Fine-Scale Classification of Dominant Vegetation Communities in Coastal Wetlands Using Color-Enhanced Aerial Images

Monitoring salt marsh vegetation in the Yellow River Delta (YRD) wetland is the basis of wetland research, which is of great significance for the further protection and restoration of wetland ecological functions. In the existing remote sensing technologies for wetland salt marsh vegetation classification, the object-oriented classification method effectively produces landscape patches similar to wetland vegetation and improves the spatial consistency and accuracy of the classification. However, the vegetation classes of the YRD are mixed with uneven distribution, irregular texture, and significant color variation. In order to solve the problem, this study proposes a fine-scale classification of dominant vegetation communities using color-enhanced aerial images. The color information is used to extract the color features of the image. Various features including spectral features, texture features and vegetation features are extracted from the image objects and used as inputs for four machine learning classifiers: random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN) and maximum likelihood (MLC). The results showed that the accuracy of the four classifiers in classifying vegetation communities was significantly improved by adding color features. RF had the highest OA and Kappa coefficients of 96.69% and 0.9603. This shows that the classification method based on color enhancement can effectively distinguish between vegetation and non-vegetation and extract each vegetation type, which provides an effective technical route for wetland vegetation classification in aerial imagery.