<![CDATA[This study aims to analyze land cover changes in Pariaman City in 2013, 2018, and 2024 using Landsat-8 imagery with the support of the Google Earth Engine (GEE) platform and the Classification and Regression Trees (CART) algorithm. Land cover change is a critical issue in Pariaman City, as population growth, infrastructure development, and economic activities have driven significant land conversion. The classification generated eight land cover classes representing the general condition of the area. The analysis revealed that rice fields were the dominant land cover in all three periods; however, they experienced a substantial decline, indicating land conversion, particularly into settlements and road networks. In addition, the area of mangrove forests and water bodies in coastal regions decreased, while plantation areas in the eastern and southern parts of the city increased. Conversely, river land cover remained the smallest category, with annual fluctuations. The identified change patterns included urban sprawl (expansion of settlements into suburban areas), the conversion of productive land into infrastructure, and coastal degradation due to human activities and natural factors. The accuracy assessment produced overall accuracy and a Kappa index above 80%, while validation using the Mapping Accuracy method through Google Earth showed per-class accuracy rates above 75%, categorized as very good. These findings indicate that the use of satellite imagery and the CART algorithm in GEE is effective for monitoring land cover dynamics while providing valuable insights for local governments in formulating sustainable development policies and managing coastal environments such as those in Pariaman City.]]>
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