Automated and refined wetland mapping of Dongting Lake using migrated training samples based on temporally dense Sentinel 1/2 imagery

作     者：Yawen Deng Weiguo Jiang Ziyan Ling Xiaoya Wang Kaifeng Peng Zhuo Li 

作者机构：Faculty of Geographical ScienceState Key Laboratory of Remote Sensing ScienceBeijing Normal UniversityBeijingPeople’s Republic of China Faculty of Geographical ScienceBeijing Key Laboratory for Remote Sensing of Environment and Digital CitiesBeijing Normal UniversityBeijingPeople’s Republic of China School of Geography and PlanningNanning Normal UniversityNanningPeople’s Republic of China School of Remote Sensing and Geomatics EngineeringNanjing University of Information Science and TechnologyNanjingPeople’s Republic of China College of Geographic and Environmental SciencesTianjin Normal UniversityTianjinPeople’s Republic of China 

出 版 物：《International Journal of Digital Earth》 (国际数字地球学报（英文）)

年 卷 期：2023年第16卷第1期

页      面：3199-3221页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：supported by the National Natural Science Foundation of China(grant numbers 42071393 U1901219 and U21A2022) 

主　　题：Wetland classification continuous change detection algorithm sample migration time series Dongting Lake wetland Google Earth Engine 

摘      要：Wetlands provide vital ecological services for both humans and environment,necessitating continuous,refined and up-to-date mapping of wetlands for conservation and *** this study,we developed an automated and refined wetland mapping framework integrating training sample migration method,supervised machine learning and knowledge-driven rules using Google Earth Engine(GEE)platform and open-source geospatial *** applied the framework to temporally dense Sentinel-1/2 imagery to produce annual refined wetland maps of the Dongting Lake Wetland(DLW)during ***,the continuous change detection(CCD)algorithm was utilized to migrate stable training ***,annual 10 m preliminary land cover maps with 9 classes were produced using random forest algorithm and migrated ***,annual 10 m refined wetland maps were generated based on preliminary land cover maps via knowledge-driven rules from geometric features and available water-related inventories,with Overall Accuracy(OA)ranging from 81.82%(2015)to 93.84%(2020)and Kappa Coefficient(KC)between 0.73(2015)and 0.91(2020),demonstrating satisfactory performance and substantial potential for accurate,timely and type-refined wetland *** methodological framework allows rapid and accurate monitoring of wetland dynamics and could provide valuable information and methodological support for monitoring,conservation and sustainable development of wetland ecosystem.