Combining spatiotemporal fusion and object-based image analysis for improving wetland mapping in complex and heterogeneous urban landscapes |
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| Authors: | Meng Zhang Wei Huang Songnian Li |
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| Affiliation: | 1. School of Geoscience and Info-Physics, Central South University, Changsha, China;2. Spatial Information Technology and Sustainable Development Research Center, Central South University, Changsha, China;3. Institute of Geography, Heidelberg University, Heidelberg, Germany;4. Department of Civil Engineering, Ryerson University, Toronto, Canada |
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| 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. |
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| Keywords: | Wetland mapping spatiotemporal fusion object-based image analysis complex heterogeneity urban landscape |
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