02267naa a2200397 a 450000100080000000500110000800800410001902200140006002400460007410000230012024501540014326000090029752010790030665000180138565000150140365000130141865000170143165000170144865300280146565300230149365300220151665300240153865300240156265300210158665300110160765300180161865300220163670000170165870000200167570000160169570000180171170000200172970000210174970000180177077300810178821559792023-08-18       2023    bl uuuu     u00u1 u    #d  a2220-99647 ahttps://doi.org/10.3390/ijgi120803422DOI1 aMACARRINGUE, L. S.  aLand use and land cover classification in the northern region of Mozambique based on Landsat time series and machine learning.h[electronic resource]  c2023  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.  aDeforestation  aLand cover  aLand use  aDesmatamento  aUso da Terra  aAprendizado de máquina  aCobertura da terra  aFeature selection  aFloresta aleatória  aGoogle Earth Engine  aMachine learning  aMiombo  aRandom forest  aSéries temporais1 aBOLFE, E. L.1 aDUVERGER, S. G.1 aSANO, E. E.1 aCALDAS, M. M.1 aFERREIRA, M. C.1 aZULLO JUNIOR, J.1 aMATIAS, L. F.  tISPRS International Journal of Geo-Informationgv. 12, n. 8, 342, Aug. 2023.