Analyzing lead information from SAR images

©1998 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.Leads are relatively linear features in the sea ice cover, which are composed of open water or new, thin ice, Because of their composition, leads impact the ocean/air heat exchange, Automated analysis of leads from sea ice imagery may provide a means of gathering important information about the sea ice cover and its climatic influence, This paper describes: 1) a method for extracting and analyzing leads from ERS-1 synthetic aperture radar (SAR) images classified by ice type and 2) the results of using this method on images of the Beaufort Sea, The methodology consists of identifying potential lead features in the image and measuring their characteristics both before and after using a thinning or skeletonization technique on the features. The measurements obtained using this method include lead area, average width, number of leads in an area, amount of branching, and linearity of the lead, These measurements were analyzed with respect to the time of year and the latitude of the images. Results indicate that the measurements produced by the methodology are consistent with lead measurement distributions that others have found, The results of the study suggest that the methodology is appropriate to study lead characteristics on a large scale

Leads and ridges in Arctic sea ice from RGPS data and a new tracking algorithm

Leads and pressure ridges are dominant features of the Arctic sea ice cover. Not only do they affect heat loss and surface drag, but they also provide insight into the underlying physics of sea ice deformation. Due to their elongated shape they are referred to as linear kinematic features (LKFs). This paper introduces two methods that detect and track LKFs in sea ice deformation data and establish an LKF data set for the entire observing period of the RADARSAT Geophysical Processor System (RGPS). Both algorithms are available as open-source code and applicable to any gridded sea ice drift and deformation data. The LKF detection algorithm classifies pixels with higher deformation rates compared to the immediate environment as LKF pixels, divides the binary LKF map into small segments, and reconnects multiple segments into individual LKFs based on their distance and orientation relative to each other. The tracking algorithm uses sea ice drift information to estimate a first guess of LKF distribution and identifies tracked features by the degree of overlap between detected features and the first guess. An optimization of the parameters of both algorithms, as well as an extensive evaluation of both algorithms against handpicked features in a reference data set, is presented. A LKF data set is derived from RGPS deformation data for the years from 1996 to 2008 that enables a comprehensive description of LKFs. LKF densities and LKF intersection angles derived from this data set agree well with previous estimates. Further, a stretched exponential distribution of LKF length, an exponential tail in the distribution of LKF lifetimes, and a strong link to atmospheric drivers, here Arctic cyclones, are derived from the data set. Both algorithms are applied to output of a numerical sea ice model to compare the LKF intersection angles in a high-resolution Arctic sea ice simulation with the LKF data set.</p

Resolving Leads in Sea-Ice Models : New Analysis Methods for Frontier Resolution Arctic Simulations

Sea ice deforms constantly under the forcing of winds and ocean currents. Eventually the ice cover of the Arctic Ocean breaks into a multitude of ice floes. Strips of open ocean, so-called leads, and pressure ridges, where the collision of floes piled up the ice, are found along the floe boundaries. These features have a strong impact on the interaction of sea ice with the atmosphere and the ocean, as they affect heat loss and surface drag. Currently, climate models do not resolve leads and pressure ridges in simulated sea ice fields due to their coarse resolution. They parameterize the effects of leads on the Arctic climate, if at all. The goal of this thesis is to develop Arctic simulations that reproduce leads sufficiently to be used in climate simulations. By decreasing the horizontal grid-spacing, a numerical ocean sea-ice model is shown to resolve leads explicitly. To test how realistic these lead-resolving sea-ice simulations are, the following research questions are addressed: (1) what are good metrics to evaluate the simulated leads with observational data? (2) Which observed characteristics of sea ice deformation and deformation features are reproduced by the model? In a first step, the sea ice deformation in a 1-km lead-resolving sea-ice simulation is analyzed with a spatio-temporal scaling analysis. The simulated sea ice deformation is strongly localized in failure zones and dominated by spontaneous fracture. This heterogeneity and intermittency of sea ice deformation shows that the simulation captures the fracture processes that form leads. In a second step, two new algorithms are described that detect and track leads and pressure ridges, combined into Linear Kinematic Features (LKFs). Both algorithms are applied to deformation data observed from satellite to establish a data set of deformation features that can be used as a reference in model evaluation. LKFs in two lead-resolving sea-ice simulations are extracted with the same algorithms, and found to agree with the LKF data set with respect to their spatial characteristics and temporal evolutions. In conclusion, high resolution sea-ice simulations can explicitly resolve leads. These simulations reproduce the characteristics of sea ice deformation and the representation of LKFs that are both observed from satellite. In future work, these simulations could be used as prototypes for the configuration of the sea-ice component in a climate model to directly simulate air-ice-ocean interaction processes in the Arctic