Determinants of household food security in Nepal: A binary logistic regression analysis

The study reveals that 10.2% of the sampled households in Nepal suffer from chronic food insecurity,i.e.,neither are they able to produce sufficient food from their farms nor earn the food security threshold income for deficit months.With the highest and the lowest exponential value of coefficient obtained from binary logistic regression model,it is concluded that any program targeting occupational caste and small landholding farm category or landless will contribute significantly to reduce food-insecurity....     

Structural fabric of the Southern Indian shield as defined by gravity trends

Southern Indian shield represents a mosaic comprised of several smaller structural domains separated by discrete shear zones. Here we present a horizontal Bouguer gravity gradient map of the Indian shield, south of 14 °N, to define a continental mosaic of gravity trends domains akin to structural domains. The gravity gradient image is based on 7862 newly collected observations merged with 6359 old gravity data. This combined dataset delineates structural boundaries of the five gravity domains related to the Eastern Dharwar Craton, the Eastern Ghats Mobile Belt, the extended Eastern Ghats Mobile Belt, the Southern Granulite Terrain, and the Western Dharwar Craton. Other belts of significant gravity gradients are found associated with the Eastern and the Western coasts. The loci of Closepet granite and Kolar schist belts do not manifest themselves as boundary zones between two distinct gravity domains of the Eastern Dharwar Craton. Lack of a gravity gradient across Karur–Oddanchatram–Kodaikanal and Karur–Kambam–Painavu–Trichur Shear Zones may be attributed to a lack of gravity measurements caused by difficulties in collecting data in topographically difficult terrain. The subdued gravity gradient across the Palghat–Cauvery Shear Zone and a weak gradient across the Achankovil Shear Zone indicates a lithological and/or morphological boundary rather than a terrane boundary. Alternatively, structural domains encompassing Palghat–Cauvery and Achankovil Shear Zones may have been in a neighbouring position during the Gondwana assembly, when Pan-African thermal perturbation reactivated the structures and reworked partly or totally obliterating earlier crustal fabric.     

Structural mapping based on potential field and remote sensing data,South Rewa Gondwana Basin,India

Intracratonic South Rewa Gondwana Basin occupies the northern part of NW–SE trending Son–Mahanadi rift basin of India. The new gravity data acquired over the northern part of the basin depicts WNW–ESE and ENE–WSW anomaly trends in the southern and northern part of the study area respectively. 3D inversion of residual gravity anomalies has brought out undulations in the basement delineating two major depressions (i) near Tihki in the north and (ii) near Shahdol in the south, which divided into two sub-basins by an ENE–WSW trending basement ridge near Sidi. Maximum depth to the basement is about 5.5 km within the northern depression. The new magnetic data acquired over the basin has brought out ENE–WSW to E–W trending short wavelength magnetic anomalies which are attributed to volcanic dykes and intrusive having remanent magnetization corresponding to upper normal and reverse polarity (29N and 29R) of the Deccan basalt magnetostratigrahy. Analysis of remote sensing and geological data also reveals the predominance of ENE–WSW structural faults. Integration of remote sensing, geological and potential field data suggest reactivation of ENE–WSW trending basement faults during Deccan volcanism through emplacement of mafic dykes and sills. Therefore, it is suggested that South Rewa Gondwana basin has witnessed post rift tectonic event due to Deccan volcanism.     

Temporal MODIS data for identification of wheat crop using noise clustering soft classification approach

In this study, temporal MODIS-Terra MOD13Q1 data have been used for identification of wheat crop uniquely, using the noise clustering (NC) soft classification approach. This research also optimises the selection of date combination and vegetation index for classification of wheat crop. First, a separability analysis is used to optimise the date combination for each case of number of dates and vegetation index. Then, these scenes have undergone for NC soft classification. The resolution parameter (δ) was optimised for the NC classifier and found to be a value of 1.6 × 10⁴ for wheat crop identification. Classified outputs were analysed by receiver operating characteristics (ROC) analysis for sub-pixel detection. Highest area under the ROC curve was found for soil-adjusted vegetation index corresponding to the three different phenological stages data sets. From this study, the data sets corresponding to the Sowing, Flowering and Maturity phenological stages of wheat crop were found more suitable to identify it uniquely.     

Reconstruction of time series MODIS EVI data using de-noising algorithms

Long-term Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) data have inherent noise due to clouds and poor atmospheric conditions that limit its applicability for environmental applications. This study was carried out with an objective of noise removal and reconstruction of time series MODIS EVI data (16 day) for the period 2010–2014 using de-noising algorithms. Relative evaluation of de-noising algorithms for smoothing temporal data with ideal noise free data is not possible in actual scenario. Hence, synthetic signals were generated and introduced Gaussian noise at different variance levels for evaluation purpose. Spatial analysis was carried out by introducing noise at different variance levels into the noise free EVI images from the raw EVI stacked image. Spatio-temporal analyses of noise signals in the reconstructed EVI images were evaluated in terms of performance indicators, namely Peak Signal-to-Noise Ratio and Mean Square Error.     

Effect of input data in hydraulic modeling for flood warning systems

The main objective of this study was to quantify the error associated with input data, including various resolutions of elevation datasets and Manning’s roughness for travel time computation and floodplain mapping. This was accomplished on the test bed, the Grand River (Ohio, USA) using the HEC-RAS model. LiDAR data integrated with survey data provided conservative predictions, whereas coarser elevation datasets provided a positive difference in the travel time (11.03–15.01%) and inundation area (32.56–44.52%). The minimum differences in travel time and inundation area were 0.50–4.33% and 3.55–7.16%, respectively, when the result from LiDAR integrated with survey data was compared with a 10-m DEM integrated with survey data. The results suggest that a 10-m DEM in the channel and LiDAR data in the floodplain combined with survey data would be appropriate for a flood warning system. Additionally, Manning’s roughness of the channel section was found to be more sensitive than that of the floodplain. The decrease in inundation area was highest (8.97%) for the lower value of Manning’s roughness.     

Multivariate Regression Analysis and Error Estimation in Formation Satellite

Astronomy Reports - In this work, we aim to develop a non-linear multivariate regression model, which predicts the initial condition for periodic orbits of deputy satellite. The used parameters in...     

Segmentation-based approach for trend analysis and structural breaks in rainfall time series (1851–2006) over India

ABSTRACT

This study analysed long-term rainfall data (1851–2006) over seven climatic zones of India at seasonal and annual scales based on three techniques: (i) linear regression, (ii) multifractal detrended fluctuation analysis (MFDFA) and (iii) Bayesian algorithm. The linear regression technique was used for trend analysis of short-term (30 years) and long-term (156 years) rainfall data. The MFDFA revealed small- and large-scale fluctuations, whereas the Bayesian algorithm helped in quantifying the uncertainty in break-point detection from the rainfall time series. Major break points years identified through Bayesian algorithm were 1888, 1904 and 1976. The MFDFA technique identified that high fluctuation years were between 1871–1890, 1891–1910 and 1951–1970. Linear regression-based analysis revealed 1881–1910 and 1971–2006 as break-point periods in the North Mountainous Indian region. A similar analysis was carried out for India as a whole, as well as its seven climatic zones.