Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/84869
Registo completo
Campo DC | Valor | Idioma |
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dc.contributor.author | da Silva Junior, Juarez Antonio | por |
dc.contributor.author | Pacheco, Admilson da Penha | por |
dc.contributor.author | Ruiz-Armenteros, Antonio Miguel | por |
dc.contributor.author | Henriques, Renato F. | por |
dc.date.accessioned | 2023-06-02T10:21:21Z | - |
dc.date.available | 2023-06-02T10:21:21Z | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | da Silva Junior, J.A.; Pacheco, A.d.P.; Ruiz-Armenteros, A.M.; Henriques, R.F.F. Evaluation of the Ability of SLSTR (Sentinel-3B) and MODIS (Terra) Images to Detect Burned Areas Using Spatial-Temporal Attributes and SVM Classification. Forests 2023, 14, 32. https://doi.org/10.3390/f14010032 | por |
dc.identifier.uri | https://hdl.handle.net/1822/84869 | - |
dc.description.abstract | Forest fires are considered one of the major dangers and environmental issues across the world. In the Cerrado biome (Brazilian savannas), forest fires have several consequences, including increased temperature, decreased rainfall, genetic depletion of natural species, and increased risk of respiratory diseases. This study presents a methodology that uses data from the Sea and Land Surface Temperature Radiometer (SLSTR) sensor of the Sentinel-3B satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) of the Terra satellite to analyze the thematic accuracy of burned area maps and their sensitivity under different spectral resolutions in a large area of 32,000 km<sup>2</sup> in the Cerrado biome from 2019 to 2021. The methodology used training and the Support Vector Machine (SVM) classifier. To analyze the spectral peculiarities of each orbital platform, the Transformed Divergence (TD) index separability statistic was used. The results showed that for both sensors, the near-infrared (NIR) band has an essential role in the detection of the burned areas, presenting high separability. Overall, it was possible to observe that the spectral mixing problems, registration date, and the spatial resolution of 500 m were the main factors that led to commission errors ranging between 15% and 72% and omission errors between 51% and 86% for both sensors. This study showed the importance of multispectral sensors for monitoring forest fires. It was found, however, that the spectral resolution and burning date may gradually interfere with the detection process. | por |
dc.description.sponsorship | This research was supported by POAIUJA 2021-22 and CEACTEMA from the University of Jaén (Spain), and RNM-282 research group from the Junta de Andalucía (Spain). This work was also supported by Portuguese national funding awarded by FCT—Foundation for Science and Technology, I.P., projects UIDB/04683/2020 and UIDP/04683/2020. | por |
dc.language.iso | eng | por |
dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) | por |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04683%2F2020/PT | por |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04683%2F2020/PT | por |
dc.rights | openAccess | por |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | por |
dc.subject | Forest fires | por |
dc.subject | Remote sensing | por |
dc.subject | Space-Time Equivalence Coefficient (STEC) | por |
dc.subject | Machine learning | por |
dc.title | Evaluation of the ability of SLSTR (Sentinel-3B) and MODIS (Terra) images to detect burned areas using spatial-temporal attributes and SVM classification | por |
dc.type | article | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://www.mdpi.com/1999-4907/14/1/32 | por |
oaire.citationStartPage | 1 | por |
oaire.citationEndPage | 23 | por |
oaire.citationIssue | 1 | por |
oaire.citationVolume | 14 | por |
dc.date.updated | 2023-01-20T14:23:07Z | - |
dc.identifier.eissn | 1999-4907 | - |
dc.identifier.doi | 10.3390/f14010032 | por |
dc.subject.wos | Science & Technology | por |
sdum.journal | Forests | por |
oaire.version | VoR | por |
dc.identifier.articlenumber | 32 | por |
Aparece nas coleções: | CCT - Artigos (Papers)/Papers |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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forests-14-00032.pdf | 6,16 MB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons