The Mongolian Plateau(MP),situated in the transitional zone between the Siberian taiga and the arid grasslands of Central Asia,plays a significant role as an Ecological Barrier(EB)with crucial implications for ecologi...The Mongolian Plateau(MP),situated in the transitional zone between the Siberian taiga and the arid grasslands of Central Asia,plays a significant role as an Ecological Barrier(EB)with crucial implications for ecological and resource security in Northeast Asia.EB is a vast concept and a complex issue related to many aspects such as water,land,air,vegetation,animals,and people,et al.It is very difficult to understand the whole of EB without a comprehensive perspective,that traditional diverse studies cannot cover.Big data and artificial intelligence(AI)have enabled a shift in the research paradigm.Faced with these requirements,this study identified issues in the construction of EB on MP from a big data perspective.This includes the issues,progress,and future recommendations for EB construction-related studies using big data and AI.Current issues cover the status of theoretical studies,technical bottlenecks,and insufficient synergistic analyses related to EB construction.Research progress introduces advances in scientific research driven by big data in three key areas of MP:natural resources,the ecological environment,and sustainable development.For the future development of EB construction on MP,it is recommended to utilize big data and intelligent computing technologies,integrate extensive regional data resources,develop precise algorithms and automated tools,and construct a big data collaborative innovation platform.This study aims to call for more attention to big data and AI applications in EB studies,thereby supporting the achievement of sustainable development goals in the MP and enhancing the research paradigm transforming in the fields of resources and the environment.展开更多
基金The National Natural Science Foundation of China(32161143025)The National Key R&D Program of China(2022YFE0119200)+4 种基金The Science&Technology Fundamental Resources Investigation Program of China(2022FY101902)The Mongolian Foundation for Science and Technology(NSFC_2022/01,CHN2022/276)The Key R&D and Achievement Transformation Plan Project in Inner Mongolia Autonomous Region(2023KJHZ0027)The Key Project of Innovation LREIS(KPI006)The Construction Project of China Knowledge Center for Engineering Sciences and Technology(CKCEST-2023-1-5)。
文摘The Mongolian Plateau(MP),situated in the transitional zone between the Siberian taiga and the arid grasslands of Central Asia,plays a significant role as an Ecological Barrier(EB)with crucial implications for ecological and resource security in Northeast Asia.EB is a vast concept and a complex issue related to many aspects such as water,land,air,vegetation,animals,and people,et al.It is very difficult to understand the whole of EB without a comprehensive perspective,that traditional diverse studies cannot cover.Big data and artificial intelligence(AI)have enabled a shift in the research paradigm.Faced with these requirements,this study identified issues in the construction of EB on MP from a big data perspective.This includes the issues,progress,and future recommendations for EB construction-related studies using big data and AI.Current issues cover the status of theoretical studies,technical bottlenecks,and insufficient synergistic analyses related to EB construction.Research progress introduces advances in scientific research driven by big data in three key areas of MP:natural resources,the ecological environment,and sustainable development.For the future development of EB construction on MP,it is recommended to utilize big data and intelligent computing technologies,integrate extensive regional data resources,develop precise algorithms and automated tools,and construct a big data collaborative innovation platform.This study aims to call for more attention to big data and AI applications in EB studies,thereby supporting the achievement of sustainable development goals in the MP and enhancing the research paradigm transforming in the fields of resources and the environment.
