The main goal of this thesis was to evaluate the use of high spatial remote imagery to map the location and biophysical parameters of eelgrass in marine areas around Sidney Spit, a part of the Gulf Islands National Park Reserve of Canada (GINPRC). To meet this goal, three objectives were addressed: (1) Define key spectral variables that provide optimum separation between eelgrass and its associated benthic substrates, using in situ hyperspectral measurements, and simulated IKONOS and Landsat 7ETM+ spectral response; (2) evaluate the efficacy of these key variables in classification of the high spatial resolution imagery, AISA and IKONOS, at various levels of processing, to determine the processing methodology that offers the highest eelgrass mapping accuracy; and (3) evaluate the potential of ―value-added‖ classification of two eelgrass biophysical indicators, LAI and epiphyte type. In situ hyperspectral measurements acquired at Sidney Spit in August 2008 provided four different data sets: above water spectra, below water spectral profiles, water-corrected spectra, and pure endmember spectra. In Chapter 3, these data sets were examined with first derivative analysis to determine the unique spectral variables of eelgrass and associated benthic substrates. The most effective variables in discriminating eelgrass from all other substrates were selected using data reduction statistics: M-statistic analysis and multiple discriminant analyses (MDA). These selected spectral variables enabled eelgrass classification accuracy of 98% when separating six classes on above water data: shallow (< 3 m deep) eelgrass, deep (> 3 m) eelgrass, shallow sand, deep sand, shallow green algae, and spectrally deep water. The variables were located mainly in the green wavelengths, where light penetrates to the greatest depth: the slope from 500 – 530 nm, and the first derivatives at 566 nm, 580 nm, and 602 nm. The same data were classified with 96% accuracy after correcting for the water column, using the ratios 566:600 and 566:710. The only source of confusion for all data sets was between green algae and eelgrass, presumably due to their similar pigment composition. IKONOS and Landsat 7ETM+ simulated datasets performed similarly well, with 92% and 94% eelgrass classification accuracy respectively. In Chapter 4, the efficacy of the selected features was tested in the classification of airborne hyperspectral AISA imagery and satellite platform multispectral IKONOS imagery, and compared with various other classifiers, both supervised and unsupervised: K-means, minimum distance (MD), linear spectral unmixing (LSU), and spectral angle mapper (SAM). The selected features achieved the highest eelgrass classification accuracies in the study, when combined with atmospheric correction, glint correction, and optically deep water masking. AISA achieved eelgrass producer and user accuracies of 85% in water shallower than 3 m, and 93% in deeper areas. IKONOS achieved 79% for shallow waters and 82% for deep waters. Endmember classification also showed accuracies over 84% and 71% in AISA and IKONOS imagery respectively. Again, the largest source of confusion was between eelgrass and green algae, as well as between exposed vegetation (sea asparagus and green algae) and exposed eelgrass. Incompatibilities of the automatable processing steps (Tafkaa, LSU and SAM) made automated mapping less accurate than supervised mapping, but suggestions are made toward improvement. The value-added classification of eelgrass LAI and epiphyte type produced poor results in all cases except one; epiphyte presence / absence could be delineated with 87% accuracy. Before applying the findings of this study, one must consider the spatial scale of the intended management goal and select imagery with suitable spatial resolution. Tidal variations, as well as seasonal variability in water conditions and eelgrass phenology must also be considered as they may affect classification accuracies.