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

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 classific...

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Hauptverfasser: Zhang, Meng (VerfasserIn) , Zeng, Yongnian (VerfasserIn) , Huang, Wei (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 2019
In: Geocarto international
Year: 2018, Jahrgang: 34, Heft: 10, Pages: 1144-1161
ISSN:1752-0762
DOI:10.1080/10106049.2018.1474275
Online-Zugang:Verlag, Volltext: https://doi.org/10.1080/10106049.2018.1474275
Volltext
Verfasserangaben:Meng Zhang, Yongnian Zeng, Wei Huang, Songnian Li
Beschreibung
Zusammenfassung: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.
Beschreibung:Gesehen am 12.11.2019
Published online: 17 May 2018
Beschreibung:Online Resource
ISSN:1752-0762
DOI:10.1080/10106049.2018.1474275

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