Ground truth data collection for species-level mapping is made challenging by limited access and hazardous conditions in some wetland ecosystems. Support Vector Machine (SVM), and the relationship between kernel smoothness parameter of SVM and spectral separability are investigated with a limited number of sample. The overall accuracy (OA) for 8 classes was around 56.25% (kappa = 0.50) for MLC, 78.12 % (kappa=0.75) for SVM (radial basis function) and 78.90% (kappa=0.76) for SVM (polynomial). When the polynomial kernel increased from 2 to 4, producer accuracy (%) increased from 81.25% to 87.50% and 53.22% to 66.67 % for Mangrove (Avicennia marina) and Swamp She-oak (Casuarina glauca) tree species respectively. This accuracy is acceptable as 15% of the required sample provided 79% overall accuracy from SVM and is comparable to other previous studies.