Regional nitrogen cycle: an Indian perspective

During the past century through food and energy production, human activities have altered the world's nitrogen cycle by accelerating the rate of reactive nitrogen creation. India has made impressive strides in the agricultural front, in which N fertilizer plays a major role. There has been a marked change in the supply and use of land, water, fertilizers, seeds and livestock, but the N use efficiency remained at a low level. Exploring the nature of these changes and quantification of the impacts on the N cycle has become essential. Hence we have presented data on various N pools and fluxes based on a conceptual N model. In India, efforts should focus on understanding the fate and consequences of the applied N and to increase the efficiency of N use

Wheat crop inventory using high spectral resolution IRS-P3 MOS-B spectrometer data

Modular Optoelectronic Scanner (MOS-B) spectrometer data over parts of Northern India was evaluated for wheat crop monitoring involving (a) sub pixel wheat tractional area estimation using spectral unmixmg approach and (b) growth assessment b3 red edge shift at different phenological stages. Red shift of 10 nm was observed between crown root initiation stage to flowering stage. Wheat fraction estimates using linear spectral unmixing on Feb. 13. 1999 acquisition of MOS-B data bad high correlatiol7 {0.82) with estimates from Wide Field Sensor (WiFS) data acquired on same date by IRS-P3 platfonn. It was observed that live bands 14.5.8.12.13 MOS-B bands) are saffieient for signature separability of major land cover classes viz. wheat, urban, wasteland, and water based on purely spectral separability, criterion using Transformed Divergence (T.D.) approach. Higher number of bands saturated the T.D. values. [n contrast, performanee of sub pixel fractional area estimation using unmixing decreased drastically for eight bands (4.5.6,728.9. 12,13 MOS-B bands l chosen from optimal band selection criteria in comparison to full set of 13 bands. The relative deviation between area estimated from Wifs and MOS-B increased from 1.72 percent when all thirteen bands were used in unmixing to 26. I0 percent for the above eight bands

Future Indian earth observation systems

Indian Earth Observation (EO) capability has increased manifold since the launch of Bhasakra-I in 1979 to Cartosat-Z in 2007. Improvements are not only in spatial, spectral. temporal and radiometric resolutions but also in their coverage and value added products. It has also entered into the arena of passive and active microwave remote sensing. stereo viewing and viewing from the geo-synchronous platform at moderately high resolution. Observations specific to oceans and atmosphere are getting further emphasis. Demand for a constellation of satellites for monitoring disaster situations is strongly made. In this context, India has made extensive plans for continuity and enhancement in EO capability. not only towards its OWn national needs. but also as a contributing participant towards Global Earth Observation System of Systems (GEOSS). Major emphasis of the future plan has been to consolidate theme-specific satellites. in order to fill the gaps in observation including those for disaster monitoring and mitigation, and also to develop synergy with international missions for complementing and supplementing Indian missions. The future Indian EO systems include those for land applications-Resourcesat witli wide swath LlSS- Ill, high resolution Cartosat (0.3 m) and Imaging Radar (RISAT: C-band, multi- polarization). It also proposes to develop space based hyper-spectral sensor and atmospheric corrector. The future ocean application sensors include improved Ocean Color Monitor, Ku band scatterometer and a thermal IR sensor. The two major satellites dedicated for atmospheric observations are INSAT-3D with 6 channel imager and 19 channel sounder. and the ISRO-CNES joint venture Megha Tropiques with three sensors viz. MADRAS, SAPHIRE and ScaRab. Satellite for Argos and Ka band radio altimeter (SA RA I,). a joint ISRO-CNES mission is also underway. L-band polarimetric radiometer. hyper spectral sounder. rain radar, millimeter wave sounder, high resolution imager from geo-synchronous platform are some of the sensors being considered for future missions

Discrimination of maize crop with hybrid polarimetric RISAT1 data

Microwave remote sensing provides an attractive approach to determine the spatial variability of crop characteristics. Synthetic aperture radar (SAR) image data provide unique possibility of acquiring data in all weather conditions. Several studies have used fully polarimetric data for extracting crop information, but it is limited by swath width. This study aimed to delineate maize crop using single date hybrid dual polarimetric Radar Imaging Satellite (RISAT)-1, Fine Resolution Stripmap mode (FRS)-1 data. Raney decomposition technique was used for explaining different scattering mechanisms of maize crop. Supervised classification on the decomposition image discriminated maize crop from other land-cover features. Results were compared with Resourcesat-2, Linear Imaging Self Scanner (LISS)-III optical sensor derived information. Spatial agreement of 91% was achieved between outputs generated from Resourcesat-2, LISS-III sensor and RISAT-1 data

Procjena prizemnog neto Sunčevog zračenja iz podataka s tornja za mjerenje turbuletnih tokova iznad tropske šume mangrova u Sundarbanu, Zapadni Bengal

In this study, net surface radiation (Rn) was estimated using artificial neural network (ANN) and Linear Model (LM). Then, estimated Rn with both the models (ANN and LM) were compared with measured Rn from eddy covariance (EC) flux tower. The routinely measured meteorological variables namely air temperature, relative humidity and wind velocity were used as input to the ANN and global solar radiation as input to the LM. All the input data are from the EC flux tower. Sensitivity analysis of ANN with all the meteorological variables is carried out by excluding one by one meteorological variable. The validation results demonstrated that, ANN and LM estimated Rn values were in good agreement with the measured values, with root mean square error (RMSE) varying between 21.63 W/m2 and 34.94 W/m2, mean absolute error (MAE) between 17.93 W/m2 and 22.28 W/m2 and coefficient of residual mass (CRM) between –0.007 and –0.04 respectively. Further we have computed modelling efficiency (0.97 for ANN and 0.99 for LM) and coefficient of determination (R2 = 0.97 for ANN and 0.99 for LM) for both the models. Even though both the models could predict Rn successfully, ANN was better in terms of minimum number of routinely measured meteorological variables as input. The results of the ANN sensitivity analysis indicated that air temperature is the more important parameter followed by relative humidity, wind speed and wind direction.U ovom je istraživanju pomoću umjetnih neuronskih mreža (ANN) i linearnog modela (LM) procijenjeno prizemno neto Sunčevo zračenje (Rn). Potom su tako procjenjeni Rn iz oba modela (ANN i LM) uspoređeni s onima izmjerenim na tornju za mjerenje kovarijance turbuluentnih tokova (EC). Kao ulazni podaci u ANN korišteni su rutinski mjerene meteorološke varijable (temperatura zraka, relativna vlaga i brzina vjetra), a za LM globalno Sunčevo zračenje, koji su dobiveni na meteorološkom tornju za mjerenje turbulentnih tokova. Uslijedila je analiza osjetljivosti ANN s uključenim svim meteorološkim varijablama te su testirani ANN iz kojih su isključeni jedna po jedna meteorološka varijabla. Rezultati validacije pokazuju da se Rn procjenjeni pomoću ANN i LM dobro slažu s izmjerenim vrijednostima, pri čemu korijen srednje kvadratne pogreške (RMSE) varira između 21,63 W/m2 i 34,94 W/m2, srednja apsolutna pogreška (MAE) između 17,93 W/m2 i 22,28 W/m2, a koeficijent preostale mase (CRM) između –0,007 i –0,04 respektivno. Nadalje smo izračunali učinkovitost modeliranja (0,97 za ANN i 0,99 za LM) i koeficijente korelacije (R2 = 0,97 za ANN i 0,99 za LM). Iako su oba modela mogla uspješno predvidjeti Rn, ANN je bio bolji u smislu korištenja minimalnog broja rutinski izmjerenih meteoroloških varijabli kao ulaza. Rezultati analize osjetljivosti ANN pokazali su da je temperatura zraka najvažniji ulazni parametar, koju slijede relativna vlažnost te brzina i smjer vjetra

INTEGRATED FLOOD STUDY OF BAGMATI RIVER BASIN WITH HYDRO PROCESSING, FLOOD INUNDATION MAPPING & 1-D HYDRODYNAMIC MODELING USING REMOTE SENSING AND GIS

Flood is one of the most the most re-occurring natural hazard in the state of Bihar, as well as in India. The major rivers responsible for flood in the state of Bihar are Kosi, Gandak, Ghagra and Bagmati, which are the tributary rivers of Ganges. The head water catchment area of these rivers lies in the Himalayan state of Nepal. The high rainfall in Nepal, siltation of hydraulic structures, rivers and low topography of North Bihar causes flood occurrence in these areas on regular basis. Remote sensing and GIS plays an important role in mapping, monitoring and providing spatial database for all flood related studies. The present work focuses on the use remote sensing based topography and images in GIS environment for integrated flood study of Bagmati River, which is one of the most flood prone rivers of North Bihar. The Digital Elevation Model (DEM) from shuttle radar topography mission (SRTM) was used to create detailed sub-basin and river network map of entire Bagmati basin. The floods of July–August 2002 were mapped using RADARSAT-1 data using threshold based method. The SRTM DEM and ground based river cross-section from Dheng to Benibad stretch of Bhagmati River were used to create 1-dimensional hydrodynamic (1-D HD) model for simulating flood water level, discharge and flood inundation. Validation of simulated flood flows was done using observed water level of central water commission (CWC) from Dheng to Runisaidpur stations, with coefficient of correlation of 0.85. Finally, an integrated framework for flood modelling and management system is proposed

Analysis of parcel-based image classification methods for monitoring the activities of the Land Bank of Galicia (Spain)

[EN] The abandonment of agricultural plots entails a low economic productivity of the land and a higher vulnerability to wildfires and degradation of affected areas. In this sense, the local government of Galicia is promoting new methodologies based on high-resolution images in order to classify the territory in basic and generic land uses. This procedure will be used to control the sustainable management of plots belonging to the Land Bank. This paper presents an application study for maintaining and updating land use/land cover geospatial databases using parcel-oriented classification. The test is performed over two geographic areas of Galicia, in the northwest of Spain. In this region, forest and shrublands in mountain environments are very heterogeneous with many private unproductive plots, some of which are in a high state of abandonment. The dataset is made of high spatial resolution multispectral imagery, cadastral cartography employed to define the image objects (plots), and field samples used to define evaluation and training samples. A set of descriptive features is computed quantifying different properties of the objects, i.e. spectral, texture, structural, and geometrical. Additionally, the effect on the classification and updating processes of the historical land use as a descriptive feature is tested. Three different classification methodologies are analyzed: linear discriminant analysis, decision trees, and support vector machine. The overall accuracies of the classifications obtained are always above 90 % and support vector machine method is proved to provide the best performance. Forest and shrublands areas are especially undefined, so the discrimination between these two classes is low. The results enable to conclude that the use of automatic parcel-oriented classification techniques for updating tasks of land use/land cover geospatial databases, is effective in the areas tested, particularly when broad and well defined classes are required.The authors appreciate the collaboration and support provided by Xunta de Galicia, Sociedade para o Desenvolvemento Comarcal de Galícia, and Banco de Terras de Galicia. The financial support provided by the Spanish Ministerio de Ciencia e Innovación in the framework of the projects CGL2010-19591/BTE and CGL2009-14220 is also acknowledged.Hermosilla, T.; Díaz Manso, J.; Ruiz Fernández, LÁ.; Recio Recio, JA.; Fernández-Sarría, A.; Ferradáns Nogueira, P. (2012). Analysis of parcel-based image classification methods for monitoring the activities of the Land Bank of Galicia (Spain). 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ASSESSMENT OF INDIAN CARBON CYCLE COMPONENTS USING EARTH OBSERVATION SYSTEMS AND GROUND INVENTORY

Improved national carbon assessments are important for UNFCC communications, policy studies and improving the global assessment. Use of EO for land cover dynamics, forest type, cover and phytomass carbon density, productivity and related soil carbon density and regional extrapolation of point flux measurements. A National Carbon Project (NCP) under the Indian Space Research Organisation - Geosphere Biosphere Programme (ISRO – GBP) aims at improving the understanding and quantification of net carbon balance. The NCP has been implemented with three major components – (A) vegetation carbon pools, (B) Soil carbon pools and (C) Soil and Vegetation – Atmosphere Fluxes. A total of 6500 field plot data from forests and trees outside forests have been collected. 1500 field plots have been inventoried for the soil carbon based on the remotely sensed data stratification. A nationwide network of carbon flux towers in different ecosystems for the measurement and modeling of the net carbon flux using eddy covariance techniques is being established and upscaling using satellite remote sensing data and modelling is under process. The amplitude of the diurnal variation in NEE increased with growth of wheat and reached its peak around the pre-anthesis stage. Besides, under NCP, satellite diurnal CO2 have also analyzed the data obtained from AIRS and SCIAMACHY over India and surrounding oceans and was correlated with surface fluxes. The CASA model simulations over India using NOAA AVHRR NDVI