利用Google Earth Engine和机器学习测量过去20年天山冰川的变化

作     者：庄立超 柯长青 蔡宇 努拉尼·瓦希德 ZHUANG Lichao;KE Changqing;CAI Yu;NOURANI Vahid

作者机构：Jiangsu Provincial Key Laboratory of Geographic Information Science and TechnologyKey Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural ResourcesSchool of Geography and Ocean ScienceNanjing UniversityNanjing 210023China Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjing 210023China Collaborative Innovation Center of South China Sea StudiesNanjing 210023China Center of Excellence in HydroinformaticsFaculty of Civil EngineeringUniversity of TabrizTabrizIran 

出 版 物：《地理学报:英文版》 (Journal of Geographical Sciences)

年 卷 期：2023年第33卷第9期

页      面：1939-1964页

核心收录：

学科分类：07[理学] 0705[理学-地理学] 070501[理学-自然地理学] 

基　　金：National Natural Science Foundation of China No.41830105 No.42011530120 

主　　题：glacier change big remote sensing data classification machine learning google earth engine Tianshan Mountians climatic change 

摘      要：Glaciers in the Tianshan Mountains are an essential water resource in Central Asia,and it is necessary to identify their variations at large spatial scales with high *** combined optical and SAR images,based on several machine learning algorithms and ERA-5 land data provided by Google Earth Engine,to map and explore the glacier distribution and changes in the Tianshan in 2001,2011,and *** forest was the best performing classifier,and the overall glacier area retreat rate showed acceleration from 0.87%/a to 1.49%/a,while among the sub-regions,Dzhungarsky Alatau,Central and Northern/Western Tianshan,and Eastern Tianshan showed a slower,stable,and sharp increase rates after 2011,*** retreat was more severe in the mountain periphery,low plains and valleys,with more area lost near the glacier equilibrium *** sustained increase in summer temperatures was the primary driver of accelerated glacier *** work demonstrates the advantage and reliability of fusing multisource images to map glacier distributions with high spatial and temporal resolutions using Google Earth *** high recognition accuracy helped to conduct more accurate and time-continuous glacier change studies for the study area.