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 

机构地区：[1]Faculty of Geographical Science,State Key Laboratory of Remote Sensing Science,Beijing Normal University,Beijing,People’s Republic of China [2]Faculty of Geographical Science,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities,Beijing Normal University,Beijing,People’s Republic of China [3]School of Geography and Planning,Nanning Normal University,Nanning,People’s Republic of China [4]School of Remote Sensing and Geomatics Engineering,Nanjing University of Information Science and Technology,Nanjing,People’s Republic of China [5]College of Geographic and Environmental Sciences,Tianjin Normal University,Tianjin,People’s Republic of China

出　　处：《International Journal of Digital Earth》2023年第1期3199-3221,共23页国际数字地球学报（英文）

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

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 management.in 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 tools.We applied the framework to temporally dense Sentinel-1/2 imagery to produce annual refined wetland maps of the Dongting Lake Wetland(DLW)during 2015-2021.First,the continuous change detection(CCD)algorithm was utilized to migrate stable training samples.Then,annual 10 m preliminary land cover maps with 9 classes were produced using random forest algorithm and migrated samples.Ultimately,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 mapping.Our 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.

关 键 词：Wetland classification continuous change detection algorithm sample migration time series Dongting Lake wetland Google Earth Engine 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]