Classification and Density Inversion of Wetland Vegetation Based on the Feature Variables Optimization of Random Forest Model

Abstract:

Taking the coastal wetland of the Yangtze River Estuary as the research area， the random forest model was used to classify the vegetation of wetland. Besides the vegetation index and water index extracted from Landsat-8 OLI image， the seasonal difference value of vegetation index based on plant phenology characteristics was proposed in this paper as the optimization of feature variables to analyze the spatial characteristics of vegetation distribution in the coastal wetland of the Yangtze River Estuary. Bassd on the vegetation classifications， the multiple linear regression model combined with the field data was used to estimate the vegetation density of the Spartina alterniflora， which was an invasive species occupying the largest area. It is indicated that the proposed multi-temporal data combined with the optimization of feature variable of random forest model can be used to conveniently analyze the spatially vegetation distributions in wetland . Compared with the maximum likelihood classification method， the proposed method in this paper greatly enhances the classification accuracy with an overall accuracy and Kappa coefficient of the classification results increasing from 78.35% and 0.72 to 86.02% and 0.82， respectively. The proposed method is proved to be applicable for solving the problem of “same spectrum for different surface features” in the study of wetland plant community distributions.