A GIS can easily analyze remote-sensed data. Sampson et al. (2000)
provided a numerical assessment of two types of kelp (Ecklonia maxima
and Laminaria pallida) biomass off the West Cape coast of South Africa.
High concentrations of kelp occur along this coast in relatively pristine
conditions. In recent years, the importance of this resource has been emphasized
in relation to its use in alginate extraction and as a commercially
highly valuable food source for abalone. Therefore, there is a distinct need
to manage this resource, including obtaining estimates of absolute biomass.
Sampson et al. (2000) compared past photographic (qualitative) and
new quantitative GIS methods. Results showed that the biomass of surface
kelp had been overestimated, on average, by 230% using the old methodology.
The GIS method based on remote sensing inputs, proved to be a more
successful tool in mapping and estimating the biomass of the kelp and it is
being modified to model the amount of alginate and abalone that can be
produced per year.
A similar system developed by Long et al. (1994b) used aerial photography
to identify and delineate seagrass beds, and to estimate seagrass
biomass after digitization. Welch et al. (1992) approached coastal zone
management using data from image analysis and aerotriangulation to build
up a time series of changes in salt marshes. Their GIS, using thematic overlaying
and Boolean logic, quantified and visualized changes induced through
human impact over a 40-year period. Exciting progress is also being made
using 3D GIS to improve our understanding of fish distribution and abundance
using hydroacoustics and echo-integration (Wazenböck and Gassner
2000).
Warning et al. (1999) used a GIS to investigate the basic ecological characteristics
of beaked whale (Ziphiidae) and sperm whale (Physeter macrocephalus)
habitats in shelf-edge and deeper waters off the northeastern
United States. Using sighting data and corresponding information on
bathymetry, slope, oceanic fronts, and SST, logistic regression analyses
was conducted to determine that the distribution of sperm whales was
more dependent on depth and slope, while that of beaked whales was more
dependent on SST.