Land Cover Classification 2020 (raster)
A land cover classification for Metro Vancouver plus a 5km buffer, providing a contiguous surface mapped to broad biophysical classes. Created using PlanetScope 5m multi-spectral satellite imagery and full feature LiDAR data (wherever available). The imagery dates from August 2020.
LiDAR data were obtained from the Metro Vancouver member jurisdictions where available. As a result, the LiDAR data used in the project represents a mosaic of vintages (from 2016 to 2020). LiDAR data were available for much of the study area, including the Capilano and Seymour watersheds; however, none were available for the rest of the Mountain Wilderness subregion. Three terrain surfaces were generated from the LiDAR data: a digital elevation model (DEM), a digital surface model (DSM) derived from first return values from each LiDAR pulse, and a canopy height model (CHM), which includes vegetation and building heights. These surfaces were resampled to a 5 m resolution to match the PlanetScope imagery.
A random forest approach was selected to perform a supervised classification of the study area. This iterative process generates hundreds of unique decision trees, each producing a classified image. The benefit of using a random forest algorithm is that the ensemble method compensates for any model weaknesses present in the individual trees; thereby, producing better classification results. Additionally, it is a data-driven approach that is flexible enough to accommodate a variety of predictor variables and can be easily scaled to fit the study area. For this project, a random forest model was built to match the class framework and definitions used in the 2014 Land Cover Classification. Table 1 below summarizes the land cover classes and associated definitions.
Table 1. Land cover classification classes and definitions
Classification
Criteria *
Code
Buildings
Housing, warehouses, towers, industrial structures, etc.
1
Paved
Asphalt and concrete surfaces
2
Other Built
Sports surfaces, transit or rail areas, other impervious surfaces, etc.
3
Barren
Beaches, alpine rock, shoreline rock, quarries, gravel pits, gravel roads, lacking vegetation, but not soil
4
Soil
Agricultural soils (light or dark), cleared/open areas where darker colours indicate organic matter present
5
Coniferous
Predominantly coniferous (>75%)
6
Deciduous
Predominantly broadleaf (>75%)
7
Shrub
Woody, leafy, and rough-textured vegetation (~ <3-4m)
8
Modified Herb
Most crops, golf course greens, city park grass, lawns, etc.
9
Natural Herb
Alpine meadows, near-shore grass areas, fine-textured bog/wetland areas
10
Non-Photosynthetic
Vegetation
Dead grass, cutblock slash, and log booms.
11
Water
Lakes, rivers, inlets, irrigation channels, retention ponds, pools, etc.
12
Shadow
Dark pixels with very low reflectance values
13
Snow/Ice
Snow or Ice features with high reflectance
14
Conifer/Paved
Asphalt and concrete surfaces covered by coniferous
15
Deciduous/Paved
Asphalt and concrete surfaces covered by deciduous
16
The accompanying technical report titled “Metro Vancouver Regional District Regional Land Cover Classification and Sensitive Ecosystem Inventory Update” and dated “December 2022” can be found by searching “land cover classification” on metrovancouver.org.
Enquiries regarding the 2020 Land Cover Classification dataset should be directed to [email protected].
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Additional Info
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| Last Updated | March 24, 2026, 22:48 (UTC) |
| Created | March 24, 2026, 22:48 (UTC) |
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Domain / Topic
Domain or topic of the dataset being cataloged.
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Title
Title for the Dataset.
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Land Cover Classification 2020 (raster) |
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Description
A description of the dataset.
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A land cover classification for Metro Vancouver plus a 5km buffer, providing a contiguous surface mapped to broad biophysical classes. Created using PlanetScope 5m multi-spectral satellite imagery and full feature LiDAR data (wherever available). The imagery dates from August 2020. LiDAR data were obtained from the Metro Vancouver member jurisdictions where available. As a result, the LiDAR data used in the project represents a mosaic of vintages (from 2016 to 2020). LiDAR data were available for much of the study area, including the Capilano and Seymour watersheds; however, none were available for the rest of the Mountain Wilderness subregion. Three terrain surfaces were generated from the LiDAR data: a digital elevation model (DEM), a digital surface model (DSM) derived from first return values from each LiDAR pulse, and a canopy height model (CHM), which includes vegetation and building heights. These surfaces were resampled to a 5 m resolution to match the PlanetScope imagery. A random forest approach was selected to perform a supervised classification of the study area. This iterative process generates hundreds of unique decision trees, each producing a classified image. The benefit of using a random forest algorithm is that the ensemble method compensates for any model weaknesses present in the individual trees; thereby, producing better classification results. Additionally, it is a data-driven approach that is flexible enough to accommodate a variety of predictor variables and can be easily scaled to fit the study area. For this project, a random forest model was built to match the class framework and definitions used in the 2014 Land Cover Classification. Table 1 below summarizes the land cover classes and associated definitions. Table 1. Land cover classification classes and definitions Classification Criteria * Code Buildings Housing, warehouses, towers, industrial structures, etc. 1 Paved Asphalt and concrete surfaces 2 Other Built Sports surfaces, transit or rail areas, other impervious surfaces, etc. 3 Barren Beaches, alpine rock, shoreline rock, quarries, gravel pits, gravel roads, lacking vegetation, but not soil 4 Soil Agricultural soils (light or dark), cleared/open areas where darker colours indicate organic matter present 5 Coniferous Predominantly coniferous (>75%) 6 Deciduous Predominantly broadleaf (>75%) 7 Shrub Woody, leafy, and rough-textured vegetation (~ <3-4m) 8 Modified Herb Most crops, golf course greens, city park grass, lawns, etc. 9 Natural Herb Alpine meadows, near-shore grass areas, fine-textured bog/wetland areas 10 Non-Photosynthetic Vegetation Dead grass, cutblock slash, and log booms. 11 Water Lakes, rivers, inlets, irrigation channels, retention ponds, pools, etc. 12 Shadow Dark pixels with very low reflectance values 13 Snow/Ice Snow or Ice features with high reflectance 14 Conifer/Paved Asphalt and concrete surfaces covered by coniferous 15 Deciduous/Paved Asphalt and concrete surfaces covered by deciduous 16 The accompanying technical report titled “Metro Vancouver Regional District Regional Land Cover Classification and Sensitive Ecosystem Inventory Update” and dated “December 2022” can be found by searching “land cover classification” on metrovancouver.org. Enquiries regarding the 2020 Land Cover Classification dataset should be directed to [email protected]. |
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23356.77 |
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Metadata Identifier
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Published Date
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2023-09-14 |
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Time Period Data Span (start date)
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Time Period Data Span (end date)
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Access category
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public |
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License
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Open Government License |
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Limits on use
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Location
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https://open-data-portal-metrovancouver.hub.arcgis.com/datasets/metrovancouver::land-cover-classification-2020-raster |
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Data Service
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Owner
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mvagoladmin |
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Contact Point
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Metro Vancouver |
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Contact Point Email
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[email protected] |
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Publisher
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Metro Vancouver |
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Publisher Email
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Author
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mvagoladmin |
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Author Email
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Accessed At
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2024-07-09 |
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Identifier
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5dd153684b9b41249c0dcf09e79c9b25 |
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Language
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English |
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https://open-data-portal-metrovancouver.hub.arcgis.com/datasets/metrovancouver::land-cover-classification-2020-raster |
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Contains data about individuals
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Contains Indigenous Data
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Version
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None |
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Source
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None |
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Provenance Text
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Metro Vancouver |
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Indigenous Community Permission
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Community Permission
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The Indigenous communities the dataset is about
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