Long-term spatio-temporal changes of wetlands in Tibetan Plateau and their response to climate change

Abstract

Understanding the changes of wetlands on the Tibetan Plateau (TP) is important for action to ensure ecosystem resilience in Asia. However, mapping long-term changes of wetlands at high resolutions remains challenging. Here, we quantify the spatio-temporal changes of TP wetlands from 1990 to 2019, by combining Landsat imagery with deep learning to map TP wetlands. The deep learning model combined with transfer learning strategies achieves high classification performance using a few class samples. The validation results show that the user’s accuracy is 95.5% and the producer's accuracy is 90.1% for wetland extraction, satisfying with subsequent analysis of wetland spatio-temporal changes. Based on the wetland extraction model, we have created annual wetland map in the TP for the first time. We find that the areal extent of TP wetlands has increased by 31.2 ± 6.6 % over the past 30 years. The growth is particularly noticeable (by 22.5 ± 6.2 %) during 2015–2019. Spatially, the wetland areal extent on the Qiangtang Plateau (in the inner part of TP and as habitats of various birds and rare wild animals) and the source region of Yangtze River show the largest expansions by 55.3 ± 9.3 % and 44.0 ± 8.9 %, respectively. Such rapid wetland expansions are associated with increasing rainfall and temperature which have heterogeneous influences on wetland changes across the TP. Our findings provide evidence for the impact of climate change on wetland area. The marked wetland changes highlight that climate mitigation is a priority for high-latitude ecosystems.

Keywords

• Tibetan Plateau
• Wetlands
• Deep learning
• Climate change