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
Journal article, 2025

This study examined wetland trends in the St. Lawrence Seaway (similar to 500,000 km(2)) 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 km(2)) and 55% (220,778 km(2)) of the entire study area are covered by wetland and non-wetland categories, respectively. It was also observed that 2.46% (12,495 km(2)) 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 km(2) and 5,701 km(2), 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.

change detection

cloud computing

continuous change detection and classification (CCDC)

wetlands

Remote sensing

satellite

Google Earth Engine (GEE)

Author

Meisam Amani

Natural Resources Canada

WSP Group

Mohammad Kakooei

Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI

University of Gothenburg

Rebecca Warren

WSP Group

Sahel Mahdavi

WSP Group

Kevin Murnaghan

Natural Resources Canada

Arsalan Ghorbanian

K. N. Toosi University of Technology

Amin Naboureh

Chinese Academy of Sciences

Big Earth Data

2096-4471 (ISSN) 2574-5417 (eISSN)

Vol. 9 1 47-71

Subject Categories (SSIF 2025)

Oceanography, Hydrology and Water Resources

Physical Geography

Earth Observation

DOI

10.1080/20964471.2025.2454044

More information

Latest update

3/28/2025