<![CDATA[The urban areas of Yogyakarta and its surroundings have experienced highly dynamic growth. Therefore, research is needed to analyze urban development, starting with land cover and land use analysis using Landsat TM and Landsat 8 OLI imagery. Random Forest (RF) classification, a machine learning-based method, was employed to accelerate the land cover and land use classification process. This study combines the Random Forest and Landscape Expansion Index (LEI) methods to analyze land use changes and urban growth patterns. LEI was utilized to identify urban development types, such as outlying (diffusion), edge expansion, and infilling. The study highlights the roles of RF and Google Earth Engine (GEE) in land cover and land use classification. The results show that Yogyakarta and its surrounding urban areas are predominantly expanding outward, following an edge expansion pattern, especially toward Sleman and Bantul regencies. Additionally, infilling growth is observed in Gondomanan and Gondokusuman sub-districts, which are already densely populated areas. Outlying development patterns were found in Sleman and Bantul regencies, occurring far from the Yogyakarta city center. This study underscores that the combination of RF, GEE, and LEI is an effective method for analyzing urban growth patterns.]]>
Copyrights © 2025
