Unitemporal approach to fire severity mapping using multispectral synthetic databases and Random Forests

dc.contributor.authorMontorio, Raquel
dc.contributor.authorPérez-Cabello, Fernando
dc.contributor.authorBorini Alves, Daniel [UNESP]
dc.contributor.authorGarcía-Martín, Alberto
dc.contributor.institutionUniversity of Zaragoza
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionAcademia General Militar
dc.date.accessioned2020-12-12T01:34:32Z
dc.date.available2020-12-12T01:34:32Z
dc.date.issued2020-11-01
dc.description.abstractFire severity assessment is crucial for predicting ecosystem response and prioritizing post-fire forest management strategies. Although a variety of remote sensing approaches have been developed, more research is still needed to improve the accuracy and effectiveness of fire severity mapping. This study proposes a unitemporal simulation approach based on the generation of synthetic spectral databases from linear spectral mixing. To fully exploit the potential of these training databases, the Random Forest (RF) machine learning algorithm was applied to build a classifier and regression model. The predictive models parameterized with the synthetic datasets were applied in a case study, the Sierra de Luna wildfire in Spain. Single date Landsat-8 and Sentinel-2A imagery of the immediate post-fire environment were used to develop the validation spectral datasets and a Pléiades orthoimage, providing the ground truth data. The four defined severity categories – unburned (UB), partial canopy unburned (PCU), canopy scorched (CS), and canopy consumed (CC) – demonstrated high accuracy in the bootstrapped (about 95%) and real validation sets (about 90%), with a slightly better performance observed when the Sentinel-2A dataset was used. Abundance of four ground covers (green vegetation, non-photosynthetic vegetation, soil, and ash) was also quantified with moderate (~45% for NPV) or high accuracy (higher than 75% for the remaining covers). No specific pattern in the comparison of sensors was observed. Variable importance analysis highlighted the complementary behavior of the spectral bands, although the contrast between the near and shortwave infrared regions stood out above the rest. Comparison of procedures reinforced the usefulness of the approach, as RF image-derived models and the multiple endmember spectral unmixing technique (MESMA) showed lower accuracy. The capabilities for detailed mapping are reflected in the development of different types of cartography (classification maps and fraction cover maps). The approach holds great potential for fire severity assessment, and future research needs to extend the predictive modeling to other burned areas – also in different ecosystems – and analyze its competence and the possible adaptations needed.en
dc.description.affiliationDepartment of Geography and Spatial Management University of Zaragoza, C/Pedro Cerbuna 12
dc.description.affiliationGEOFOREST-IUCA research group Environmental Sciences Institute (IUCA) University of Zaragoza, C/Pedro Cerbuna 12
dc.description.affiliationLab of Vegetation Ecology Instituto de Biociências Universidade Estadual Paulista (UNESP), Avenida 24-A 1515
dc.description.affiliationCentro Universitario de la Defensa de Zaragoza Academia General Militar, Ctra. Huesca s/n
dc.description.affiliationUnespLab of Vegetation Ecology Instituto de Biociências Universidade Estadual Paulista (UNESP), Avenida 24-A 1515
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipIdFAPESP: 2019/07357-8
dc.identifierhttp://dx.doi.org/10.1016/j.rse.2020.112025
dc.identifier.citationRemote Sensing of Environment, v. 249.
dc.identifier.doi10.1016/j.rse.2020.112025
dc.identifier.issn0034-4257
dc.identifier.scopus2-s2.0-85089268347
dc.identifier.urihttp://hdl.handle.net/11449/199243
dc.language.isoeng
dc.relation.ispartofRemote Sensing of Environment
dc.sourceScopus
dc.subjectFire severity
dc.subjectLandsat-8
dc.subjectLinear spectral mixing
dc.subjectMachine learning
dc.subjectPost-fire ground covers
dc.subjectSentinel-2
dc.titleUnitemporal approach to fire severity mapping using multispectral synthetic databases and Random Forestsen
dc.typeArtigo
unesp.author.orcid0000-0001-7403-1764 0000-0001-7403-1764[1]
unesp.author.orcid0000-0003-2610-7749 0000-0003-2610-7749[4]

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