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The present study aims to develop a steady two-dimensional suspended sediment transport model in an open channel for turbulent flow-carrying sediments. In such a flow, if sediment concentration is high, hindered settling occurs, meaning that the settling velocity of a particle reduces than that in clear water flow and should be considered in the mathematical modeling. Inclusion of hindered settling into the governing equation of transport leads to a highly nonlinear parabolic-type partial differential equation (PDE) with variable coefficients. Physically realistic and generalized boundary conditions are considered at the free surface and at the bed. A semianalytical solution of the nonlinear PDE together with these boundary conditions is proposed using two different approaches, namely, the Laplace transform-based homotopy analysis method (Laplace-HAM) and the method of lines-based HAM (MOL-HAM). Unlike existing analytical/semianalytical methods, the convergence behavior of the proposed solution can be handled efficiently through a convergence-control parameter. The proposed solutions have been validated with the numerical solution and the solution of an existing model under limited conditions. Further, the derived solution is also validated with experimental data for the far-field solution. The two derived solutions are compared with each other, and both are found to provide an approximate series solution. The concentration profiles of suspended sediment particles with different physical parameters and conditions are interpreted physically. This study can be extended to address other suspended sediment transport problems. [ABSTRACT FROM AUTHOR]

An optimal model was developed for accounting forest carbon stock from synergistic use of optical data from Landsat TM and synthetic aperture radar (SAR) data from COSMO-Skymed (X-band), Radarsat-2 (C-band) and ALOS PALSAR (L-band) sensors over a tropical deciduous heterogeneous forest of India. The best-fit integrated multiple linear regression model had a model accuracy of 83%, r2 = 0.96, root mean square error = 10.02 Mg/ha and Willmott's index of agreement of 0.98. The model further validated using chi-squared and t-test. Results of models for calculating the aboveground biomass (AGB) were converted to C and CO2 using conversion factors. Average AGB, C and CO2 were 70.5, 35.26 and 130.89 Mg/ha, respectively. The synergistic use of optical and multi-frequency SAR data enhanced the AGB saturation threshold to about 150 Mg/ha for tropical deciduous mixed forests. Hence, the synergistic use of this data is suggested for large-scale AGB and C estimations for tropical forests. Optical remote sensing sensors are extensively used due to greater data availability despite their poor sensitivity toward forest parameters. In contrast, SAR signals are highly sensitive toward forest biophysical and structural parameters, providing a better alternative. This unique integrated approach provides valuable information regarding the spatial distribution and quantification of forest biomass and carbon. [ABSTRACT FROM AUTHOR]

To understand the sediment-transport process in an open-channel turbulent flow, the time-averaged profiles of streamwise fluid velocity and volumetric particle concentration in suspension must be given simultaneous treatment because they are closely interrelated through particle–turbulence interaction. Presence of sediment particles increases the density of a fluid-sediment mixture, which makes the flow stratified and obstructs the settling of sediment particles. The greater the amount of sediment particles in fluid, the stronger the effects of stratification and hindered settling. Therefore, generalizing existing works, this study attempts to model the velocity and concentration simultaneously, incorporating the aforementioned effects. The coupled system of odes arising from the derivation is strongly nonlinear in nature, and the analytical solution needs a special mathematical tool. To that end, a novel analytical method called the homotopy analysis method (HAM) is employed to obtain the explicit series solution to the system. The methodology is a nonperturbation approach, and the convergence can be tackled easily through some convergence control parameters. The solutions obtained are found to be stable and are validated with numerical solution as well as with relevant experimental data available in the literature. Further, the models have been physically interpreted through the effects of the turbulent factors incorporated. [ABSTRACT FROM AUTHOR]

Groundwater assists farmers to irrigate crops for fulfilling the crop-water requirement. Indian agriculture system is characterized by three cropping seasons known as Kharif (monsoon), Rabi (post-monsoon) and summer (pre-monsoon). In tropical countries like India, monitoring cropping practices using optical remote sensing during Kharif and Rabi seasons is constraint due to the cloud cover, which can be well addressed by microwave remote sensing. In the proposed research, the strength of C-band polarimetric Synthetic Aperture Radar (SAR) time series images were evaluated to classify groundwater irrigated croplands for the Kharif and Rabi cropping seasons of the year 2013. The present study was performed in the Berambadi experimental watershed of Kabini river basin, southern peninsular India. A total of fifteen polarimetric variables were estimated includes four backscattering coefficients (HH, HV, VH, VV) and eleven polarimetric indices for all Radarsat-2 SAR images. The cumulative temporal sum (seasonal and dual-season) of these parameters was supervised classified using Support Vector Machine (SVM) classifier with intensive ground observation samples. Classification results using the best equation (highest accuracy and kappa) shows that the Kharif, Rabi and irrigated double croplands are respectively 9.58 km2 (20.6%), 16.14 km2 (34.7%) and 6.22 km2 (13.4%) with a kappa coefficient respectively 0.84, 0.74 and 0.94. [ABSTRACT FROM AUTHOR]

There are various classification techniques available which produce desired results. However, some of the land use/land cover (LU/LC) classes are not discernible in such classifications. The present study attempts for improving LU/LC classification accuracy by applying data fusion techniques. For this, we considered combinations of: (1) Synthetic Aperture Radar (SAR) multi-looked intensity and optical, (2) backscatter with optical and (3) terrain corrected backscatter with optical data. The fusion of terrain corrected backscatter with optical has been considered in this study to negate the effect of topographic undulations on backscatter. The classification accuracy for combinations of cross-polarised terrain corrected backscatter data with Advanced Visible and Near-Infrared Radiometer type 2 (AVNIR-2) (90.33%), co-polarised terrain corrected backscatter data with AVNIR-2 (89.66%), cross-polarised backscatter data with AVNIR-2 (89.0%) and cross-polarised multi-look intensity with AVNIR-2 (87.0%) were found to be better than classified outputs of AVNIR-2 data alone (84.6%), combinations of co-polarised backscatter and AVNIR-2 data (82.7%) and co-polarised multi-look intensity with AVNIR-2 data (80.1%), and combinations of multi-date terrain corrected backscatter (80.66%), multi-date co-polarised backscatter (80.0%) and multi-date co-polarised multi-look intensity (79.0%). The highest accuracy achieved in LU/LC classification is with cross-polarised terrain corrected backscatter with AVNIR-2 (90.33%) data. Data fusion techniques can be an alternative for LU/LC classification. [ABSTRACT FROM AUTHOR]

Topographic corrections of synthetic aperture radar (SAR) images over hilly regions are vital for retrieval of correct backscatter values associated with natural targets. The coarse resolution external digital elevation models (DEM) available for topographic corrections of high resolution SAR images often result into degradation of spatial resolution or improper estimation of backscatter values in SAR images. Also, many a times the external DEMs do not spatially co-register well with the SAR data. The present study showcases the methodology and results of topographic correction of ALOS-PALSAR image using high resolution DEM generated from the same data. High resolution DEMs of Jaipur region, India were generated using multiple pair SAR images acquired from ALOS-PALSAR using interferometric (InSAR) techniques. The DEMs were validated using differential global positioning system measured elevation values as ground control points and were compared with photogrammetric DEM (advanced spaceborne thermal emission and reflection radiometer – ASTER) and SRTM (Shuttle Radar Topography Mission) DEM. It was observed that ALOS-PALSAR images with optimum baseline parameters produced high resolution DEM with better height accuracy. Finally, the validated DEM was used for topographic correction of ALOS-PALSAR images of the same region and were found to produce better result as compared with ASTER and SRTM-DEM. [ABSTRACT FROM PUBLISHER]

A polarimetric radar system measures the complete scattering matrix of a target in the backscattered field that includes magnitudes of linearly polarized scattering amplitudes and the co- polarised and cross-polarised phase angles. Apart from backscattering intensity, the co-polarization phase difference (CPD) calculated from polarimetric synthetic aperture radar (SAR) data produces important information about target physical, geometrical and dielectric properties. In the present work, the distribution of CPD in C-band polarimetric SAR data corresponding to major kharif and rabi crops (denoting the monsoon and the winter season) and other land cover features have been studied over Central State Farm, Hisar, Haryana. The probability density functions (PDF) of CPD have been compared with dominant scattering contributions from these targets as obtained from polarimetric target decompositions. The results show that crops and other land cover features show characteristic CPD distributions, which relates well with crop physical and geometrical properties. An intuition of the rate of growth and plant vigour is indicative from the temporal PDF pattern. [ABSTRACT FROM AUTHOR]

Information on phenological metrics of individual plant species is meager. Phenological metrics generation for a specific plant species can prove beneficial if the species is ecologically or economically important. Teak, a dominating tree in most regions of the world has been focused on in the present study due to its multiple benefits. Forecasts on such species can attain a substantial improvement in their productivity. MODIS NDVI time series when subjected to statistical smoothing techniques exhibited good output with Tukey's smoothing (TS) with a low RMSE of 0.042 compared to single exponential (SE) and double exponential (DE). Phenological metrics, namely, the start of the season (SOS), end of the season (EOS), maximum of the season (MAX), and length of the season (LOS) were generated using Tukey-smoothed MODIS NDVI data for the years 2003–2004 and 2013–2014. Post shifts in SOS and EOS by 14 and 37 days respectively with a preshift of 28 days in MAX were observed in the year 2013–2014. Preshift in MAX was accompanied by an increase in greenness exhibiting increased NDVI value.LOS increased by 24 days in the year 2013–2014, showing an increase in the duration of the season of teak. Dates of these satellite-retrieved phenological occurrences were validated with ground phenological data calculated using crown cover assessment. The present study demonstrated the potential of a spatial approach in the generation of phenometrics for an individual plant species, which is significant in determining productivity or a crucial trophic link for a given region. [ABSTRACT FROM AUTHOR]