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dc.contributorFacultad de Ciencias Biologicas y Ambientaleses_ES
dc.contributor.authorFernández Guisuraga, José Manuel 
dc.contributor.authorFernández Manso, Alfonso 
dc.contributor.authorQuintano Pastor, Carmen
dc.contributor.authorFernández García, Víctor 
dc.contributor.authorCerrillo Cuenca, Alberto
dc.contributor.authorMarqués Rodríguez, Guillermo
dc.contributor.authorCascallana Prol, Gaspar
dc.contributor.authorCalvo Galván, María Leonor 
dc.contributor.otherEcologiaes_ES
dc.date2024
dc.date.accessioned2024-04-15T12:44:58Z
dc.date.available2024-04-15T12:44:58Z
dc.identifier.citationFernández-Guisuraga, J. M., Fernández Manso, A., Quintano, C., Fernández-García, V., Cerrillo, A., Marqués, G. & Calvo, L. (2024). FIREMAP: Cloud-based software to automate the estimation of wildfire-induced ecological impacts and recovery processes using remote sensing techniques. Ecological Informatics, 81, Article e102591. https://doi.org/10.1016/j.ecoinf.2024.102591es_ES
dc.identifier.issn1574-9541
dc.identifier.otherhttps://www.sciencedirect.com/science/article/pii/S157495412400133Xes_ES
dc.identifier.urihttps://hdl.handle.net/10612/19738
dc.description.abstract[EN] The formulation and planning of integrated fire management strategies must be strengthened by decision support systems about fire-induced ecological impacts and ecosystem recovery processes, particularly in the context of extreme wildfire events that challenge land management initiatives. Wildfire data collection and analysis through remote sensing earth observations is of utmost importance for this purpose. However, the needs of land managers are not always met because the exploitation of the full potential of remote sensing techniques requires a high level of technical expertise. In addition, data acquisition and storage, database management, networking, and computing requirements may present technical difficulties. Here, we present FIREMAP software, which leverages the potential of Google Earth Engine (GEE) cloud-based platform, an intuitive graphical user interface (GUI), and the European Forest Fire Information System (EFFIS) wildfire database for wildfire analyses through remote sensing techniques and data collections. FIREMAP software allows automatic computing of (i) machine learning-based burned area (BA) detection algorithms to facilitate the mapping of (historical) fire perimeters, (ii) fire severity spectral indices, and (iii) post-fire recovery trajectories through the inversion of physically-based radiative transfer models. We introduce (i) the FIREMAP platform architecture and the GUI, (ii) the implementation of well-established algorithms for wildfire science and management in GEE, (iii) the validation of the algorithm implementation in fifteen case-study wildfires across the western Mediterranean Basin, and (iv) the near-future and long-term planned expansion of FIREMAP featureses_ES
dc.languageenges_ES
dc.publisherElsevieres_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectEcología. Medio ambientees_ES
dc.subjectIngeniería forestales_ES
dc.subject.otherBurned areaes_ES
dc.subject.otherFire severityes_ES
dc.subject.otherGoogle earth enginees_ES
dc.subject.otherGraphical user interfacees_ES
dc.subject.otherVegetation recoveryes_ES
dc.titleFIREMAP: Cloud-based software to automate the estimation of wildfire-induced ecological impacts and recovery processes using remote sensing techniqueses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.identifier.doi10.1016/j.ecoinf.2024.102591
dc.description.peerreviewedSIes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Programa Estatal para Impulsar la Investigación Científico-Técnica y su Transferencia/PID2022-139156OB-C21/ES/Susceptibilidad y vulnerabilidad del paisaje frente a megaincendios severos en el eje atlántico-mediterráneo: soluciones basadas en la naturaleza para una gestión proactivaes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/MICIN//TED2021-130925B-I00/ES/Tecnologías disruptivas para la toma de decisiones en la gestión post-incendio basadas en las soluciones de computación en la nube/FIREMAPes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.essn1878-0512
dc.journal.titleEcological Informaticses_ES
dc.volume.number81es_ES
dc.page.initial102591es_ES
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES
dc.subject.unesco3106 Ciencia Forestales_ES
dc.subject.unesco2506.16 Teledetección (Geología)es_ES
dc.subject.unesco3106.99 Otras (Incendios forestales)es_ES
dc.description.projectThis study was financially supported by the Spanish Ministry of Science and Innovation in the framework of LANDSUSFIRE project (PID2022-139156OB-C21) within the National Program for the Promotion of Scientific-Technical Research (2021-2023), and with Next-Generation Funds of the European Union (EU) in the framework of the FIREMAP project (TED2021-130925B-I00); and by the Regional Government of Castile and León in the framework of the IA-FIREXTCyL project (LE081P23). Víctor Fernández-García was supported by a Margarita Salas post-doctoral fellowship from the Ministry of Universities of Spain, financed with European Union-NextGenerationEU and Ministerio de Universidades Fundses_ES


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