Land use and land cover classification in the northern region of Mozambique based on Landsat time series and machine learning.

• Autor(a) principal: MACARRINGUE, L. S.
• Data de Publicação: 2023
• Outros Autores: BOLFE, E. L., DUVERGER, S. G., SANO, E. E., CALDAS, M. M., FERREIRA, M. C., ZULLO JUNIOR, J., MATIAS, L. F.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
• Texto Completo: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1155979; https://doi.org/10.3390/ijgi12080342
• topic: Cobertura da terra; Floresta aleatória; Séries temporais; Aprendizado de máquina; Google Earth Engine; Feature selection; Miombo; Random forest; Machine learning; Desmatamento; Uso da Terra; Deforestation; Land use; Land cover

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.