The sparse and low mangroves are difficult to be accurately extracted due to the periodic changes of the tide level. Taking the Beibu Gulf of Guangxi as the research area, the paper constructed a mangrove identification decision tree model using Landsat8 OLI images at low and high tidal levels and DEM (Digital Elevation Model) data, and took SVM (support vector machine) method to evaluate the feasibility of the decision tree method. The research results show that: (1) The spectra of mangroves were different due to the height and density of mangroves canopy and the tidal levels. There was a serious "same spectrum with different species" effect between sparse and low-lying mangroves and the water-terrestrial vegetation mixed pixel and shady slope forest. (2) Mangroves were classified into high-density mangroves and sparse-dwarf mangroves according to the differences in height and density of canopy. Whether using low-tide, high-tide or multi-tide images for the SVM methods, this can bring about that the overall accuracy values (Mangroves were divided into mangrove and non-mangrove categories) were improved by 4.65%, 4.41% and 7.22% respectively, (3) The overall accuracy and Kappa coefficient of decision tree model based on multi-tide image and DEM were 98.80% and 0.973 respectively, which were 1.62% and 0.035 higher than the best value in SVM method. Therefore, considering the characteristics of mangrove height, density, tide level and DEM could significantly improve the accuracy of remote sensing identification of mangroves.