An automatic classification method with weak supervision for large-scale wetland mapping in transboundary (Irtysh River) basin using Sentinel 1/2 imageries
Luo K. Samat A. Van de voorde T. Jiang W. Li W. Abuduwaili J.
April 2025Academic Press
Journal of Environmental Management
2025#380
Wetlands are essential ecosystems that play a significant role in biodiversity conservation and environmental stability. Monitoring their changes is crucial for understanding ecological dynamics and informing conservation strategies, particularly those in transboundary basins. This study introduces a novel automatic classification method for mapping and detecting wetland changes in the Irtysh River Basin. Utilizing Google Earth Engine (GEE) as the primary platform, this method integrates unsupervised classification, sample transfer techniques, and object-oriented random forest (OORF) algorithms to generate accurate training samples and delineate wetlands. Using Sentinel-1 and Sentinel-2 satellite data, we created high-resolution wetland distribution maps. The process begins with unsupervised classification to identify wetland inundation zones, followed by overlaying permanent water bodies and surface depressions to refine the sample set. Sample transfer, using spectral similarity metrics with the GWL_FCS30 product, further enhances the robustness of the training data. The selected features from Sentinel-1 and Sentinel-2 data, including spectral indices, phenological parameters, and textural features, were optimized, resulting in 18 optimal features for the OORF classification. The classification achieved a high overall accuracy of 96.96 %, with a sample accuracy of 98.1 %, and both Users and Producers Accuracies consistently above 88 %. Spatiotemporal analysis of wetland changes from 2017 to 2023 revealed significant fluctuations, including a net loss of approximately 1,743.92 km2 of wetlands in the Irtysh River Basin. This study provides an effective and innovative method for large-scale wetland monitoring, offering valuable insights to support conservation and management efforts.
Irtysh River Basin , Object-oriented random forest (OORF) , Sample transferring , Sentinel 1/2 , Spatiotemporal analysis , Wetland classification
Text of the article Перейти на текст статьи
State Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China
College of Geography and Remote Sensing Science, Xinjiang University, Urumqi, 830017, China
Department of Geography, Ghent University, Ghent, Belgium
Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
China-Kazakhstan Joint Laboratory for RS Technology and Application, Al-Farabi Kazakh National University, Almaty, 050012, Kazakhstan
University of Chinese Academy of Sciences, Beijing, 100049, China
State Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands
College of Geography and Remote Sensing Science
Department of Geography
Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities
China-Kazakhstan Joint Laboratory for RS Technology and Application
University of Chinese Academy of Sciences
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026