Land Use and Land Cover Classification in the Northern Region of Mozambique Based on Landsat Time Series and Machine Learning

Autor: Lucrêncio Silvestre Macarringue, Édson Luis Bolfe, Soltan Galano Duverger, Edson Eyji Sano, Marcellus Marques Caldas, Marcos César Ferreira, Jurandir Zullo Junior, Lindon Fonseca Matias
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: ISPRS International Journal of Geo-Information, Vol 12, Iss 8, p 342 (2023)
Druh dokumentu: article
ISSN: 2220-9964
DOI: 10.3390/ijgi12080342
Popis: 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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