This study aimed to assess land use/land cover (LULC) changes in the Southeastern Mediterranean Coastal Zone of Turkey, using remote sensing techniques and geographical information systems (GIS). Landscape-level changes in the Çukurova coastal area between 1984 and 2000 were determined using remotely sensed satellite data and digital change detection methods. Three Landsat TM and ETM+ datasets from 1984, 1993 and 2000 were used to quantify LULC changes from 1984 to 1993 and from 1993 to 2000. Pre-classification change detection and post-classification comparison were the techniques employed to identify changes for the periods of 1984-1993 and 1993-2000, respectively. The quantitative information resulted from the change detection applications were used as inputs to evaluate LULC change trends. Total change areas for the first and second periods were calculated to be 2448.2 and 6065.6 hectares, respectively. Urbanization over fertile agricultural lands and expansion of agriculture over adjacent marginal areas such as macchia and bare land were the major LULC changes in both periods. Two change detection approaches were compared in terms of their utility for landscape-level monitoring of the Mediterranean coastal environment. Though the pre-classification method used in this study has been reported to provide high accuracy in coastal change studies, it generated some confusion due to spatial and temporal variations of some LULC classes. However, the post-classification comparison was found more useful especially for the change detection of such LULC that show high spatial, seasonal and temporal variation. As employed in this study, digital change detection based on 30 m ground resolution has provided detailed information for the monitoring of coastal environmental indicators such as changes in the size of semi-natural habitats, man-made features and vegetation cover that can directly be related to environmental quality. The importance of finer spatial resolution imagery and frequency of change detection were highlighted as key points to provide more accurate monitoring in the future.