Publicação
Enhancing Forest Fire Detection and Monitoring Through Satellite Image Recognition: A Comparative Analysis of Classification Algorithms Using Sentinel-2 Data
| Resumo: | 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. |
|---|---|
| Autores principais: | Brito, Thadeu |
| Outros Autores: | Pereira, Ana I.; Costa, Paulo Gomes da; Lima, José |
| Assunto: | Machine learning Classification algorithm Satellite Imagery Wildfires Tree cover density |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| _version_ | 1867172806284804096 |
|---|---|
| author | Brito, Thadeu |
| author2 | Pereira, Ana I. Costa, Paulo Gomes da Lima, José |
| author2_role | author author author |
| author_facet | Brito, Thadeu Pereira, Ana I. Costa, Paulo Gomes da Lima, José |
| author_role | author |
| contributor_name_str_mv | Biblioteca Digital do IPB |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Brito, Thadeu\",\"Person.identifier.orcid\":\"0000-0002-5962-0517\"},{\"Person.name\":\"Pereira, Ana I.\",\"Person.identifier.orcid\":\"0000-0003-3803-2043\"},{\"Person.name\":\"Costa, Paulo Gomes da\"},{\"Person.name\":\"Lima, José\",\"Person.identifier.orcid\":\"0000-0001-7902-1207\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Biblioteca Digital do IPB |
| datacite.creators.creator.creatorName.fl_str_mv | Brito, Thadeu Pereira, Ana I. Costa, Paulo Gomes da Lima, José |
| datacite.date.Accepted.fl_str_mv | 2024-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2024-11-07T11:05:56Z |
| datacite.date.embargoed.fl_str_mv | 2024-11-07T11:05:56Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_16ec |
| datacite.subjects.subject.fl_str_mv | Machine learning Classification algorithm Satellite Imagery Wildfires Tree cover density |
| datacite.titles.title.fl_str_mv | Enhancing Forest Fire Detection and Monitoring Through Satellite Image Recognition: A Comparative Analysis of Classification Algorithms Using Sentinel-2 Data |
| dc.contributor.none.fl_str_mv | Biblioteca Digital do IPB |
| dc.creator.none.fl_str_mv | Brito, Thadeu Pereira, Ana I. Costa, Paulo Gomes da Lima, José |
| dc.date.Accepted.fl_str_mv | 2024-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2024-11-07T11:05:56Z |
| dc.date.embargoed.fl_str_mv | 2024-11-07T11:05:56Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10198/30511 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Springer Nature |
| dc.rights.cclincense.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_16ec |
| dc.subject.none.fl_str_mv | Machine learning Classification algorithm Satellite Imagery Wildfires Tree cover density |
| dc.title.fl_str_mv | Enhancing Forest Fire Detection and Monitoring Through Satellite Image Recognition: A Comparative Analysis of Classification Algorithms Using Sentinel-2 Data |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_5794 |
| description | 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. |
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| format | conferencePaper |
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| funding.funder.alternateName_str_mv | FCT FCT FCT |
| funding.funder.identifier_str_mv | http://doi.org/10.13039/501100001871 http://doi.org/10.13039/501100001871 http://doi.org/10.13039/501100001871 |
| funding.funder.name_str_mv | Fundação para a Ciência e a Tecnologia Fundação para a Ciência e a Tecnologia Fundação para a Ciência e a Tecnologia |
| funding.name_str_mv | 6817 - DCRRNI ID 6817 - DCRRNI ID 6817 - DCRRNI ID |
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| identifier.url.fl_str_mv | http://hdl.handle.net/10198/30511 |
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| institution | Instituto Politécnico de Bragança |
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| language | eng |
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| person_str_mv | Brito, Thadeu Brito, Thadeu https://www.ciencia-id.pt/C911-A95D-712F C911-A95D-712F http://orcid.org/0000-0002-5962-0517 0000-0002-5962-0517 Pereira, Ana I. Pereira, Ana I. https://www.ciencia-id.pt/0716-B7C2-93E4 0716-B7C2-93E4 http://orcid.org/0000-0003-3803-2043 0000-0003-3803-2043 Costa, Paulo Gomes da Lima, José Lima, José https://www.ciencia-id.pt/6016-C902-86A9 6016-C902-86A9 http://orcid.org/0000-0001-7902-1207 0000-0001-7902-1207 |
| publishDate | 2024 |
| publisher.none.fl_str_mv | Springer Nature |
| reponame_str | Biblioteca Digital do IPB |
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| spelling | engSpringer Naturept_PTWorldwide, 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.application/pdfpt_PTEnhancing Forest Fire Detection and Monitoring Through Satellite Image Recognition: A Comparative Analysis of Classification Algorithms Using Sentinel-2 DataPersonalBrito, ThadeuDSpacehttp://dspace.org/items/c8641c9a-a994-4ab2-836d-c758c0e44cc9DSpacehttp://dspace.org/items/c8641c9a-a994-4ab2-836d-c758c0e44cc9BritoThadeuCiência IDhttps://www.ciencia-id.ptC911-A95D-712FORCIDhttp://orcid.org0000-0002-5962-0517Scopus Author IDhttps://www.scopus.com57200694948PersonalPereira, Ana I.DSpacehttp://dspace.org/items/e9981d62-2a2b-4fef-b75e-c2a14b0e7846DSpacehttp://dspace.org/items/e9981d62-2a2b-4fef-b75e-c2a14b0e7846PereiraAna I.Ciência IDhttps://www.ciencia-id.pt0716-B7C2-93E4ORCIDhttp://orcid.org0000-0003-3803-2043Researcher IDhttps://www.researcherid.comF-3168-2010Scopus Author IDhttps://www.scopus.com15071961600Costa, Paulo Gomes daPersonalLima, JoséDSpacehttp://dspace.org/items/d88c2b2a-efc2-48ef-b1fd-1145475e0055DSpacehttp://dspace.org/items/d88c2b2a-efc2-48ef-b1fd-1145475e0055LimaJoséCiência IDhttps://www.ciencia-id.pt6016-C902-86A9ORCIDhttp://orcid.org0000-0001-7902-1207Researcher IDhttps://www.researcherid.comL-3370-2014Scopus Author IDhttps://www.scopus.com55851941311HostingInstitutionOrganizationalBiblioteca Digital do IPBe-mailmailto:dspace@ipb.ptdspace@ipb.ptISBNIsPartOf978-3-031-53035-7ISBNIsPartOf978-3-031-53036-4DOIIsPartOf10.1007/978-3-031-53036-4_62024-11-07T11:05:56Z20242024-01-01T00:00:00ZHandlehttp://hdl.handle.net/10198/30511http://purl.org/coar/access_right/c_16ecrestricted accessMachine learningClassification algorithmSatellite ImageryWildfiresTree cover density7941558 bytesFundação para a Ciência e a TecnologiaResearch Centre in Digitalization and Intelligent Robotics6817 - DCRRNI IDCrossref Funder IDhttp://doi.org/10.13039/501100001871Fundação para a Ciência e a TecnologiaResearch Centre in Digitalization and Intelligent Robotics6817 - DCRRNI IDCrossref Funder IDhttp://doi.org/10.13039/501100001871Fundação para a Ciência e a TecnologiaAssociate Laboratory for Sustainability and Tecnology in Mountain Regions6817 - DCRRNI IDCrossref Funder IDhttp://doi.org/10.13039/501100001871other research producthttp://purl.org/coar/resource_type/c_5794conference paper2024http://creativecommons.org/licenses/by/4.0/http://purl.org/coar/access_right/c_16ecapplication/pdffulltexthttps://bibliotecadigital.ipb.pt/bitstreams/2c29d77e-6ee2-49ea-8b74-8e369ddfbc92/download3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023)27892 |
| spellingShingle | Enhancing Forest Fire Detection and Monitoring Through Satellite Image Recognition: A Comparative Analysis of Classification Algorithms Using Sentinel-2 Data Brito, Thadeu Machine learning Classification algorithm Satellite Imagery Wildfires Tree cover density |
| status | SINGLETON |
| subject.fl_str_mv | Machine learning Classification algorithm Satellite Imagery Wildfires Tree cover density |
| title | Enhancing Forest Fire Detection and Monitoring Through Satellite Image Recognition: A Comparative Analysis of Classification Algorithms Using Sentinel-2 Data |
| title_full | Enhancing Forest Fire Detection and Monitoring Through Satellite Image Recognition: A Comparative Analysis of Classification Algorithms Using Sentinel-2 Data |
| title_fullStr | Enhancing Forest Fire Detection and Monitoring Through Satellite Image Recognition: A Comparative Analysis of Classification Algorithms Using Sentinel-2 Data |
| title_full_unstemmed | Enhancing Forest Fire Detection and Monitoring Through Satellite Image Recognition: A Comparative Analysis of Classification Algorithms Using Sentinel-2 Data |
| title_short | Enhancing Forest Fire Detection and Monitoring Through Satellite Image Recognition: A Comparative Analysis of Classification Algorithms Using Sentinel-2 Data |
| title_sort | Enhancing Forest Fire Detection and Monitoring Through Satellite Image Recognition: A Comparative Analysis of Classification Algorithms Using Sentinel-2 Data |
| topic | Machine learning Classification algorithm Satellite Imagery Wildfires Tree cover density |
| topic_facet | Machine learning Classification algorithm Satellite Imagery Wildfires Tree cover density |
| url | http://hdl.handle.net/10198/30511 |
| visible | 1 |