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.     

Water-removed spectra increase the retrieval accuracy when estimating savanna grass nitrogen and phosphorus concentrations

Information about the distribution of grass foliar nitrogen (N) and phosphorus (P) is important for understanding rangeland vitality and for facilitating the effective management of wildlife and livestock. Water absorption effects in the near-infrared (NIR) and shortwave-infrared (SWIR) regions pose a challenge for nutrient estimation using remote sensing. The aim of this study was to test the utility of water-removed (WR) spectra in combination with partial least-squares regression (PLSR) and stepwise multiple linear regression (SMLR) to estimate foliar N and P, compared to spectral transformation techniques such as first derivative, continuum removal and log-transformed (Log(1/R)) spectra. The study was based on a greenhouse experiment with a savanna grass species (Digitaria eriantha). Spectral measurements were made using a spectrometer. The D. eriantha was cut, dried and chemically analyzed for foliar N and P concentrations. WR spectra were determined by calculating the residual from the modelled leaf water spectra using a nonlinear spectral matching technique and observed leaf spectra. Results indicated that the WR spectra yielded a higher N retrieval accuracy than a traditional first derivative transformation (R²=0.84, RMSE = 0.28) compared to R²=0.59, RMSE = 0.45 for PLSR. Similar trends were observed for SMLR. The highest P retrieval accuracy was derived from WR spectra using SMLR (R²=0.64, RMSE = 0.067), while the traditional first derivative and continuum removal resulted in lower accuracy. Only when using PLSR did the first derivative result in a higher P retrieval accuracy (R²=0.47, RMSE = 0.07) than the WR spectra (R²=0.43, RMSE = 0.070). It was concluded that the water removal technique is a promising technique to minimize the perturbing effect of foliar water content when estimating grass nutrient concentrations.     

ITRF2008: an improved solution of the international terrestrial reference frame

ITRF2008 is a refined version of the International Terrestrial Reference Frame based on reprocessed solutions of the four space geodetic techniques: VLBI, SLR, GPS and DORIS, spanning 29, 26, 12.5 and 16?years of observations, respectively. The input data used in its elaboration are time series (weekly from satellite techniques and 24-h session-wise from VLBI) of station positions and daily Earth Orientation Parameters (EOPs). The ITRF2008 origin is defined in such a way that it has zero translations and translation rates with respect to the mean Earth center of mass, averaged by the SLR time series. Its scale is defined by nullifying the scale factor and its rate with respect to the mean of VLBI and SLR long-term solutions as obtained by stacking their respective time series. The scale agreement between these two technique solutions is estimated to be 1.05 ± 0.13 ppb at epoch 2005.0 and 0.049 ± 0.010?ppb/yr. The ITRF2008 orientation (at epoch 2005.0) and its rate are aligned to the ITRF2005 using 179 stations of high geodetic quality. An estimate of the origin components from ITRF2008 to ITRF2005 (both origins are defined by SLR) indicates differences at epoch 2005.0, namely: ?0.5, ?0.9 and ?4.7?mm along X, Y and Z-axis, respectively. The translation rate differences between the two frames are zero for Y and Z, while we observe an X-translation rate of 0.3?mm/yr. The estimated formal errors of these parameters are 0.2?mm and 0.2?mm/yr, respectively. The high level of origin agreement between ITRF2008 and ITRF2005 is an indication of an imprecise ITRF2000 origin that exhibits a Z-translation drift of 1.8?mm/yr with respect to ITRF2005. An evaluation of the ITRF2008 origin accuracy based on the level of its agreement with ITRF2005 is believed to be at the level of 1?cm over the time-span of the SLR observations. Considering the level of scale consistency between VLBI and SLR, the ITRF2008 scale accuracy is evaluated to be at the level of 1.2?ppb (8?mm at the equator) over the common time-span of the observations of both techniques. Although the performance of the ITRF2008 is demonstrated to be higher than ITRF2005, future ITRF improvement resides in improving the consistency between local ties in co-location sites and space geodesy estimates.     

VLBI-derived troposphere parameters during CONT08

Time-series of zenith wet and total troposphere delays as well as north and east gradients are compared, and zenith total delays (ZTD) are combined on the level of parameter estimates. Input data sets are provided by ten Analysis Centers (ACs) of the International VLBI Service for Geodesy and Astrometry (IVS) for the CONT08 campaign (12?C26 August 2008). The inconsistent usage of meteorological data and models, such as mapping functions, causes systematics among the ACs, and differing parameterizations and constraints add noise to the troposphere parameter estimates. The empirical standard deviation of ZTD among the ACs with regard to an unweighted mean is 4.6?mm. The ratio of the analysis noise to the observation noise assessed by the operator/software impact (OSI) model is about 2.5. These and other effects have to be accounted for to improve the intra-technique combination of VLBI-derived troposphere parameters. While the largest systematics caused by inconsistent usage of meteorological data can be avoided and the application of different mapping functions can be considered by applying empirical corrections, the noise has to be modeled in the stochastic model of intra-technique combination. The application of different stochastic models shows no significant effects on the combined parameters but results in different mean formal errors: the mean formal errors of the combined ZTD are 2.3?mm (unweighted), 4.4?mm (diagonal), 8.6?mm [variance component (VC) estimation], and 8.6?mm (operator/software impact, OSI). On the one hand, the OSI model, i.e. the inclusion of off-diagonal elements in the cofactor-matrix, considers the reapplication of observations yielding a factor of about two for mean formal errors as compared to the diagonal approach. On the other hand, the combination based on VC estimation shows large differences among the VCs and exhibits a comparable scaling of formal errors. Thus, for the combination of troposphere parameters a combination of the two extensions of the stochastic model is recommended.     

Application of Spatial Database in Quantitative Analysis of Litho-Paleogeography—A Case Study of a Middle Ordovician Sequence Interval in the Ordos Basin

In the study of sequence stratigraphy and litho-paleogeography,quantitative analysis,precise calculation and detailed comparison of tremendous geological data,such as field profiles,logging records and seismic curves from different areas,are the basic requirements.In order to obtain a more reliable and precise result,this paper presents a novel method that combines spatial database analysis with the single-factor mapping technology to establish sequence stratigraphical succession and to map the Ordo-vician litho-paleogeography of the Ordos Basin,one of the largest oil-gas bearing basins in North China Platform.By using this method,all of the related basic geological data can be quantitatively analyzed and effectively managed.Various attributes of the ba-sic stratigraphic units and their characters,such as sequence thickness,penecontemporaneous dolostone content,shallow water parget content,and terrigenous material content,can be fully utilized statistically in facies analysis and in mapping.Based on this analysis,this paper has be exerted single-factor isopachous mapping quantitatively for each of the Ordovician sequences in the ba-sin,and finally synthesized multiple factors to reconstruct the litho-paleogeography for each of the sequence intervals.The study shows that the proposed method is quite effective and has a much higher resolution in recognizing litho-paleogeographic subunits compared with traditional ways.For example,in one of the Middle Ordovician sequence intervals(SQ19 in the Lower Majiagou Formation) of the Ordos Basin,the authors have successfully developed a mathematical formula to divide the distribution of various facies units substantially,such as old lands,submarine uplifts,supratidal zones,intertidal zones and subtidal zones.     

Precise ionosphere-free single-frequency GNSS positioning

Ionospheric delays can be efficiently eliminated from single-frequency data using a combination of carrier phases and code ranges. Unfortunately, GPS and GLONASS ranges are relatively noisy which can limit the use of the positioning method. Nevertheless, position standard deviations are in the range of 6–8 cm (horizontal) and 7–9 cm (3d) obtained from diurnal data batches from selected IGS reference stations can be further reduced to 2–3 cm (3d) for weekly smoothed averages. GPS data sets collected in Ghana (Africa) reveal a typical level of 10 cm of deviation that must be anticipated under average conditions. Looking at the future of GNSS, the European Galileo system will, in contrast to GPS, provide the broadband signal E5 that is by far less affected by multipath thus providing rather precise range measurements. Simulated processing runs featuring both high ionospheric and tropospheric delay variations show a 3d position precision of 4 cm even for a data batch as short as just 1 h, whereas GPS L1/Galileo E1 performance is close to 13 cm for the same data set.