Accuracy improvement of ASTER stereo satellite generated DEM using texture filter

The grid DEM(digital elevation model) generation can be from any of a number of sources:for instance,analogue to digital conversion of contour maps followed by application of the TIN model,or direct elevation point modelling via digital photogrammetry applied to airborne images or satellite images.Currently,apart from the deployment of point-clouds from LiDAR data acquisition,the generally favoured approach refers to applications of digital photogrammetry.One of the most important steps in such deployment i...     

Quasar–galaxy and galaxy–galaxy cross-correlations: model predictions with realistic galaxies

Several measurements of quasi-stellar object (QSO)–galaxy correlations have reported signals much larger than predictions of magnification by large-scale structure. We find that the expected signal depends strongly on the properties of the foreground galaxy population. On arcmin scales, it can be either larger or smaller by a factor of 2 for different galaxy types in comparison with a linearly biased version of the mass distribution. Thus the resolution of some of the excess measurements may lie in examining the halo occupation properties of the galaxy population sampled by a given survey; this is also the primary information such measurements will provide.
We use the halo model of clustering and simulations to predict the magnification-induced cross-correlations and errors for forthcoming surveys. With the full Sloan Digital Sky Survey, the statistical errors will be below 1 per cent for the galaxy–galaxy correlations and significantly larger for QSO–galaxy correlations. Thus accurate constraints on parameters of the galaxy halo occupation distribution can be obtained from small-scale measurements and on the bias parameter from large scales. Since the lensing-induced cross-correlation measures the first moment of the halo occupation number of galaxies, these measurements can provide the basis for interpreting galaxy clustering measurements that measure the second- and higher-order moments.     

Utility of Hyperspectral Data for Potato Late Blight Disease Detection

The study was carried out to investigate the utility of hyperspectral reflectance data for potato late blight disease detection. The hyperspectral data was collected for potato crop at different level of disease infestation using hand-held spectroradiometer over the spectral range of 325–1075 nm. The data was averaged into 10-nm wide wavebands, resulting in 75 narrowbands. The reflectance curve was partitioned into five regions, viz. 400–500 nm, 520–590 nm, 620–680 nm, 770–860 nm and 920–1050 nm. The notable differences in healthy and diseased potato plants were noticed in 770–860 nm and 920–1050 nm range. Vegetation indices, namely NDVI, SR, SAVI and red edge were calculated using reflectance values. The differences between the vegetation indices for plants at different levels of disease infestation were found highly significant. The optimal hyperspectral wavebands to discriminate the healthy plants from disease infested plants were 540, 610, 620, 700, 710, 730, 780 and 1040 nm whereas upto 25% infestation could be discriminated using reflectance at 710, 720 and 750 nm.     

Prediction of the Maximum Amplitude and Timing of Sunspot Cycle 24

Precursor techniques, in particular those using geomagnetic indices, often are used in the prediction of the maximum amplitude for a sunspot cycle. Here, the year 2008 is taken as being the sunspot minimum year for cycle 24. Based on the average aa index value for the year of the sunspot minimum and the preceding four years, we estimate the expected annual maximum amplitude for cycle 24 to be about 92.8±19.6 (1-sigma accuracy), indicating a somewhat weaker cycle 24 as compared to cycles 21 – 23. Presuming a smoothed monthly mean sunspot number minimum in August 2008, a smoothed monthly mean sunspot number maximum is expected about October 2012±4 months (1-sigma accuracy).     

Monitoring landuse and landcover changes using landsat images

It is well known that changes in landuse and landcover e.g., vegetation, forests, soils, water quality etc. have a great impact on the economic and social development of a region. In most of these cases, a knowledge of existing landuse and landcover and its rate of change is essential prior to any planning activity. Remote Sensing, particularly from a satellite platform is potentially an effective and economic means to collect the data and to monitor changes occurring in landuse categories. This paper deals with a study carried out in Dehradun - Roorkee region for preparation of the landuse and landcover maps and monitoring changes, if any, over a period of 5 years, from landsat images using Visual Interpretation Techniques..     

A circular statistical method for extracting rotation measures

We propose a new method for the extraction of Rotation Measures from spectral polarization data. The method is based on maximum likelihood analysis and takes into account the circular nature of the polarization data. The method is unbiased and statistically more efficient than the standard χ² procedure.     

Assessment of greenhouse gas emissions from coal and natural gas thermal power plants using life cycle approach

The importance of mitigation of climate change due to greenhouse gas (GHG) emissions from various developmental and infrastructure projects has generated interest at global level to reduce environmental impacts. Life cycle assessment may be used as a tool to assess GHG emissions and subsequent environmental impacts resulting from electricity generation from thermal power plants. This study uses life cycle approach for assessing GHG emissions and their impacts due to natural gas combined cycle (NGCC) and imported coal thermal power plants using the IPCC 2001 and Eco-Indicator 99(H) methods in India for the first time. The total GHG emission from the NGCC thermal power plant was 584 g CO₂ eq/kWh electricity generation, whereas in case of imported coal, it was 1,127 g CO₂ eq/kWh electricity generation. This shows that imported coal has nearly ~2 times more impacts when compared to natural gas in terms of global warming potential and human health as disability-adjusted life years from climate change due to GHG emissions such as carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O).     

New age constraints on the cooling and unroofing history of the Trans-Himalayan Ladakh Batholith (Kargil area), N. W. India

Thermotectonic history of the Trans-Himalayan Ladakh Batholith in the Kargil area, N. W. India, is inferred from new age data obtained here in conjunction with previously published ages. Fission-track (FT) ages on apatite fall around 20±2 Ma recording cooling through temperatures of ∼100°C and indicating an unroofing of 4 km of the Ladakh Range since the Early Miocene. Coexisting apatite and zircon FT ages from two samples in Kargil show the rocks to have cooled at an average rate of 5–6°C/Ma in the past 40 Ma. Zircon FT ages together with mica K−Ar cooling ages from the Ladakh Batholith cluster around 40–50 Ma, probably indicating an Eocene phase of uplift and erosion that affected the bulk of the batholith after the continental collision of India with the Ladakh arc at 55 Ma. Components of the granitoids in Upper Eocene-Lower Oligocene sediments of the Indus Molasse in Ladakh supports this idea. Three hornblende K−Ar ages of 90 Ma, 55 Ma, and 35 Ma are also reported; these distinctly different ages probably reflect cooling through 500–550°C of three phases of I-type plutonism in Ladakh also evidenced by other available radiometric data: 102 Ma (mid-Cretaceous), 60 Ma (Palaeocene), and 40 Ma (Late Eocene); the last phase being localised sheet injections. The geodynamic implications of the age data for the India-Asia collision are discussed.     

Snow and forest cover assessment of uttaranachal state using irs 1c wifs data

In this study, IRS 1C WiFS data have been used for the assessment of two natural resources i.e. forest cover and snow cover. These two resources have a great role to play in various hydrological studies such as floods, soil erosion and water pollution etc. Therefore their assessment is very useful in various hydrological studies and management of these resources. The assessment of snow and forest cover have been made on the basis of multispectral classification and classification of NDVI images. Newly created Uttaranchal state has been taken as the study area. These two resources have been estimated for all the thirteen districts of the state separately. The forest cover area estimated in this study is compared with the available data sets of Forest Survey of India (FSI). The estimated forest is 52%, whereas the forest cover reported by the FSI is 44.5% of the total geographical area of the state. The snow cover is estimated for the period after winter season i.e. maximum snow cover and before next winter season i.e. minimum snow cover. It is found that one quarter of the state is under snow cover covering six districts of the state. As such no estimate of snow cover at regional scale has been made so far therefore comparison of the present assessment could not be made.