Data-driven regionalization of forested and non-forested ecosystems in coastal British Columbia with LiDAR and RapidEye imagery

Shanley D. Thompson, Trisalyn Nelson, Ian Giesbrecht, Gordon Frazer, Sari C. Saunders

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Traditionally, forest inventory and ecosystem mapping at local to regional scales rely on manual interpretation of aerial photographs, based on standardized, expert-driven classification schemes. These current approaches provide the information needed for forest ecosystem management but constrain the thematic and spatial resolution of mapping and are infrequently repeated. The goal of this research was to demonstrate the utility of an unsupervised, quantitative technique based on Light Detection And Ranging (LiDAR) data and multi-spectral satellite imagery for mapping local-scale ecosystems over a heterogeneous landscape of forested and non-forested ecosystems. We derived a range of metrics characterizing local terrain and vegetation from LiDAR and RapidEye imagery for Calvert and Hecate Islands, British Columbia. These metrics were used in a cluster analysis to classify and quantitatively characterize ecological units across the island. A total of 18 clusters were derived. The clusters were attributed with quantitative summary statistics from the remotely sensed data inputs and contextualized through comparison to ecological units delineated in a traditional expert-driven mapping method using aerial photographs. The 18 clusters describe ecosystems ranging from open shrublands to dense, productive forest and include a riparian zone and many wetter and wetland ecosystems. The clusters provide detailed, spatially-explicit information for characterizing the landscape as a mosaic of units defined by topography and vegetation structure. This study demonstrates that using various types of remotely sensed data in a quantitative classification can provide scientists and managers with multivariate information unique from that which results from traditional, expert-based ecosystem mapping methods.

Original languageEnglish (US)
Pages (from-to)35-50
Number of pages16
JournalApplied Geography
Volume69
DOIs
StatePublished - Apr 1 2016
Externally publishedYes

Fingerprint

lidar
regionalization
British Columbia
imagery
expert
mapping method
ecosystems
ecosystem
aerial photograph
forest ecosystem
forest ecosystems
photographs
wetland
cluster analysis
riparian zone
ecosystem management
taxonomy
forest inventory
vegetation structure
shrubland

Keywords

  • Classification
  • Cluster analysis
  • Ecoregion
  • Ecosystem
  • Ecosystem structure
  • Terrain

ASJC Scopus subject areas

  • Forestry
  • Geography, Planning and Development
  • Environmental Science(all)
  • Tourism, Leisure and Hospitality Management

Cite this

Data-driven regionalization of forested and non-forested ecosystems in coastal British Columbia with LiDAR and RapidEye imagery. / Thompson, Shanley D.; Nelson, Trisalyn; Giesbrecht, Ian; Frazer, Gordon; Saunders, Sari C.

In: Applied Geography, Vol. 69, 01.04.2016, p. 35-50.

Research output: Contribution to journalArticle

Thompson, Shanley D. ; Nelson, Trisalyn ; Giesbrecht, Ian ; Frazer, Gordon ; Saunders, Sari C. / Data-driven regionalization of forested and non-forested ecosystems in coastal British Columbia with LiDAR and RapidEye imagery. In: Applied Geography. 2016 ; Vol. 69. pp. 35-50.
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