Kadi, FYilmaz, OS2025-04-102025-04-10http://hdl.handle.net/20.500.14701/39445This study aims to detect temporal changes in treeless forest areas using remote sensing techniques on the Google Earth Engine Platform. In this direction, ten treeless forest areas were determined from stand maps. A general study area was determined to include these areas, and the status of treeless forest areas was obtained by classifying the study area with a random forest algorithm on Sentinel-2 images. Then, normalized difference vegetation index (NDVI) time series analyses were performed on Landsat images to reveal vegetation changes in these treeless forest areas. In the classification study conducted with Sentinel-2 images, the land was divided into three classes: forest, treeless forest areas, and bare land. The overall accuracy of the classification study was calculated as 89.46%, and the Kappa value as 0.810. When the obtained treeless forest areas were compared with the areas on the stand map, it was seen that there was an average change of about 52.56% in terms of forest cover for ten regions. As a result of the analyses made with NDVI time series, it was observed that vegetation in treeless forest areas increased and therefore these areas tended to close.TurkishArticle2564-6761