Preprint Article Version 1 This version is not peer-reviewed

Estimating the Speed of Icegoing Ships by Integrating SAR Imagery and Ship Data From Automatic Identification System

Version 1 : Received: 31 May 2018 / Approved: 4 June 2018 / Online: 4 June 2018 (10:28:38 CEST)

A peer-reviewed article of this Preprint also exists.

Similä, M.; Lensu, M. Estimating the Speed of Ice-Going Ships by Integrating SAR Imagery and Ship Data from an Automatic Identification System. Remote Sens. 2018, 10, 1132. Similä, M.; Lensu, M. Estimating the Speed of Ice-Going Ships by Integrating SAR Imagery and Ship Data from an Automatic Identification System. Remote Sens. 2018, 10, 1132.

Journal reference: Remote Sens. 2018, 10, 1132
DOI: 10.3390/rs10071132

Abstract

Ship speeds extracted from AIS data vary with ice conditions. We extrapolated this variation with SAR data to a chart of expected icegoing speed. The study is for the Gulf of Bothnia in March 2013 and for ships with ice class 1A Super that are able to navigate without icbreaker assistance. The speed was normalized to 0-10 for each ship. As the matching between AIS and SAR was complicated by ice drift during the time gap, from hours to two days, we calculated a set of local SAR statistics over several scales. We used random tree regression to estimate the speed. The accuracy was quantified by mean squared error (MSE), and the fraction of estimates close to the actual speeds. These depended strongly on the route and the day. MSE varied from 0.4 to 2.7 units2 for daily routes. 65 % of the estimates deviated less than one unit and 82 % less than 1.5 units from the AIS speeds. The estimated daily mean speeds were close to the observations. Largest speed decreases were provided by the estimator in a dampened form or not at all. This improved when ice chart thickness was included as one predictor.

Subject Areas

synthetic aperture radar; automatic identification system; ice thickness; regression

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