Abstract: Mangroves are typically made up of salt tolerant species of vegetation with great diversity of flora and fauna which is mainly found in tropical and subtropical climate country. However, these forest ecosystems have been declining over time due to the various kinds of direct and indirect pressure. Thus, there is increasing need and efforts to monitor and assess this ecosystem for better conservation and management. In this study, multispectral RapidEye satellite image was analysed to identify the mangrove vegetation species within the Matang Mangrove Forest Reserve in Perak, Malaysia. The Maximum Likelihood Classifier (MLC) was used to classify the mangrove vegetation species with integration of Normalized Difference Vegetation Index (NDVI) using NDVIRed and NDVIRed Edge data. Eleven species of mangrove vegetation were found within the study area including from the genus of Rhizophora, Avicennia, Bruguiera, Sonneratia and Xylocarpus. The overall classification accuracy assessment of RapidEye multispectral image integrated with NDVIRed Edge was higher at 87.5% with overall kappa statistics recorded of 0.85 compared to with employment of NDVIRed at 85% and kappa statistics of 0.80. The results indicated the applicability of red edge band in the RapidEye satellite imagery in combination with ancillary and field data to classify the mangrove species within the study area. It also helps for better management and conservation process to ensure the sustainability of these valuable resources.