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TitleA LiDAR-based decision-tree classification of open water surfaces in an Arctic delta
AuthorCrasto, N; Hopkinson, C; Forbes, D LORCID logo; Lesack, L; Marsh, P; Spooner, I; van der Sanden, J JORCID logo
SourceRemote Sensing of Environment vol. 164, 2015 p. 90-102,
Alt SeriesEarth Sciences Sector, Contribution Series 20140594
PublisherElsevier BV
Mediapaper; on-line; digital
File formatpdf
ProvinceNorthwest Territories
NTS107A; 107B
AreaMackenzie Delta
Lat/Long WENS-136.0000 -134.0000 69.0000 67.5000
Subjectsgeophysics; hydrogeology; Economics and Industry; Nature and Environment; remote sensing; surface waters; lakes; rivers; deltas; LiDAR
Illustrationslocation maps; tables; images
ProgramClimate Change Geoscience Coastal Infrastructure
Released2015 07 01
AbstractIn 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 characterising terrain hydrological characteristics.
Summary(Plain Language Summary, not published)
Using LiDAR data from the Mackenzie Delta, this paper presents a technique for automatically mapping the distribution of open water, thus allowing calculation of off-channel storage capacity and hydraulic gradients.

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