An iterative algorithm for removing the effect of thin cloud cover from LANDSAT imagery

In this paper, we propose an iterative algorithm for removing the effect of thin cloud cover from LANDSAT imagery. It is seen that the noise in such images is multiplicative as well as additive. The recorded image is first processed to update different parameters of the image formation model to known values. Processing algorithm and knowledge of parameter values are developed by considering the physics of the situation. A low-pass filter is then applied to the processed image to remove the effect of the cloud. The filter is of a tapered shape, and its parameters are adjusted to minimize the estimation error.     

Compressibility and Strength Behaviour of Sand–tyre Chip Mixtures

Scrap tyres can be shredded into chips and can easily be mixed with granular soils. To assess the behaviour of the admixtures, compressibility and triaxial compression tests were carried out by varying chip size and chip content. The results demonstrated that sand–tyre chip mixtures up to 20% could be a potential material for highway construction and embankment construction up to around 10 m height.     

Simulation–Regression Approximations for Value of Information Analysis of Geophysical Data

Value of information analysis is useful for helping a decision maker evaluate the benefits of acquiring or processing additional data. Such analysis is particularly beneficial in the petroleum industry, where information gathering is costly and time-consuming. Furthermore, there are often abundant opportunities for discovering creative information gathering schemes, involving the type and location of geophysical measurements. A consistent evaluation of such data requires spatial modeling that realistically captures the various aspects of the decision situation: the uncertain reservoir variables, the alternatives and the geophysical data under consideration. The computational tasks of value of information analysis can be daunting in such spatial decision situations; in this paper, a regression-based approximation approach is presented. The approach involves Monte Carlo simulation of data followed by linear regression to fit the conditional expectation expression that is needed for value of information analysis. Efficient approximations allow practical value of information analysis for the spatial decision situations that are typically encountered in petroleum reservoir evaluation. Applications are presented for seismic amplitude data and electromagnetic resistivity data, where one example includes multi-phase fluid flow simulations.     

Molecular composition and paleobotanical origin of Eocene resin from northeast India

The molecular composition of fossil resins from early to middle Eocene coal from northeast India, has been analyzed for the first time to infer their paleobotanical source. The soluble component of fossil resin was analyzed using gas chromatography–mass spectrometry (GC–MS). The resin extracts are composed of cadalene-based C₁₅ sesquiterpenoids and diagenetically altered triterpenoids. The macromolecular composition was investigated using pyrolysis gas chromatography-mass spectrometry (Py-GC–MS) and Fourier transform infrared (FTIR) spectroscopy. The major pyrolysis products are C₁₅ bicyclic sesquiterpenoids, alkylated naphthalenes, benzenes and a series of C₁₇–C₃₄ n-alkene-n-alkane pairs. Spectroscopic analysis revealed the dominance of aliphatic components. The presence of cadalene-based sequiterpenoids confirms the resin to be Class II or dammar resin, derived from angiosperms of Dipterocarpaceae family. These sesquiterpenoids are often detected in many SE Asian fluvio-deltaic oils. Dipterocarpaceae are characteristic of warm tropical climate suggesting the prevalence of such climate during early Eocene in northeast India.     

Analysis of water resources in the Mahanadi River Basin,India under projected climate conditions

The paper presents the outcomes of a study conducted to analyse water resources availability and demand in the Mahanadi River Basin in India under climate change conditions. Climate change impact analysis was carried out for the years 2000, 2025, 2050, 2075 and 2100, for the months of September and April (representing wet and dry months), at a sub‐catchment level. A physically based distributed hydrologic model (DHM) was used for estimation of the present water availability. For future scenarios under climate change conditions, precipitation output of Canadian Centre for Climate Modelling and Analysis General Circulation Model (CGCM2) was used as the input data for the DHM. The model results show that the highest increase in peak runoff (38%) in the Mahanadi River outlet will occur during September, for the period 2075–2100 and the maximum decrease in average runoff (32·5%) will be in April, for the period 2050–2075. The outcomes indicate that the Mahanadi River Basin is expected to experience progressively increasing intensities of flood in September and drought in April over the considered years. The sectors of domestic, irrigation and industry were considered for water demand estimation. The outcomes of the analysis on present water use indicated a high water abstraction by the irrigation sector. Future water demand shows an increasing trend until 2050, beyond which the demand will decrease owing to the assumed regulation of population explosion. From the simulated future water availability and projected water demand, water stress was computed. Among the six sub‐catchments, the sub‐catchment six shows the peak water demand. This study hence emphasizes on the need for re‐defining water management policies, by incorporating hydrological response of the basin to the long‐term climate change, which will help in developing appropriate flood and drought mitigation measures at the basin level. Copyright © 2008 John Wiley & Sons, Ltd.     

National Wheat Acreage Estimation for 1995-96 Using Multi-Date IRS-1C WiFS Data

I he aim of the piesent study was to suggest an approach for national level acreage estimation for wheat using satellite remote sensing data and demonstrate its perfromantee Multi-date moderate resolution (188 m) IRS-IC WiFS data sets were used as the core data Sampie segment approach with stratified random sampling was used for the data analysis For making meaningful comparisons over time, multi-date sets were geometncally registered and radiometrically normalised by extracting pseudo-invariant Features and performing regression analysis on the digital numbers of such features The corrected multi-temporal data sets were used in hierarchical classification scheme. The results of this exercise are presented. It appeals that there is an overestimatoin of wheat acreage The sampling effieieney was also low, indicating need to improve sampling strategy Some of the problems encountered and the corrections planned to overcome them are also discussed