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On the Use of Satellite Sentinel 2 Data for Automatic Mapping of Burnt Areas and Burn Severity

Author

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  • Rosa Lasaponara

    (Institute of Methodologies for Environmental Analysis, Italian Research Council, C.da S. Loja, Tito Scalo, 85050 Potenza, Italy)

  • Biagio Tucci

    (Institute of Methodologies for Environmental Analysis, Italian Research Council, C.da S. Loja, Tito Scalo, 85050 Potenza, Italy)

  • Luciana Ghermandi

    (Laboratorio Ecotono, Institute for Research on Biodiversity and the Environment, National Scientific and Technical Research Council, Sarmiento 440, Buenos Aires, Argentina)

Abstract

In this paper, we present and discuss the preliminary tools we devised for the automatic recognition of burnt areas and burn severity developed in the framework of the EU-funded SERV_FORFIRE project. The project is focused on the set up of operational services for fire monitoring and mitigation specifically devised for decision-makers and planning authorities. The main objectives of SERV_FORFIRE are: (i) to create a bridge between observations, model development, operational products, information translation and user uptake; and (ii) to contribute to creating an international collaborative community made up of researchers and decision-makers and planning authorities. For the purpose of this study, investigations into a fire burnt area were conducted in the south of Italy from a fire that occurred on 10 August 2017, affecting both the protected natural site of Pignola (Potenza, South of Italy) and agricultural lands. Sentinel 2 data were processed to identify and map different burnt areas and burn severity levels. Local Index for Statistical Analyses LISA were used to overcome the limits of fixed threshold values and to devise an automatic approach that is easier to re-apply to diverse ecosystems and geographic regions. The validation was assessed using 15 random plots selected from in situ analyses performed extensively in the investigated burnt area. The field survey showed a success rate of around 95%, whereas the commission and omission errors were around 3% of and 2%, respectively. Overall, our findings indicate that the use of Sentinel 2 data allows the development of standardized burn severity maps to evaluate fire effects and address post-fire management activities that support planning, decision-making, and mitigation strategies.

Suggested Citation

  • Rosa Lasaponara & Biagio Tucci & Luciana Ghermandi, 2018. "On the Use of Satellite Sentinel 2 Data for Automatic Mapping of Burnt Areas and Burn Severity," Sustainability, MDPI, vol. 10(11), pages 1-13, October.
  • Handle: RePEc:gam:jsusta:v:10:y:2018:i:11:p:3889-:d:178350
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    References listed on IDEAS

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    1. Trombetta, Andrea & Iacobellis, Vito & Tarantino, Eufemia & Gentile, Francesco, 2016. "Calibration of the AquaCrop model for winter wheat using MODIS LAI images," Agricultural Water Management, Elsevier, vol. 164(P2), pages 304-316.
    2. Lucia Saganeiti & Antonella Favale & Angela Pilogallo & Francesco Scorza & Beniamino Murgante, 2018. "Assessing Urban Fragmentation at Regional Scale Using Sprinkling Indexes," Sustainability, MDPI, vol. 10(9), pages 1-23, September.
    3. Federico Amato & Biagio Antonio Maimone & Federico Martellozzo & Gabriele Nolè & Beniamino Murgante, 2016. "The Effects of Urban Policies on the Development of Urban Areas," Sustainability, MDPI, vol. 8(4), pages 1-22, March.
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    Cited by:

    1. Zina Mitraka & Sofia Siachalou & Georgia Doxani & Petros Patias, 2020. "Decision Support on Monitoring and Disaster Management in Agriculture with Copernicus Sentinel Applications," Sustainability, MDPI, vol. 12(3), pages 1-20, February.
    2. Luciano Telesca & Michele Lovallo & Gianfranco Cardettini & Angelo Aromando & Nicodemo Abate & Monica Proto & Antonio Loperte & Nicola Masini & Rosa Lasaponara, 2023. "Urban and Peri-Urban Vegetation Monitoring Using Satellite MODIS NDVI Time Series, Singular Spectrum Analysis, and Fisher–Shannon Statistical Method," Sustainability, MDPI, vol. 15(14), pages 1-20, July.

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