Rapid seagrass meadow expansion in an Indian Ocean bright spot.

The areal extent of seagrass meadows is in rapid global decline, yet they provide highly valuable societal benefits. However, their conservation is hindered by data gaps on current and historic spatial extents. Here, we outline an approach for national-scale seagrass mapping and monitoring using an open-source platform (Google Earth Engine) and freely available satellite data (Landsat, Sentinel-2) that can be readily applied in other countries globally. Specifically, we map contemporary (2021) and historical (2000-2021; n = 10 maps) shallow water seagrass extent across the Maldives. We found contemporary Maldivian seagrass extent was ~ 105 km2 (overall accuracy = 82.04%) and, notably, that seagrass area increased threefold between 2000 and 2021 (linear model, + 4.6 km2 year-1, r2 = 0.93, p < 0.001). There was a strongly significant association between seagrass and anthropogenic activity (p < 0.001) that we hypothesize to be driven by nutrient loading and/or altered sediment dynamics (from large scale land reclamation), which would represent a beneficial anthropogenic influence on Maldivian seagrass meadows. National-scale tropical seagrass expansion is unique against the backdrop of global seagrass decline and we therefore highlight the Maldives as a rare global seagrass 'bright spot' highly worthy of increased attention across scientific, commercial, and conservation policy contexts.

Interannual Change Detection of Mediterranean Seagrasses Using RapidEye Image Time Series.

Recent research studies have highlighted the decrease in the coverage of Mediterranean seagrasses due to mainly anthropogenic activities. The lack of data on the distribution of these significant aquatic plants complicates the quantification of their decreasing tendency. While Mediterranean seagrasses are declining, satellite remote sensing technology is growing at an unprecedented pace, resulting in a wealth of spaceborne image time series. Here, we exploit recent advances in high spatial resolution sensors and machine learning to study Mediterranean seagrasses. We process a multispectral RapidEye time series between 2011 and 2016 to detect interannual seagrass dynamics in 888 submerged hectares of the Thermaikos Gulf, NW Aegean Sea, Greece (eastern Mediterranean Sea). We assess the extent change of two Mediterranean seagrass species, the dominant Posidonia oceanica and Cymodocea nodosa, following atmospheric and analytical water column correction, as well as machine learning classification, using Random Forests, of the RapidEye time series. Prior corrections are necessary to untangle the initially weak signal of the submerged seagrass habitats from satellite imagery. The central results of this study show that P. oceanica seagrass area has declined by 4.1%, with a trend of -11.2 ha/yr, while C. nodosa seagrass area has increased by 17.7% with a trend of +18 ha/yr throughout the 5-year study period. Trends of change in spatial distribution of seagrasses in the Thermaikos Gulf site are in line with reported trends in the Mediterranean. Our presented methodology could be a time- and cost-effective method toward the quantitative ecological assessment of seagrass dynamics elsewhere in the future. From small meadows to whole coastlines, knowledge of aquatic plant dynamics could resolve decline or growth trends and accurately highlight key units for future restoration, management, and conservation.

Mapping Mediterranean seagrasses with Sentinel-2 imagery.

Mediterranean seagrasses have been hailed for their numerous ecosystem services, yet they are undergoing a decline in their coverage. The major complication with resolving this tendency is the sparsity of data on their overall distribution. This study addresses the suitability of the recently launched Sentinel-2 satellite for mapping the distribution of Mediterranean seagrass meadows. A comprehensive methodology is presented which applies atmospheric and analytical water column corrections and compares the performance of three different supervised classifiers. Remote sensing of the Thermaikos Gulf, northwestern Aegean Sea (Greece, eastern Mediterranean Sea) reveals that the utilization of Support Vector Machines on water column corrected reflectances yields best accuracies. Two Mediterranean seagrasses, Posidonia oceanica and Cymodocea nodosa, cover a total submerged area of 1.48km2 between depths of 1.4-16.5m. With its 10-m spatial resolution and 5-day revisit frequency, Sentinel-2 imagery can mitigate the Mediterranean seagrass distribution data gap and allow better management and conservation in the future in a retrospective, time- and cost-effective fashion.