An Improved Hilbert Curve for Parallel Spatial Data Partitioning

A novel Hilbert-curve is introduced for parallel spatial data partitioning, with consideration of the huge-amount property of spatial information and the variable-length characteristic of vector data i...     

Gibbs sampler by sampling-importance-resampling

Among the Markov chain Monte Carlo methods, the Gibbs sampler has the advantage that it samples from the conditional distributions for each unknown parameter, thus decomposing the sample space. In the case the conditional distributions are not tractable, the Gibbs sampler by means of sampling-importance-resampling is presented here. It uses the prior density function of a Bayesian analysis as the importance sampling distribution. This leads to a fast convergence of the Gibbs sampler as demonstrated by the smoothing with preserving the edges of 3D images of emission tomography.     

Spatial Technologies in Deriving the Morphotectonic Characteristics of Tectonically Active Western Tripura Region,Northeast India

Employing integrated remote sensing and GIS technology the western most part of Tripura region (Northeast India) and adjoining Bangladesh region has been investigated in the light of its geomorphological characteristics. Nature of fold ridges, several streams and the respective drainage basins are well depicted in satellite images and digital elevation model providing meaningful information. Quantitative parameters such as stream sinuosity, drainage basin asymmetry, basin elongation ratio have been computed. Main rivers of the study area, namely the Gomti and Khowai follows extremely meandering path and crosses through the transversely faulted anticlinal ridges. Fluvial anomalies viz. shift in stream channel and the abandoned meandering loops have been inferred and mapped. The Haora river in the study area exhibits northward shift in some part. Development of drainage system towards north and south from the drainage divide along the latitude 23°45^′N indicated up arching of the region which is also corroborated by the extracted topographic profiles. It has been observed that several tributary streams have gone dry and agricultural fields are developed along the dried up stream. These derived parameters remained useful to understand the nature of topographical modification attributed to the possible tectonic activity.     

Discrimination of Spectrally-Close Crops Using Ground-Based Hyperspectral Data

Hyperspectral remote sensing, because of its large number of narrow bands, has shown possibility of discriminating the crops. Current study was carried out to select the optimum bands for discrimination among pulses, cole crops and ornamental plants using the ground-based Hyperspectral data in Patha village, Lalitpur district, Uttar Pradesh state and Kolkata, West Bengal state. The field observations of reflectance were taken using a 512-channel spectroradiometer with a range of 325–1075 nm. The stepwise discriminant analysis was carried out and separability measures, such as Wilks’ lambda and F-Value were used as criteria for identifying the narrow bands. The analysis showed that, the best four bands for pulse crop discrimination lie mostly in NIR and early MIR regions i.e. 750, 800, 940 and 960 nm. Within cole crops discrimination is primarily determined by the green, red and NIR bands of 550, 690, 740, 770 and 980 nm. The separability study showed the bands 420,470,480,570,730,740, 940, 950, 970, 1030 nm are useful for discriminating flowers.     

Universal time from VLBI single-baseline observations during CONT08

The IVS Intensive sessions are single-baseline, 1-h VLBI sessions carried out everyday in order to determine Universal Time (UT1). We investigate different possibilities to improve the results of such sessions. We do this investigation by extracting 2-h single-baseline sessions from the CONT08 data set. These are analysed like normal Intensives, and the results are compared to the results of the analysis of the full CONT08 data set. We find that tropospheric asymmetry is the major error source for the single-baseline sessions. It is possible to improve the accuracy of the estimated UT1 either by using accurate a priori tropospheric gradients or by estimating gradients in the data analysis.     

Hyperspectral Radiometry to Quantify the Grades of Iron Ores of Noamundi and Joda Mines,Eastern India

This paper present the results of a preliminary study to assess the potential of the visible, NIR and SWIR energy of the EMR in differentiating iron ores of different grades in a rapid manner using hyperspectral radiometry. Using different iron ore samples from Noamundi and Joda mines, Jharkhand and Orissa, states of India, certain spectro-radiometric measurements and geochemical analysis were carried out and the results have been presented. It was observed that the primary spectral characteristics of these iron ores lie in the 850 to 900 nm and 650–750 nm regions. The spectral parameters for each curve used for studying the iron ores are: (i) the slopes of the spectral curve in 685–725 nm region; (ii) position of the peak with respect to wavelength in 730–750 nm region and (iii) radius of curvature of the absorption trough in the 850–900 nm region. Comparison of these spectral parameters and the geochemistry of the samples indicates that the position of the peak of the curve in 730–750 nm region shifts towards longer wavelength with increasing iron oxide content, while the slope of the curvature in the 685–725 nm region has a strong negative correlation with the iron oxide content of the samples. Similarly, a strong negative correlation is observed between the radius of curvature of the 850–900 nm absorption trough and the iron oxide content. Such strong correlations indicate that hyperspectral radiometry in the visible and NIR regions can give a better estimate and quantification of the grades of iron ores. This study has demonstrated that generation of empirical models using hyperspectral radiometric techniques is helpful to quantify the grade of iron ores with limited geochemical analysis.     

Analysis of Seasonal Variability of Vegetation and Methane Concentration over India using SPOT-VEGETATION and ENVISAT-SCIAMACHY Data

This paper highlights the spatial and temporal variability of atmospheric columnar methane (CH₄) concentration over India and its correlation with the terrestrial vegetation dynamics. SCanning IMaging Absorption spectrometer for Atmospheric CHartographY (SCIAMACHY) on board ENVIronmental SATellite (ENVISAT) data product (0.5° × 0.5°) was used to analyze the atmospheric CH₄ concentration. Satellite Pour l'Observation de la Terre (SPOT)-VEGETATION sensor’s Normalized Difference Vegetation Index (NDVI) product, aggregated at 0.5° × 0.5° grid level for the same period (2004 and 2005), was used to correlate the with CH₄ concentration. Analysis showed mean monthly CH₄ concentration during the Kharif season varied from 1,704 parts per billion volume (ppbv) to 1,780 ppbv with the lowest value in May and the highest value in September. Correspondingly, mean NDVI varied from 0.28 (May) to 0.53 (September). Analysis of correlation between CH₄ concentration and NDVI values over India showed positive correlation (r = 0.76; n = 6) in Kharif season. Further analysis using land cover information showed characteristic low correlation in natural vegetation region and high correlation in agricultural area. Grids, particularly falling in the Indo-Gangetic Plains showed positive correlation. This could be attributed to the rice crop which is grown as a predominant crop during this period. The CH₄ concentration pattern matched well with growth pattern of rice with the highest concentration coinciding with the peak growth period of crop in the September. Characteristically low correlation was observed (r = 0.1; n = 6) in deserts of Rajasthan and forested Himalayan ecosystem. Thus, the paper emphasizes the synergistic use of different satellite based data in understanding the variability of atmospheric CH₄ concentration in relation to vegetation.     

Comparison of Two Data Smoothing Techniques for Vegetation Spectra Derived From EO-1 Hyperion

Hyperspectral data are generally noisier compared to broadband multispectral data because their narrow bandwidth can only capture very little energy that may be overcome by the self-generated noise inside the sensors. It is desirable to smoothen the reflectance spectra. This study was carried out to see the effect of smoothing algorithms - Fast-Fourier Transform (FFT) and Savitzky–Golay (SG) methods on the statistical properties of the vegetation spectra at varying filter sizes. The data used in the study is the reflectance spectra data obtained from Hyperion sensor over an agriculturally dominated area in Modipuram (Uttar Pradesh). The reflectance spectra were extracted for wheat crop at different growth stages. Filter sizes were varied between 3 and 15 with the increment of 2. Paired t-test was carried out between the original and the smoothed data for all the filter sizes in order to see the extent of distortion with changing filter sizes. The study showed that in FFT, beyond filter size 11, the number of locations within the spectra where the smooth spectra were statistically different from its original counterpart increased to 14 and reaches 21 at the filter size 15. However, in SG method, number of statistically different locations were more than those found in the FFT, but the number of locations did not changing drastically. The number of statistically disturbed locations in SG method varied between 16 and 19. The optimum filter size for smoothing the vegetation spectra was found to be 11 in FFT and 9 in SG method.     

Evaluating the Sensitivity of Image Fusion Quality Metrics to Image Degradation in Satellite Imagery

Referring to the high potential of topographic satellite in collecting high resolution panchromatic imagery and high spectral, multi spectral imagery, the purpose of image fusion is to produce a new image data with high spatial and spectral characteristics. It is necessary to evaluate the quality of fused image by some quality metrics before using this product in various applications. Up to now, several metrics have been proposed for image quality assessment; which are also applicable for quality evaluation of fused images. However, it seems more investigations are needed to inspect the potentials of proposed Image Fusion Quality Metrics (IFQMs) to registration accuracy, especially in high resolution satellite imagery. This paper focuses on such studies and, using different image fusion quality metrics, experiments are conducted to evaluate the sensitivity of such metrics to a set of high resolution satellite imagery covering urban areas. The obtained results clearly reveal that these metrics sometimes do not behave robust in the whole area and also their obtained results are inconsistence in different patch areas in comparison with the whole image. These limitations are in minimum situation for an image quality metric such as SAM and are completely tangible for image quality metrics such as ERGAS in case of multi modal and DIV and CC from mono modal category.