Comparison of hybrid-Pol with quad-Pol scheme based on polarimetric information content

Recently, there has been growing interest in dual-polarimetric (dual-Pol) systems, especially in hybrid-Pol mode. In comparison to quad-Pol systems, a dual-Pol system has the advantage of halved average transmitted power and doubled swath coverage. In this article, we present a comparison of information provided by hybrid-Pol and quad-Pol synthetic aperture radar (SAR) data. To be compared, hybrid-Pol data are converted to pseudo-quad-Pol data using compact polarimetric scattering models. We have further compared both configurations based on the information extractable about the scattering process. The scattering contribution of each of the three basic scattering mechanisms, namely single bounce, double bounce and volume scattering, can be evaluated by Freeman and Durden decomposition techniques. Applying such a decomposition technique to quad-Pol and pseudo-quad-Pol data, we evaluate and compare the scattering contribution of each of the three basic scattering mechanisms for clusters of pixels in polarimetric SAR images.     

Variability of mesopause temperature derived from two independent methods using meteor radar and its comparison with SABER and EOS MLS and a collocated multi-wavelength dayglow photometer over an equatorial station,Thumba (8.5° N, 76.5° E)

Two independent methods for deriving mesopause temperature using meteor radar installed at an equatorial station, Thumba (8.5° N, 76.5° E), are discussed in this article. This meteor radar-derived mesopause temperature is then compared with two different types of spaceborne measurement, namely (i) Sounding the Atmosphere using Broadband Emission Radiometry (SABER) and (ii) the Earth Observing System Microwave Limb Sounder (EOS MLS), and a collocated multi-wavelength dayglow photometer (DGPM). The meteor radar-derived temperature is in fairly good agreement with all the three measurement techniques, with an uncertainty of ±10°. This study focuses on a detailed evaluation and inter-comparison of mesopause temperature derived from different measurement techniques. An attempt is also made to compare the suitability of these observations to study planetary waves and other oscillation activities in the mesospheric region.     

Stratification of forest density and its validation by NDVI analysis in a part of western Himalaya,India using Remote sensing and GIS techniques

This study deals with the assessment of the status of forest density in the Kangra district of Himachal Pradesh, western Himalaya. A classified forest map of the area with an accuracy of 88.17% was produced using the hybrid classification method in Erdas Imagine. An IRS 1D LISS III satellite image was used for mapping and classification. Forest density was calculated in the ArcGIS environment by overlaying a mesh of uniform resolution cells (500 m×500 m) on a classified forest map. The forest density value of each cell was later used for the preparation of forest density contours. The forest density output was verified by Normalized Difference Vegetation Index (NDVI) analyses. The forest cover of the study area was found to be 34.3%. Baroh area had the highest (45.87%) forest density and Baijnath (18.65%) the lowest. Central and western regions of the district showed high‐value forest density contours (>50%). The derived NDVI values were compared against the forest density classes for assessing the accuracy of the results obtained. A positive correlation (r = 0.99) between NDVI values and forest density confirms the accuracy of the results.     

Estimating and mapping grass phosphorus concentration in an African savanna using hyperspectral image data

We tested the utility of imaging spectroscopy and neural networks to map phosphorus concentration in savanna grass using airborne HyMAP image data. We also sought to ascertain the key wavelengths for phosphorus prediction using hyperspectral remote sensing. The remote sensing of foliar phosphorus has received very little attention as compared to nitrogen, yet it plays an equally important role in explaining the distribution and feeding patterns of herbivores. Band depths from two continuum‐removed absorption features as well as the red edge position (REP) were input into a backpropagation neural network. Following a series of experiments to ascertain the optimum wavelengths, the best trained neural network was used to predict and ultimately to map grass phosphorus concentration in the Kruger National Park. The results indicate that the best trained neural network could predict phosphorus distribution with a coefficient of determination of 0.63 and a root mean square error (RMSE) of 0.07 (28% of the mean observed phosphorus concentration) on an independent test data set. Our results also show that the absorption feature located in the shortwave infrared (R _2015–2199) contains more information on phosphorus distribution, a region that has hardly been explored before in most spectroscopic experiments for phosphorus as compared to the visible bands. Overall, the study demonstrates the potential of imaging spectroscopy in mapping grass phosphorus concentration in savanna rangelands.     

Impact on the chlorophyll concentration in the Bay of Bengal and Arabian Sea during Indian Ocean dipole mode

The chlorophyll (Chl) concentration in the seawater of the Bay of Bengal (BoB) and the Arabian Sea (AS) changes most during the Indian Ocean dipole (IOD) mode event. The empirical orthogonal function (EOF) of several oceanographic and atmospheric data was studied, using 9 years (1998–2006) of reanalysed satellite data. The variation of Chl during the period of September to November (SON) over the 9 years has been studied in this article. It has been found that significant enhancement of Chl in the BoB takes place during IOD years owing to the surface wind action, that is, wind-stress curl (WC), which favours the upwelling process, whereas the AS shows a decline in concentration of Chl owing to a reduced open ocean upwelling process.     

Parallel Godunov smoothed particle hydrodynamics (SPH) with improved treatment of Boundary Conditions and an application to granular flows

Smoothed Particle Hydrodynamics has been successfully used for various fluid-dynamics problems, such as breaking-waves, flooding etc., since it was originally proposed. While the Lagrangian approach is naturally suitable for free-surface flows, enforcing boundary conditions and poor approximations in the presence of discontinuities in the solution are major difficulties with the method. In this paper we present an enhanced conservative Godunov SPH based on the work of Inutsuka [S. Inutsuka, Reformulation of smoothed particle hydrodynamics with Riemann solver, Journal of Computational Physics 179 (2002) 238–267] that accurately resolves discontinuities without the need to use artificial viscosity, preserves partition of unity everywhere in the domain, correctly and flexibly enforces necessary essential and frictional slip boundary conditions to approximately solve free-surface granular flows. The development is motivated by the need to improve upon depth averaged grid based models of large scale debris flows and avalanches often characterized as granular flows. Simple validation of the results is obtained by comparison to table-top experiments.     

Broadcast stochastic receding horizon control of multi-agent systems

Optimal regulation of stochastically behaving agents is essential to achieve a robust aggregate behavior in a swarm of agents. How optimally these behaviors are controlled leads to the problem of designing optimal control architectures. In this paper, we propose a novel broadcast stochastic receding horizon control architecture as an optimal strategy for stabilizing a swarm of stochastically behaving agents. The goal is to design, at each time step, an optimal control law in the receding horizon control framework using collective system behavior as the only available feedback information and broadcast it to all agents to achieve the desired system behavior. Using probabilistic tools, a conditional expectation based predictive model is derived to represent the ensemble behavior of a swarm of independently behaving agents with multi-state transitions. A stochastic finite receding horizon control problem is formulated to stabilize the aggregate behavior of agents. Analytical and simulation results are presented for a two-state multi-agent system. Stability of the closed-loop system is guaranteed using the supermartingale theory. Almost sure (with probability 1) convergence of the closed-loop system to the desired target is ensured. Finally, conclusions are presented.     

Optimal band selection from hyperspectral data for Lantana camara discrimination

The primary objective of this research was to determine the optimal hyperspectral wavelengths based on spectroscopy data over the spectral range of 450–2500 nm for the detection of the invasive species Lantana camara L. (lantana) from seven of its co-occurring species. A procedure based on statistical analysis of the reflectance and the first derivative reflectance (FDR) identified 86 and 18 bands, respectively, where lantana significantly differed from its co-occurring species. The effectiveness of the identified optimal bands was then evaluated using Hyperion imagery. The original Hyperion image with 155 bands gave an overall accuracy of 80% compared to 77% and 76% from the 86- and 18-band spectral subsets, respectively. A pairwise comparison of the three error matrices showed no significant difference in the accuracy achieved. The FDR analysis combined with the statistical analysis proved to be a useful procedure for data reduction by refining the discrimination to fewer optimal bands for lantana detection with no adverse impact on classification accuracy.     

Prediction of hopper discharge rate using combined discrete element method and artificial neural network

An Artificial Neural Network (ANN) was developed to predict the mass discharge rate from conical hoppers. By employing Discrete Element Method (DEM), numerically simulated flow rate data from different internal angles (20°–80°) hoppers were used to train the model. Multi-component particle systems (binary and ternary) were simulated and mass discharge rate was estimated by varying different parameters such as hopper internal angle, bulk density, mean diameter, coefficient of friction (particle-particle and particle-wall) and coefficient of restitution (particle-particle and particle-wall). The training of ANN was accomplished by feed forward back propagation algorithm. For validation of ANN model, the authors carried out 22 experimental tests on different mixtures (having different mean diameter) of spherical glass beads from different angle conical hoppers (60° and 80°). It was found that mass discharge rate predicted by the developed neural network model is in a good agreement with the experimental discharge rate. Percentage error predicted by ANN model was less than ±13%. Furthermore, the developed ANN model was also compared with existing correlations and showed a good agreement.