Fusion of Sentinel-1A and Sentinel-2A data for land cover mapping: a case study in the lower Magdalena region, Colombia

Resultado de la investigación: Contribución a RevistaArtículo

18 Citas (Scopus)

Resumen

Land cover–land use (LCLU) classification tasks can take advantage of the fusion of radar and
optical remote sensing data, leading generally to increase mapping accuracy. Here we
propose a methodological approach to fuse information from the new European Space
Agency Sentinel-1 and Sentinel-2 imagery for accurate land cover mapping of a portion of
the Lower Magdalena region, Colombia. Data pre-processing was carried out using the
European Space Agency’s Sentinel Application Platform and the SEN2COR toolboxes. LCLU
classification was performed following an object-based and spectral classification approach,
exploiting also vegetation indices. A comparison of classification performance using three
commonly used classification algorithms was performed. The radar and visible-near infrared
integrated dataset classified with a Support Vector Machine algorithm produce the most
accurate LCLU map, showing an overall classification accuracy of 88.75%, and a Kappa
coefficient of 0.86. The proposed mapping approach has the main advantages of combining
the all-weather capability of the radar sensor, spectrally rich information in the visible-near
infrared spectrum, with the short revisit period of both satellites. The mapping results
represent an important step toward future tasks of aboveground biomass and carbon
estimation in the region.
Idioma originalEnglish (US)
Páginas (desde-hasta)718-726
Número de páginas8
PublicaciónJournal of Maps
Volumen13
N.º2
EstadoPublished - ago 24 2017

Huella dactilar

Colombia
land cover
radar
near infrared
land use
aboveground biomass
vegetation index
imagery
sensor
remote sensing
weather
carbon
performance
land use classification

Citar esto

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title = "Fusion of Sentinel-1A and Sentinel-2A data for land cover mapping: a case study in the lower Magdalena region, Colombia",
abstract = "Land cover–land use (LCLU) classification tasks can take advantage of the fusion of radar andoptical remote sensing data, leading generally to increase mapping accuracy. Here wepropose a methodological approach to fuse information from the new European SpaceAgency Sentinel-1 and Sentinel-2 imagery for accurate land cover mapping of a portion ofthe Lower Magdalena region, Colombia. Data pre-processing was carried out using theEuropean Space Agency’s Sentinel Application Platform and the SEN2COR toolboxes. LCLUclassification was performed following an object-based and spectral classification approach,exploiting also vegetation indices. A comparison of classification performance using threecommonly used classification algorithms was performed. The radar and visible-near infraredintegrated dataset classified with a Support Vector Machine algorithm produce the mostaccurate LCLU map, showing an overall classification accuracy of 88.75{\%}, and a Kappacoefficient of 0.86. The proposed mapping approach has the main advantages of combiningthe all-weather capability of the radar sensor, spectrally rich information in the visible-nearinfrared spectrum, with the short revisit period of both satellites. The mapping resultsrepresent an important step toward future tasks of aboveground biomass and carbonestimation in the region.",
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Fusion of Sentinel-1A and Sentinel-2A data for land cover mapping: a case study in the lower Magdalena region, Colombia. / Clerici, Nicola; Calderón , Cesar Augusto; Posada Hostettler, Juan Manuel Roberto.

En: Journal of Maps, Vol. 13, N.º 2, 24.08.2017, p. 718-726.

Resultado de la investigación: Contribución a RevistaArtículo

TY - JOUR

T1 - Fusion of Sentinel-1A and Sentinel-2A data for land cover mapping: a case study in the lower Magdalena region, Colombia

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AU - Calderón , Cesar Augusto

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PY - 2017/8/24

Y1 - 2017/8/24

N2 - Land cover–land use (LCLU) classification tasks can take advantage of the fusion of radar andoptical remote sensing data, leading generally to increase mapping accuracy. Here wepropose a methodological approach to fuse information from the new European SpaceAgency Sentinel-1 and Sentinel-2 imagery for accurate land cover mapping of a portion ofthe Lower Magdalena region, Colombia. Data pre-processing was carried out using theEuropean Space Agency’s Sentinel Application Platform and the SEN2COR toolboxes. LCLUclassification was performed following an object-based and spectral classification approach,exploiting also vegetation indices. A comparison of classification performance using threecommonly used classification algorithms was performed. The radar and visible-near infraredintegrated dataset classified with a Support Vector Machine algorithm produce the mostaccurate LCLU map, showing an overall classification accuracy of 88.75%, and a Kappacoefficient of 0.86. The proposed mapping approach has the main advantages of combiningthe all-weather capability of the radar sensor, spectrally rich information in the visible-nearinfrared spectrum, with the short revisit period of both satellites. The mapping resultsrepresent an important step toward future tasks of aboveground biomass and carbonestimation in the region.

AB - Land cover–land use (LCLU) classification tasks can take advantage of the fusion of radar andoptical remote sensing data, leading generally to increase mapping accuracy. Here wepropose a methodological approach to fuse information from the new European SpaceAgency Sentinel-1 and Sentinel-2 imagery for accurate land cover mapping of a portion ofthe Lower Magdalena region, Colombia. Data pre-processing was carried out using theEuropean Space Agency’s Sentinel Application Platform and the SEN2COR toolboxes. LCLUclassification was performed following an object-based and spectral classification approach,exploiting also vegetation indices. A comparison of classification performance using threecommonly used classification algorithms was performed. The radar and visible-near infraredintegrated dataset classified with a Support Vector Machine algorithm produce the mostaccurate LCLU map, showing an overall classification accuracy of 88.75%, and a Kappacoefficient of 0.86. The proposed mapping approach has the main advantages of combiningthe all-weather capability of the radar sensor, spectrally rich information in the visible-nearinfrared spectrum, with the short revisit period of both satellites. The mapping resultsrepresent an important step toward future tasks of aboveground biomass and carbonestimation in the region.

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