We present a methodology to detect oil spills using MODIS near-infrared sun glittered radiance imagery. The methodology was developed by using a set of seven MODIS images (training dataset) and validated using four other images (validation dataset). The method is based on the ratio image R = L'_GN/L_GN, where L'_GN is the MODIS-retrieved normalized sun glint radiance image and L_GN the same quantity, but obtained from the Cox and Munk isotropic (independent of wind direction) sun glint model. We show that in the R image, while clean water pixel values tend to one, oil spills stand out as anomalies. Moreover, we provide a criterion to distinguish between positive and negative oil-water contrast. A pixel in an R image is classified as a potential oil spill or water via a variable threshold R_s as a function of L'_GN, where the threshold values are obtained from the slicks of our training dataset. Two different fitting curves are provided for R_s, according to the contrast sign. The selection of the correct fitting curve is based on the contrast type, resulting from the criterion above. Results indicate that the thresholding is able to isolate the spills and that the spills of the validation dataset are successfully detected. Spurious look-alike features, such as clouds, and other non-spill features, e.g., large areas at the glint region border, are also detected as oil spills and must be eliminated. We believe that our methodology represents a novel and promising, though preliminary, approach towards automatic oil spill detection in optical satellite images.

The interannual variability in the transport of anchovy eggs and larvae in the Sicily Channel, relatively to the period 1999–2012, is studied by means of numerical simulations of the Mediterranean Forecasting System (MFS) circulation model provided by INGV. Subgrid-scale dynamics not resolved by the MFS model is parameterized in terms of kinematic fields. The latter affect small-scale tracer relative dispersion, while leaving the mean large-scale advection substantially unchanged. A Lagrangian Transport Index (LTI) can be defined to characterize the efficiency of the main currents, e.g., the Atlantic Ionian Stream, in connecting spawning and nursery areas to each other. In our case, this indicator comes from the first arrival time statistics of tracers traveling from a spawning area near Sciacca to a nursery area in proximity of Cape Passero. We observe, on the basis of LTI values, that there are years when the Lagrangian connectivity is very efficient (2004, 2008, 2012) and years when it is weak (2000, 2001, 2003, 2010). Lagrangian indicators like the LTI concur to explain observed fluctuations of larval density and, also, can be employed, more in general, in multivariate models of population dynamics.