Publicação
Enhancing Forest Fire Detection and Monitoring Through Satellite Image Recognition: A Comparative Analysis of Classification Algorithms Using Sentinel-2 Data
| dc.contributor.author | Brito, Thadeu | |
| dc.contributor.author | Pereira, Ana I. | |
| dc.contributor.author | Costa, Paulo Gomes da | |
| dc.contributor.author | Lima, José | |
| dc.date.accessioned | 2024-11-07T11:05:56Z | |
| dc.date.available | 2024-11-07T11:05:56Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Worldwide, forests have been harassed by fire in recent years. Either by human intervention or other reasons, the history of the burned area is increasing considerably, harming fauna and flora. It is essential to detect an early ignition for fire-fighting authorities can act quickly, decreasing the impact of forest damage impacts. The proposed system aims to improve nature monitoring and improve the existing surveillance systems through satellite image recognition. The soil recognition via satellite images can determine the sensor modules’ best position and provide crucial input information for artificial intelligence-based systems. For this, satellite images from the Sentinel-2 program are used to generate forest density maps as updated as possible. Four classification algorithms make the Tree Cover Density (TCD) map, consisting of the Gaussian Mixture Model (GMM), Random Forest (RF), Support Vector Machine (SVM), and KNearest Neighbors (K-NN), which identify zones by training known regions. The results demonstrate a comparison between the algorithms through their performance in recognizing the forest, grass, pavement, and water areas by Sentinel-2 images. | pt_PT |
| dc.description.sponsorship | The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021). Thadeu Brito is supported by FCT PhD Grant Reference SFRH/BD/08598/2020. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Brito, Thadeu; Pereira, Ana I.; Costa, Paulo; Lima, José (2024). Enhancing Forest Fire Detection and Monitoring Through Satellite Image Recognition: A Comparative Analysis of Classification Algorithms Using Sentinel-2 Data. In 3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023). Cham: Springer Nature, Vol. 2, p. 78-92. ISBN 978-3-031-53035-7. | pt_PT |
| dc.identifier.doi | 10.1007/978-3-031-53036-4_6 | pt_PT |
| dc.identifier.isbn | 978-3-031-53035-7 | |
| dc.identifier.isbn | 978-3-031-53036-4 | |
| dc.identifier.uri | http://hdl.handle.net/10198/30511 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Springer Nature | pt_PT |
| dc.relation | Research Centre in Digitalization and Intelligent Robotics | |
| dc.relation | Research Centre in Digitalization and Intelligent Robotics | |
| dc.relation | Associate Laboratory for Sustainability and Tecnology in Mountain Regions - LA/P/0007/2020 | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Machine learning | pt_PT |
| dc.subject | Classification algorithm | pt_PT |
| dc.subject | Satellite Imagery | pt_PT |
| dc.subject | Wildfires | pt_PT |
| dc.subject | Tree cover density | pt_PT |
| dc.title | Enhancing Forest Fire Detection and Monitoring Through Satellite Image Recognition: A Comparative Analysis of Classification Algorithms Using Sentinel-2 Data | pt_PT |
| dc.type | conference paper | |
| dspace.entity.type | Publication | |
| oaire.awardNumber | UIDB/05757/2020 | |
| oaire.awardNumber | UIDP/05757/2020 | |
| oaire.awardNumber | LA/P/0007/2020 | |
| oaire.awardTitle | Research Centre in Digitalization and Intelligent Robotics | |
| oaire.awardTitle | Research Centre in Digitalization and Intelligent Robotics | |
| oaire.awardTitle | Associate Laboratory for Sustainability and Tecnology in Mountain Regions - LA/P/0007/2020 | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT | |
| oaire.citation.endPage | 92 | pt_PT |
| oaire.citation.startPage | 78 | pt_PT |
| oaire.citation.title | 3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023) | pt_PT |
| oaire.citation.volume | 2 | pt_PT |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| person.familyName | Brito | |
| person.familyName | Pereira | |
| person.familyName | Lima | |
| person.givenName | Thadeu | |
| person.givenName | Ana I. | |
| person.givenName | José | |
| person.identifier | BjSISEAAAAAJ | |
| person.identifier | R-000-8GD | |
| person.identifier.ciencia-id | C911-A95D-712F | |
| person.identifier.ciencia-id | 0716-B7C2-93E4 | |
| person.identifier.ciencia-id | 6016-C902-86A9 | |
| person.identifier.orcid | 0000-0002-5962-0517 | |
| person.identifier.orcid | 0000-0003-3803-2043 | |
| person.identifier.orcid | 0000-0001-7902-1207 | |
| person.identifier.rid | F-3168-2010 | |
| person.identifier.rid | L-3370-2014 | |
| person.identifier.scopus-author-id | 57200694948 | |
| person.identifier.scopus-author-id | 15071961600 | |
| person.identifier.scopus-author-id | 55851941311 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | restrictedAccess | pt_PT |
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