BIG IMAGERY AND HIGH PERFORMANCE COMPUTING AS RESOURCES TO UNDERSTAND CHANGING ARCTIC POLYGONAL TUNDRA
Permafrost thaw has been observed at several locations across the Arctic tundra in recent decades; however, the pan-Arctic extent and spatiotemporal dynamics of thaw remains poorly explained. Thaw-induced differential ground subsidence and dramatic microtopographic transitions, such as transformatio...
Published in: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
---|---|
Main Authors: | , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Copernicus Publications
2020
|
Subjects: | |
Online Access: | https://doi.org/10.5194/isprs-archives-XLIV-M-2-2020-111-2020 https://doaj.org/article/0cdf4ca0122745cc83a84c468f6fcf06 |
id |
ftdoajarticles:oai:doaj.org/article:0cdf4ca0122745cc83a84c468f6fcf06 |
---|---|
record_format |
openpolar |
spelling |
ftdoajarticles:oai:doaj.org/article:0cdf4ca0122745cc83a84c468f6fcf06 2023-05-15T14:43:20+02:00 BIG IMAGERY AND HIGH PERFORMANCE COMPUTING AS RESOURCES TO UNDERSTAND CHANGING ARCTIC POLYGONAL TUNDRA C. Witharana M. A. E. Bhuiyan A. K. Liljedahl 2020-11-01T00:00:00Z https://doi.org/10.5194/isprs-archives-XLIV-M-2-2020-111-2020 https://doaj.org/article/0cdf4ca0122745cc83a84c468f6fcf06 EN eng Copernicus Publications https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIV-M-2-2020/111/2020/isprs-archives-XLIV-M-2-2020-111-2020.pdf https://doaj.org/toc/1682-1750 https://doaj.org/toc/2194-9034 doi:10.5194/isprs-archives-XLIV-M-2-2020-111-2020 1682-1750 2194-9034 https://doaj.org/article/0cdf4ca0122745cc83a84c468f6fcf06 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIV-M-2-2020, Pp 111-116 (2020) Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820 article 2020 ftdoajarticles https://doi.org/10.5194/isprs-archives-XLIV-M-2-2020-111-2020 2022-12-31T14:22:05Z Permafrost thaw has been observed at several locations across the Arctic tundra in recent decades; however, the pan-Arctic extent and spatiotemporal dynamics of thaw remains poorly explained. Thaw-induced differential ground subsidence and dramatic microtopographic transitions, such as transformation of low-centered ice-wedge polygons (IWPs) into high-centered IWPs can be characterized using very high spatial resolution (VHSR) commercial satellite imagery. Arctic researchers demand for an accurate estimate of the distribution of IWPs and their status across the tundra domain. The entire Arctic has been imaged in 0.5 m resolution by commercial satellite sensors; however, mapping efforts are yet limited to small scales and confined to manual or semi-automated methods. Knowledge discovery through artificial intelligence (AI), big imagery, and high performance computing (HPC) resources is just starting to be realized in Arctic science. Large-scale deployment of VHSR imagery resources requires sophisticated computational approaches to automated image interpretation coupled with efficient use of HPC resources. We are in the process of developing an automated Mapping Application for Permafrost Land Environment (MAPLE) by combining big imagery, AI, and HPC resources. The MAPLE uses deep learning (DL) convolutional neural nets (CNNs) algorithms on HPCs to automatically map IWPs from VHSR commercial satellite imagery across large geographic domains. We trained and tasked a DLCNN semantic object instance segmentation algorithm to automatically classify IWPs from VHSR satellite imagery. Overall, our findings demonstrate the robust performances of IWP mapping algorithm in diverse tundra landscapes and lay a firm foundation for its operational-level application in repeated documentation of circumpolar permafrost disturbances. Article in Journal/Newspaper Arctic Ice permafrost Tundra wedge* Directory of Open Access Journals: DOAJ Articles Arctic The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-M-2-2020 111 116 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820 |
spellingShingle |
Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820 C. Witharana M. A. E. Bhuiyan A. K. Liljedahl BIG IMAGERY AND HIGH PERFORMANCE COMPUTING AS RESOURCES TO UNDERSTAND CHANGING ARCTIC POLYGONAL TUNDRA |
topic_facet |
Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820 |
description |
Permafrost thaw has been observed at several locations across the Arctic tundra in recent decades; however, the pan-Arctic extent and spatiotemporal dynamics of thaw remains poorly explained. Thaw-induced differential ground subsidence and dramatic microtopographic transitions, such as transformation of low-centered ice-wedge polygons (IWPs) into high-centered IWPs can be characterized using very high spatial resolution (VHSR) commercial satellite imagery. Arctic researchers demand for an accurate estimate of the distribution of IWPs and their status across the tundra domain. The entire Arctic has been imaged in 0.5 m resolution by commercial satellite sensors; however, mapping efforts are yet limited to small scales and confined to manual or semi-automated methods. Knowledge discovery through artificial intelligence (AI), big imagery, and high performance computing (HPC) resources is just starting to be realized in Arctic science. Large-scale deployment of VHSR imagery resources requires sophisticated computational approaches to automated image interpretation coupled with efficient use of HPC resources. We are in the process of developing an automated Mapping Application for Permafrost Land Environment (MAPLE) by combining big imagery, AI, and HPC resources. The MAPLE uses deep learning (DL) convolutional neural nets (CNNs) algorithms on HPCs to automatically map IWPs from VHSR commercial satellite imagery across large geographic domains. We trained and tasked a DLCNN semantic object instance segmentation algorithm to automatically classify IWPs from VHSR satellite imagery. Overall, our findings demonstrate the robust performances of IWP mapping algorithm in diverse tundra landscapes and lay a firm foundation for its operational-level application in repeated documentation of circumpolar permafrost disturbances. |
format |
Article in Journal/Newspaper |
author |
C. Witharana M. A. E. Bhuiyan A. K. Liljedahl |
author_facet |
C. Witharana M. A. E. Bhuiyan A. K. Liljedahl |
author_sort |
C. Witharana |
title |
BIG IMAGERY AND HIGH PERFORMANCE COMPUTING AS RESOURCES TO UNDERSTAND CHANGING ARCTIC POLYGONAL TUNDRA |
title_short |
BIG IMAGERY AND HIGH PERFORMANCE COMPUTING AS RESOURCES TO UNDERSTAND CHANGING ARCTIC POLYGONAL TUNDRA |
title_full |
BIG IMAGERY AND HIGH PERFORMANCE COMPUTING AS RESOURCES TO UNDERSTAND CHANGING ARCTIC POLYGONAL TUNDRA |
title_fullStr |
BIG IMAGERY AND HIGH PERFORMANCE COMPUTING AS RESOURCES TO UNDERSTAND CHANGING ARCTIC POLYGONAL TUNDRA |
title_full_unstemmed |
BIG IMAGERY AND HIGH PERFORMANCE COMPUTING AS RESOURCES TO UNDERSTAND CHANGING ARCTIC POLYGONAL TUNDRA |
title_sort |
big imagery and high performance computing as resources to understand changing arctic polygonal tundra |
publisher |
Copernicus Publications |
publishDate |
2020 |
url |
https://doi.org/10.5194/isprs-archives-XLIV-M-2-2020-111-2020 https://doaj.org/article/0cdf4ca0122745cc83a84c468f6fcf06 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Ice permafrost Tundra wedge* |
genre_facet |
Arctic Ice permafrost Tundra wedge* |
op_source |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIV-M-2-2020, Pp 111-116 (2020) |
op_relation |
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIV-M-2-2020/111/2020/isprs-archives-XLIV-M-2-2020-111-2020.pdf https://doaj.org/toc/1682-1750 https://doaj.org/toc/2194-9034 doi:10.5194/isprs-archives-XLIV-M-2-2020-111-2020 1682-1750 2194-9034 https://doaj.org/article/0cdf4ca0122745cc83a84c468f6fcf06 |
op_doi |
https://doi.org/10.5194/isprs-archives-XLIV-M-2-2020-111-2020 |
container_title |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
container_volume |
XLIV-M-2-2020 |
container_start_page |
111 |
op_container_end_page |
116 |
_version_ |
1766315003039711232 |