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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.
- Source :
- Journal of Maps; 2017, Vol. 13 Issue 2, p718-726, 9p
- Publication Year :
- 2017
-
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. [ABSTRACT FROM PUBLISHER]
- Subjects :
- LAND cover
REMOTE sensing
GEOLOGY
Subjects
Details
- Language :
- English
- ISSN :
- 17445647
- Volume :
- 13
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Journal of Maps
- Publication Type :
- Academic Journal
- Accession number :
- 127011121
- Full Text :
- https://doi.org/10.1080/17445647.2017.1372316