The present situation and shifts observed in wetlands within the St. Lawrence Seaway region of Canada, utilizing imagery from the Landsat archive and the cloud-based platform Google Earth Engine
This study examined wetland trends in the St. Lawrence Seaway (~500,000 km2) in Canada over the past four decades. To this end, historical Landsat data within the Google Earth Engine (GEE) big geo data platform were processed. Reference samples were scrutinized using the Continuous Change Detection...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-01-01
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| Series: | Big Earth Data |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/20964471.2025.2454044 |
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| author | Meisam Amani Mohammad Kakooei Rebecca Warren Sahel Mahdavi Kevin Murnaghan Arsalan Ghorbanian Amin Naboureh |
| author_facet | Meisam Amani Mohammad Kakooei Rebecca Warren Sahel Mahdavi Kevin Murnaghan Arsalan Ghorbanian Amin Naboureh |
| author_sort | Meisam Amani |
| collection | DOAJ |
| description | This study examined wetland trends in the St. Lawrence Seaway (~500,000 km2) in Canada over the past four decades. To this end, historical Landsat data within the Google Earth Engine (GEE) big geo data platform were processed. Reference samples were scrutinized using the Continuous Change Detection and Classification (CCDC) algorithm to identify spectrally unchanged samples. These spectrally unchanged samples were subsequently employed as training data within an object-based Random Forest (RF) model to generate wetland maps from 1984 to 2021. Subsequently, a change analysis was conducted to calculate the loss and gain of different wetland types. Overall, it was observed that approximately 45% (184,434 km2) and 55% (220,778 km2) of the entire study area are covered by wetland and non-wetland categories, respectively. It was also observed that 2.46% (12,495 km2) of the study area was changed during 40 years. Overall, there was a decline in the Bog and Fen classes, while the Marsh, Swamp, Forest, Grassland/Shrubland, Cropland, and Barren classes had an increase. Finally, the wetland gain and loss were 6,793 km2 and 5,701 km2, respectively. This study demonstrated that the use of Landsat data, along with advanced machine learning and GEE, could provide valuable assistance for wetland classification and change studies. |
| format | Article |
| id | doaj-art-c69ae9304066469f90fba60c0d0c61ba |
| institution | DOAJ |
| issn | 2096-4471 2574-5417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Big Earth Data |
| spelling | doaj-art-c69ae9304066469f90fba60c0d0c61ba2025-08-20T03:12:11ZengTaylor & Francis GroupBig Earth Data2096-44712574-54172025-01-0191477110.1080/20964471.2025.2454044The present situation and shifts observed in wetlands within the St. Lawrence Seaway region of Canada, utilizing imagery from the Landsat archive and the cloud-based platform Google Earth EngineMeisam Amani0Mohammad Kakooei1Rebecca Warren2Sahel Mahdavi3Kevin Murnaghan4Arsalan Ghorbanian5Amin Naboureh6Digital Environmental Group, WSP Canada Inc., Ottawa, ON, CanadaDepartment of Computer Science, Chalmers University of Technology, Gothenburg, SwedenDigital Environmental Group, WSP Canada Inc., Edmonton, AB, CanadaDigital Environmental Group, WSP Canada Inc., Ottawa, ON, CanadaIndependent ResearcherDepartment of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, IranResearch Center for Digital Mountain and Remote Sensing Application, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, ChinaThis study examined wetland trends in the St. Lawrence Seaway (~500,000 km2) in Canada over the past four decades. To this end, historical Landsat data within the Google Earth Engine (GEE) big geo data platform were processed. Reference samples were scrutinized using the Continuous Change Detection and Classification (CCDC) algorithm to identify spectrally unchanged samples. These spectrally unchanged samples were subsequently employed as training data within an object-based Random Forest (RF) model to generate wetland maps from 1984 to 2021. Subsequently, a change analysis was conducted to calculate the loss and gain of different wetland types. Overall, it was observed that approximately 45% (184,434 km2) and 55% (220,778 km2) of the entire study area are covered by wetland and non-wetland categories, respectively. It was also observed that 2.46% (12,495 km2) of the study area was changed during 40 years. Overall, there was a decline in the Bog and Fen classes, while the Marsh, Swamp, Forest, Grassland/Shrubland, Cropland, and Barren classes had an increase. Finally, the wetland gain and loss were 6,793 km2 and 5,701 km2, respectively. This study demonstrated that the use of Landsat data, along with advanced machine learning and GEE, could provide valuable assistance for wetland classification and change studies.https://www.tandfonline.com/doi/10.1080/20964471.2025.2454044Remote sensingGoogle Earth Engine (GEE)cloud computingsatellitechange detectioncontinuous change detection and classification (CCDC) |
| spellingShingle | Meisam Amani Mohammad Kakooei Rebecca Warren Sahel Mahdavi Kevin Murnaghan Arsalan Ghorbanian Amin Naboureh The present situation and shifts observed in wetlands within the St. Lawrence Seaway region of Canada, utilizing imagery from the Landsat archive and the cloud-based platform Google Earth Engine Big Earth Data Remote sensing Google Earth Engine (GEE) cloud computing satellite change detection continuous change detection and classification (CCDC) |
| title | The present situation and shifts observed in wetlands within the St. Lawrence Seaway region of Canada, utilizing imagery from the Landsat archive and the cloud-based platform Google Earth Engine |
| title_full | The present situation and shifts observed in wetlands within the St. Lawrence Seaway region of Canada, utilizing imagery from the Landsat archive and the cloud-based platform Google Earth Engine |
| title_fullStr | The present situation and shifts observed in wetlands within the St. Lawrence Seaway region of Canada, utilizing imagery from the Landsat archive and the cloud-based platform Google Earth Engine |
| title_full_unstemmed | The present situation and shifts observed in wetlands within the St. Lawrence Seaway region of Canada, utilizing imagery from the Landsat archive and the cloud-based platform Google Earth Engine |
| title_short | The present situation and shifts observed in wetlands within the St. Lawrence Seaway region of Canada, utilizing imagery from the Landsat archive and the cloud-based platform Google Earth Engine |
| title_sort | present situation and shifts observed in wetlands within the st lawrence seaway region of canada utilizing imagery from the landsat archive and the cloud based platform google earth engine |
| topic | Remote sensing Google Earth Engine (GEE) cloud computing satellite change detection continuous change detection and classification (CCDC) |
| url | https://www.tandfonline.com/doi/10.1080/20964471.2025.2454044 |
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