Combining spatiotemporal fusion and object-based image analysis for improving wetland mapping in complex and heterogeneous urban landscapes

• Verfasst von: Zhang, Meng [VerfasserIn]
• Verfasst von: Zeng, Yongnian [VerfasserIn]
• Verfasst von: Huang, Wei [VerfasserIn]
• Titel: Combining spatiotemporal fusion and object-based image analysis for improving wetland mapping in complex and heterogeneous urban landscapes
• Verf.angabe: Meng Zhang, Yongnian Zeng, Wei Huang, Songnian Li
• Jahr: 2019
• Jahr des Originals: 2018
• Umfang: 18 S.
• Fussnoten: Gesehen am 12.11.2019 ; Published online: 17 May 2018
• Titel Quelle: Enthalten in: Geocarto international
• Ort Quelle: London [u.a.] : Taylor & Francis, 1986
• Jahr Quelle: 2019
• Band/Heft Quelle: 34(2019), 10, Seite 1144-1161
• ISSN Quelle: 1752-0762
• DOI: doi:10.1080/10106049.2018.1474275
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: china
• Sach-SW: complex heterogeneity
• Sach-SW: delta
• Sach-SW: land-cover classification
• Sach-SW: object-based image analysis
• Sach-SW: poyang lake
• Sach-SW: reflectance
• Sach-SW: spatiotemporal fusion
• Sach-SW: time-series
• Sach-SW: tm
• Sach-SW: urban landscape
• Sach-SW: vegetation
• Sach-SW: Wetland mapping
• Sach-SW: worldview-2
• K10plus-PPN: 1681680238

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.