Seagrasses on the move: Tracing the multi-decadal species distribution trends in lagoon meadows using Landsat imagery

Abstract

Seagrass meadows play a vital role for lagoon ecosystems and their biota, sustaining multiple ecosystem services. Their distribution and functioning are closely tied to the environmental pressures induced by global changes. Long-term monitoring of seagrass species and communities is, hence, important to depict their response to past and future scenarios. The availability of long term open-access satellite data offers a new remote sensing perspective for monitoring seagrass communities dynamics in shallow waters, especially when combined with machine learning algorithms. In this study, seasonal multispectral images (from 1999 to 2019) were collected from Landsat 5 Thematic Mapper and 8 Operational Land Imager satellites to map the seagrass meadows, at the community and species levels, within the vast Grado and Marano lagoon (Northeast Italy) using a Random Forest (RF) algorithm. RF models were calculated using an extensive field training dataset collected in 2010 (n = 426) and reached an overall accuracy of 0.92 and 0.76 for the classification at the community and species levels, respectively. The change detection analysis revealed an increase of 14.16 km2 (+ 39%) of the whole seagrass community cover over the period, at a rate of 1.59 km2year−1. Despite the coarse spatial resolution (30 m) of the Landsat's images, the classification of seagrasses at species level achieved a good overall accuracy (0.76), evidencing Nanozostera noltei as the species with the highest cover increase (+13.87 km2 over the time period). The observed expansion is likely caused by an increase of the sea water influence that is radically modifying Adriatic brackish water bodies, emphasizing the connection between the ongoing environmental changes and the rapid responses of seagrass meadows.