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TitreA LiDAR-based decision-tree classification of open water surfaces in an Arctic delta
AuteurCrasto, N; Hopkinson, C; Forbes, D L; Lesack, L; Marsh, P; Spooner, I; van der Sanden, J J
SourceRemote Sensing of Environment vol. 164, 2015 p. 90-102, https://doi.org/10.1016/j.rse.2015.04.011
Année2015
Séries alt.Secteur des sciences de la Terre, Contribution externe 20140594
ÉditeurElsevier
Documentpublication en série
Lang.anglais
DOIhttps://doi.org/10.1016/j.rse.2015.04.011
Mediapapier; en ligne; numérique
Formatspdf
ProvinceTerritoires du Nord-Ouest
SNRC107A; 107B
Lat/Long OENS-136.0000 -134.0000 69.0000 67.5000
Sujetstélédétection; eaux de surface; lacs; rivières; deltas; géophysique; hydrogéologie
Illustrationslocation maps; tables; images
ProgrammeInfrastructure côtière, Géosciences de changements climatiques
Résumé(disponible en anglais seulement)
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 characterising terrain hydrological characteristics.
Résumé(Résumé en langage clair et simple, non publié)
En utilisant des données LiDAR pour le delta du Mackenzie, cet article démontre une technique pour la cartographie automatique de l'étendue de l'eau ouvert, permettant le calcul de la capacité d'accumulation de l'eau hors-chenal et des pentes hydrauliques.
GEOSCAN ID296278