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Remote sensing and GIS for integrated modeling and analysis for mapping of groundwater potential zones in Sarada River Basin, Visakhapatnam, India

100%

Kinthada N. R. , Gurram M. K.

tom 78

The study aimed at identifying and mapping groundwater potential zones in agricultural intensive Sarada river basin using Remote sensing and GIS technology. Zones of water potentiality were mapped integrating various information layers in GIS environment which eventually helped weighted modeling to arrive at the final outcome. Hydrogeomorphic units such as alluvial plains, valley fills, shallow weathered pediplains and deeply weathered pediplains were mapped. Eventually water potential zones in the basin were mapped and categorised them in to ‘excellent’, ‘good’, ‘moderate’ and ‘poor’. The study highlighted the effective use of Remote sensing and GIS technology for integrated analysis, identification and mapping of the groundwater potential zones in the Sarada river basin.

Accuracy of Sea Ice Data from Remote Sensing Methods, its Impact on Safe Speed Determination and Planning of Voyage in Ice-Covered Areas

84%

Pastusiak T.

tom Vol. 10, no. 2

229--248

The data related to ice floe concentration and ice thickness were analysed. Sources of data have been verified by visual observation and by comparison in between information from different remote sensing sources. The results of this work exceeded initial expectations. The discrepancies of the information provided by various data sources result from the error of the measurement method, which can be as high as 15% of the concentration of ice floes. It should also be borne in mind that the more generalized information about the state of the ice cover, the lower probability of detection of ice floe patches of a high concentration and spatial extent. Each vessel that is planning voyage in ice should take into consideration inaccurate estimation of concentration and thickness of ice floes received by means of satellite remote sensing methods. The method of determining permissible speed of various ice class vessel in ice on basis of safe speed graph for the icebreaker was developed. A well-defined equation approximates relationship between speed of the icebreaker and the vessels of specified ice classes. Average distance of 24.1 Nm from sea ice extent line was related to all analysed lines representing 30-40% ice floe concentration (IUP product excluded) and 30.6 Nm for analysed lines representing 70-81-91% ice floe concentration. The maximal average distance of the furthest analysed line (IUP product excluded) was equal 37.2 Nm. The average standard deviation of that results was equal 8.3 Nm only. Average distances of analysed lines from sea ice extent line to maximal ice data values were found as follow: 8.4 Nm (23%) for NSIDC-CCAR ice age, 12.3 Nm (33%) for minimal distance of 30-40% ice concentration, 15.4 Nm (41%) for OSISAF ice type “ambiguous” zone from Open Water side, 25 Nm (67%) for minimal distance of 70-81-91% ice concentration, 26.6 Nm (72%) for OSISAF ice type “ambiguous” zone from 1st year ice age side, 35.9 Nm (97%) for maximal distance of 30-40% ice concentration and 36.3 Nm (98%) for maximal distance of 70-81-91% ice concentration data. In the parentheses placed relative distances from first ice data including IUP 40% concentration isolines. Sea ice extent of most of available data sources delineated the edge of “area to be avoided” for vessels of ice class lower than L1. Estimated average speed of L3 ice class vessel was from 3.3 knots till 5.2 knots at average speed 5.0 knots. For L1 ice class vessel estimated average speed was from 6.5 knots till 12.1 knots at average speed 9.7 knots. Relative standard deviation of averaged speed for both ice class vessels was equal 18%. The highest relative deviations were found up to 50% below the average speed value. The highest relative deviations upward were equal 22%. Above speeds for L3 and L1 ice class vessels corresponded well with average technical speed of “Norilsk SA-15” ULA class vessel equal 12,6 knots. The results of the work were not intended to be used for decision making on spot - “on-scene” - during direct guiding vessel in ice. They should be useful for initial voyage planning to allow decision-makers to identify the best freely available data sources for considered voyage and vessel of defined ice class; to understand advantages and limitations of available in the internet data sources; to estimate vessel’s maximal safe speed in encountered ice conditions, to estimate spatial distribution and correlations in between various levels of sea ice concentration and thickness. All above data allow estimate voyage time that is, in addition to fuel consumption, basic criterion of maritime transport economics.