LiDAR-based Ecosystem Classification for Calvert Island

The purpose of this work was to define and map a set of repeating ecohydrological classes on Calvert and Hecate Islands using remote sensing data and an unsupervised classification technique. The resulting map provides a new tool for characterizing the extent and internal properties of different ecosystem classes, for stratifying future study designs, and for evaluating the influence of terrestrial landscape characteristics on watershed processes. "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 multi- variate information unique from that which results from traditional, expert-based ecosystem mapping methods." - Abstract from Thompson et al. 2016. A complete explanation of methods is available in Thompson et al. 2016. Data-driven regionalization of forested and non-forested ecosystem in coastal British Columbia with LiDAR and RapidEye imagery. The manuscript is available here: Thompson et al. 2016 A small number of data voids in the 2012 LiDAR coverage were present and were excluded from the analysis. Although the voids have since been filled with new LiDAR data acquired in 2014, the new data were not included in the analysis of Thompson et al. Other “gaps” in the spatial coverage of the final map are a result of the exclusion of non-vegetated areas (as guided by the Normalized Difference Vegetation Index (NDVI) and the provincial Freshwater Atlas (FWA): http://geobc.gov.bc.ca/base-mapping/atlas/fwa/index.html). In addition to small waterbodies, these non-vegetated areas include a few small areas at high elevation that were snow-covered at the time of the RapidEye image acquisition. DOI: http://dx.doi.org/10.21966/1.135248

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Licence: Creative Commons Attribution 4.0
Limitations: Appropriate credit must be given to Hakai Institute and the authors of the dataset.

Data and Resources

Dates

Metadata Reference Date(s) August 27, 2021, 17:32 (UTC) (Revision)
September 23, 2021, 00:00 (UTC) (Publication)
Dataset Reference Date(s) August 04, 2012 (Creation)
January 05, 2016 (Publication)
Frequency of Update As Needed

Citation

Dataset extent

Map data © OpenStreetMap contributors

Additional Info

Field Value
Ocean Variables Other
Scope Dataset
Status On Going
Topic Category oceans
Maintenance Note Generated from https://cioos-siooc.github.io/metadata-entry-form
Point of Contact
  • Name: Hakai Geospatial Technology Team
    Affiliation: Hakai Institute
    Contact Info Email: data@hakai.org
    Role: Point of Contact
  • Name: Hakai Geospatial Technology Team
    Affiliation: Hakai Institute
    Contact Info Email: data@hakai.org
    Role: Point of Contact
  • Name: Hakai Geospatial Technology Team
    Affiliation: Hakai Institute
    Contact Info Email: data@hakai.org
    Role: Custodian
  • Name: Keith Holmes
    Affiliation: Hakai Institute
    Role: Point of Contact
  • Name: Keith Holmes
    Affiliation: Hakai Institute
    Role: Custodian
  • Name: Shanley Thompson
    Affiliation: University of Victoria
    Contact Info Email: sdthomps@uvic.ca
    Role: Point of Contact
Responsible Party
  • Individual Name: Keith Holmes
    Organisation Name / Affiliation: Hakai Institute
    Role: Processor
  • Individual Name: Shanley Thompson
    Organisation Name / Affiliation: University of Victoria
    Contact Info Email: sdthomps@uvic.ca
    Role: Author
  • Individual Name: Shanley Thompson
    Organisation Name / Affiliation: University of Victoria
    Contact Info Email: sdthomps@uvic.ca
    Role: Owner
  • Organisation Name / Affiliation: Hakai Institute
    Contact Info Email: data@hakai.org
    Role: Resource Provider
Spatial Extent {"type": "Polygon", "coordinates": [[[-128.226928711, 51.4094858956], [-127.809448242, 51.4094858956], [-127.809448242, 51.7423368769], [-128.226928711, 51.7423368769], [-128.226928711, 51.4094858956]]]}
North Bounding Latitude 51.74233687689102
South Bounding Latitude 51.40948589555509
East Bounding Longitude -127.80944824218746
West Bounding Longitude -128.22692871093747
Temporal Extent Begin: 2012-08-04
Vertical Extent Min: 0.0
Max: 1014.0
Default Locale English
Citation identifier
Code: http://dx.doi.org/10.21966/1.135248
Version
Associated Datasets