首页|Spatio-temporal change and driving mechanisms of land use/cover in Qarhan Salt Lake area during from 2000 to 2020,based on machine learning
Spatio-temporal change and driving mechanisms of land use/cover in Qarhan Salt Lake area during from 2000 to 2020,based on machine learning
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Spatio-temporal change and driving mechanisms of land use/cover in Qarhan Salt Lake area during from 2000 to 2020,based on machine learning
The significance of land use classification has garnered attention due to its implications for climate and ecosys-tems.This paper establishes a connection by introducing and applying automatic machine learning(Auto ML)techniques to salt lake landscape,with a specific focus on the Qarhan Salt Lake area.Utilizing Landsat-5 Thematic Mappe(TM)and Landsat-8 Operational Land Imager(OLI)imagery,six machine learning algorithms were employed to classify eight land use types from 2000 to 2020.Results show that XGBLD performed optimally with 77％accuracy.Over two decades,salt fields,construction land,and water areas increased due to transformations in saline land and salt flats.The exposed lakes area exhibited a rise followed by a decline,mainly transforming into salt flats.Agricultural land areas slightly increased,influenced by both human activities and climate.Our analysis reveals a strong correlation between salt fields and precipitation,while exposed lakes demonstrate a significant negative correlation with evaporation and temperature,highlighting their vulnerability to climate change.Additionally,human water usage was identified as a significant factor impacting land use change,emphasizing the dual influence of anthropogenic activities and natural factors.This paper addresses the void in the application of Auto ML in salt lake environments and provides valuable insights into the dynamic evolution of land use types in the Qarhan Salt Lake region.
Automatic machine learningQarhan Salt LakeLand use classicificationTransformation
Chao Yue、ZiTao Wang、JianPing Wang
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Key Laboratory of Comprehensive and Highly Efficient Utilization of Salt Lake Resources,Qinghai Institute of Salt Lakes,Chinese Academy of Sciences,Xining,Qinghai 810008,China
Qinghai Provincial Key Laboratory of Geology and Environment of Salt Lakes,Xining,Qinghai 810008,China
University of Chinese Academy of Sciences,Beijing 100049,China
Automatic machine learning Qarhan Salt Lake Land use classicification Transformation
NETL
NSTL
万方数据
