Carla Micheli

Posidonia oceanica genetic and biometry mapping through high-resolution satellite spectral vegetation indices and sea-truth calibration

In the framework of Posidonia oceanica (PO) preservation activities, a small-scale restoration pilot project was implemented in 2005 at a Santa Marinella site to replace the loss of this important species of seagrass in this zone of the central Tyrrhenian coast via an innovative transplantation approach. In this context, taking into account the recent advances in the fields of high-resolution (HR) satellite/airborne remote-sensing and genetics laboratory analysis techniques, we propose this integrated methodology for monitoring changes in transplanted meadows in regard to perspective to provide support in the assessment of the entire local PO and seagrass population dynamic. According to specific information requirements in terms of radiometric and spectral/spatial resolution, the multispectral data currently available from the QuickBird polar satellite’s four-band (red, green, blue visible and near-infrared) HR sensor were exploited for methodology implementation using a practical ‘image-based’ approach to account for atmospheric and water column turbidity typical of this mid-coastal Mediterranean region. First, the extents and types of seagrass cover were suitably mapped, and then also the distributions of specific vegetation parameters related to PO dynamics and health were assessed by exploiting the remotely sensed satellite-derived radiance signals and point sea-truth calibration measurements of the bio-genetic parameters. In particular, we implemented maps of leaf area index, genetic similarity, and density Giraud indices corresponding to distributions of PO patches using multivariate and data-mining models (artificial neural network) based on appropriately preprocessed radiometric and auxiliary (bathymetry) input variables.

Mapping Spatial Patterns of Posidonia oceanica Meadows by Means of Daedalus ATM Airborne Sensor in the Coastal Area of Civitavecchia (Central Tyrrhenian Sea, Italy)

The spatial distribution of sea bed covers and seagrass in coastal waters is of key importance in monitoring and managing Mediterranean shallow water environments often subject to both increasing anthropogenic impacts and climate change effects. In this context we present a methodology for effective monitoring and mapping of Posidonia oceanica (PO) meadows in turbid waters using remote sensing techniques tested by means of LAI (Leaf Area Index) point sea truth measurements. Preliminary results using Daedalus airborne sensor are reported referring to the PO meadows at Civitavecchia site (central Tyrrhenian sea) where vessel traffic due to presence of important harbors and huge power plant represent strong impact factors. This coastal area, 100 km far from Rome (Central Italy), is characterized also by significant hydrodynamic variations and other anthropogenic factors that affect the health of seagrass meadows with frequent turbidity and suspended sediments in the water column. During 2011–2012 years point measurements of several parameters related to PO meadows phenology were acquired on various stations distributed along 20 km of coast between the Civitavecchia and S. Marinella sites. The Daedalus airborne sensor multispectral data were preprocessed with the support of satellite (MERIS) derived water quality parameters to obtain here improved thematic maps of the local PO distribution. Their thematic accuracy was then evaluated as agreement (R2) with the point sea truth measurements and regressive modeling using an on purpose developd method.

Landsat 8 OLI satellite data for mapping of the Posidonia oceanica and benthic habitats of coastal ecosystems

ABSTRACT The benthic seabeds and seagrass ecosystems, in particular the vulnerable Posidonia oceanica (PO), are increasingly threatened by climate change and other anthropogenic pressures. Along the 8000 km coastline of Italy, they are often poorly mapped and monitored to properly evaluate their health status. Thus to support these monitoring needs, the improved capabilities of the Landsat 8 Operational Land Imager (OLI) Earth Observation (EO) satellite system were tested for PO mapping by coupling its atmospherically corrected multispectral data with near-synchronous sea truth information. Two different approaches for the necessary atmospheric correction were exploited focusing on the Aerosol Optical Depth (AOD) and adjacency noise effects, which typically occur at land–sea interfaces. The general achievements demonstrated the effectiveness of High Resolution (HR) spectral responses captured by OLI sensor, for monitoring seagrass and sea beds in the optically complex Tyrrhenian shallow waters, with performance level dependent on the type of applied atmospheric pre-processing. The distribution of the PO leaf area index (LAI) on different substrates has been most effectively modelled using on purpose developed spectral indices. They were based on the coastal and blue-green OLI bands, atmospherically corrected using a recently introduced method for AOD retrieval, based on the Short Wave Infrared (SWIR) reflectance. The alternative correction method including a less effective AOD assessment but the removal of adjacency effects has proven its efficacy for improving the thematic discriminability of the seabed types characterized by different PO cover–substrate combinations.