Journal
JOURNAL OF MAPS
Volume 13, Issue 2, Pages 718-726Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/17445647.2017.1372316
Keywords
Sentinel-1; Sentinel-2; land cover mapping; data fusion; segmentation; Colombia
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Funding
- project 'Strategies for natural resources valuation and ownership as a climate change adaptation mechanism in the Lower Magdalena region, Colombia', Gobernacion de Cundinamarca
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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.
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