Glacial lakes of Sikkim Himalaya: their dynamics, trends, and likely fate—a timeseries analysis through cloud-based geocomputing, and machine learning
AbstractIn the background of ongoing climate change, it is important to monitor the spatial and temporal changes of glacial lakes (GLs) since they influence snowmelt runoff, stream discharge, water resources, and glacial lake outburst flood (GLOF). However, accurate identification and mapping of GLs...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Geomatics, Natural Hazards & Risk |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19475705.2023.2286903 |
| _version_ | 1827582557435199488 |
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| author | Polash Banerjee Chandrashekhar Bhuiyan |
| author_facet | Polash Banerjee Chandrashekhar Bhuiyan |
| author_sort | Polash Banerjee |
| collection | DOAJ |
| description | AbstractIn the background of ongoing climate change, it is important to monitor the spatial and temporal changes of glacial lakes (GLs) since they influence snowmelt runoff, stream discharge, water resources, and glacial lake outburst flood (GLOF). However, accurate identification and mapping of GLs in the background of snow-clad mountains through visual interpretation of satellite data is a tedious and challenging assignment when multiyear time-series analysis is considered. To overcome this challenge, automated extraction of GLs in satellite images has been carried out in this study with the help of machine learning (ML). The novelty of this study is identification and tracking of GLs over three decades using ML and geospatial analysis using pixel-based image classification. For this, Random Forest Classifier (RFC) and Artificial Neural Network (ANN) were employed. The methodology is demonstrated here for the identification and mapping of GLs in the Sikkim Himalaya from 1987 to 2020 and for forecasting the possible fate of these GLs through time-series modelling. The geospatial time-series analysis using Google Earth Engine, ML classifiers, and GIS framework, has captured the dynamics of GLs in Sikkim and has revealed the spatial and temporal patterns in GLs’ dimensions as well as GLOF risk. |
| first_indexed | 2024-03-08T22:52:25Z |
| format | Article |
| id | doaj.art-48c172000dd14f4685468ee155393ed0 |
| institution | Directory Open Access Journal |
| issn | 1947-5705 1947-5713 |
| language | English |
| last_indexed | 2024-03-08T22:52:25Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geomatics, Natural Hazards & Risk |
| spelling | doaj.art-48c172000dd14f4685468ee155393ed02023-12-16T08:49:46ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132023-12-0114110.1080/19475705.2023.2286903Glacial lakes of Sikkim Himalaya: their dynamics, trends, and likely fate—a timeseries analysis through cloud-based geocomputing, and machine learningPolash Banerjee0Chandrashekhar Bhuiyan1Department of Computer Science & Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, Sikkim, IndiaDepartment of Civil Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, Sikkim, IndiaAbstractIn the background of ongoing climate change, it is important to monitor the spatial and temporal changes of glacial lakes (GLs) since they influence snowmelt runoff, stream discharge, water resources, and glacial lake outburst flood (GLOF). However, accurate identification and mapping of GLs in the background of snow-clad mountains through visual interpretation of satellite data is a tedious and challenging assignment when multiyear time-series analysis is considered. To overcome this challenge, automated extraction of GLs in satellite images has been carried out in this study with the help of machine learning (ML). The novelty of this study is identification and tracking of GLs over three decades using ML and geospatial analysis using pixel-based image classification. For this, Random Forest Classifier (RFC) and Artificial Neural Network (ANN) were employed. The methodology is demonstrated here for the identification and mapping of GLs in the Sikkim Himalaya from 1987 to 2020 and for forecasting the possible fate of these GLs through time-series modelling. The geospatial time-series analysis using Google Earth Engine, ML classifiers, and GIS framework, has captured the dynamics of GLs in Sikkim and has revealed the spatial and temporal patterns in GLs’ dimensions as well as GLOF risk.https://www.tandfonline.com/doi/10.1080/19475705.2023.2286903Glacial lakesGLOFmachine learningtime-seriesHimalaya |
| spellingShingle | Polash Banerjee Chandrashekhar Bhuiyan Glacial lakes of Sikkim Himalaya: their dynamics, trends, and likely fate—a timeseries analysis through cloud-based geocomputing, and machine learning Geomatics, Natural Hazards & Risk Glacial lakes GLOF machine learning time-series Himalaya |
| title | Glacial lakes of Sikkim Himalaya: their dynamics, trends, and likely fate—a timeseries analysis through cloud-based geocomputing, and machine learning |
| title_full | Glacial lakes of Sikkim Himalaya: their dynamics, trends, and likely fate—a timeseries analysis through cloud-based geocomputing, and machine learning |
| title_fullStr | Glacial lakes of Sikkim Himalaya: their dynamics, trends, and likely fate—a timeseries analysis through cloud-based geocomputing, and machine learning |
| title_full_unstemmed | Glacial lakes of Sikkim Himalaya: their dynamics, trends, and likely fate—a timeseries analysis through cloud-based geocomputing, and machine learning |
| title_short | Glacial lakes of Sikkim Himalaya: their dynamics, trends, and likely fate—a timeseries analysis through cloud-based geocomputing, and machine learning |
| title_sort | glacial lakes of sikkim himalaya their dynamics trends and likely fate a timeseries analysis through cloud based geocomputing and machine learning |
| topic | Glacial lakes GLOF machine learning time-series Himalaya |
| url | https://www.tandfonline.com/doi/10.1080/19475705.2023.2286903 |
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