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The presence of sea ice is the predominant risk for ship operations in the Arctic, and monitoring of ice condition around a vessel is crucial during all times of operation. This paper presents a system for online onboard assessment of ice condition. It is demonstrated that ice-induced accelerations in the bow section of the hull follow a bivariate t-distribution and parameters of the distribution have a one-to-one relation to ice condition. This paper suggests a methodology to monitor the ice condition in real time through estimation of parameters that characterize the distribution of hull accelerations. It is shown how a Kullback–Leibler divergence measure can classify ice condition among a set of pretrained conditions. An absolute measure of ice load is suggested as an alternative for situations when pretraining data are not available. The alternative algorithm quantifies the condition through the entropy of measured accelerations. This paper presents a computationally easy methodology and tests against data collected during Arctic transit of an icebreaker. Furthermore, the classification results are compared with the results from two standard methods from machine learning, decision tree and a support vector machine approaches. The results show that the statistical methods provide robust assessment of the prevailing ice conditions, independent of visual and weather conditions. Also, the comparison shows that the statistical classification methods, designed by process knowledge, provide steadier and more reliable results. [ABSTRACT FROM AUTHOR]

Quantifying vegetation structure and function is critical for modeling ecological processes, and an emerging challenge is to apply models at multiple spatial scales. Land surface heterogeneity is commonly characterized using rectangular pixels, whose length scale reflects that of remote sensing measurements or ecological models rather than the spatial scales at which vegetation structure and function varies. We investigated the ‘optimum’ pixel size and shape for averaging leaf area index (LAI) measurements in relatively large (85 m2 estimates on a 600 × 600-m2 grid) and small (0.04 m2 measurements on a 40 × 40-m2 grid) patches of sub-Arctic tundra near Abisko, Sweden. We define the optimum spatial averaging operator as that which preserves the information content (IC) of measured LAI, as quantified by the normalized Shannon entropy ( ES,n) and Kullback–Leibler divergence ( DKL), with the minimum number of pixels. Based on our criterion, networks of Voronoi polygons created from triangulated irregular networks conditioned on hydrologic and topographic indices are often superior to rectangular shapes for averaging LAI at some, frequently larger, spatial scales. In order to demonstrate the importance of information preservation when upscaling, we apply a simple, validated ecosystem carbon flux model at the landscape level before and after spatial averaging of land surface characteristics. Aggregation errors are minimal due to the approximately linear relationship between flux and LAI, but large errors of approximately 45% accrue if the normalized difference vegetation index (NDVI) is averaged without preserving IC before conversion to LAI due to the nonlinear NDVI-LAI transfer function. [ABSTRACT FROM AUTHOR]