Multi-Temporal Data for Land Use Change Analysis Using a Machine Learning Approach (Google Earth Engine)

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A. Aji, V.N. Husna, S.M. Purnama

2024 International Journal of Geoinformatics Vol. 20 Issue 4 Article Cited by 13 Quartile

Abstract

Land use and land cover change have significant impacts on climate, the environment, and natural ecosystems. This research analyzes land use change over time using Google Earth Engine (GEE) and provides recommendations for land use planning based on the results. This research aligns with priorities related to SDG issues, specifically the maintenance of degraded terrestrial ecosystems that negatively impact the livelihoods of the organisms that inhabit them. The study utilized Sentinel-2 with Multi Spectral Instrument, Level 2A time series from 2019-2023, which were processed using cloud computing and a classification method utilizing SMILE Random Forest. The classification model achieved an accuracy value of 95%. The calculation results indicate a total land use and land cover (LULC) area of 1007.96 hectares. The largest change in land use occurred in fields, decreasing from 10.64% to 4.96%, or 57.18 hectares, while the smallest change occurred in dryland forest, at 2.18 hectares. The total predicted LULC change area was 558.02 hectares. © Geoinformatics International.

Affiliations

Department of Geography, Faculty of Social Sciences, Universitas Negeri Semarang, Indonesia; Surveying and Mapping Study Programme, Politeknik Sinar Mas Berau Coal, Indonesia