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.

Attributes

(Note for interpretation: attribute values represent all cells of a given cluster type within the study area, rather than a specific polygon or cell on the map.)

Cluster_ID: Identification number for each cluster delineated by Thompson et al. 2016. Forest_Non: Differentiates forested and non-forested ecosystem classes of Thompson et al. 2016. Gen_EClass: The eighteen clusters delineated by Thompson et al. were each assigned to one of six generalized ecosystem classes. Extent_ha: Extent of the cluster in hectares. Elevation: Mean elevation of the cluster in m. Gap_Fractn: Mean gap fraction for the cluster. Gap Fraction is the ratio of total number of laser points = the minimum height threshold (2 m in this study). Reflects average height of tree canopy. NDVI: Mean of NDVI values measured in grid cells of this cluster. A measure of vegetation greenness, widely used as a proxy for productivity. Values near zero are considered non-vegetated or sparsely vegetated. Slope_Prct: Mean % slope for grid cells of this cluster. TPI: Mean Topographic Position Index for grid cells of this cluster. Positive values (>0.5 in this study) represent ridges and upper slopes, whereas negative values (< 0.5 in this study) represent lower slopes and valley bottoms; values near zero are mid-slope or flat (De Reu et al., 2013; Tagil & Jenness, 2008; Weiss, 2001). TRASP: Mean Topographic Radiation ASPect for grid cells of this cluster. Values near 0 (~0.5) are S, W, SW, or SE facing (warm slopes). TWI: Mean Topographic Wetness Index for grid cells of this cluster. Higher values are considered wetter, lower values drier. Count: The total count or number of 20 x 20 meter cells represented in each cluster.

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

Access and Use

Licence: Appropriate credit must be given to Hakai Institute and the authors of the dataset.

Data and Resources

Dates

Metadata Created October 26, 2018, 22:48 (UTC)
Metadata Updated October 26, 2018, 22:48 (UTC)
Reference Date(s) 2016-01-08 (Creation)
2016-01-08 (Publication)
Frequency of Update
Metadata Date September 13, 2018, 16:01 (UTC)

Graphic Preview

Dataset extent

Map data © OpenStreetMap contributors

Additional Info

Field Value
Contact Email data@hakai.org
encoding utf8
metadata-language eng
progress completed
resource-type dataset
Responsible Party University of Victoria (Author); Hakai Institute (Point of Contact, Processor)
spatial-reference-system 3857