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...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2019
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| 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
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| Verfasserangaben: | Meng Zhang, Yongnian Zeng, Wei Huang, Songnian Li |
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| 245 | 1 | 0 | |a Combining spatiotemporal fusion and object-based image analysis for improving wetland mapping in complex and heterogeneous urban landscapes |c Meng Zhang, Yongnian Zeng, Wei Huang, Songnian Li |
| 264 | 1 | |c 2019 | |
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| 500 | |a Gesehen am 12.11.2019 | ||
| 500 | |a Published online: 17 May 2018 | ||
| 520 | |a 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. | ||
| 534 | |c 2018 | ||
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| 650 | 4 | |a complex heterogeneity | |
| 650 | 4 | |a delta | |
| 650 | 4 | |a land-cover classification | |
| 650 | 4 | |a object-based image analysis | |
| 650 | 4 | |a poyang lake | |
| 650 | 4 | |a reflectance | |
| 650 | 4 | |a spatiotemporal fusion | |
| 650 | 4 | |a time-series | |
| 650 | 4 | |a tm | |
| 650 | 4 | |a urban landscape | |
| 650 | 4 | |a vegetation | |
| 650 | 4 | |a Wetland mapping | |
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| 700 | 1 | |a Huang, Wei |e VerfasserIn |0 (DE-588)1162162155 |0 (DE-627)1025611454 |0 (DE-576)507238184 |4 aut | |
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