Combining spatiotemporal fusion and object-based image analysis for improving wetland mapping in complex and heterogeneous urban landscapes. Issue 10 (24th August 2019)

• Record Type: Journal Article
• Authors: Zhang, Meng; Zeng, Yongnian; Huang, Wei; Li, Songnian
• Is Part Of: Geocarto international. Volume 34:Issue 10(2019)
• Page Start: 1144
• Page End: 1161
• Publication Date: 2019-08-24
• Subjects: Wetland mapping -- spatiotemporal fusion -- object-based image analysis -- complex heterogeneity -- urban landscape; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2018.1474275
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: Remote sensing has been proven promising in wetland mapping. However, conventional methods in a complex and heterogeneous urban landscape usually use mono temporal Landsat TM/ETM + images, which have great uncertainty due to the spectral similarity of different land covers, and pixel-based classifications may not meet the accuracy requirement. This paper proposes an approach that combines spatiotemporal fusion and object-based image analysis, using the spatial and temporal adaptive reflectance fusion model to generate a time series of Landsat 8 OLI images on critical dates of sedge swamp and paddy rice, and the time series of MODIS NDVI to calculate phenological parameters for identifying wetlands with an object-based method. The results of a case study indicate that different types of wetlands can be successfully identified, with 92.38%. The overall accuracy and 0.85 Kappa coefficient, and 85% and 90% for the user's accuracies of sedge swamp and paddy respectively.