1985-87
During the years 1985 to 1987, information on eelgrass distribution, water depth and bottom sediments was collected in conjunction with an estuarine shellfish inventory of Barnegat and Little Egg Harbor bays (Joseph et al.,1992).  These surveys spanned the spring, summer and autumn seasons.  Benthic samples were collected at approximately 0.4 km (one-quarter mile) intervals in the deeper waters (greater than 0.9 m; 3 feet) of the bay, for a total of 489 stations. The extreme northern end of the Bay (i.e., Metedeconk River) was not surveyed.  Based on this survey, the distribution of eelgrass beds was then interpolated and mapped onto a nautical chart base map and produced as figures (Joseph et al. 1992).  The resulting map included two areas that were not explicitly sampled (i.e. the boat did not enter due to the shallow depth and no benthic samples were taken) but where visual reconnaissance noted the occurrence of eelgrass beds.  These two areas were included as containing eelgrass  (for a total of 896 ha)  in our analysis. We table digitized photocopies of the maps, using existing shoreline GIS data as a base map to provide ground control points.

2003
The purpose of this study was to map the areal extent and density of submerged aquatic vegetation within the Barnegat Bay and Little Egg Harbor, New Jersey as part of ongoing monitoring for the Barnegat Bay National Estuary Program. We examined the utility of multi-scale image segmentation/object-oriented image classification approaches to seagrass mapping. Our remotely sensed approach allowed for determination of seagrass at 4 levels of density (including shallow sand/mud flats with < 10% seagrass cover), rather than a simple presence/absence, with a comparatively high degree of consistency and accuracy (68% overall accuracy for 4 categories and 83% accuracy for a simpler presence/absence map). While the aerial digital camera imagery employed in this study had the advantage of flexible acquisition, suitable image scale, fast processing return times and comparatively low cost, it had inconsistent radiometric response across the individual images. While we were not successful in using the eCognition software to develop a rule-based classification that was universally applicable across the 14 individual image mosaics that comprised our 73,000 ha study area, the manual classification approach that we developed provided a flexible and time effective approach to mapping seagrass. This multi-scale image segmentation approach coupled with field transect/point surveys has the potential to be more replicable than strictly boat-based surveys and/or visual image interpretation and allow for more robust conclusions regarding change in areal extent, location and spatial pattern of seagrass beds through time. In the present study based on imagery collected in May 2003, we mapped 5,184 ha of seagrass beds. This area is less than the 6,083 ha of seagrass documented from boat-based surveys conducted between 1996-1999. We do not believe that the difference of 899 ha represents a significant change in seagrass extent between the dates of the two studies, but most likely is an artifact of the difference in mapping techniques.