GRETL

Current MSc thesis focuses on the monitoring of seagrass P.oceanica along the northern coasts of Crete Island, Greece, and investigates the application of the remote sensing techniques for the seagrass mapping. This research was articulated in two parts, where the first one involves an ecological approach to the seagrass distribution in various regions around the globe and the experience of seagrass monitoring nowadays. The second part of this work has technical character and investigates the application of the remote sensing techniques towards seagrass mapping. It, furthermore, focuses on the optical properties of the P.oceanica and other seafloor cover types, and studies distinguishability of various seafloor cover types. Studies of the optical characteristics of separate seafloor cover types were made with purpose to clarify, whether their spectral properties change with varying environmental conditions. Special attention has been drawn on the role of environmental factors on the distribution of P.oceanica along the coasts of Crete, and in particular, how the optical properties of the seafloor cover types, i.e. spectral reflectance, are being changed under varying external conditions, e.g. water column, amount of suspended particles and sediments in the seawater, and water temperature. Fieldwork research sites were presented by separate locations on the northern coast of Crete region (Ligaria, Agia Pelagia, Xerocampos). The additional measurements of the reflectance spectra of the seawater with and without sediments have been made in aquarium tank in 2009 by means of Trios-RAMSES spectroradiometer. Parallel to the collection of spectra signatures, we captured the imagery for the seagrass mapping, which consists of the aerial images from the Google Earth website and the satellite Landsat TM and ETM+ scenes.

Gretl and R statistical libraries enables to perform data analysis using various algorithms, modules and functions. The case study of this research consists in geospatial analysis of the Mariana Trench, a hadal trench located in the Pacific Ocean. Technically, data modelling was performed using multi-functional combined approach of both Gretl and R libraries. The study aim is modelling and visualizing trends in the variations of the trench's properties: bathymetry (depths), geomorphology (steepness gradient), geology, volcanism (igneous rocks). The workflow included following statistical methods computed and visualized by Gretl and R libraries: 1) descriptive statistics; 2) box plots, normality analysis by quantile-quantile (QQ) plots; 3) local weighted polynomial regression model (loess), 4) linear regression by several methods: weighted least squares (WLS) regression, ordinary least squares (OLS) regression, maximal likelihood linear regression and heteroskedasticity regression model; 5) confidence ellipses and marginal intervals for data distribution; 6) robust estimation by Nadaraya-Watson kernel regression fit; 7) correlation analysis and matrix. The results include following conclusions. First, the slope angle gradient has a correlation with the geological settings of the trench and distribution of volcanic igneous rocks. Second, the sediment thickness varies by the tectonic plates showing unequal distribution in space. Third, there is a correlation between the slope gradient and aspect degree. Forth, geospatial analysis of the bathymetry shows that the deepest part of the trench is located in the southwest.