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| Registros recuperados : 306 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Cerrados. |
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Data corrente: |
18/08/2023 |
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Data da última atualização: |
18/08/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
MACARRINGUE, L. S.; BOLFE, E. L.; DUVERGER, S. G.; SANO, E. E.; CALDAS, M. M.; FERREIRA, M. C.; ZULLO JUNIOR, J.; MATIAS, L. F. |
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Afiliação: |
LUCRÊNCIO SILVESTRE MACARRINGUE, UNIVERSIDADE ESTADUAL DE CAMPINAS; EDSON LUIS BOLFE, CNPTIA, UNIVERSIDADE ESTADUAL DE CAMPINAS; SOLTAN GALANO DUVERGER, UNIVERSIDADE FEDERAL DA BAHIA; EDSON EYJI SANO, CPAC; MARCELLUS MARQUES CALDAS, KANSAS STATE UNIVERSITY; MARCOS CÉSAR FERREIRA, UNIVERSIDADE ESTADUAL DE CAMPINAS; JURANDIR ZULLO JUNIOR, UNIVERSIDADE ESTADUAL DE CAMPINAS; LINDON FONSECA MATIAS, UNIVERSIDADE ESTADUAL DE CAMPINAS. |
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Título: |
Land use and land cover classification in the northern region of Mozambique based on Landsat time series and machine learning. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
ISPRS International Journal of Geo-Information, v. 12, n. 8, 342, Aug. 2023. |
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ISSN: |
2220-9964 |
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DOI: |
https://doi.org/10.3390/ijgi12080342 |
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Idioma: |
Inglês |
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Conteúdo: |
Accurate land use and land cover (LULC) mapping is essential for scientific and decision-making purposes. The objective of this paper was to map LULC classes in the northern region of Mozambique between 2011 and 2020 based on Landsat time series processed by the Random Forest classifier in the Google Earth Engine platform. The feature selection method was used to reduce redundant data. The final maps comprised five LULC classes (non-vegetated areas, built-up areas, croplands, open evergreen and deciduous forests, and dense vegetation) with an overall accuracy ranging from 80.5% to 88.7%. LULC change detection between 2011 and 2020 revealed that non-vegetated areas had increased by 0.7%, built-up by 2.0%, and dense vegetation by 1.3%. On the other hand, open evergreen and deciduous forests had decreased by 4.1% and croplands by 0.01%. The approach used in this paper improves the current systematic mapping approach in Mozambique by minimizing the methodological gaps and reducing the temporal amplitude, thus supporting regional territorial development policies. |
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Palavras-Chave: |
Aprendizado de máquina; Cobertura da terra; Feature selection; Floresta aleatória; Google Earth Engine; Machine learning; Miombo; Random forest; Séries temporais. |
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Thesagro: |
Desmatamento; Uso da Terra. |
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Thesaurus NAL: |
Deforestation; Land cover; Land use. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1155979/1/AP-LandUse-2023.pdf
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Marc: |
LEADER 02267naa a2200397 a 4500
001 2155979
005 2023-08-18
008 2023 bl uuuu u00u1 u #d
022 $a2220-9964
024 7 $ahttps://doi.org/10.3390/ijgi12080342$2DOI
100 1 $aMACARRINGUE, L. S.
245 $aLand use and land cover classification in the northern region of Mozambique based on Landsat time series and machine learning.$h[electronic resource]
260 $c2023
520 $aAccurate land use and land cover (LULC) mapping is essential for scientific and decision-making purposes. The objective of this paper was to map LULC classes in the northern region of Mozambique between 2011 and 2020 based on Landsat time series processed by the Random Forest classifier in the Google Earth Engine platform. The feature selection method was used to reduce redundant data. The final maps comprised five LULC classes (non-vegetated areas, built-up areas, croplands, open evergreen and deciduous forests, and dense vegetation) with an overall accuracy ranging from 80.5% to 88.7%. LULC change detection between 2011 and 2020 revealed that non-vegetated areas had increased by 0.7%, built-up by 2.0%, and dense vegetation by 1.3%. On the other hand, open evergreen and deciduous forests had decreased by 4.1% and croplands by 0.01%. The approach used in this paper improves the current systematic mapping approach in Mozambique by minimizing the methodological gaps and reducing the temporal amplitude, thus supporting regional territorial development policies.
650 $aDeforestation
650 $aLand cover
650 $aLand use
650 $aDesmatamento
650 $aUso da Terra
653 $aAprendizado de máquina
653 $aCobertura da terra
653 $aFeature selection
653 $aFloresta aleatória
653 $aGoogle Earth Engine
653 $aMachine learning
653 $aMiombo
653 $aRandom forest
653 $aSéries temporais
700 1 $aBOLFE, E. L.
700 1 $aDUVERGER, S. G.
700 1 $aSANO, E. E.
700 1 $aCALDAS, M. M.
700 1 $aFERREIRA, M. C.
700 1 $aZULLO JUNIOR, J.
700 1 $aMATIAS, L. F.
773 $tISPRS International Journal of Geo-Information$gv. 12, n. 8, 342, Aug. 2023.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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