Annual inventories of retrogressive thaw slumps across the Qinghai-Tibet Plateau from 2016 to 2022 ...
The dataset is retrogressive thaw slump inventories with annual intervals across the plateau from 2016 to 2022. It contains the boundaries of thaw slumps delineated yearly based on the PlanetScope Scenes with high resolution (3-5 m). The inventories were compiled semi-automatically by combining deep...
Main Authors: | , , , , , , , |
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Format: | Dataset |
Language: | unknown |
Published: |
Zenodo
2024
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Subjects: | |
Online Access: | https://dx.doi.org/10.5281/zenodo.10917080 https://zenodo.org/doi/10.5281/zenodo.10917080 |
_version_ | 1821682505263087616 |
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author | Xia, Zhuoxuan Liu, Lin Mu, Cuicui Peng, Xiaoqing Zhao, Zhuoyi Huang, Lingcao Luo, Jing Fan, Chengyan |
author_facet | Xia, Zhuoxuan Liu, Lin Mu, Cuicui Peng, Xiaoqing Zhao, Zhuoyi Huang, Lingcao Luo, Jing Fan, Chengyan |
author_sort | Xia, Zhuoxuan |
collection | DataCite |
description | The dataset is retrogressive thaw slump inventories with annual intervals across the plateau from 2016 to 2022. It contains the boundaries of thaw slumps delineated yearly based on the PlanetScope Scenes with high resolution (3-5 m). The inventories were compiled semi-automatically by combining deep-learning detection and manual delineation. We assigned a unique ID to every RTS in 2022 and the corresponding polygons in previous years. For RTSs merged into one in 2022, we assigned the same ID to make them traceable. We also clustered the RTSs based on the locations. It is the first of this kind to provide annual large-scale thaw slump observations across the QTP and can potentially be a benchmark for monitoring permafrost and evaluating its impact. The vector file in shapefile format contains the boundary of each hot melt collapse. The name of the file is “QTP_RTS_YYYY”, with the ‘YYYY’ representing the year of the boundaries. Relevant attribute tables include unique numbers, area (units: m2), longitude and ... |
format | Dataset |
genre | permafrost |
genre_facet | permafrost |
id | ftdatacite:10.5281/zenodo.10917080 |
institution | Open Polar |
language | unknown |
op_collection_id | ftdatacite |
op_doi | https://doi.org/10.5281/zenodo.1091708010.5281/zenodo.1092834610.5281/zenodo.10917081 |
op_relation | https://dx.doi.org/10.5281/zenodo.10928346 https://dx.doi.org/10.5281/zenodo.10917081 |
op_rights | Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
publishDate | 2024 |
publisher | Zenodo |
record_format | openpolar |
spelling | ftdatacite:10.5281/zenodo.10917080 2025-01-17T00:16:29+00:00 Annual inventories of retrogressive thaw slumps across the Qinghai-Tibet Plateau from 2016 to 2022 ... Xia, Zhuoxuan Liu, Lin Mu, Cuicui Peng, Xiaoqing Zhao, Zhuoyi Huang, Lingcao Luo, Jing Fan, Chengyan 2024 https://dx.doi.org/10.5281/zenodo.10917080 https://zenodo.org/doi/10.5281/zenodo.10917080 unknown Zenodo https://dx.doi.org/10.5281/zenodo.10928346 https://dx.doi.org/10.5281/zenodo.10917081 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Dataset dataset 2024 ftdatacite https://doi.org/10.5281/zenodo.1091708010.5281/zenodo.1092834610.5281/zenodo.10917081 2024-05-13T13:29:17Z The dataset is retrogressive thaw slump inventories with annual intervals across the plateau from 2016 to 2022. It contains the boundaries of thaw slumps delineated yearly based on the PlanetScope Scenes with high resolution (3-5 m). The inventories were compiled semi-automatically by combining deep-learning detection and manual delineation. We assigned a unique ID to every RTS in 2022 and the corresponding polygons in previous years. For RTSs merged into one in 2022, we assigned the same ID to make them traceable. We also clustered the RTSs based on the locations. It is the first of this kind to provide annual large-scale thaw slump observations across the QTP and can potentially be a benchmark for monitoring permafrost and evaluating its impact. The vector file in shapefile format contains the boundary of each hot melt collapse. The name of the file is “QTP_RTS_YYYY”, with the ‘YYYY’ representing the year of the boundaries. Relevant attribute tables include unique numbers, area (units: m2), longitude and ... Dataset permafrost DataCite |
spellingShingle | Xia, Zhuoxuan Liu, Lin Mu, Cuicui Peng, Xiaoqing Zhao, Zhuoyi Huang, Lingcao Luo, Jing Fan, Chengyan Annual inventories of retrogressive thaw slumps across the Qinghai-Tibet Plateau from 2016 to 2022 ... |
title | Annual inventories of retrogressive thaw slumps across the Qinghai-Tibet Plateau from 2016 to 2022 ... |
title_full | Annual inventories of retrogressive thaw slumps across the Qinghai-Tibet Plateau from 2016 to 2022 ... |
title_fullStr | Annual inventories of retrogressive thaw slumps across the Qinghai-Tibet Plateau from 2016 to 2022 ... |
title_full_unstemmed | Annual inventories of retrogressive thaw slumps across the Qinghai-Tibet Plateau from 2016 to 2022 ... |
title_short | Annual inventories of retrogressive thaw slumps across the Qinghai-Tibet Plateau from 2016 to 2022 ... |
title_sort | annual inventories of retrogressive thaw slumps across the qinghai-tibet plateau from 2016 to 2022 ... |
url | https://dx.doi.org/10.5281/zenodo.10917080 https://zenodo.org/doi/10.5281/zenodo.10917080 |