. We then selected the optimal features for the study of desert grassland vegetation classification,and its refinement. It was found that:1) Feature selection can effectively improve the classification accuracy and should be fully utilized,and the highest classification accuracy is achieved when the selected feature combination has a contribution degree greater than 1.00％;2) Based on the vegetation spectral and texture features mined from UAV remote sensing images,combining with the object-oriented classification method can effectively achieve the refined classification of the typical vegetation of the desert grassland at the eastern foothills of the Helan Mountains. In this research the RF classification accuracy was the highest,and the overall classification accuracy reached 87.77％ with a Kappa coefficient of 0.79. The results of this study provide a reference for the study of vegetation classification in desert grasslands. This research will be of great significance for the conservation and management of desert grassland biodiversity and ecological sustainable development.