Obtaining the shoreline of a water body and spatial changes on it provide valuable information regarding the fact that freshwater resources constitute a small fraction of available water resources in the world. Nowadays, various types of image are used to extract shoreline details with the contribution of near infrared response (NIR) band. In this study, the potential utility of light-detection-and-ranging LIDAR-derived intensity image (LDII) as an infrared band in shoreline extraction was investigated. Study area was Kestel Dam operated in Izmir province of Turkey. Orthophoto, multispectral Pleiades image (PI), and LDII were processed and evaluated to obtain the shoreline of the dam lake. Mean-shift segmentation was applied on the LDII as smoothing to maintain the edge details while eliminating noise. Noise-free LDII was then added to red, green and blue (RGB) bands of PI instead of NIR band to obtain layer stacked image. Two shorelines were extracted from these two imageries by means of rule-based object-oriented classification. Implemented rules were directly based on threshold values of normalized difference water indexes derived from imageries. Areal-based change detection analysis was carried out with reference to the occupancy rates at the minimum and maximum operating volumes of the dam lake. Also, change detection analysis based on minimum distance differences between extracted and digitized shorelines was performed to examine the subpixel values. Both analyses proved that LDII created from point cloud produced by a beam of 1064 nm can be used as an infrared band in object-based shoreline extraction and may provide better distinction between water and nonwater objects.