Mapping Ice Covered Waters from Space

Under a leaden April Arctic sky, the United States Coast Guard Cutter Healy left the swell of the southern Labrador Sea and entered the Arctic ice pack for the first time, settling in as if she had finally found her home. The deck log notes that at about 4 pm on the 4th April 2000, the ship’s commanding officer, Capt. Bob Garrett, assumed both the deck and the con for the momentous occasion (Figure 1). The ship entered the ice at 51° 33.0’ N, 54° 33.33’ W. The ice trials of the newest U.S. icebreaker were underway

ARKTOS: An intelligent system for SAR sea ice image classification

©2004 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.We present an intelligent system for satellite sea ice image analysis named Advanced Reasoning using Knowledge for T ping Of Sea ice (ARKTOS). ARKTOS performs fully automated analysis of synthetic aperture radar (SAR) sea ice images by mimicking the reasoning process of sea ice experts. ARKTOS automatically segments a SAR image of sea ice, generates descriptors for the segments of the image, and then uses expert system rules to classify these sea ice features. ARKTOS also utilizes multisource data fusion to improve classification and performs belief handling using Dempster-Shafer. As a software package, ARKTOS comprises components in image processing, rule-based classification, multisource data fusion, and graphical user interface-based knowledge engineering and modification. As a research project over the past ten years, ARKTOS has undergone phases such as knowledge acquisition, prototyping, refinement, evaluation, deployment, and operationalization at the U.S. National Ice Center. In this paper, we focus on the methodology, evaluations, and classification results of ARKTOS

Evaluation of Special Sensor Microwave / Imager Sea-Ice Products

Existing SSM/I algorithms are imperfect at mapping total and partial ice concentrations. This paper reviews recent findings based on comparisons of sea-ice products against other satellite data and U.S. National Ice Center (NIC) ice charts

Mapping Ice Covered Waters from Space

Under a leaden April Arctic sky, the United States Coast Guard Cutter Healy left the swell of the southern Labrador Sea and entered the Arctic ice pack for the first time, settling in as if she had finally found her home. The deck log notes that at about 4 pm on the 4th April 2000, the ship’s commanding officer, Capt. Bob Garrett, assumed both the deck and the con for the momentous occasion (Figure 1). The ship entered the ice at 51° 33.0’ N, 54° 33.33’ W. The ice trials of the newest U.S. icebreaker were underway

Evaluation of operational SSM/I ice-concentration algorithms

The United States National Ice Center (NIC) provides weekly ice analyses of the Arctic and Antarctic using information from ice reconnaissance, ship reports and high-resolution satellite imagery. In cloud-covered areas and regions lacking imagery, the higher-resolution sources are augmented by ice concentrations derived from Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSMII) passive-microwave imagery. However, the SSMII-derived ice concentrations are limited by low resolution and uncertainties in thin-ice regions. Ongoing research at NIC is attempting to improve the utility of these SSMII products for operational sea-ice analyses. The refinements of operational algorithms may also aid future scientific studies. Here we discuss an evaluation of the standard operational ice-concentration algorithm, Cal/Val, with a possible alternative, a modified NASA Team algorithm. The modified algorithm compares favorably with CallVal and is a substantial improvement over the standard NASA Team algorithm in thin-ice regions that are of particular interest to operational activities

Data fusion for use of passive microwave data in operational sea-ice monitoring

A new SSM/I algorithm is described that is based on near real-time data fusion with portions of operational ice charts derived from RADARSAT, OLS or AVHRR data. The aim of this is to enable parts of the ice chart where there is no cloud-free imagery or SAR data to be completed using an SSM/I algorithm that is tuned to the region and time associated with the ice chart. The algorithm is a linear combination of partial concentrations from the NASA Team and Bootstrap algorithms together with lower variance principal components of SSM/I data. The algorithm is designed for near real time use in production of operational ice charts

ARKTOS: An Intelligent System for SAR Sea Ice Image Classification

We present an intelligent system for satellite sea ice image analysis named Advanced Reasoning using Knowledge for Typing Of Sea ice (ARKTOS). ARKTOS performs fully automated analysis of synthetic aperture radar (SAR) sea ice images by mimicking the reasoning process of sea ice experts. ARKTOS automatically segments a SAR image of sea ice, generates descriptors for the segments of the image, and then uses expert system rules to classify these sea ice features. ARKTOS also utilizes multisource data fusion to improve classification and performs belief handling using Dempster–Shafer. As a software package, ARKTOS comprises components in image processing, rule-based classification, multisource data fusion, and graphical user interface-based knowledge engineering and modification. As a research project over the past ten years, ARKTOS has undergone phases such as knowledge acquisition, prototyping, refinement, evaluation, deployment, and operationalization at the U.S. National Ice Center. In this paper, we focus on the methodology, evaluations, and classification results of ARKTOS

Transition from Research to Operations: ARKTOS - A Knowledge-Based Sea Ice Classification System

ARKTOS is a fully automated intelligent system that classifies sea ice and that is now being used by the U.S. National Ice Center (NIC) for daily operations related to the NIC’s task of mapping the ice covered oceans. In this paper we describe the process of taking a research project and transitioning it to an operational environment. We discuss the theoretical methodologies implemented in ARKTOS, and how ARKTOS was developed, tested, and finally moved to operations

Global Ice and Land Climate Studies Using Scatterometer Image Data

Spaceborne scatterometers have provided continuous synoptic microwave coverage of the Earth for nearly a decade. Though these scatterometers were originally designed to measure oceanic surface winds, their data are also extremely useful in a broad range of ice and land applications, including the use of extensive scatterometer time series to determine seasonal and interannual variability and possible relationships to climate change. Under a NASA Earth Science Enterprise grant, the Scatterometer Climate Record Pathfinder (SCP) project has produced non-ocean scatterometer imagery and data products that are now publicly available for the first time (http://www.scp.byu.edu/)

Operational Evaluation of a Knowledge-Based Sea Ice Classification System

ARKTOS (Advanced Reasoning Using Knowledge for Typing of Sea Ice) is a fully automated intelligent sea ice classification system. ARKTOS is in use at the U.S. National Ice Center (NIC) for daily operations related to the NIC’S task of mapping the ice covered oceans. ARKTOS incorporates image processing, input from ancillary data, and artificial intelligence (AI) to analyze and classify RADARSAT Synthetic Aperture Radar (SAR) imagery. The NIC and Naval Research Laboratory (NRL/ERIM) have been testing and evaluating ARKTOS through the freeze-up, winter, melt-out and summer seasons of the Beaufort Sea. In this paper we outline the development and evolution of ARKTOS, evaluate current output, and discuss future implementation