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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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  • Nicola Clerici
  • Cesar Augusto Valbuena Calderón
  • Juan Manuel Posada

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.

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  • Nicola Clerici & Cesar Augusto Valbuena Calderón & Juan Manuel Posada, 2017. "Fusion of Sentinel-1A and Sentinel-2A data for land cover mapping: a case study in the lower Magdalena region, Colombia," Journal of Maps, Taylor & Francis Journals, vol. 13(2), pages 718-726, November.
    • Handle: RePEc:taf:tjomxx:v:13:y:2017:i:2:p:718-726
    DOI: 10.1080/17445647.2017.1372316
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    1. Kuemmerle, Tobias & Erb, Karlheinz & Meyfroidt, Patrick & Müller, Daniel & Verburg, Peter H & Estel, Stephan & Haberl, Helmut & Hostert, Patrick & Jepsen, Martin R. & Kastner, Thomas & Levers, Christi, 2013. "Challenges and opportunities in mapping land use intensity globally," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 5(5), pages 484-493.
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    1. Odile Close & Beaumont Benjamin & Sophie Petit & Xavier Fripiat & Eric Hallot, 2018. "Use of Sentinel-2 and LUCAS Database for the Inventory of Land Use, Land Use Change, and Forestry in Wallonia, Belgium," Land, MDPI, vol. 7(4), pages 1-16, December.
    2. Guste Metrikaityte & Jurate Suziedelyte Visockiene & Kestutis Papsys, 2022. "Digital Mapping of Land Cover Changes Using the Fusion of SAR and MSI Satellite Data," Land, MDPI, vol. 11(7), pages 1-20, July.
    3. Paolo De Fioravante & Tania Luti & Alice Cavalli & Chiara Giuliani & Pasquale Dichicco & Marco Marchetti & Gherardo Chirici & Luca Congedo & Michele Munafò, 2021. "Multispectral Sentinel-2 and SAR Sentinel-1 Integration for Automatic Land Cover Classification," Land, MDPI, vol. 10(6), pages 1-35, June.
    4. Per Arild Garnåsjordet & Margrete Steinnes & Zofie Cimburova & Megan Nowell & David N. Barton & Iulie Aslaksen, 2020. "Urban Green. Integrating ecosystem extent and condition as a basis for ecosystem accounts. Examples from the Oslo region," Discussion Papers 941, Statistics Norway, Research Department.
    5. Tesfamariam Engida Mengesha & Lulseged Tamene Desta & Paolo Gamba & Getachew Tesfaye Ayehu, 2024. "Multi-Temporal Passive and Active Remote Sensing for Agricultural Mapping and Acreage Estimation in Context of Small Farm Holds in Ethiopia," Land, MDPI, vol. 13(3), pages 1-29, March.
    6. Willie Doaemo & Midhun Mohan & Esmaeel Adrah & Shruthi Srinivasan & Ana Paula Dalla Corte, 2020. "Exploring Forest Change Spatial Patterns in Papua New Guinea: A Pilot Study in the Bumbu River Basin," Land, MDPI, vol. 9(9), pages 1-18, August.
    7. Spicciarelli Renato & Mirauda Domenica, 2020. "Lago Grande in Monticchio Area (Southern Italy): Integrating Multitemporal Airborne Images, Satellite Images (Sentinel-2)," International Journal of Agriculture & Sustainable Development, 50sea, vol. 2(1), pages 1-23, March.
    8. Boudewijn van Leeuwen & Zalán Tobak & Ferenc Kovács, 2020. "Sentinel-1 and -2 Based near Real Time Inland Excess Water Mapping for Optimized Water Management," Sustainability, MDPI, vol. 12(7), pages 1-21, April.

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