Technical Abstract: Mangrove wetlands are economically and ecologically important ecosystems and accurate assessment of these wetlands with remote sensing can assist in their management and conservation. This study was conducted to evaluate airborne hyperspectral imagery and image compression and classification techniques for mapping black mangrove [Avicennia germinans (L.) L.] populations on the south Texas Gulf coast. AISA+ hyperspectral imagery was acquired from two study sites and a minimum noise fraction (MNF) transformation was used to reduce the spectral dimensionality of the imagery. Four classification methods, including minimum distance, Mahalanobis distance, maximum likelihood, and spectral angle mapper (SAM), were applied to the hyperspectral imagery and to the MNF imagery for distinguishing black mangrove from associated plant species [cattail (Typha domingensis Pers.), mixed herbaceous species, and mixed woody species] and other cover types (algae flats, bare soil, roads, and water). Accuracy assessment showed that overall accuracy varied from 84% to 95% for site 1 and from 69% to 91% for site 2 among the eight classifications for each site. The transformed MNF imagery provided more consistent classification results than the original hyperspectral imagery among the four classification methods. Producer’s and user’s accuracies for black mangrove were 91% and 94%, respectively, for site 1 and both 91% for site 2 based on the maximum likelihood method applied to the MNF imagery. These results indicate that airborne hyperspectral imagery combined with image processing and classification techniques can be a useful tool for monitoring and mapping black mangrove distributions in coastal environments.