Geostatistical and statistical classification of sea-ice properties and provinces from SAR data

Recent drastic reductions in the Arctic sea-ice cover have raised an interest in understanding the role of sea ice in the global system as well as pointed out a need to understand the physical processes that lead to such changes. Satellite remote-sensing data provide important information about remote ice areas, and Synthetic Aperture Radar (SAR) data have the advantages of penetration of the omnipresent cloud cover and of high spatial resolution. A challenge addressed in this paper is how to extract information on sea-ice types and sea-ice processes from SAR data. We introduce, validate and apply geostatistical and statistical approaches to automated classification of sea ice from SAR data, to be used as individual tools for mapping sea-ice properties and provinces or in combination. A key concept of the geostatistical classification method is the analysis of spatial surface structures and their anisotropies, more generally, of spatial surface roughness, at variable, intermediate-sized scales. The geostatistical approach utilizes vario parameters extracted from directional vario functions, the parameters can be mapped or combined into feature vectors for classification. The method is flexible with respect to window sizes and parameter types and detects anisotropies. In two applications to RADARSAT and ERS-2 SAR data from the area near Point Barrow, Alaska, it is demonstrated that vario-parameter maps may be utilized to distinguish regions of different sea-ice characteristics in the Beaufort Sea, the Chukchi Sea and in Elson Lagoon. In a third and a fourth case study the analysis is taken further by utilizing multi-parameter feature vectors as inputs for unsupervised and supervised statistical classification. Field measurements and high-resolution aerial observations serve as basis for validation of the geostatistical-statistical classification methods. A combination of supervised classification and vario-parameter mapping yields best results, correctly identifying several sea-ice provinces in the shore-fast ice and the pack ice. Notably, sea ice does not have to be static to be classifiable with respect to spatial structures. In consequence, the geostatistical-statistical classification may be applied to detect changes in ice dynamics, kinematics or environmental changes, such as increased melt ponding, increased snowfall or changes in the equilibrium line

An Algorithm for Detection of Ground and Canopy Cover in Micropulse Photon-Counting Lidar Altimeter Data in Preparation of the ICESat-2 Mission

The Ice, Cloud and Land Elevation Satellite-II (ICESat-2) mission has been selected by NASA as a Decadal Survey mission, to be launched in 2016. Mission objectives are to measure land ice elevation, sea ice freeboard/ thickness and changes in these variables and to collect measurements over vegetation that will facilitate determination of canopy height, with an accuracy that will allow prediction of future environmental changes and estimation of sea-level rise. The importance of the ICESat-2 project in estimation of biomass and carbon levels has increased substantially, following the recent cancellation of all other planned NASA missions with vegetation-surveying lidars. Two innovative components will characterize the ICESat-2 lidar: (1) Collection of elevation data by a multi-beam system and (2) application of micropulse lidar (photon counting) technology. A micropulse photon-counting altimeter yields clouds of discrete points, which result from returns of individual photons, and hence new data analysis techniques are required for elevation determination and association of returned points to reflectors of interest including canopy and ground in forested areas. The objective of this paper is to derive and validate an algorithm that allows detection of ground under dense canopy and identification of ground and canopy levels in simulated ICESat-2-type data. Data are based on airborne observations with a Sigma Space micropulse lidar and vary with respect to signal strength, noise levels, photon sampling options and other properties. A mathematical algorithm is developed, using spatial statistical and discrete mathematical concepts, including radial basis functions, density measures, geometrical anisotropy, eigenvectors and geostatistical classification parameters and hyperparameters. Validation shows that the algorithm works very well and that ground and canopy elevation, and hence canopy height, can be expected to be observable with a high accuracy during the ICESat-2 mission. A result relevant for instrument design is that even the two weaker beam classes considered can be expected to yield useful results for vegetation measurements (93.01-99.57% correctly selected points for a beam with expected return of 0.93 mean signals per shot (msp9) and 72.85% - 98.68% for 0.48 msp (msp4)). Resampling options affect results more than noise levels. The algorithm derived here is generally applicable for analysis of micropulse lidar altimeter data collected over forested areas as well as other surfaces, including land ice, sea ice and land surfaces

The trough-system algorithm and its application to spatial modeling of Greenland subglacial topography

This is the published version. Copyright 2014 Herzfeld et al.Dynamic ice-sheet models are used to assess the contribution of mass loss from the Greenland ice sheet to sea-level rise. Mass transfer from ice sheet to ocean is in a large part through outlet glaciers. Bed topography plays an important role in ice dynamics, since the acceleration from the slow-moving inland ice to an ice stream is in many cases caused by the existence of a subglacial trough or trough system. Problems are that most subglacial troughs are features of a scale not resolved in most ice-sheet models and that radar measurements of subglacial topography do not always reach the bottoms of narrow troughs. The trough-system algorithm introduced here employs mathematical morphology and algebraic topology to correctly represent subscale features in a topographic generalization, so the effects of troughs on ice flow are retained in ice-dynamic models. The algorithm is applied to derive a spatial elevation model of Greenland subglacial topography, integrating recently collected radar measurements (CReSIS data) of the Jakobshavn Isbræ, Helheim, Kangerdlussuaq and Petermann glacier regions. The resultant JakHelKanPet digital elevation model has been applied in dynamic ice-sheet modeling and sea-level-rise assessment

Resolution of ice streams and outlet glaciers in large-scale simulations of the Greenland ice sheet

The dynamic/thermodynamic shallow-ice model SICOPOLIS is applied to the Greenland ice sheet. Paleoclimatic spin-ups from 125 ka BP until today, as well as future-climate experiments 500 years into the future, are carried out with three different grid spacings, namely 20,10 and 5 km. The scenarios are a subset of those specified by the SeaRISE (Sea-level Response to Ice Sheet Evolution) community effort. The bed topography includes improved troughs for Jakobshavn Isbrae, Helheim, Kangerdlugssuaq and Petermann glaciers, processed by an algorithm that preserves shape, orientation and continuity of the troughs on the 5 km scale. Comparison of simulated and observed present-day surface velocities shows that these ice streams and outlet glaciers are resolved with different accuracies, ranging from poor (20 km grid) to reasonably good (5 km grid). In the future-climate experiments, the simulated absolute ice volumes depend significantly on the resolution, while the sensitivities (ice volumes relative to the constant-climate control run) vary only by a few centimeters of sea-level equivalent

Projecting the response of the Greenland ice sheet to future climate change with the ice sheet model SICOPOLIS

Numerical modelling has become established as an important tool for understanding ice sheet dynamics in general, and in particular for assessing the contribution of the Greenland and Antarctic ice sheets to future sea level change under global warming conditions. In this paper, we review related work carried out with the ice sheet model SICOPOLIS (SImulation COde for POLythermal Ice Sheets). As part of a group of eight models, it was applied to a set of standardised experiments for the Greenland ice sheet defined by the SeaRISE (Sea-level Response to Ice Sheet Evolution) initiative. A main finding of SeaRISE was that, if climate change continues unabatedly, the ice sheet may experience a significant decay over the next centuries. However, the spread of results across different models was very large, mainly because of differences in the applied initialisation methods and surface mass balance schemes. Therefore, the new initiative ISMIP6 (Ice Sheet Modeling Intercomparison Project for CMIP6) was launched. An early sub-project is InitMIP-Greenland, within which we showed that two different initialisations computed with SICOPOLIS lead indeed to large differences in the simulated response to schematic future climate scenarios. Further work within ISMIP6 will thus focus on improved initialisation techniques. Based on this, refined future climate simulations for the Greenland ice sheet, driven by forcings derived from AOGCM (atmosphere-ocean general circulation model) simulations, will be carried out. The goal of ISMIP6 is to provide significantly improved estimates of ice sheet contribution to sea level rise in the coming years

Geostatistical Methods For Determination of Roughness, Topography, And Changes of Antarctic Ice Streams From SAR And Radar Altimeter Data

The central objective of this project has been the development of geostatistical methods fro mapping elevation and ice surface characteristics from satellite radar altimeter (RA) and Syntheitc Aperture Radar (SAR) data. The main results are an Atlas of elevation maps of Antarctica, from GEOSAT RA data and an Atlas from ERS-1 RA data, including a total of about 200 maps with 3 km grid resolution. Maps and digital terrain models are applied to monitor and study changes in Antarctic ice streams and glaciers, including Lambert Glacier/Amery Ice Shelf, Mertz and Ninnis Glaciers, Jutulstraumen Glacier, Fimbul Ice Shelf, Slessor Glacier, Williamson Glacier and others

Derivation of deformation characteristics in fast-moving glaciers

Crevasse patterns are the writings in a glacier’s history book – the movement, strain and deformation frozen in ice. Therefore by analysis of crevasse patterns we can learn about the ice-dynamic processes which the glacier has experienced. Direct measurement of ice movement and deformation is time-consuming and costly, in particular for large glaciers; typically, observations are lacking when sudden changes occur. Analysis of crevasse patterns provides a means to reconstruct past and ongoing deformation processes mathematically. This is especially important for fast-moving ice. Ice movement and deformation are commonly described and analyzed using continuum mechanics and measurements of ice velocities or strain rates. Here, we present a different approach to the study of ice deformation based on principles of structural geology. Fast ice movement manifests itself in the occurrence of crevasses. Because crevasses remain after the deformation event and may be transported, overprinted or closed, their analysis based on aerial videography and photography or satellite data gives information on past deformation events and resulting strain states. In our treatment, we distinguish (A) continuously fastmoving glaciers and ice streams, and (B) surge-type glaciers, based on observations of two prototypes, Jakobshavns Isbræ, Greenland, for (A), and Bering Glacier, Alaska, during the 1993-1995 surge, for (B). Classes of ice-deformation types are derived from aerial images of ice surfaces using structural geology, i.e. structural glaciology. For each type, the deformation gradient matrix is formed. Relationships between invariants used in structural geology and continuum mechanics and the singular value decomposition are established and applied to ice-surface classification. Deformation during a surge is mostly one of the extensional deformation types. Continuously, or infinitesimally repeated, deformation acting in continuously fast-moving ice causes different typical crevasse patterns. The structural-geology approach also includes a way to treat the problem of shear, as observed in the margins of fast-moving ice streams within slow-moving surrounding ice. In this paper we provide the first link between a physical analysis of ice-surface deformation and a connectionist-geostatistical analysis of the same problem