This study proposed three spectral metrics, namely spectral match degree (SMD), normalized difference mangrove index (NDMI) and shortwave infrared absorption depth (SIAD), to enhance the separability between mangrove forests and terrestrial vegetation in remote sensing imagery. The Landsat 8 OLI image of an interest area in Beilunhekou National Nature Reserve was used to test the spectral metrics. The derived spectral metrics and raw band reflectance data were classified using a support vector machine classifier. Mangrove forest maps were then identified from the classified images. Identification accuracies were compared and evaluated by determining the user’s accuracy (UA), producer’s accuracy (PA), overall accuracy (OA) and by conducting McNemar’s test. Results showed that the use of spectral metrics (UA = 85%, PA = 94%, OA = 95%) outperformed the use of raw band reflectance data (UA = 72%, PA = 82%, OA = 90%). McNemar’s test confirmed that the spectral metrics were significantly better than the raw band reflectance data (Z = 4.63, p < 0.05). therefore, the proposed spectral metrics could improve the accuracy of mangrove forest identification.