Nitrification in Kochi backwaters     

Joereen Miranda  K.K. Balachandran  R. Ramesh  Mohideen Wafar 《Estuarine, Coastal and Shelf Science》2008

Nitrification rates, as oxidation of ¹⁵N-labelled ammonium and loss of nitrite from N-Serve treated samples, were measured in Kochi backwaters during three seasons. Nitrification rates ranged from undetectable to 166 nmol N L⁻¹ h⁻¹ in the water column and up to 17 nmol N (g wet wt)⁻¹ h⁻¹ in sediments. Nitrification rates were higher in intermediate salinities than in either freshwater or seawater end. Within this salinity range, nitrification rates could be related to ammonium concentrations. As shown by the relation between ammonification and nitrification rates, it is also likely that nitrification is more regulated by renewal rates, rather than by in situ concentrations, of substrate. Among other environmental parameters, temperature and pH may have an influence on nitrification. Potential nitrification rates calculated from loss of nitrite from N-Serve treated, nitrite-enriched samples were about 800 nmol N L⁻¹ h⁻¹ in the water column and 40 nmol N (g wet wt)⁻¹ h⁻¹ in sediments. While these rates are in balance with those of biological ammonium production they may be inadequate to mitigate ammonium pollution in this estuary.  相似文献   

A global map of rainfed cropland areas (GMRCA) at the end of last millennium using remote sensing     

Chandrashekhar M. Biradar  Prasad S. Thenkabail  Praveen Noojipady  Yuanjie Li  Venkateswarlu Dheeravath  Hugh Turral  Manohar Velpuri  Murali K. Gumma  Obi Reddy P. Gangalakunta  Xueliang L. Cai  Xiangming Xiao  Mitchell A. Schull  Ranjith D. Alankara  Sarath Gunasinghe  Sadir Mohideen 《International Journal of Applied Earth Observation and Geoinformation》2009

The overarching goal of this study was to produce a global map of rainfed cropland areas (GMRCA) and calculate country-by-country rainfed area statistics using remote sensing data. A suite of spatial datasets, methods and protocols for mapping GMRCA were described. These consist of: (a) data fusion and composition of multi-resolution time-series mega-file data-cube (MFDC), (b) image segmentation based on precipitation, temperature, and elevation zones, (c) spectral correlation similarity (SCS), (d) protocols for class identification and labeling through uses of SCS R²-values, bi-spectral plots, space-time spiral curves (ST-SCs), rich source of field-plot data, and zoom-in-views of Google Earth (GE), and (e) techniques for resolving mixed classes by decision tree algorithms, and spatial modeling. The outcome was a 9-class GMRCA from which country-by-country rainfed area statistics were computed for the end of the last millennium. The global rainfed cropland area estimate from the GMRCA 9-class map was 1.13 billion hectares (Bha). The total global cropland areas (rainfed plus irrigated) was 1.53 Bha which was close to national statistics compiled by FAOSTAT (1.51 Bha). The accuracies and errors of GMRCA were assessed using field-plot and Google Earth data points. The accuracy varied between 92 and 98% with kappa value of about 0.76, errors of omission of 2–8%, and the errors of commission of 19–36%.  相似文献