Spatio-temporal change and driving mechanisms of land use/cover in Qarhan Salt Lake area during from 2000 to 2020,based on machine learning  

作　　者：Chao Yue ZiTao Wang JianPing Wang 

机构地区：[1]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 [2]Qinghai Provincial Key Laboratory of Geology and Environment of Salt Lakes,Xining,Qinghai 810008,China [3]University of Chinese Academy of Sciences,Beijing 100049,China

出　　处：《Research in Cold and Arid Regions》2024年第5期239-249,共11页寒旱区科学(英文版)

基　　金：supported cooperatively by the Second Tibetan Plateau Scientific Expedition and Research Program(2019QZKK0805);the National Natural Science Foundation of China(U20A2088);the Innovation Team Foundation of Qinghai Office of Science and Technology(2022-ZJ-903);CITIC Top 10 Technological Innovation Projects Comprehensive development and utilization of salt lake resources(2023ZXKYA05100).

摘　　要：The significance of land use classification has garnered attention due to its implications for climate and ecosystems.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.

关 键 词：Automatic machine learning Qarhan Salt Lake Land use classicification TRANSFORMATION 

分 类 号：S126[农业科学—农业基础科学] P901[天文地球—自然地理学]