Self-Supervised Learning and Multi-Sensor Fusion for Alpine Wetland Vegetation Mapping: Bayinbuluke, China
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| Vydáno v: | Plants vol. 14, no. 20 (2025), p. 3153-3170 |
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| Hlavní autor: | |
| Další autoři: | , , , , |
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MDPI AG
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| On-line přístup: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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MARC
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| 001 | 3265937220 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2223-7747 | ||
| 024 | 7 | |a 10.3390/plants14203153 |2 doi | |
| 035 | |a 3265937220 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231551 |2 nlm | ||
| 100 | 1 | |a Zaka Muhammad Murtaza |u 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 | |
| 245 | 1 | |a Self-Supervised Learning and Multi-Sensor Fusion for Alpine Wetland Vegetation Mapping: Bayinbuluke, China | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Accuracy | ||
| 653 | |a Invasive species | ||
| 653 | |a Datasets | ||
| 653 | |a Classification | ||
| 653 | |a Image resolution | ||
| 653 | |a Ecological monitoring | ||
| 653 | |a Flowers & plants | ||
| 653 | |a Indigenous species | ||
| 653 | |a Wetlands | ||
| 653 | |a Satellite imagery | ||
| 653 | |a Biodiversity | ||
| 653 | |a Ecosystem integrity | ||
| 653 | |a Data integration | ||
| 653 | |a Plants (botany) | ||
| 653 | |a Vegetation mapping | ||
| 653 | |a Vegetation surveys | ||
| 653 | |a Data collection | ||
| 653 | |a Vegetation patterns | ||
| 653 | |a Heterogeneity | ||
| 653 | |a Self-supervised learning | ||
| 653 | |a Remote sensing | ||
| 653 | |a Invasive plants | ||
| 653 | |a Ground truth | ||
| 653 | |a Sensors | ||
| 653 | |a Spatial heterogeneity | ||
| 653 | |a Introduced species | ||
| 653 | |a Mapping | ||
| 653 | |a Annotations | ||
| 653 | |a Multisensor fusion | ||
| 653 | |a Semantics | ||
| 700 | 1 | |a Samat Alim |u 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 | |
| 700 | 1 | |a Jilili, Abuduwaili |u 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 | |
| 700 | 1 | |a Zhu Enzhao |u 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 | |
| 700 | 1 | |a Arslan, Akhtar |u University of Chinese Academy of Sciences, Beijing 100049, China | |
| 700 | 1 | |a Li, Wenbo |u Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy | |
| 773 | 0 | |t Plants |g vol. 14, no. 20 (2025), p. 3153-3170 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3265937220/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3265937220/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3265937220/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |