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 remo...
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ftdoajarticles:oai:doaj.org/article:b74c07d9ac9844f1a363f1914727f1b9 2023-05-15T15:16:35+02:00 Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data Ute C. Herzfeld Scott Williams John Heinrichs James Maslanik Steven Sucht 2016-07-01T00:00:00Z https://doi.org/10.3390/rs8080616 https://doaj.org/article/b74c07d9ac9844f1a363f1914727f1b9 EN eng MDPI AG http://www.mdpi.com/2072-4292/8/8/616 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs8080616 https://doaj.org/article/b74c07d9ac9844f1a363f1914727f1b9 Remote Sensing, Vol 8, Iss 8, p 616 (2016) geostatistical and statistical classification vario function feature vector satellite data Chukchi Sea Beaufort Sea Point Barrow/Alaska Science Q article 2016 ftdoajarticles https://doi.org/10.3390/rs8080616 2022-12-31T15:12:21Z 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 ... Article in Journal/Newspaper Arctic Barrow Beaufort Sea Chukchi Chukchi Sea Point Barrow Sea ice Alaska Directory of Open Access Journals: DOAJ Articles Arctic Chukchi Sea Sea Point ENVELOPE(-61.481,-61.481,57.517,57.517) Remote Sensing 8 8 616 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
geostatistical and statistical classification vario function feature vector satellite data Chukchi Sea Beaufort Sea Point Barrow/Alaska Science Q |
spellingShingle |
geostatistical and statistical classification vario function feature vector satellite data Chukchi Sea Beaufort Sea Point Barrow/Alaska Science Q Ute C. Herzfeld Scott Williams John Heinrichs James Maslanik Steven Sucht Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data |
topic_facet |
geostatistical and statistical classification vario function feature vector satellite data Chukchi Sea Beaufort Sea Point Barrow/Alaska Science Q |
description |
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 ... |
format |
Article in Journal/Newspaper |
author |
Ute C. Herzfeld Scott Williams John Heinrichs James Maslanik Steven Sucht |
author_facet |
Ute C. Herzfeld Scott Williams John Heinrichs James Maslanik Steven Sucht |
author_sort |
Ute C. Herzfeld |
title |
Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data |
title_short |
Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data |
title_full |
Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data |
title_fullStr |
Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data |
title_full_unstemmed |
Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data |
title_sort |
geostatistical and statistical classification of sea-ice properties and provinces from sar data |
publisher |
MDPI AG |
publishDate |
2016 |
url |
https://doi.org/10.3390/rs8080616 https://doaj.org/article/b74c07d9ac9844f1a363f1914727f1b9 |
long_lat |
ENVELOPE(-61.481,-61.481,57.517,57.517) |
geographic |
Arctic Chukchi Sea Sea Point |
geographic_facet |
Arctic Chukchi Sea Sea Point |
genre |
Arctic Barrow Beaufort Sea Chukchi Chukchi Sea Point Barrow Sea ice Alaska |
genre_facet |
Arctic Barrow Beaufort Sea Chukchi Chukchi Sea Point Barrow Sea ice Alaska |
op_source |
Remote Sensing, Vol 8, Iss 8, p 616 (2016) |
op_relation |
http://www.mdpi.com/2072-4292/8/8/616 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs8080616 https://doaj.org/article/b74c07d9ac9844f1a363f1914727f1b9 |
op_doi |
https://doi.org/10.3390/rs8080616 |
container_title |
Remote Sensing |
container_volume |
8 |
container_issue |
8 |
container_start_page |
616 |
_version_ |
1766346883193634816 |