Mapping native grasses and alien species on river dikes is crucial for vegetation management, which influences native species conservation and river dike maintenance. We aimed to improve alien species distribution mapping of Solidago altissima and Sorghum halepense on river dikes along Tone River, by fusing satellite and drone images. The methodology includes fusing pan-sharpened WorldView-3 satellites with drone images for one sample dike. The object-based approach segmented images into objects and extracted statistical data to form datasets. Regression models were constructed from the sample dike to predict fused satellite–drone datasets for other dikes. The random forest classification model was then applied to map the distribution of alien species. Our findings highlight the enhanced mapping accuracy achieved using a fused satellite–drone dataset approach. For Solidago altissima, the fused dataset reaches the highest overall accuracy (OA) of 98.39% and a kappa coefficient of 0.976. Likewise, for Sorghum halepense, the fused dataset achieved the highest OA of 97.78% and a kappa coefficient of 0.936, compared with that of pan-sharpened and original satellite datasets. These outcomes underscore the contribution of the fused satellite–drone imagery for alien species distribution mapping in the river dike environment.
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