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.
基金
supported by the National Natural Science Foundation of China(grant numbers 42071393,U1901219 and U21A2022).
作者简介
CONTACT:Weiguo Jiang,jiangweiguo@bnu.edu.cn,State Key Laboratory of Remote Sensing Science,Faculty of Geographical Science,Beijing Normal University,Beijing,100875,People’s Republic of China,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities,Faculty of Geographical Science,Beijing Normal University,Beijing,100875,People’s Republic of China;CONTACT:Ziyan Ling,lingziyan@nnnu.edu.cn,School of Geography and Planning,Nanning Normal University,Nanning 530001,People’s Republic of China,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities,Faculty of Geographical Science,Beijing Normal University,Beijing,100875,People’s Republic of China;ORCID:Yawen Deng,http:/orcid.org/0000-0002-0734-037X。
