Data/hora: 27/07/2024 06:40:26

Biblioteca(s): 	Embrapa Agricultura Digital; Embrapa Cerrados.	
Data corrente: 	18/08/2023	
Data da última atualização: 	18/08/2023	
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.	
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.	
Título: 	Land use and land cover classification in the northern region of Mozambique based on Landsat time series and machine learning.	
Ano de publicação: 	2023	
Fonte/Imprenta: 	ISPRS International Journal of Geo-Information, v. 12, n. 8, 342, Aug. 2023.	
ISSN: 	2220-9964	
DOI: 	https://doi.org/10.3390/ijgi12080342	
Idioma: 	Inglês	
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.	
Palavras-Chave: 	Aprendizado de máquina; Cobertura da terra; Feature selection; Floresta aleatória; Google Earth Engine; Machine learning; Miombo; Random forest; Séries temporais.	
Thesagro: 	Desmatamento; Uso da Terra.	
Thesaurus Nal: 	Deforestation; Land cover; Land use.	
Categoria do assunto: 	--	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1155979/1/AP-LandUse-2023.pdf	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
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CPAC - (37620-1) - UPC - AP - DD - DIGITAL -  - DIGITAL - DIS