## 5 Conclusions  
This research presented the capability of multispectral LiDAR data, in the format of discrete returns, for land/water discrimination in coastal areas. The multispectral data of a study area at Lake Ontario were collected using the Optech Titan sensor operating at three channels with wavelengths of 1550, 1064 and 532 nm. The interaction of the water bodies at the C1 and C2 is almost identical, whereas the water surface is detected. The green wavelength can penetrate the water surface and detect both the water surface and benthic layer. A set of LiDAR point features based on elevation, intensity, and geometry were extracted and evaluated for land/water discrimination. The elevation-based features (\(HV\) and \(HSD\)) revealed an average overall accuracy of 75.1%, while the \(PD\) feature revealed an average overall accuracy of 58.3%. Those features showed various overall accuracies due to the different interaction of the wavelengths with land objects and water bodies. The \(NOR\) feature achieved relatively high overall accuracy of 90.2% due to the large number of points that have double returns from the water bodies in C3. The intensity-based features show relatively low water detection rates with an average overall accuracy of 63.9%, because the water points have the same intensity range of other land features (e.g., vegetation).  
A fully automated and unsupervised land/water discrimination method, based on seeded region growing algorithm, was also presented. This method started with the automatic selection of water seed points using two point features, \(NOR\) and \(HV\). Subsequently, a criterion based on \(HV\) was grown point by point in order to label all water points in the scene. It has been shown that our method achieves high land/water discrimination with an overall accuracy of more than 99% and an average RMSE of 0.51 m for the land/water boundary.  
The use of multispectral LiDAR systems is required to fully automate the land/water discrimination process. However, dual-wavelength LiDAR systems are suitable for this application if the green wavelength (532 nm) is used. The multispectral data could be more effective in land/water discrimination if more LiDAR point features are explored and combined. Consequently, features selection and the assessment of their relevance to land/water discrimination will be required [50; 51]. The land/water discrimination could be extended to consider shorelines with submerged vegetation and debris and inland water areas such as rivers in urban or mountain areas.  
This research work is supported by the Discovery Grant from the Natural Sciences and Engineering Research Council of Canada (NSERC) [RGPIN-2015-03960] and the Ontario Trillium Scholarship (OTS). The authors also would like to thank [PERSON] and Teledyne Optech for providing the LiDAR data from the world's first multispectral LiDAR system, Optech Titan.  
[PERSON], [PERSON], and [PERSON] conceived the research idea and carried out the design. [PERSON] implemented the experiment and produced and analyzed the results under the supervision of [PERSON] and [PERSON]. [PERSON] drafted the manuscript. [PERSON] and [PERSON] reviewed the manuscript.  
The authors declare no conflict of interest.