Using MODIS data for understanding changes in seagrass meadow health: A case study in the Great Barrier Reef (Australia)

• We use MODIS data to map turbid plume waters in the Great Barrier Reef.
• We use these data to understand changes in seagrass meadow areas and abundance.
• Correlations are found between seagrass changes and exposure to turbid plume waters.
• Potential of remote sensing data to predict GBR seagrasses health changes is proved.

Stretching more than 2000 km along the Queensland coast, the Great Barrier Reef Marine Park (GBR) shelters over 43,000 square km of seagrass meadows. Despite the status of marine protected area and World Heritage listing of the GBR, local seagrass meadows are under stress from reduced water quality levels; with reduction in the amount of light available for seagrass photosynthesis defined as the primary cause of seagrass loss throughout the GBR. Methods have been developed to map GBR plume water types by using MODIS quasi-true colour (hereafter true colour) images reclassified in function of their dominant colour. These data can be used as an interpretative tool for understanding changes in seagrass meadow health (as defined in this study by the seagrass area and abundance) at different spatial and temporal scales. We tested this method in Cleveland Bay, in the northern GBR, where substantial loss in seagrass area and biomass was detected by annual monitoring from 2007 to 2011. A strong correlation was found between bay-wide seagrass meadow area and biomass and exposure to turbid Primary (sediment-dominated) water type. There was also a strong correlation between the changes of biomass and area of individual meadows and exposure of seagrass ecosystems to Primary water type over the 5-year period. Seagrass meadows were also grouped according to the dominant species within each meadow, irrespective of location within Cleveland Bay. These consolidated community types did not correlate well with the exposure to Primary water type, and this is likely to be due to local environmental conditions with the individual meadows that comprise these groupings. This study proved that remote sensing data provide the synoptic window and repetitivity required to investigate changes in water quality conditions over time. Remote sensing data provide an opportunity to investigate the risk of marine-coastal ecosystems to light limitation due to increased water turbidity when in situ water quality data is not available or is insufficient.