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

Pastusiak T.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2016

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.

Możliwości wykorzystania danych satelitarnych do wyznaczania początku i końca okresu wegetacyjnego

Siłuch M., Bartoszek K.

Woda-Środowisko-Obszary Wiejskie

2012

T. 12, z. 2

245-255

Celem pracy jest ocena możliwości wykorzystania danych satelitarnych do określania dat początku i końca okresu wegetacyjnego. Analizowane charakterystyki zostały wyznaczone na podstawie wartości wskaźnika wegetacji Enhanced Vegetation Index (EVI) oraz obrazów satelitarnych o rozdzielczości przestrzennej 500 m, pochodzących ze skanera MODIS (produkt MOD12Q2). Stosując tę metodę, daty początku i końca okresu wegetacyjnego wyznaczono dla obszarów w promieniu 10 km od miejsca położenia trzech posterunków meteorologicznych na terenie Lubelszczyzny: Czesławic k. Nałęczowa, Felina (wschodnia część Lublina) oraz Bezka k. Chełma. Okres badań obejmował lata 2001-2009, zaś daty odnosiły się do wybranych rodzajów pokrycia terenu (gruntów ornych, łąk i lasów). Stwierdzono, że na podstawie danych wyznaczonych na bazie wskaźnika EVI okres wegetacyjny trwał średnio o miesiąc krócej w stosunku do charakterystyk, obliczonych metodami tradycyjnymi, tj. Gumińskiego i Huculaka-Makowca. Ponadto początek okresu wegetacyjnego, wyznaczonego metodą teledetekcyjną, był istotnie statystycznie skorelowany ze średnią wartością temperatury powietrza w okresie styczeń-marzec oraz z liczbą dni z pokrywą śnieżną od grudnia do marca. Z kolei daty końca okresu wegetacyjnego wykazywały największą współzmienność z sumami promieniowania całkowitego we wrześniu

The aim of this study is to evaluate the possibility of using satellite data to determine dates of the onset and end of the growing season. The analysed characteristics were determined based on the Enhanced Vegetation Index (EVI) and satellite images with spatial resolution of 500 m, derived from MODIS scanner (MOD12Q2 product). Based on this method, dates of the onset and end of the growing season were determined for areas within 10 km from the location of three meteorological stations in the Lublin Region: Czesławice near Nałęczów, Felin (eastern district of Lublin) and Bezek near Chełm. The study period covered the years 2001-2009 and dates referred to the selected land cover types (arable lands, meadows and forests). It was found that the growing season determined with the remote sensing method was on average shorter by one month compared with that estimated with traditional methods such as those by Gumiński and Huculak-Makowiec. The onset of the growing season was significantly correlated with the mean air temperature in January-March period and the number of days with snow cover from December to March. In addition, dates of the end of growing season showed the highest correlation with the sum of the total radiation in September.