Mangroves are a tropical and sub-tropical dominant ecosystem that flourishes in the coastal areas of the Philippines and offer both ecological and economic benefits for coastal communities. However, even with its benefits, its forest cover has experienced substantial damages and mangrove species are critically approaching extinctions. With this implications, mangrove forest cover mapping is an important aid to environmental policies and processes that will contribute to its resources protection and preservation. This study aimed to produce a rhizophora-specific map in a mangrove forest of Magnesia, Virac, Catanduanes, Philippines using Orthophoto and the derivatives from LiDAR Data. LiDAR derivatives such as DSM, DTM, normalized DSM, and Slope using Geographic Information Systems (GIS) were used to analyze the features for rhizophora extraction. An object-based classification scheme was developed using eCognition to extract rhizophora mangroves. The scheme includes Geographic Coordinates Systems (GCS) transformation, Georeferencing, Multiresolution Segmentation, and Classification using Nearest-Neighbor algorithm. Accuracy of the produced map for Magnesia is 99.63% showing two classes namely Rhizophora and Non-Rhizophora.