Inland waters have vital importance in terrestrial ecosystems as they contribute to the total diversity in surrounding areas as well as enhancing horizontal and vertical ecological connectivity of various habitats. Therefore, temporal monitoring of changes on water bodies is crucial. Morphological changes of inland waters can be conveniently determined using Remote Sensing (RS) techniques. For instance, optical satellite images are widely used for change detection studies; however, it might be difficult to get a proper optical image of an area of interest all the time as the area might be covered by clouds or haze. Moreover, aerial images collected by an optical sensor mounted on aircraft can also be employed for monitoring inland water change. On the other hand, optical images are two dimensional which makes very difficult to detect changes in three dimensions such as for inland water bodies. Hence, alternative technologies such as Light Detection and Ranging (LiDAR) which has direct and fast 3D data acquisition can be used instead of images for change detection. Since LiDAR sensors are mounted on an aircraft, the data collection time can be scheduled according to weather conditions for avoiding from rain and haze. Therefore, in this study, LiDAR technology was chosen as the source of the data and two algorithms are proposed for extracting boundary of inland water bodies. It is known that inland water bodies are generally planar, as a first step of the proposed methodology, point cloud of the water surface was extracted using RANdom SAmple Consensus (RANSAC) algorithm from LiDAR data. For the second step, two algorithms were proposed for delineating of inland water surface boundary. The first algorithm is the Angles of Points (ADP) which is mainly based on line and angle properties of point cloud. The second algorithm is the LiDAR to Image (LTI) and it basically involves conversion of point clouds to binary images for extraction of boundary of water bodies. The two algorithms for boundary extraction of water bodies were tested with three different inland water bodies. Finally, the results produced by the algorithms were compared to each other and with manually extracted boundaries for different time periods. The experimental results showed that the proposed algorithms are capable of extracting the boundaries of water bodies; however, the LTI algorithm performed better than the AOP when applied on water surfaces with complex geometry. (C) 2015 Elsevier Ltd. All rights reserved.