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

• Published in: International journal of remote sensing 2013, Vol.34 (13), p.4680-4701
• Main Authors: Borfecchia, Flavio, De Cecco, Luigi, Martini, Sandro, Ceriola, Giulio, Bollanos, Stelios, Vlachopoulos, George, Valiante, Luigi M, Belmonte, Alessandro, Micheli, Carla
• Format: Article
• Language: English
• Subjects: Animal, plant and microbial ecology; Applied geophysics; Biological and medical sciences; biometry; Dynamic tests; Dynamics; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; genetic techniques and protocols; Genetics; Internal geophysics; leaf area index; Marine; Mathematical models; meadows; Methodology; monitoring; neural networks; plant genetics; Posidonia oceanica; radiometry; remote sensing; Satellites; seagrasses; Spectra; spectral analysis; Teledetection and vegetation maps; turbidity; Vegetation
• ISSN: 1366-5901; 0143-1161; 1366-5901
• DOI: 10.1080/01431161.2013.781701

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.