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Progress,Challenges and Opportunities of Permafrost Mapping in China
Authors:Youhua Ran  Xin Li
Institution:1. Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;3. Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China;4. CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing 100101, China
Abstract:Permafrost mapping is the basic research direction of geocryology. This paper summarized the development history of permafrost mapping in China. The classification system, permafrost (region) area, and the progress of mapping methods at three aspects, i.e. statistical learning, empirical model and physical model in China were discussed. According to the differences of tools, data availability, models, and methods, permafrost mapping in China has experienced three development stages, including initial stage (1960s-1980s), preliminary application of remote sensing and GIS (1990s-2000s), and fusion of multi-source observation and integrated model (2010-). There are differences in the understanding of permafrost distribution and area in different stages. With the improvement of spatial resolution and maps accuracy, the new permafrost map represents the true permafrost distribution and area better. For methodology, the development of empirical model and physical model runs through three stages. The combination of remote sensing and empirical-based model is the dominated method at present, but statistical learning shows great potential with the accumulation of ground-based and remote sensed data, and physical-based model develops rapidly in China. Coupling with other models, especially with distributed hydrological models, physical-based model provides an important tool for simulating the ecological and hydrological effects of frozen soil changes. The development of earth observation system provides unprecedented opportunities for monitoring permafrost. The optimization of ground survey, data accumulation and open sharing, further development of remote sensing methods, deepening processes understanding of deeper permafrost, further improvement of physical models and their integration with multi-source observations will help break through the challenges faced by permafrost mapping in China and promote the understanding of past, present and future changes of permafrost in China.
Keywords:Remote sensing  Empirical model  Physical model  Permafrost area  Statistical learning    
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