Bremner, Julie, Caroline Petus, Tony Dolphin, Jon Hawes, Benoît Beguet, and Michelle J. Devlin. 2023. “A Seagrass Mapping Toolbox for South Pacific Environments.” Remote Sensing 15(3):834. doi: 10.3390/rs15030834.
Seagrass beds provide a range of ecosystem services but are at risk from anthropogenic pressures. While recent progress has been made, the distribution and condition of South Pacific seagrass is relatively poorly known and selecting an appropriate approach for mapping it is challenging. A variety of remote sensing tools are available for this purpose and here we develop a mapping toolbox and associated decision tree tailored to the South Pacific context. The decision tree considers the scale at which data are needed, the reason that monitoring is required, the finances available, technical skills of the monitoring team, data resolution, site safety/accessibility and whether seagrass is predominantly intertidal or subtidal. Satellite mapping is recommended for monitoring at the national and regional scale, with associated ground-reference data where possible but without if time and funds are limiting. At the local scale, satellite, remotely piloted aircraft (RPA), kites, underwater camera systems and in situ surveys are all recommended. In the special cases of community-based initiatives and emergency response monitoring, in situ or satellite/RPA are recommended, respectively. For other types of monitoring the primary driver is funding, with in situ, kite and satellite recommended when finances are limited and satellite, underwater camera, RPA or kites otherwise, dependent on specific circumstances. The tools can be used individually or in combination, though caution is recommended when combining tools due to data comparability.
Figure 4. The data-poor seagrass-monitoring toolbox decision tree. The tree asks a series of questions relating to monitoring requirements; orange lines represent ‘yes’ and blue lines represent ‘no’. Green boxes show the suggested tools. ‘Ground reference’ is the collection of site-based data to train or validate the remote sensing tools; this is mainly interpreted as in situ surveys, though RPA and underwater imagery is used for this purpose by some. The circles represent the degree of confidence in the ability to classify the seagrass and in estimations of areal cover for each tool (confidence in areal cover estimations is low for tools with low spatial resolution because they will be less able to detect localised patchiness in seagrass cover).