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Multispectral Sentinel-2 and SAR Sentinel-1 Integration for Automatic Land Cover Classification

Author

Listed:
  • Paolo De Fioravante

    (Italian Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Rome, Italy
    Department of Innovation in Biology, Agri-Food and Forest systems (DIBAF), University of Tuscia, Via San Camillo de Lellis SNC, 01100 Viterbo, Italy)

  • Tania Luti

    (Italian Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Rome, Italy
    Dipartimento di Scienze della Terra, Università degli Studi di Firenze, Via La Pira 4, 50121 Firenze, Italy)

  • Alice Cavalli

    (Department of Innovation in Biology, Agri-Food and Forest systems (DIBAF), University of Tuscia, Via San Camillo de Lellis SNC, 01100 Viterbo, Italy)

  • Chiara Giuliani

    (Italian Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Rome, Italy)

  • Pasquale Dichicco

    (geoLAB-Laboratory of Forest Geomatics, Department of Agricultural, Food and Forestry Systems, University of Florence, Via San Bonaventura, 13, 50145 Firenze, Italy)

  • Marco Marchetti

    (Dipartimento di Bioscienze e Territorio, Università degli Studi del Molise, C/da Fonte Lappone, 86090 Pesche, Italy)

  • Gherardo Chirici

    (geoLAB-Laboratory of Forest Geomatics, Department of Agricultural, Food and Forestry Systems, University of Florence, Via San Bonaventura, 13, 50145 Firenze, Italy)

  • Luca Congedo

    (Italian Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Rome, Italy)

  • Michele Munafò

    (Italian Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Rome, Italy)

Abstract

The study of land cover and land use dynamics are fundamental to understanding the radical changes that human activity is causing locally and globally and to analyse the continuous metamorphosis of landscape. In Europe, the Copernicus Program offers numerous territorial monitoring tools to users and decision makers, such as Sentinel data. This research aims at developing and implementing a land cover mapping and change detection methodology through the classification of Copernicus Sentinel-1 and Sentinel-2 satellite data. The goal is to create a versatile and economically sustainable algorithm capable of rapidly processing large amounts of data, allowing the creation of national-scale products with high spatial resolution and update frequency for operational purposes. Great attention was paid to compatibility with the main activities planned in the near future at the national and European level. In this sense, a land cover classification system consistent with the European specifications of the EAGLE group has been adopted. The methodology involves the definition of distinct sets of decision rules for each of the land cover macro-classes and for the land cover change classes. The classification refers to pixels’ spectral and backscatter characteristics, exploiting the main multi-temporal indices while proposing two new ones: the NDCI to distinguish between broad-leaved and needle-leaved trees, and the Burned Index (BI) to identify burned areas. This activity allowed for the production of a land cover map for 2018 and the change detection related to forest disturbances and land consumption for 2017–2018, reaching an overall accuracy of 83%.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jlands:v:10:y:2021:i:6:p:611-:d:570538
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    References listed on IDEAS

    as
    1. Fiona Ngadze & Kudzai Shaun Mpakairi & Blessing Kavhu & Henry Ndaimani & Monalisa Shingirayi Maremba, 2020. "Exploring the utility of Sentinel-2 MSI and Landsat 8 OLI in burned area mapping for a heterogenous savannah landscape," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-13, May.
    2. 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.
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    Cited by:

    1. Giulia Cecili & Paolo De Fioravante & Luca Congedo & Marco Marchetti & Michele Munafò, 2022. "Land Consumption Mapping with Convolutional Neural Network: Case Study in Italy," Land, MDPI, vol. 11(11), pages 1-19, October.
    2. Paolo De Fioravante & Andrea Strollo & Alice Cavalli & Angela Cimini & Daniela Smiraglia & Francesca Assennato & Michele Munafò, 2023. "Ecosystem Mapping and Accounting in Italy Based on Copernicus and National Data through Integration of EAGLE and SEEA-EA Frameworks," Land, MDPI, vol. 12(2), pages 1-22, January.
    3. Angela Cimini & Paolo De Fioravante & Nicola Riitano & Pasquale Dichicco & Annagrazia Calò & Giuseppe Scarascia Mugnozza & Marco Marchetti & Michele Munafò, 2023. "Land Consumption Dynamics and Urban–Rural Continuum Mapping in Italy for SDG 11.3.1 Indicator Assessment," Land, MDPI, vol. 12(1), pages 1-24, January.
    4. Giulia Cecili & Paolo De Fioravante & Pasquale Dichicco & Luca Congedo & Marco Marchetti & Michele Munafò, 2023. "Land Cover Mapping with Convolutional Neural Networks Using Sentinel-2 Images: Case Study of Rome," Land, MDPI, vol. 12(4), pages 1-20, April.

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