Self-Supervised Learning and Multi-Sensor Fusion for Alpine Wetland Vegetation Mapping: Bayinbuluke, China
Zaka M.M. Samat A. Abuduwaili J. Zhu E. Akhtar A. Li W.
October 2025Multidisciplinary Digital Publishing Institute (MDPI)
Plants
2025#14Issue 20
Accurate mapping of wetland vegetation is essential for ecological monitoring and conservation, yet it remains challenging due to the spatial heterogeneity of wetlands, the scarcity of ground-truth data, and the spread of invasive species. Invasive plants alter native vegetation patterns, making their early detection critical for preserving ecosystem integrity. This study proposes a novel framework that integrates self-supervised learning (SSL), supervised segmentation, and multi-sensor data fusion to enhance vegetation classification in the Bayinbuluke Alpine Wetland, China. High-resolution satellite imagery from PlanetScope-3 and Jilin-1 was fused, and SSL methods—including BYOL, DINO, and MoCo v3—were employed to learn transferable feature representations without extensive labeled data. The results show that SSL methods exhibit consistent variations in classification performance, while multi-sensor fusion significantly improves the detection of rare and fragmented vegetation patches and enables the early identification of invasive species. Overall, the proposed SSL–fusion strategy reduces reliance on labor-intensive field data collection and provides a scalable, high-precision solution for wetland monitoring and invasive species management.
alpine wetlands (Bayinbuluke, China) , invasive plant species (IPS) , multi-modal data fusion , remote sensing of wetlands , self-supervised learning , vegetation mapping
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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
University of Chinese Academy of Sciences, Beijing, 100049, China
China-Kazakhstan Joint Laboratory for RS Technology and Application, Al-Farabi Kazakh National University, Almaty, 050012, Kazakhstan
CAS Research Center for Ecology and Environment of Central Asia, Urumqi, 830011, China
National Engineering Technology Research Center for Desert-Oasis Ecological Construction, Urumqi, 830011, China
Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, 27100, Italy
State Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands
University of Chinese Academy of Sciences
China-Kazakhstan Joint Laboratory for RS Technology and Application
CAS Research Center for Ecology and Environment of Central Asia
National Engineering Technology Research Center for Desert-Oasis Ecological Construction
Department of Electrical
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