A LiDAR-based decision-tree classification of open water surfaces in an Arctic delta

Abstract

In the Mackenzie Delta, western Arctic Canada, decisions relating to navigation, socio-economics, infrastructure stability, wildlife, vegetation and emergency preparedness are closely related to the delta hydrology. Presented here is a remote sensing decision-tree approach to delineate open-water hydrological features using high-resolution LiDAR terrain, intensity and derivative data. The proposed classification scheme exploits the propensity of LiDAR point attributes and data metrics such as point density and standard deviation (of intensity and elevation) to cluster around characteristic response values over water and non-water surfaces. Due to the impracticability of validating an Arctic water surface classification over such a huge and remote area, results of the hierarchical classification were compared to alternative classifications derived from Radarsat-2 and a manually intensive digitisation technique. Open-water features were identified with >95% accuracy when compared to manually interpreted data. The spatially extensive but temporally distinct information on the hydrological setting of the delta thus extracted forms the basis for calculation of time-invariant parameters such as off-channel storage capacity and hydraulic gradients. In situations where LiDAR data are primarily collected in support of terrain-based watershed hydrologic or floodplain hydraulic assessments, contemporaneous water extent and associated level data are valuable in further characterizing terrain hydrological characteristics.