bluuebunny/crossref_metadata_2025 huggingface dataset:
Climatology Of Thin Cirrus Clouds at Gadanki (13.5°N, 79.2°E) Using Ground Based Lidar And Satellite Based Measurements
Authors: Motty G. S. , Jayeshlal G. S. , Satyanarayana M.
Published: November 2014

Abstract. High altitude cirrus clouds play a significant role in the radiative balance of Earth atmosphere system. Information on cirrus occurrences and their optical properties is essential for climate modeling studies. The influence of high altitude thin cirrus clouds on the climate is important due to their optical and thermodynamic properties. In order to quantify their effect on atmosphere, the vertical structure and optical properties of these thin cirrus clouds are to be characterized. The Lidar technique has become a unique tool for detecting and characterizing cirrus clouds for their optical properties. Ground based LIDAR system offers an excellent way to obtain characteristic values on the cirrus formations, although the microphysical and optical properties of thin cirrus clouds can also obtained on global scale by the observations from Earth-orbiting Satellites .The ground-based lidar observations could provide more intensive measurements on continuous basis, compared to satellite observations. Utilising observations from both, the statistical characteristics, physical and optical properties of thin cirrus clouds can be retrieved more precisely. The present study is based on the ground based lidar measurements using the pulsed monostatic LIDAR system at the National Atmospheric Research Laboratory [NARL], Gadanki (13.5° N, 79.2° E), Andhra Pradesh, India. The data obtained in the altitude range of 8−20 km are used for this study. Cirrus observations made using CALIPSO and MODIS satellites are compared with the ground based lidar data for systematic statistical study of cirrus climatology. Optically thin cirrus clouds (τ < 0.3) observed during 2009 are selected and their microphysical and geometrical properties are studied. The microphysical properties such as optical depth, lidar ratio and depolarisation ratio for cirrus clouds were obtained. It is observed that the variability in optical depth depends on the composition and thickness of the clouds. The relationships between various quantities were also processed. The study shows that the thin cirrus generally was present in higher altitudes and the optical properties show correlation with the height and the temperature.
Evaluation of NOAA Carbon Tracker Global Carbon Dioxide Products
Authors: Nayak R. K. , Deepthi E. N. , Dadhwal V. K. , Rao K. H. , Dutt C. B. S.
Published: November 2014

Abstract. Inter-comparison between National Oceanic and Atmospheric Administration Carbon Tracker (NOAACT) CO2 with satellite observations were carried out in this study. The satellite observations used here are mid troposphere CO2 based on Atmosphere Infrared Sounder (AIRS) on board NASA’s Aqua and lower troposphere CO2 based on Greenhouse-gas Observing Satellite (GOSAT) of Japanese Aerospace Exploration Agency (JAXA). There exists good agreement between the seasonal cycles as estimated by NOAACT and Satellite observations. The mid troposphere CO2 exhibits distinct annual cycle in the northern hemisphere with positive detrended value during January–June and negative values during July–December. In the southern hemisphere, the annual cycle is less prominent and opposite phase with respect to the northern hemisphere. The lower tropospheric CO2 in both the hemispheres exhibits mixed signature of annual and semi-annual cycle. The amplitudes of the variability are significantly larger in the northern hemisphere than the southern hemisphere. The inter-annual variability of annual growth rates from the NOAACT is comparable with satellite observations however NOAACT could not resolved the spatial patterns of long-term growth rate as observed in the satellite observations.
Estimation of Net Radiation using satellite based data inputs
Authors: Sai Krishna S. V. S. , Manavalan P. , Rao P. V. N.
Published: November 2014

Abstract. Daily net surface radiation fluxes are estimated for Indian land mass at spatial grid intervals of 0.1 degree. Two approaches are employed to obtain daily net radiation for four sample days viz., November 19, 2013, December 16, 2013, January 8, 2014 and March 20, 2014. Both the approaches compute net shortwave and net longwave fluxes, separately and sum them up to obtain net radiation. The first approach computes net shortwave radiation using daily insolation product of Kalpana VHRR and 15 days time composited broadband albedo product of Oceansat OCM2. The net outgoing longwave radiation is computed using Stefan Boltzmann equation corrected for humidity and cloudiness. In the second approach, instantaneous clear-sky net-shortwave radiation is estimated using computed clear-sky incoming shortwave radiation and the gridded MODIS 16-day time composited albedo product. The net longwave radiation is obtained by estimating outgoing and incoming longwave radiation fluxes, independently. In this, MODIS derived surface emissivity and skin temperature parameters are used for estimating outgoing longwave radiation component. In both the approaches, surface air temperature data required for estimation of net longwave radiation fluxes are extracted from India Meteorological Department’s (IMD) Automatic Weather Station (AWS) records. Estimates by the two different approaches are evaluated by comparing daily net radiation fluxes with CERES based estimates corresponding to the sample days, through statistical measures. The estimated all sky daily net radiation using the first approach compared well with CERES SYN1deg daily average net radiation with r2 values of the order of 0.7 and RMS errors of the order of 8–16 w/m2.
Temporal changes in semivariogram of ocean surface latent heat flux under linear trend
Authors: Singh M. K. , Venkatachalam P.
Published: November 2014

Abstract. One of the ways to study spatio-temporal variability of a process is to consider it as a temporal variation of a spatial process. Semivariogram is a measure of spatial variation in a process. If a process is undergoing a linear trend, then semivariogram parameters such as range, sill and nugget are bound to change. In this paper, a mathematical closed form of range, sill, and nugget and in turn semivariogram were expressed for a process under linear trend. The derived semivariogram was used to study the latent heat flux (LHF) over the Indian Ocean. LHF values depend on sea surface temperature (SST) and wind speed (WS) over ocean surface. Universal kriging (UK) was used to estimate the LHF with WS and SST as covariables. UK coefficients corresponding to covariables were found out for the years 2010, 2020, 2030, 2040 and 2050. In similar line, study has been attempted to see how empirical orthogonal function modes of a spatio-temporal process change with time under linear trend.
Mapping of coupling hot spots of satellite derived latent heat flux in indian agro-climatic regions
Authors: Choudhury I.
Published: November 2014

Abstract. This study focuses on the understanding and mapping of coupling hotspots of LE versus terrestrial and meteorological parameters. Single source surface energy balance model was used to derive surface energy balance parameters. Agro climatic region wise monthly information of terrestrial, energy balance and meteorological parameters were derived during June- September from decadal analysis of MODIS data (2003–2012) over India (68–100°E, 5–40°N) at 5 km spatial resolution. Information on rainfall was obtained from gridded rainfall data (1° × 1° spatial resolution) from Indian Meteorological Department (IMD). The spatiotemporal variability of the parameters such as rainfall, evapotranspiration (ET), evaporative fraction (EF), soil water index (SWI), land surface temperature (LST) and air temperature (Ta) showed strong influence on seasonal LE fluctuation. LE showed positive linear coupling with ET (0.52
Assessment of slope stability using PS-InSAR technique
Authors: Dwivedi R. , Varshney P. , Tiwari A. , Singh A. K. , Dikshit O.
Published: November 2014

Abstract. In this research work, PS-InSAR approach is envisaged to monitor slope stability of landslides prone areas in Nainital and Tehri region of Uttarakhand, India. For the proposed work, Stanford Method for Persistent Scatterers (StaMPS) based PS-InSAR is used for processing ENVISAT ASAR C-Band data stacks of study area which resulted in a time series 1D-Line of Sight (LOS) map of surface displacement. StaMPS efficiently extracted the PS pixels on the unstable slopes in both areas and the time series 1D-LOS displacement map of PS pixels indicates that those areas in Nainital and Tehri region have measurement pixels with maximum displacement away from the satellite of the order of 22 mm/year and 17.6 mm/year respectively
Evaluation of TRMM Precipitation Products over Indian Subcontinent
Authors: Indu J. , Nagesh Kumar D.
Published: November 2014

Abstract. Quantitatively accurate estimates of precipitation at high spatial resolutions often rely on a combination of individual satellite retrieved estimates merged using suitable techniques. Precipitation estimates from these merged data products are extensively being used by scientists and researchers for various hydro meteorological and climatological applications. The intent of this study is to evaluate the performance of gridded precipitation estimates from Tropical Rainfall Measuring Mission (TRMM) namely the 3A12 (from passive radar TRMM Microwave Imager i.e. TMI), 3A25 (from active radar Precipitation Radar i.e., PR) and 3B43 (combined data product). For this purpose, the present study utilizes 6 years of daily rainfall of spatial resolution 1° × 1°. Data period chosen is the Indian summer monsoonal months of JJAS from June 2002 to September 2007. Results indicate that over India, the TMI derived 3A12 product tends to overestimate rainfall compared to the PR derived 3A25 product for the seasonal data period. The 3A12 data product represents gridded monthly accumulations of the level 2 TMI derived 2A12 data product. The land rainfall algorithm of 2A12 is well known to be dependent on the 85 GHz high frequency channel. The present study conducts sensitivity analysis of 20 combinations of TMI frequency channels with respect to rainfall rate. The results conclude that a combination of TMI low frequency channels are better suited to model rainfall thereby questioning the usage of 85 GHz frequency channel for the generation of 2A12 rainfall estimates.
Validation of Satellite Soil Moisture Retrievals using Precipitation Records in India
Authors: Karthikeyan L. , Nagesh Kumar D.
Published: November 2014

Abstract. Soil moisture plays crucial role in influencing the components of hydrologic cycle and thus used for large range of applications such as climate predictions, agriculture management and flood/drought modelling. The current work focuses on establishing a measure to check the performance of passive microwave satellite soil moisture data using rainfall information over India. The measure is developed based on the concepts of information theory and copulas. Two soil moisture products developed by, VUA-NASA (jointly by Vrije Universiteit Amsterdam and NASA) and university of Montana are tested with the proposed measure using IMD rainfall data at 0.25° latitude-longitude spatial resolution. The measure conveyed that soil moisture product by university of Montana has outperformed over its counterpart. Further analysis concluded that under moderate climate conditions, Montana product could be used for analysis whereas for study in extreme weather conditions it may be necessary to check the usefulness of VUA-NASA product.
Assessing Agricultural Vulnerability in India using NDVI Data Products
Authors: Kaushalya R. , Praveen Kumar V. , Shubhasmita S.
Published: November 2014

Abstract. Impact of climate change on Indian rainfed agriculture was assessed using temporal NDVI data products from AVHRR and MODIS. Agricultural vulnerability was analysed using CV of Max NDVI from NOAA-AVHRR (15-day, 8 km) and MODIS-TERRA (16-day, 250 m) NDVI data products from 1982–2012. AVHRR dataset was found suitable for estimating regional vulnerability at state and agro-eco-sub-region (AESR) level while MODIS dataset was suitable for drawing district-level strategy for adaptation and mitigation. Methodology was developed to analyse NDVI variations with spatial pattern of rainfall using 10 X 10 girded data and spatially interpolating it to estimate Standard Precipitation Index. Study indicated large variations in vegetation dynamics across India owing to bio-climate and natural resource base. IPCC framework of vulnerability and exposure was used to identify vulnerable region extending from arid western India to semi-arid and dry sub-humid regions in central India and southern peninsula. This is a major agricultural region in the country with sizable human and livestock population with millions of marginal and small farm holdings. Exposure to climatic variability at local and regional levels have national implications and study indicated that over 122 districts extending over 110 mha was vulnerable to climate change that spread across 26 typical AESR in 11 states in India. Of the 74 mha under agriculture in the region, MODIS dataset indicated 47 mha as agriculturally vulnerable while coarser resolution of AVHRR dataset indicated a conservative estimate of 29 mha. First ever estimates of agricultural vulnerability for India indicates 20.4 to 33.1 % agricultural land under risk from climate change.
Energy Balance of Rural Ecosystems In India
Authors: Chhabra A. , Madhava Rao V. , Hermon R. R. , Garg A. , Nag T. , Bhaskara Rao N. , Sharma A. , Parihar J. S.
Published: November 2014

Abstract. India is predominantly an agricultural and rural country. Across the country, the villages vary in geographical location, area, human and livestock population, availability of resources, agricultural practices, livelihood patterns etc. This study presents an estimation of net energy balance resulting from primary production vis-a-vis energy consumption through various components in a "Rural Ecosystem". Seven sites located in different agroclimatic regions of India were studied. An end use energy accounting "Rural Energy Balance Model" is developed for input-output analysis of various energy flows of production, consumption, import and export through various components of crop, trees outside forest plantations, livestock, rural households, industry or trade within the village system boundary. An integrated approach using field, ancillary, GIS and high resolution IRS-P6 Resourcesat-2 LISS IV data is adopted for generation of various model inputs. The primary and secondary field data collection of various energy uses at household and village level were carried out using structured schedules and questionnaires. High resolution multi-temporal Resourcesat-2 LISS IV data (2013–14) was used for generating landuse/landcover maps and estimation of above-ground Trees Outside Forests phytomass. The model inputs were converted to energy equivalents using country-specific energy conversion factors. A comprehensive geotagged database of sampled households and available resources at each study site was also developed in ArcGIS framework. Across the study sites, the estimated net energy balance ranged from −18.8 Terra Joules (TJ) in a high energy consuming Hodka village, Gujarat to 224.7 TJ in an agriculture, aquaculture and plantation intensive Kollaparru village, Andhra Pradesh. The results indicate that the net energy balance of a Rural Ecosystem is largely driven by primary production through crops and natural vegetation. This study provides a significant insight to policy relevant recommendations for Energy Sustainable Rural India.
GIS based 3D visualization of subsurface and surface lineaments / faults and their geological significance, northern tamil nadu, India
Authors: Saravanavel J. , Ramasamy S. M.
Published: November 2014

Abstract. The study area falls in the southern part of the Indian Peninsular comprising hard crystalline rocks of Archaeozoic and Proterozoic Era. In the present study, the GIS based 3D visualizations of gravity, magnetic, resistivity and topographic datasets were made and therefrom the basement lineaments, shallow subsurface lineaments and surface lineaments/faults were interpreted. These lineaments were classified as category-1 i.e. exclusively surface lineaments, category-2 i.e. surface lineaments having connectivity with shallow subsurface lineaments and category-3 i.e. surface lineaments having connectivity with shallow subsurface lineaments and basement lineaments. These three classified lineaments were analyzed in conjunction with known mineral occurrences and historical seismicity of the study area in GIS environment. The study revealed that the category-3 NNE-SSW to NE-SW lineaments have greater control over the mineral occurrences and the N-S, NNE-SSW and NE-SW, faults/lineaments control the seismicities in the study area.
Mineralogical characterization of Juventae Chasma, Mars: evidences from MRO-CRISM
Authors: Singh M. , Rajesh V. J.
Published: November 2014

Abstract. Juventae Chasma is a long depression associated with Valles Marineris and this study discusses the spectral observation made after analysing the Mars Reconnaissance Orbiter (MRO) Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) Full Resolution targeted (FRT) Images in the wavelength region of 1000–2600 nm. Observations reveal the presence of hydrous sulphates, pyroxenes (mafic minerals) and silica rich minerals, more likely opaline silica or glass. Hydrous mineral identified shows the absorption bands, which are more consistent with gypsum. Absorption bands at 2300–2350 nm, and 2500–2550 nm, which are characteristics of carbonates, have been also identified in the region. Presence of this mineral assemblage, confirmed by the observed spectral signatures ranging from volcanic to sedimentary origin, implies a relatively geologically active area, and a significant region in terms of aqueous activity.
Hydat-A Hyperspectral Data Processing Tool for Field Spectroradiometer Data
Authors: Singh S. , Dutta D. , Singh U. , Sharma J. R. , Dadhwal V. K.
Published: November 2014

Abstract. A hyperspectral data processing tool "HyDAT" has been developed in MATLAB environment for processing of Field Spectroradiometer data for vegetation studies. Several basic functions e.g. data visualization, pre-processing, noise removal and data transformation and features like automatic absorption feature recovery and their characterization have been introduced. A new concept of spectral geometry has been included as a separate module which is conceptualized as triangle formed over spectral space joining the vertices of green reflectance peak, red well and inflection point and is extremely useful for vegetation health analysis. A large variety of spectral indices both static and dynamic, have been introduced which is useful for remote estimation of foliar biochemicals. Keeping in view the computational requirement, MATLAB was used in the programming environment. It has various in-built functions for statistical and mathematical analysis, signal processing functions like FFT (Fast Fourier Transform), CWT (Continuous Wavelet Transform), direct smoothing function for moving average, Savitzky-Golay smoothing technique, etc. which can be used with ease for the signal processing and field data analysis. FSF (Field Spectroscopy Facility) Post processing Toolbox can also be freely downloaded and can be used for the direct importing and pre-processing of Spectroradiometer data for detector overlap correction, erroneous water band removal and smoothing. The complete package of the software has been bundled for standalone application of shared libraries with additional files for end users. The software is powered by creation of spectral library and customized report generation. An online help menu guides the user for performing different functions. The tool is capable of reducing the time required for processing field based hyperspectral data significantly and eliminate the need for different software to process the raw data and spectral features extraction.
Origin of collapsed pits and branched valleys surrounding the Ius chasma on Mars
Authors: Vamshi G. T. , Martha T. R. , Vinod Kumar K.
Published: November 2014

Abstract. Chasma is a deep, elongated and steep sided depression on planetary surfaces. Several hypothesis have been proposed regarding the origin of chasma. In this study, we analysed morphological features in north and south of Ius chasma. Collapsed pits and branched valleys alongwith craters are prominent morphological features surrounding Ius Chasma, which forms the western part of the well known Valles Marineris chasma system on Martian surface. Analysis of images from the High Resolution Stereo Camera (HRSC) in ESA’s Mars Express (MEX) with a spatial resolution of 10 m shows linear arrangement of pits north of the Ius chasma. These pits were initially developed along existing narrow linear valleys parallel to Valles Merineris and are conical in shape unlike flat floored impact craters found adjacent to them. The width of conical pits ranges 1–10 km and depth ranges 1–2 km. With more subsidence, size of individual pits increased gradually and finally coalesced together to create a large depression forming a prominent linear valley. Arrangement of pits in this particular fashion can be attributed to collapse of the surface due to l arge hollows created in the subsurface because of the withdrawal of either magma or dry ice. Branched valleys which are prominent morphologic features south of the Ius chasma could have been formed due to groundwater sapping mechanism as proposed by previous researchers. Episodic release of groundwater in large quantity to the surface could have resulted in surface runoff creating V-shaped valleys, which were later modified into U-shaped valleys due to mass wasting and lack of continued surface runoff.
Dynamics of surface melting over Amery and Ross ice shelf in Antarctic using OSCAT data
Authors: Bothale R. V. , Rao P. V. N. , Dutt C. B. S. , Dadhwal V. K.
Published: November 2014

Abstract. Antarctic sea ice sheets play an important role in modulating the climate system. The present study investigates the dynamics of melt/freeze over Amery and Ross ice shelf located in Eastern and Southern part of continent using OSCAT, the microwave scatterometer data from OCEANSAT2. The study utilizes the sensitivity of backscatter coefficient values of scatterometer data to presence of liquid water in the snow caused due to melt conditions. The analysis carried out for four austral winters from 2010–2013 and five austral summer from 2009–2014 showed spatial and temporal variations in average backscatter coefficient over Amery and Ross shelf areas. A dynamic threshold based on the austral winter mean and standard deviation of HH polarization is considered for pixel by pixel analysis for the shelf area. There is significant spatio-temporal variability in melt extent, duration and melt index as observed in the analysis. Spatially, the melt over Amery shelf moves from South to North along coast and West towards inner shelf area. Maximum mean melt occurs on 9th January with January 1–15 fortnight accounting for 80 % of the melt. Extreme low melt conditions were observed during summer 2010–11 and 2011–12 indicating cold summer. Summer 2012–13 and 2013–14 were warm summer. Year 2014 experienced melt only in the month of January with entire shelf under melt conditions. Practically no melt was observed over Ross ice shelf.
Geodynamics of the Indian Lithospheric Plate relative to the neighbouring Plates as revealed by Space Geodetic Measurements
Authors: Krishna S. , Mathew J. , Majumdar R. , Roy P. , Vinod Kumar K.
Published: November 2014

Abstract. The Indian Plate is highly dynamic in nature which in turn makes the Indo-Eurassian collision zone the foci of most of the historic large magnitude earthquakes. Processing of positional information from continuously observing reference stations is one of the space based geodetic techniques used globally and nationally to understand the crustal dynamics. The present study evaluates the dynamic nature of the Indian plate relative to its adjoining plates using the permanent GPS data (2011 to 2013) of 12 International GNSS Service (IGS), which are spread across the Indian, Eurassian, Australian, Somaliyan and African plates. The data processing was carried out using GAMIT/GLOBK software. The results indicate that the average velocity for the two IGS stations on the Indian Plate (Hyderabad and Bangalore) is 54.25 mm/year towards NE in the ITRF-2008 reference frame. The relative velocity of various stations with respect to the Indian plate has been estimated using the Bangalore station and has been found that the stations in the Eurasian plate (Lhasa, Urumqi, Bishkek and Kitab) are moving with velocity ranging from 25 to 33 mm/year in the SE direction resulting in compressional interaction with the Indian plate. This study reveals and confirms to the previous studies that the Indian- Eurassian-Australian Plates are moving at different relative velocities leading to compressional regimes at their margins leading to seismicity in these zones.
Spatio-temporal assessment of ecological disturbance and its intensity in the Mangrove forest using MODIS derived disturbance index
Authors: Dutta D. , Das P. K. , Paul S. , Sharma J. R. , Dadhwal V. K.
Published: November 2014

Abstract. The mangrove ecosystem of Sundarbans region plays an important ecological and socio-economical role in both India and Bangladesh. The ecological disturbance in the coastal mangrove forests are mainly attributed to the periodic cyclones caused by deep depression formed over the Bay of Bengal. In the present study, three of the major cyclones in the Sundarbans region were analyzed to establish the cause-and-effect relationship between cyclones and the resultant ecological disturbance. The Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data was used to generate MODIS global disturbance index (MGDI) and its potential was explored to assess the instantaneous ecological disturbance caused by cyclones with varying landfall intensities and at different stages of mangrove phenology. The time-series MGDI was converted into the percentage change in MGDI using its multi-year mean for each pixel, and its response towards several cyclonic events was studied. The affected areas were identified by analyzing the Landsat-8 satellite data before and after the cyclone and the MGDI values of the affected areas were utilized to develop the threshold for delineation of the disturbed pixels. The selected threshold was applied on the time-series MGDI images to delineate the disturbed areas for each year individually to identify the frequently disturbed areas. The classified intensity map could able to detect the chronically affected areas, which can serve as a valuable input towards modelling the biomigration of the invasive species and efficient forest management.
Study of temporal variation of vegetation indices and phenology of tropical deciduous broadleaf forest in eastern India
Authors: Gupta U.
Published: November 2014

Abstract. In the recent years study of vegetation variation has taken significance as a quintessential part of a bigger research agenda of climate change. This study has attempted to (a) Analyse the inter-annual and seasonal variation of vegetation (b) Observe the variation in phenological transition dates of tropical deciduous broadleaf forest (DBF) in Eastern India from 2003 to 2012. The study was conducted on 1500 sq. km area of dense forest in the districts of West Singhbhum and Sundargarh. MODIS EVI data of 250 meter resolution was used. Land cover mask was used to study only the DBF pixels. Least squares convolution method proposed by Savitzky and Golay was used for noise reduction and smoothing. For the determination of phenological parameters iterative Savitzky-Golay method followed by threshold method was used in TIMESAT software. The results showed that (i) EVI varied between 0.41 in early April and 0.71 in mid October (ii) The overall trend was decreasing with a slope of 0.0022, representing a degradation trend (iii) EVI of summer season was found to be more stable than of rainy and winter season (iv) Summer and rainy months showed a decreasing trend whereas the winter months showed an overall increase (v) Day of start of season varied between 2nd May and 20th June whereas the day of end of season varied between 1st February and 7th March (vi) Length of season was longest for 2007 at 302 days and shortest for 2010 at 235 days and showed an overall decreasing trend.
Integration of multispectral satellite and hyperspectral field data for aquatic macrophyte studies
Authors: John C. M. , Kavya N.
Published: November 2014

Abstract. Aquatic macrophytes (AM) can serve as useful indicators of water pollution along the littoral zones. The spectral signatures of various AM were investigated to determine whether species could be discriminated by remote sensing. In this study the spectral readings of different AM communities identified were done using the ASD Fieldspec® Hand Held spectro-radiometer in the wavelength range of 325–1075 nm. The collected specific reflectance spectra were applied to space borne multi-spectral remote sensing data from Worldview-2, acquired on 26th March 2011. The dimensionality reduction of the spectro-radiometric data was done using the technique principal components analysis (PCA). Out of the different PCA axes generated, 93.472 % variance of the spectra was explained by the first axis. The spectral derivative analysis was done to identify the wavelength where the greatest difference in reflectance is shown. The identified wavelengths are 510, 690, 720, 756, 806, 885, 907 and 923 nm. The output of PCA and derivative analysis were applied to Worldview-2 satellite data for spectral subsetting. The unsupervised classification was used to effectively classify the AM species using the different spectral subsets. The accuracy assessment of the results of the unsupervised classification and their comparison were done. The overall accuracy of the result of unsupervised classification using the band combinations Red-Edge, Green, Coastal blue & Red-edge, Yellow, Blue is 100%. The band combinations NIR-1, Green, Coastal blue & NIR-1, Yellow, Blue yielded an accuracy of 82.35 %. The existing vegetation indices and new hyper-spectral indices for the different type of AM communities were computed. Overall, results of this study suggest that high spectral and spatial resolution images provide useful information for natural resource managers especially with regard to the location identification and distribution mapping of macrophyte species and their communities.
Comparison of Bioclimatic, NDVI and Elevation variables in assessing extent of Commiphora wightii (Arnt.) Bhand.
Authors: Kulloli R. N. , Kumar S.
Published: November 2014

Abstract. Commiphora wightii (Arnt.) Bhand., is an important medicinal plant of Indian Medicine System (IMS) since ancient time. It is used in different ailments of obesity, arthritis, rheumatism and high cholesterol. Due to overexploitation its natural populations declined to large extent. IUCN has put it under Data Deficient (DD) category due to lack of data on its extent of occurrence in nature. Hence, the study was carried out using MaxEnt distribution modelling algorithm to estimate its geographic distribution and to identify potential habitats for its reintroduction. For modelling employed 68 presence locality data, 19 bioclimatic variables, Normalize Difference Vegetation Index (NDVI) and elevation data. These were tested for multicollinearity and those variables having r-value less than 0.8 were selected for further analysis, which was carried out in two ways i) Bioclimatic variables and elevation; ii) NDVI and elevation. Area Under the Curve (AUC) in both analysis was above 0.9 for all variables, indicating very high accuracy of prediction. Variables governing distribution of C. wightii in the analysis using bioclimatic and elevation data set are precipitation seasonality (56.6 %), annual precipitation (16.4 %) and elevation (14.7 %). Extent of occurrence of C.wightii predicted by model closely matched in the districts of Jaisalmer and Barmer. In the second analysis elevation (48.3 %), NDVI of June (11.1 %) and August (11.2 %) contributed for NDVI and Elevation data set. NDVI of June corresponds to its leafing phase while NDVI of August to flowering phase. Area of its occurrence predicted for NDVI and elevation data set are Bikaner, Churu, Jhunjhunun some part of Jodhpur which are completely sandy, where C. wightii is totally absent. Extent of occurrence was also validated in ground survey. Potential areas for its reintroduction were identified as Jaisalmer and Barmer districts in Indian arid zone.
Spatial and seasonal characterization of terrestrial biospheric carbon flux over India using GOSAT data
Authors: Pradhan R. , Goroshi S. K. , Singh R. P.
Published: November 2014

Abstract. Carbon plays a crucial role in determining the ecosystem balance and slight changes in its concentration in the atmosphere can have significant impacts. The launch of JAXA’s GOSAT (Greenhouse gases Observing SATellite) in 2009 has started a new era of high accuracy CO2 concentration and flux measurements from space borne sensors. This paper reports the spatial and temporal variability of terrestrial biospheric carbon fluxes over the agro-climatic zones of India derived using GOSAT data for the period June 2009 to October 2011. The country averaged biospheric carbon flux varied from −0.47 (October) to 0.37 (April) gC m−2 day−1. Maximum variability in fluxes was observed for the North-Eastern region (−2.18 to +1.38 gC m−2 day−1) whereas the dry region of Rajasthan showed extremely low values (−0.1 to +0.1 gC m−2 day−1). The temporal variation in flux values was compared to averaged NDVI for each zone and indicated that growing season corresponds to more sequestration of carbon from the atmosphere. We compared GOSAT derived biospheric flux with Carbon Tracker (CT) data and observed that the two values show good agreement for all months except June and July. This study provides new estimates of biospheric carbon flux using satellite data driven models to better understand the carbon dynamics associated with terrestrial biosphere over India.
Improved Classification of Mangroves Health Status Using Hyperspectral Remote Sensing Data
Authors: Vidhya R. , Vijayasekaran D. , Ahamed Farook M. , Jai S. , Rohini M. , Sinduja A.
Published: November 2014

Abstract. Mangrove ecosystem plays a crucial role in costal conservation and provides livelihood supports to humans. It is seriously affected by the various climatic and anthropogenic induced changes. The continuous monitoring is imperative to protect this fragile ecosystem. In this study, the mangrove area and health status has been extracted from Hyperspectral remote sensing data (EO- 1Hyperion) using support vector machine classification (SVM). The principal component transformation (PCT) technique is used to perform the band reduction in Hyperspectral data. The soil adjusted vegetation Indices (SAVI) were used as additional parameters. The mangroves are classified into three classes degraded, healthy and sparse. The SVM classification is generated overall accuracy of 73 % and kappa of 0.62. The classification results were compared with the results of spectral angle mapper classification (SAM). The SAVI also included in SVM classification and the accuracy found to be improved to 82 %. The sparse and degraded mangrove classes were well separated. The results indicate that the mapping of mangrove health is accurate when the machine learning classifier like SVM combined with different indices derived from hyperspectral remote sensing data.
Gaussian Kernel Based Classification Approach for Wheat Identification
Authors: Aggarwal R. , Kumar A. , Raju P. L. N. , Krishna Murthy Y. V. N.
Published: November 2014

Abstract. Agriculture holds a pivotal role in context to India, which is basically agrarian economy. Crop type identification is a key issue for monitoring agriculture and is the basis for crop acreage and yield estimation. However, it is very challenging to identify a specific crop using single date imagery. Hence, it is highly important to go for multi-temporal analysis approach for specific crop identification. This research work deals with implementation of fuzzy classifier; Possibilistic c-Means (PCM) with and without kernel based approach, using temporal data of Landsat 8- OLI (Operational Land Imager) for identification of wheat in Radaur City, Haryana. The multi- temporal dataset covers complete phenological cycle that is from seedling to ripening of wheat crop growth. The experimental results show that inclusion of Gaussian kernel, with Euclidean Norm (ED Norm) in Possibilistic c-Means (KPCM), soft classifier has been more robust in identification of the wheat crop. Also, identification of all the wheat fields is dependent upon appropriate selection of the temporal date. The best combination of temporal data corresponds to tillering, stem extension, heading and ripening stages of wheat crop. Entropy at testing sites of wheat has been used to validate the classified results. The entropy value at testing sites was observed to be low, implying lower uncertainty of existence of any other class at wheat test sites and high certainty of existence of wheat crop.
Desertification Change Analysis in Siwalik Hills of Haryana Using Geo-informatics
Authors: Arya V. S. , Singh H. , Hooda R. S. , Arya A. S.
Published: November 2014

Abstract. Desertification constitutes one of the international environment problems whose global importance has been recognized by the international community. Desertification is a problem that affects a number of regions of the world in the developed and developing countries. Desertification is even more closely associated with the development process insofar as it impacts on peoples livelihoods much more directly than other environmental problem. One of the central challenges of environment management in the coming years, the loss of productive land is of major concern in a world where hundreds millions of individuals already go hungry today. Availability of remote sensing data from earth observation satellite and GIS techniques has made it convenient to map and monitor land use /land cover of desertification areas. In the present study Desertification Change analysis in Panchkula district Haryana was carried out by using LISS-III satellite data of 2002 and 2011. The main objective of the study was to monitor the changes in degraded lands in the district. Onscreen digitization technique was followed to interpret the satellite data. The two dates maps were overlaid and changes in area under various degraded lands were calculated. It was observed that Total geographical area of under investigation is 1021.86 sq. km.
Geomorphic determinanats of landuse intensity
Authors: Chidi C. L.
Published: November 2014

Abstract. Increasing population growth and lacking of further spatial expansion of various landuse have compelled to look intensity dimension of landuse. Grid cell based data extraction can give unique character of each locality of small land unit area which is very suitable technique for data extraction in the hill areas like Nepal. Landform plays an important role determining landuse intensity. Freely available 30m resolution Aster GDEM image has been used to extract geomorphic variables. Landuse data has been taken from aerial photo 1996. Quantitative weight has been given to landuse categories on the basis input, output and ecological importance for human life. One hectare square grid cells have been generated to extract geomorphic and landuse intensity information. The result shows that the average intensity of landuse is highly related to summarized categories of each geomorphic variable. Graphical presentation and quantitative analysis have been used to identify the effect of geomorphic variables on land use intensity. Simple and multiple correlation and regression analysis shows that there is higher control of altitude, slope gradient and slope aspect on landuse intensity. The average landuse intensity is highly determined by geomorphic variable then in local level. Multiple correlation coefficients is found to be 0.42 and the adjusted R² representing coefficient of determination is 0.178 which is significant at more than 99 % of confidence level. So, it can be concluded that geomorphic factors have contributed 17.8 % of the total variation determining landuse intensity in the study area.
Object oriented classification of high resolution data for inventory of horticultural crops
Authors: Hebbar R. , Ravishankar H. M. , Trivedi S , Subramoniam S. R. , Uday R. , Dadhwal V. K.
Published: November 2014

Abstract. High resolution satellite images are associated with large variance and thus, per pixel classifiers often result in poor accuracy especially in delineation of horticultural crops. In this context, object oriented techniques are powerful and promising methods for classification. In the present study, a semi-automatic object oriented feature extraction model has been used for delineation of horticultural fruit and plantation crops using Erdas Objective Imagine. Multi-resolution data from Resourcesat LISS-IV and Cartosat-1 have been used as source data in the feature extraction model. Spectral and textural information along with NDVI were used as inputs for generation of Spectral Feature Probability (SFP) layers using sample training pixels. The SFP layers were then converted into raster objects using threshold and clump function resulting in pixel probability layer. A set of raster and vector operators was employed in the subsequent steps for generating thematic layer in the vector format. This semi-automatic feature extraction model was employed for classification of major fruit and plantations crops viz., mango, banana, citrus, coffee and coconut grown under different agro-climatic conditions. In general, the classification accuracy of about 75–80 per cent was achieved for these crops using object based classification alone and the same was further improved using minimal visual editing of misclassified areas. A comparison of on-screen visual interpretation with object oriented approach showed good agreement. It was observed that old and mature plantations were classified more accurately while young and recently planted ones (3 years or less) showed poor classification accuracy due to mixed spectral signature, wider spacing and poor stands of plantations. The results indicated the potential use of object oriented approach for classification of high resolution data for delineation of horticultural fruit and plantation crops. The present methodology is applicable at local levels and future development is focused on up-scaling the methodology for generation of fruit and plantation crop maps at regional and national level which is important for creation of database for overall horticultural crop development.
Texture Based Hyperspectral Image Classification
Authors: Kumar B. , Dikshit O.
Published: November 2014

Abstract. This research work presents a supervised classification framework for hyperspectral data that takes into account both spectral and spatial information. Texture analysis is performed to model spatial characteristics that provides additional information, which is used along with rich spectral measurements for better classification of hyperspectral imagery. The moment invariants of an image can derive shape characteristics, elongation, and orientation along its axis. In this investigation second order geometric moments within small window around each pixel are computed which are further used to compute texture features. The textural and spectral features of the image are combined to form a joint feature vector that is used for classification. The experiments are performed on different types of hyperspectral images using multi-class one-vs-one support vector machine (SVM) classifier to evaluate the robustness of the proposed methodology. The results demonstrate that integration of texture features produced statistically significantly better results than spectral classification.
Geo-information Based Spatio-temporal Modeling of Urban Land Use and Land Cover Change in Butwal Municipality, Nepal
Authors: Mandal U. K.
Published: November 2014

Abstract. Unscientific utilization of land use and land cover due to rapid growth of urban population deteriorates urban condition. Urban growth, land use change and future urban land demand are key concerns of urban planners. This paper is aimed to model urban land use change essential for sustainable urban development. GI science technology was employed to study the urban change dynamics using Markov Chain and CA-Markov and predicted the magnitude and spatial pattern. It was performed using the probability transition matrix from the Markov chain process, the suitability map of each land use/cover types and the contiguity filter. Suitability maps were generated from the MCE process where weight was derived from the pair wise comparison in the AHP process considering slope, land capability, distance to road, and settlement and water bodies as criterion of factor maps. Thematic land use land cover types of 1999, 2006, and 2013 of Landsat sensors were classified using MLC algorithm. The spatial extent increase from 1999 to 2013 in built up , bush and forest was observed to be 48.30 percent,79.48 percent and 7.79 percent, respectively, while decrease in agriculture and water bodies were 30.26 percent and 28.22 percent. The predicted urban LULC for 2020 and 2027 would provide useful inputs to the decision makers. Built up and bush expansion are explored as the main driving force for loss of agriculture and river areas and has the potential to continue in future also. The abandoned area of river bed has been converted to built- up areas.
Remote Sensing Based Methane Emission Inventory Vis-A-Vis Rice Cultural Types Of South Asia
Authors: Manjunath K. R. , More R. , Chauhan P. , Vyas A. , Panigrahy S. , Parihar J. S.
Published: November 2014

Abstract. Rice cultivation has been recognized as one of the major anthropogenic source for methane (CH4) emissions which is a microbial mediated anaerobic activity, mainly favoured by the flooded conditions in the rice fields. Information available on CH4 emission is limited, especially in a spatial domain, mainly because of the complexity involved in generating such data. The current approach demonstrates the use of secondary data sources available on the methane emission scaling factors, coupled with the information derived on rice cultural types and crop calendar. Methane emission from each type of rice field was firstly calculated by multiplying the emission factor by the corresponding cultivation area and length of cropping period. The values were then extrapolated over each country with respect to the rice area and crop duration for under each cultural type. The rice cultural type wise methane emission value for South Asia was derived by summation of individual emission values for the respective cultural type within each country. The total methane emission derived for South Asia region is (4.7817 Tg/yr). The mean methane emission estimates derived for each country are viz. India (3.3860 Tg/yr), Bangladesh (0.9136 Tg/yr), Pakistan (0.2675 Tg/yr), Sri Lanka (0.1073 Tg/yr) and Nepal (0.1074 Tg/yr). The derived methane emission estimates could be used to study the regional variations within the country and also to adopt the mitigation strategies to combat the high methane emission values within specific cultural type by means of altering the farming practise or water regime.