Coastal landforms are constantly changed due to both natural and anthropogenic forces. An attempt is made to analyze short-term coastal landform changes on the east coast of Sri Lanka using remote sensing and geographic information system (GIS) techniques. Such landform changes can be classified as either negative (e.g. erosion) or positive (e.g. accretion) impacts. Surface reflectance images of Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) were analyzed for extracting spatial and temporal changes, based on cross-shore profile analysis and geomorphic change detection. In this study, we developed a novel script model built-in ArcMap for image pre-processing and processing. This model covers six functioning areas of mosaic, land feature extraction, cloud layer generation, vectorization, cloud masking, and smoothing. The green band and mid-infrared images were selected due to the largest reflectance difference between water and non-water bodies. These images have proceeded with modified normalized difference water index (i.e. algorithm of the script model for extracting water and land features). We also quantified the landform changes such as net shoreline movement, shoreline change envelope, endpoint rate, and linear regression rate, using a digital shoreline analysis system (i.e. statistical software combined with ArcMap™). Therefore, landform changes were classified as erosion (>–2.5 m/year), minor seasonal changes (−2.5 m/year to +2.5 m/year), accretion (>+2.5 m/year), based on the annual variations of endpoint rate (i.e. the distance between the oldest and the youngest shorelines to particular time interval). We visualized spatial and temporal coastal landform changes along the east of Sri Lanka, and also identified short-term landform change drives such as tsunamis, cyclones, and anthropogenic activities (e.g. engineering constructions). These landform changes were observed with a global positioning system (GPS)-based field survey. The understanding of such impacts can be directly applied to sustainable coastal zone management.