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dc.contributor.author
Lasaponara, Rosa
dc.contributor.author
Tucci, Biagio
dc.contributor.author
Ghermandi, Luciana
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dc.date.available
2020-03-26T16:25:24Z
dc.date.issued
2018-10
dc.identifier.citation
Lasaponara, Rosa; Tucci, Biagio; Ghermandi, Luciana; On the use of satellite Sentinel 2 data for automatic mapping of burnt areas and burn severity; MDPI AG; Sustainability; 10; 11; 10-2018; 1-13
dc.identifier.issn
2071-1050
dc.identifier.uri
http://hdl.handle.net/11336/100929
dc.description.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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
MDPI AG
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/
dc.subject
BURN SEVERITY
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BURNT AREAS
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CLASSIFICATION
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FIRE
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SATELLITE
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SENTINEL 2
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SPACE DATA
dc.subject.classification
Ecología
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dc.subject.classification
Ciencias Biológicas
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
On the use of satellite Sentinel 2 data for automatic mapping of burnt areas and burn severity
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2019-10-28T19:31:08Z
dc.journal.volume
10
dc.journal.number
11
dc.journal.pagination
1-13
dc.journal.pais
Suiza
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dc.journal.ciudad
Basilea
dc.description.fil
Fil: Lasaponara, Rosa. Consiglio Nazionale delle Ricerche; Italia
dc.description.fil
Fil: Tucci, Biagio. Consiglio Nazionale delle Ricerche; Italia
dc.description.fil
Fil: Ghermandi, Luciana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina. Universidad Nacional del Comahue. Centro Regional Universitario Bariloche. Laboratorio de Ecotono; Argentina
dc.journal.title
Sustainability
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.3390/su10113889
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2071-1050/10/11/3889
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