Assessment of urban expansion in the city of Jaen (Peru) using satellite remote sensing and Random Forest classification

The rapid growth of urban areas poses challenges for land use planning and sustainable development. In this context, the objective of the research was to estimate the urban expansion of the city of Jaen during the period 2013-2025, using satellite remote sensing and the Random Forest classification algorithm. Satellite images of the urban area were collected and analyzed from the PERUSAT-1, LANDSAT-8, and LANDSAT-9 sensors, using the Semi-Automatic Classification Plugin for QGIS and the supervised classification technique developed in three stages: pre-processing, processing, and post-processing. Three coverage categories were evaluated: urban, vegetation, and unclassified (water bodies, clouds, and shadows), and the accuracy of the classification was assessed using the kappa coefficient. The results showed a sustained increase in urban coverage, which rose from 627.43 ha in 2017 to 673.45 ha in 2020 according to PERUSAT-1, and from 452.43 ha in 2013 to 733.05 ha in 2025 according to LANDSAT. Kappa index values of 0.81 and 0.79 were obtained for PERUSAT-1 (2017 and 2020), and 0.69 and 0.65 for LANDSAT (2013 and 2025). In percentage terms, urban coverage grew by 7.33\% during the period 2017–2020 (PERUSAT-1) and by 62.03\% between 2013 and 2025 (LANDSAT). These findings highlight the effectiveness of satellite remote sensing and the Random Forest algorithm as tools for monitoring and analyzing urban dynamics, contributing to the design of sustainable land management strategies