Evaluating Hyperspectral Imager for the Coastal Ocean (HICO) data for seagrass mapping in Indian River Lagoon,FL

Differentiation between benthic habitats, particularly seagrass and macroalgae, using satellite data is complicated because of water column effects plus the presence of chlorophyll-a in both seagrass and algae that result in similar spectral patterns. Hyperspectral imager for the coastal ocean data over the Indian River Lagoon, Florida, USA, was used to develop two benthic classification models, Slope_RED and Slope_NIR. Their performance was compared with iterative self-organizing data analysis technique and spectral angle mapping classification methods. The slope models provided greater overall accuracies (63–64%) and were able to distinguish between seagrass and macroalgae substrates more accurately compared to the results obtained using the other classifications methods.     

Ocean Model Simulation of Southern Indian Ocean Surface Currents

The dynamic importance of the Southern Indian Ocean (SIO) lies in the fact that it connects the three major world oceans: the Pacific, Atlantic, and Indian Oceans. Modeling study has been used to understand the circulation pattern of this very important region. Simulation of SIO (10°N–60°S and 30°E–120°E) is performed with z-coordinate Ocean General Circulation Model (OGCM) viz; MOM3.0 and the results have been compared with observed ship drift data. It is found that except near coastal boundaries and in equatorial region, the simulated current reproduce most well known current pattern such as Antarctic Circumpolar Current (ACC), South Equatorial Current (SEC) etc. and bears a resemblance to that of the observed data; however the magnitude of the surface current is weaker in model than the observed data, which may be due to deficiency in the forcing field and boundary condition and problem with observed data. The annual mean wind stress curl computed over the oceanic domain reveals about ACC and its similar importance. The way in which the ocean responds to the windstress and vertically integrated transport using model output is fascinating and rather good.     

Long-Term Modulation of Cosmic-Ray Intensity and Correlation Analysis Using Solar and Heliospheric Parameters

Based on the monthly sunspot numbers (SSNs), the solar-flare index (SFI), grouped solar flares (GSFs), the tilt angle of heliospheric current sheet (HCS), and cosmic-ray intensity (CRI) for Solar Cycles 21?–?24, a detailed correlation study has been performed using the cycle-wise average correlation (with and without time lag) method as well as by the “running cross-correlation” method. It is found that the slope of regression lines between SSN and SFI, as well as between SSN and GSF, is continuously decreasing from Solar Cycle 21 to 24. The length of regression lines has significantly decreased during Cycles 23 and 24 in comparison to Cycles 21 and 22. The cross-correlation coefficient (without time lag) between SSN–CRI, SFI–CRI, and GSF–CRI has been found to be almost the same during Cycles 21 and 22, while during Cycles 23 and 24 it is significantly higher between SSN–CRI and HCS–CRI than for SFI–CRI and GSF–CRI. Considering time lags of 1 to 20 months, the maximum correlation coefficient (negative) amongst all of the sets of solar parameters is observed with almost the same time lags during Cycles 21?–?23, whereas exceptional behaviour of the time lag has been observed during Cycle 24, as the correlation coefficient attains its maximum value with two time lags (four and ten months) in the case of the SSN–CRI relationship. A remarkably large time lag (22 months) between HCS and CRI has been observed during the odd-numbered Cycle 21, whereas during another odd cycle, Cycle 23, the lag is small (nine months) in comparison to that for other solar/flare parameters (13?–?15 months). On the other hand, the time lag between SSN–CRI and HCS–CRI has been found to be almost the same during even-numbered Solar Cycles 22 and 24. A similar analysis has been performed between SFI and CRI, and it is found that the correlation coefficient is maximum at zero time lag during the present solar cycle. The GSFs have shown better maximum correlation with CRI as compared to SFI during Cycles 21 to 23, indicating that GSF could also be used as a significant solar parameter to study the cosmic-ray modulation. Furthermore, the running cross-correlation coefficient between SSN–CRI and HCS–CRI, as well as between solar-flare activity parameters (SFI and GSF) and CRI is observed to be strong during the ascending and descending phases of solar cycles. The level of cosmic-ray modulation during the period of investigation shows the appropriateness of different parameters in different cycles, and even during the different phases of a particular solar cycle. We have also studied the galactic cosmic-ray modulation in relation to combined solar and heliospheric parameters using the empirical model suggested by Paouris et al. (Solar Phys.280, 255, 2012). The proposed model for the calculation of the modulated cosmic-ray intensity obtained from the combination of solar and heliospheric parameter gives a very satisfactory value of standard deviation as well as \(R^{2}\) (the coefficient of determination) for Solar Cycles 21?–?24.     

Correction to: A Size-Dependent Bonded-Particle Model for Transversely Isotropic Rock and its Application in Studying the Size Effect of Shale

Geotechnical and Geological Engineering -