ABSTRACT
These datasets derived from our mapping protocol are presented as a research article in the Journal of Environmental Management [1]. In particular, by using a Structure from Motion photogrammetric workflow we produced high spatial resolution 2D raster maps and 3D outputs such as dense points clouds and textured meshes of an underwater seagrass restoration site. In this area transplanted fragments of Posidonia oceanica were planted to restore this impacted site after the Costa Concordia shipwrecking which occurred on 13 January 2012 along the NE coast of Giglio Island (Tuscany, Italy). Photogrammetric outputs were used to render the underwater environment by using the open-source software Blender allowing a fine 3D modelling and immersive visualization of the mapped area. This data other than providing an exceptional tool for analysing the benthic habitats from a biological point of view, following over time the progress transplanting operations, might also provide a new way to visualize and share the perception of such underwater shallow environments to a large plethora of users, increasing the public awareness on restoration programmes and promoting new action aimed at restored underwater habitats restoration.
ABSTRACT
Seagrasses rank among the most productive yet highly threatened ecosystems on Earth. Loss of seagrass habitat because of anthropogenic disturbances and evidence of their limited resilience have provided the impetus for investigating and monitoring habitat restoration through transplantation programmes. Although Structure from Motion (SfM) photogrammetry is becoming a more and more relevant technique for mapping underwater environments, no standardised methods currently exist to provide 3-dimensional high spatial resolution and accuracy cartographic products for monitoring seagrass transplantation areas. By synthesizing various remote sensing applications, we provide an underwater SfM-based protocol for monitoring large seagrass restoration areas. The data obtained from consumer-grade red-green-blue (RGB) imagery allowed the fine characterization of the seabed by using 3D dense point clouds and raster layers, including orthophoto mosaics and Digital Surface Models (DSM). The integration of high spatial resolution underwater imagery with object-based image classification (OBIA) technique provided a new tool to count transplanted Posidonia oceanica fragments and estimate the bottom coverage expressed as a percentage of seabed covered by such fragments. Finally, the resulting digital maps were integrated into Geographic Information Systems (GIS) to run topographic change detection analysis and evaluate the mean height of transplanted fragments and detect fine-scale changes in seabed vector ruggedness measure (VRM). Our study provides a guide for creating large-scale, replicable and ready-to-use products for a broad range of applications aimed at standardizing monitoring protocols in future seagrass restoration actions.
