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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

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Autores
Clerici, Nicola
Calderón, Cesar Augusto Valbuena
Posada Hostettler, Juan Manuel Roberto

Fecha
2017

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Taylor and Francis Ltd.

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Abstract
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. © 2017 The Author(s).
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Colombia , Data fusion , Land cover mapping , Segmentation , Sentinel-1 , Sentinel-2
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