Abstract：This paper established machine learning models to classify swamp vegetation communities based on high-resolution UAV multispectral images. In Honghe National Nature Reserve, typical sample areas were selected in the core area, buffer zone and experimental area and ortho-images of these areas were acquired using low-altitude UAVs with RGB and multispectral cameras. Multidimensional datasets were then derived from multiresolution segmentation of ortho-images, and established four classification scenarios. The object-based random forest (RF) algorithm was used to classify vegetation communities after feature selection and parameters (mtry and ntree) optimization and tuning. This algorithm also could rank the importance of each feature in multidimensional datasets and eliminat data redundancy accordingly. The results showed that:The optimized object-based RF algorithm had a high recognition ability for swamp vegetation. The scenario 4 (combination of spectral bands, texture features, geometric features, location features, surface elevation information and vegetation indexes) in the core area obtained the highest overall accuracy (87.12%), and the kappa value was 0.850 at the 95% confidence interval, which was 12.27% higher than scenario 2 (combining spectral bands, geometric features and location features), and the kappa value improved 0.140; For an identification accuracy of typical swamp vegetation, the classification of the reed achieved the highest user's accuracy of above 88%, and its producer's accuracy was higher than 90%. The classification of Calamagrostisangustifolia also achieved over 85% of producer's accuracy, but its user's accuracy (78%) was lower in the core area. This method can be used as an effective method to identify swamp vegetation communities and provide more accurate data support for studying dynamic changes of wetland ecological environment.
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