Geographical information systems (GIS) consist of computer-based methods of recording, analyzing, combining, and displaying geographic information such as shorelines, bathymetry, habitat types, sensitive areas, or any other feature that can be mapped. GIS are especially useful in management planning and resource-use decisions.
In oceanography, GIS are used in the mapping and measurement of major ocean processes (e.g., ocean currents, upwelling, nutrient regimes) that affect the state of marine environment. In fisheries, GIS provide a suitable framework for complex fisheries management processes.
Ocean Ecology has used a variety of commercial and open source GIS software. Given below is an example of a study on eelgrass distribution which illustrates one way in which GIS can be used in coastal oceanography.
Eelgrass in British Columbia
Eelgrass (Zostera marina) meadows represent one of the habitat types that are threatened by estuarine development. Seagrasses, including eelgrass, have been used as indicators of nearshore ecosystem health. Eelgrass provides critical habitat for numerous species including outmigrating juvenile salmon, Pacific herring, Dungeness crab, and black brant.
Two species of eelgrass are found in British Columbia – the native species Zostera marina and the introduced species Zostera japonica. Fortunately, the introduced species can not compete with the native species due to its smaller size, thus it is not a threat to the native eelgrass. There are three ecotypes of Zostera marina occurring in British Columbia: (1) Z. marina typica (primarily intertidal with low tolerance to current; has shorter, narrower blades); (2) Z. marina phillipsi (found between 0 and -4 m with moderate tolerance to current; has intermediate blade length and width); (3) Z. marina latifolia (found between -0.5 and -10 m with strongest tolerance to current; has larger, wider blades).
Eelgrass Habitat Study
A study was carried out at a bay where the potential of eelgrass rehabilitation existed in order to determine if eelgrass habitat was available, and if any eelgrass was currently present at the site. Using our sounder towfish and charting/bathymetric software, we were able to create realtime bathymetry and bottom hardness contour plots. This information allowed us to selectively choose different regions of the bay to carry out benthic videography to assess the presence or absence of eelgrass and/or eelgrass habitat.
Shown below is a video transect through an eelgrass bed.
Concurrent to the realtime bathymetric display, the depth and bottom hardness data were also logged and later imported into GIS software, where further data analysis was carried out. Using this data, we were able to correlate the presence of eelgrass with both depth and bottom hardness.
The image above above shows the 3D depth contour plot for part of the bay. Green represents shallow depths and purple represents deeper depths.
In this image, reds and browns indicate harder substrates (rock, boulders), whereas greens, blues, and pinks represent softer substrates (sand, silt, mud).
Based on observations taken from our benthic videos, we were able to plot the location and density of eelgrass. Darker green regions represent areas of higher eelgrass density in the picture above.
The information on eelgrass density, bathymetry, and bottom hardness can be overlaid in order to visually observe correlations between these factors, as show in the stacked plot above.
Stacked plots can be rotated through three dimensions to provide better angles for viewing relationships. This image shows the eelgrass density plot lying directly above the bottom hardness plot. Note that regions of greatest eelgrass density occur in areas of fairly soft substrate (probably sand).
Interactive 3-dimensional model of eelgrass distribution in this bay. Left mouse button rotates, right mouse button (or wheel) zooms, middle mouse button (or left mouse button + Ctrl) pans. Press “r” to reset the view and “a” to show all.
View the fly through of the eelgrass distribution model below.
Finally, a number of factors affecting eelgrass habitat can be shown on a single plot. The base layer for this graphic is the bottom hardness plot. The green outlines are the regions in which 95% of the eelgrass was observed. The lowest red line is the -10 m depth contour, which is the lower limit for Z. marina latifolia. The orange line is the -4 m depth contour, which is the lower limit for Z. marina phillipsi. The yellow line is the 0 m depth contour, marking the start of the intertidal region, and the area where Z. marina typica might be found. Note that the vast majority of the eelgrass lies between the -4 and -10 m contours, suggesting that the species present is most likely Z. marina phillipsi (which concurs with visual observations of the plants in the video). The eelgrass also prefers substrates shown in the green through orange colors on the chart (silt ranging to sand), but avoids very soft (purple and white areas; most likely mud) and very hard (red and brown areas; mostly like rock and boulder) substrates. This agrees well with what is presently understood about eelgrass habitat. Furthermore, this study shows how realtime depth and hardness data can be used to assist in designing video surveys for eelgrass.