Deriving Rich Coastal Morphology and Shore Zone Classification from LIDAR Terrain Models

Wiebe Nijland, Luba Y. Reshitnyk, Brian M. Starzomski, John D. Reynolds, Chris T. Darimont, Trisalyn Nelson

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Comprehensive mapping of shore-zone morphology supports evaluation of shore habitat, monitoring of environmental hazards, and characterization of the transfer of nutrients between marine and terrestrial environments. This article shows how rich shore-zone morphological metrics can be derived from LIDAR terrain models and evaluates the application of LIDAR to classify shore-zone substrates. The utility of LIDAR methods was tested in comparison with the current best-practice method of photo interpretation (i.e. The BC ShoreZone system) on Calvert Island, British Columbia, Canada. Wider applications are considered. Indicators of shore-zone morphology (i.e. slope, width, roughness, backshore elevation) were calculated from LIDAR terrain models for regularly spaced transects perpendicular to the coastline. A combination of boosted regression-Tree modeling and direct-rule application was used to classify the shore-zone morphology according to the British Columbia (BC) ShoreZone system. Classification accuracy was assessed against existing ShoreZone classification data. Shore-zone substrate was classified from LIDAR-derived morphometric indicators with 90% accuracy (five classes). A full classification, which combined substrate with shore width and slope, results in lower correspondence (40%; 25 classes) when compared with ShoreZone classes. Differences can likely be attributed, in part, to variation in spatial resolution of elevation-based methods and photo interpretation. It is concluded that LIDAR data can be used to support characterization of shore-zone morphology. Differences in processing and interpretation cause a low direct correspondence with the current image-based classification system, but LIDAR has the advantage of higher resolution, rich terrain information, speed, and an objective and repeatable method for monitoring future change in coastal environments.

Original languageEnglish (US)
Pages (from-to)949-958
Number of pages10
JournalJournal of Coastal Research
Volume33
Issue number4
DOIs
StatePublished - Jul 1 2017
Externally publishedYes

Keywords

  • British Columbia
  • coast
  • digital elevation model
  • substrate

ASJC Scopus subject areas

  • Ecology
  • Water Science and Technology
  • Earth-Surface Processes

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