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961.
Multipath error is considered one of the major errors affecting GPS observations. One can benefit from the repetition of satellite
geometry approximately every sidereal day, and apply filtering to help minimize this error. For GPS data at 1 s interval processed
using a double-difference strategy, using the day-to-day coordinate or carrier-phase residual autocorrelation determined with
a 10-h window leads to the steadiest estimates of the error-repeat lag, although a window as short as 2 h can produce an acceptable
value with > 97% of the optimal lag’s correlation. We conclude that although the lag may vary with time, such variation is
marginal and there is little advantage in using a satellite-specific or other time-varying lag in double-difference processing.
We filter the GPS data either by stacking a number of days of processed coordinate residuals using the optimum “sidereal”
lag (23 h 55 m 54 s), and removing these stacked residuals from the day in question (coordinate space), or by a similar method
using double-difference carrier-phase residuals (observational space). Either method results in more consistent and homogeneous
set of coordinates throughout the dataset compared with unfiltered processing. Coordinate stacking reduces geometry-related
repeating errors (mainly multipath) better than carrier-phase residual stacking, although the latter takes less processing
time to achieve final filtered coordinates. Thus, the optimal stacking method will depend on whether coordinate precision
or computational time is the over-riding criterion. 相似文献
962.
K. R. Koch 《Journal of Geodesy》2007,81(9):581-591
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. 相似文献
963.
K. R. Manjunath Shibendu Shankar Ray Sushma Panigrahy 《Journal of the Indian Society of Remote Sensing》2011,39(4):599-602
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. 相似文献
964.
Sheshakumar K. Goroshi R. P. Singh S. Panigrahy J. S. Parihar 《Journal of the Indian Society of Remote Sensing》2011,39(3):315-321
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. 相似文献
965.
Comparison of Two Data Smoothing Techniques for Vegetation Spectra Derived From EO-1 Hyperion 总被引:1,自引:0,他引:1
Anshu Miglani Shibendu S. Ray D. P. Vashishta Jai Singh Parihar 《Journal of the Indian Society of Remote Sensing》2011,39(4):443-453
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. 相似文献
966.
R. Heinkelmann J. B?hm S. Bolotin G. Engelhardt R. Haas R. Lanotte D. S. MacMillan M. Negusini E. Skurikhina O. Titov H. Schuh 《Journal of Geodesy》2011,85(7):377-393
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. 相似文献
967.
Mandyam B. Rajani Satadru Bhattacharya A. S. Rajawat 《Journal of the Indian Society of Remote Sensing》2011,39(4):519-527
Talakadu is a well known historic place situated on bank of the river Cauvery in Mysore district of Karnataka. The place is
close to concave side of a prominent meander where large amount of sand has accumulated. It is believed that after construction
of a reservoir upstream, sand was exposed to wind action burying the structures of Ganga dynasty and other later kingdoms.
A number of buried sites have been identified by archaeological excavations conducted so far. Presently the area forms sand
dunes with thick plantation cover. Analysis of RADAR data (fine beam RADARSAT and ENVISAT ASAR) led to identifying a hitherto
unknown buried channel through the Old Talakadu town adjoining the excavated archaeological sites. The study suggests that
RADAR penetration through the plantation canopy seems to have occurred as observed by comparing with corresponding optical
data of LISS-IV. Below the canopy, sand and shrubs on top of the channel (topographically low area) are acting as smooth surface
providing dark tone on radar imagery. During field validation GPS was extensively used to navigate through the forest canopy
and locate the buried channel, excavated archaeological sites as well as other anomalous patterns. Synergistic application
of optical (RESOURCESAT-1 LISS-IV and CARTOSAT-1 & 2) and radar (fine beam RADARSAT and ENVISAT ASAR) data led to identifying
remote sensing based guides for archaeological exploration. Integration of known archaeological sites with the identified
anomalous patterns was done in GIS environment. This study adds on to the knowledge base of the site and compliments already
known information and suggested new areas for further archaeological exploration. 相似文献
968.
Sandip R. Oza R. K. K. Singh N. K. Vyas B. S. Gohil Abhijit Sarkar 《Journal of the Indian Society of Remote Sensing》2011,39(2):147-152
The identification of sea-ice has frequently been cited as one of the most important tasks for deriving the sea-ice parameters
and to avoid erroneous retrieval of wind vector over sea-ice infested oceans using space-borne scatterometer data. Discrimination
between sea-ice and ocean is ambiguous under the high wind and/or thin/scattered ice conditions. The pre-launch technique
developed for Oceansat-2, utilizes the dual-polarized QuikSCAT scatterometer data by using the spatio-temporal coherence properties
of sea ice in addition to backscatter coefficient and the Active Polarization Ratio. Results were compared with the operational
sea-ice products from National Snow and Ice Data Center. The threshold API value of −0.025 was found optimum for sea-ice and
ocean discrimination. The overall sea-ice identification accuracy achieved was of the order of 95 per cent, ranging from 92.5%
(during December in Southern Hemisphere) to 98% (during March in Northern Hemisphere). The applicability of the algorithm
for both the Arctic as well as Antarctic makes it suitable for its operational use with the Oceansat-2 scatterometer data. 相似文献
969.
Sushma Panigrahy Shibendu Shankar Ray K. R. Manjunath P. S. Pandey S. K. Sharma Anil Sood Manoj Yadav P. C. Gupta N. Kundu Jai Singh Parihar 《Journal of the Indian Society of Remote Sensing》2011,39(3):355-364
Cropping system study is not only useful to understand the overall sustainability of agricultural system, but also it helps
in generating many important parameters which are useful in climate change impact assessment. Considering its importance,
Space Applications Centre, took up a project for mapping and characterizing major cropping systems of Indo-Gangetic Plains
of India. The study area included the five states of Indo-Gangetic Plains (IGP) of India, i.e. Punjab, Haryana, Uttar Pradesh,
Bihar and West Bengal. There were two aspects of the study. The first aspect included state and district level cropping system
mapping using multi-date remote sensing (IRS-AWiFS and Radarsat ScanSAR) data. The second part was to characterize the cropping
system using moderate spatial resolution multi-date remote sensing data (SPOT VGT NDVI) and ground survey. The remote sensing
data was used to compute three cropping system performance indices (Multiple Cropping Index, Area Diversity Index and Cultivated
Land Utilization Index). Ground survey was conducted using questionnaires filled up by 1,000 farmers selected from 103 villages
based on the cropping systems map. Apart from ground survey, soil and water sampling and quality analysis were carried out
to understand the effect of different cropping systems and their management practices. The results showed that, rice-wheat
was the major cropping system of the IGP, followed by Rice-Fallow-Fallow and Maize-Wheat. Other major cropping systems of
IGP included Sugarcane based, Pearl millet-Wheat, Rice-Fallow-Rice, Cotton-Wheat. The ground survey could identify 77 cropping
systems, out of which 38 are rice-based systems. Out of these 77 cropping systems, there were 5 single crop systems, occupying
6.5% coverage (of all cropping system area), 56 double crop systems with 72.7% coverage, and 16 triple crop systems with 20.8%
coverage. The cropping system performance analysis showed that the crop diversity was found to be highest in Haryana, while
the cropping intensity was highest in Punjab state. 相似文献
970.
C. Jeganathan N.A.S. Hamm S. Mukherjee P.M. Atkinson P.L.N. Raju V.K. Dadhwal 《International Journal of Applied Earth Observation and Geoinformation》2011
Fine spatial resolution (e.g., <300 m) thermal data are needed regularly to characterise the temporal pattern of surface moisture status, water stress, and to forecast agriculture drought and famine. However, current optical sensors do not provide frequent thermal data at a fine spatial resolution. The TsHARP model provides a possibility to generate fine spatial resolution thermal data from coarse spatial resolution (≥1 km) data on the basis of an anticipated inverse linear relationship between the normalised difference vegetation index (NDVI) at fine spatial resolution and land surface temperature at coarse spatial resolution. The current study utilised the TsHARP model over a mixed agricultural landscape in the northern part of India. Five variants of the model were analysed, including the original model, for their efficiency. Those five variants were the global model (original); the resolution-adjusted global model; the piecewise regression model; the stratified model; and the local model. The models were first evaluated using Advanced Space-borne Thermal Emission Reflection Radiometer (ASTER) thermal data (90 m) aggregated to the following spatial resolutions: 180 m, 270 m, 450 m, 630 m, 810 m and 990 m. Although sharpening was undertaken for spatial resolutions from 990 m to 90 m, root mean square error (RMSE) of <2 K could, on average, be achieved only for 990–270 m in the ASTER data. The RMSE of the sharpened images at 270 m, using ASTER data, from the global, resolution-adjusted global, piecewise regression, stratification and local models were 1.91, 1.89, 1.96, 1.91, 1.70 K, respectively. The global model, resolution-adjusted global model and local model yielded higher accuracy, and were applied to sharpen MODIS thermal data (1 km) to the target spatial resolutions. Aggregated ASTER thermal data were considered as a reference at the respective target spatial resolutions to assess the prediction results from MODIS data. The RMSE of the predicted sharpened image from MODIS using the global, resolution-adjusted global and local models at 250 m were 3.08, 2.92 and 1.98 K, respectively. The local model consistently led to more accurate sharpened predictions by comparison to other variants. 相似文献