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

Artikel i vetenskaplig tidskrift, 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.