Determination of Parameters Affecting the Estimation of Iceberg Draft

Recent offshore oil and gas loading facilities developed in the Arctic area have led to a considerable awareness of the iceberg draft approximation, where deep keel icebergs may gouge the ocean floor, and these submarine infrastructures would be damaged in the shallower waters. Developing reliable solutions to estimate the iceberg draft requires a profound understanding of the problem’s dominant parameters. As such, the dimensionless groups of the parameters affecting the iceberg draft estimation were determined for the first time in the present study. Using the dimensionless groups recognized and the linear regression(LR) analysis, nine LR models(i.e., LR 1 to LR 9) were developed and then validated using a comprehensive dataset, which has been constructed in this study. A sensitivity analysis distinguished the premium LR models and important dimensionless groups. The best LR model, as a function of all dimensionless parameters, was able to estimate the iceberg draft with the highest level of precision and correlation along with the lowest degree of complexity. The ratio of iceberg length to iceberg height as the “iceberg length ratio” and the ratio of iceberg width to iceberg height as the “iceberg width ratio” was detected as the important dimensionless groups in the estimation of the iceberg draft. An uncertainty analysis demonstrated that the best LR model was biased towards underestimating the iceberg drafts. The premium LR model outperformed the previous empirical models.Ultimately, a set of LR-based relationships were derived for estimating the iceberg drafts for practical engineering applications, e.g., the early stages of the iceberg management projects.

A generalized supervised classification scheme to produce provincial wetland inventory maps:an application of Google Earth Engine for big geo data processing

Wetlands are important natural resources due to their numerous ecological services.Consequently,identifying their locations and extents is imperative.The stability,repeatability,cost-effectiveness,multi-scale coverage,and proper spatial resolution imagery of satellites provide a valuable opportunity for their use in various large-scale applications,such as provincial wetland mapping.To do so,it is required to(1)process and classify big geo data(i.e.a large amount of satellite datasets)in a time-and computationally-efficient approach and(2)collect a large amount of field samples.In this study,Google Earth Engine(GEE)and machine learning algorithms were utilized to process thousands of remote sensing images and produce provincial wetland inventory maps of the three Canadian provinces of Manitoba,Quebec,and Newfoundland and Labrador(NL).Additionally,using GEE,a generalized supervised classification method is proposed to produce a regional wetland map from a large area(e.g.,a province)when lacking field samples.In fact,using the field data from only Manitoba and assuming that all wetlands in Canada have similar characteristics,the wetland maps were generated for the other two provinces.The overall classification accuracies for Manitoba,Quebec,and NL were 84%,78%,and 82%,respectively,indicating the high potential of the proposed method for aiding provincial wetland inventory systems.

Speckle filtering of Synthetic Aperture Radar images using filters with object-size-adapted windows

Speckle degrades the radiometric quality of a Synthetic Aperture Radar(SAR)image.Previous methods for speckle reduction have used a fixedsize window for filtering the entire image.This,however,may not be effective for the entire image,as land covers of different sizes require different filtering windows.In this paper,a novel method is proposed by which each pixel in the image is filtered with a window appropriate for the size of object within it.The real in-phase and the imaginary quadrature components of the SAR images determine the best window size and the pixels in the intensity image are filtered using their own optimal windows.The proposed method is presented for both singleand multi-polarized SAR images,and the results of several common filters that were modified are presented.This approach is applied to two RADARSAT-2 images:one over San Francisco,California,USA and the other over St.John’s,Newfoundland and Labrador,Canada,producing results that were similar to,or outperformed,comparable filters while retaining details and suppressing speckle effectively.While the method was successful for single-look intensity data,it offers great potential for multi-look and amplitude data as well.