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Scale Issues of wetland classification and Mapping Using Remote Sensing Images: A Case of Honghe National Nature Reserve in Sanjiang Plain, Northeast China

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Chinese Geographical Science 2011年第2期21卷 230-240页

作者： GONG Huili JIAO Cuicui ZHOU Demin LI Na Base of State Key Laboratory of Urban Environmental Process and Digital Modeling Capital Normal University Beijing 100048 China Key Laboratory of 3D Information Acquisition and Application of Ministry of Education Capital Normal University Beijing 100048 China

wetland research has become a hot spot linking multiple disciplines presently. wetland classification and mapping is the basis for wetland research. It is difficult to generate wetland data sets using traditional methods because of the low accessibility of wetlands, hence remote sensing data have become one of the primary data sources in wetland research. This paper presents a case study conducted at the core area of Honghe National Nature Reserve in the Sanjiang Plain, Northeast China. In this study, three images generated by airship, from Thematic Mapper and from SPOT 5 were selected to produce wetland maps at three different wetland landscape levels. After assessing classification accuracies of the three maps, we compared the different wetland mapping results of 11 plant communities to the airship image, 6 plant ecotypes to the TM image and 9 landscape classifications to the SPOT 5 image. We discussed the different characteristics of the hierarchical ecosystem classifications based on the spatial scales of the different images. The results indicate that spatial scales of remote sensing data have an important link to the hierarchies of wetland plant ecosystems displayed on the wetland landscape maps. The richness of wetland landscape information derived from an image closely relates to its spatial resolution. This study can enrich the ecological classification methods and mapping techniques dealing with the spatial scales of different remote sensing images. With a better understanding of classification accuracies in mapping wetlands by using different scales of remote sensing data, we can make an appropriate approach for dealing with the scale issue of remote sensing images.

关键词： wetland classification remote sensing image spatial resolution scale mapping wetland

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

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International Journal of Digital Earth 2023年第1期16卷 3199-3221页

作者： Yawen Deng Weiguo Jiang Ziyan Ling Xiaoya Wang Kaifeng Peng Zhuo Li Faculty of Geographical Science State Key Laboratory of Remote Sensing ScienceBeijing Normal UniversityBeijingPeople’s Republic of China Faculty of Geographical Science Beijing Key Laboratory for Remote Sensing of Environment and Digital CitiesBeijing Normal UniversityBeijingPeople’s Republic of China School of Geography and Planning Nanning Normal UniversityNanningPeople’s Republic of China School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science and TechnologyNanjingPeople’s Republic of China College of Geographic and Environmental Sciences Tianjin Normal UniversityTianjinPeople’s Republic of China

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.

关键词： wetland classification continuous change detection algorithm sample migration time series Dongting Lake wetland Google Earth Engine

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