IBL - Imagery, basemaps, and land cover (imageryBaseMapsEarthCover) Basemaps. For example, resources describing land cover, topographic maps, and classified and unclassified images

The National Ecological Framework for Canada's "Land Cover by Ecoprovince” dataset provides land cover information within the ecoprovince framework polygon. It provides landcover codes and their English and French language description as well as information about the percentage of the polygon that the component occupies.

The National Ecological Framework for Canada's "Land Cover by Ecodistrict” dataset provides land cover information within the ecodistrict framework polygon. It provides landcover codes and their English and French language description as well as information about the percentage of the polygon that the component occupies.

The National Ecological Framework for Canada's "Land Cover by Ecoregion” dataset provides land cover information within the ecoregion framework polygon. It provides landcover codes and their English and French language description as well as information about the percentage of the polygon that the component occupies.

The National Ecological Framework for Canada's "Land Cover by Ecozone” dataset provides land cover information within the ecozone framework polygon. It provides landcover codes and their English and French language description as well as information about the percentage of the polygon that the component occupies.

Wall-to-wall map of water bodies across Canada's forested ecosystems for the year 2022, derived from the "water" class of the annual Virtual Land Cover of Engine (VLCE) product. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). The VLCE maps are based on Landsat image time-series composites and represent annual land cover classifications from 1984 to 2022 at a spatial resolution of 30 m. The classification process integrates forest change information and ancillary topographic and hydrologic variables, applying a regional modeling framework based on a 150x150 km tiling system ( Hermosilla et al., 2022). Training data are drawn from multiple land cover sources and selected proportionally to land cover distributions using a distance-weighted approach. Classifications are refined over time using a Hidden Markov Model to ensure consistency and reduce classification noise between years.Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C. 2022. Land cover classification in an era of big and open data: Optimizing localized implementation and training data selection to improve mapping outcomes. Remote Sensing of Environment. 268, 112780. https://doi.org/10.1016/j.rse.2021.112780. ( Hermosilla et al., 2022)Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W. 2018. Disturbance-Informed Annual Land Cover Classification Maps of Canada's Forested Ecosystems for a 29-Year Landsat Time Series. Canadian Journal of Remote Sensing. 44(1) 67-87. https://doi.org/10.1080/07038992.2018.1437719.( Hermosilla et al., 2018)

FCOVER corresponds to the amount of the ground surface that is covered by vegetation, including the understory, when viewed vertically (from nadir). FCOVER is an indicator of the spatial extent of vegetation independent of land cover class. It is a dimensionless quantity that varies from 0 to 1, and as an intrinsic property of the canopy, is not dependent on satellite observation conditions.This product consists of FCOVER indicator during peak-season (June-July-August) at 100m resolution covering Canada's land mass.

Canada’s NFI survey was designed to provide an unbiased probability sample of Canada’s forests for long-term strategic monitoring purposes. The target population is Canada’s entire non-Arctic land area. A National Terrestrial Monitoring Framework (NTMF) was created by establishing a systematic 4 km by 4 km sampling grid over all of Canada from a random offshore point. Prior to T0, NFI partners determined that the NFI program would be able to affordably achieve its mission by establishing a 2 km by 2 km (400 ha) “photo plot” at every fifth sampling point on the NTMF (i.e. every 20 km), thereby providing a one percent sample of the target population. This sampling intensity was considered sufficient for national reporting and possible to sustain over the long term with anticipated funding.Photo plots were established across Canada during 2000-2006 (T0). There are 26,139 photo plot survey locations on the 20 km by 20 km grid, of which 18,570 lie inside the target population area. For each photo plot, information is collected on land cover, land use, ownership and protection status.NFI photo plot survey data are stratified by “NFI Unit” for standard estimation and reporting purposes. NFI Units were created by the geographic intersection of Canada’s 10 provinces, 3 territories and 12 non-Arctic terrestrial ecozones. Estimates produced for NFI Units are rolled up to produce standard reports for ecozones, jurisdictions (provinces and territories) and Canada. Some NFI Units are too small to produce robust estimates for with the current sampling intensity, so NFI Unit estimates are not publicly reported. Prince Edward Island (PEI) Atlantic Maritime, for example, is PEI’s only NFI Unit and it is small (1% sampling intensity achieved with only 19 photo plots), so the NFI avoids publishing provincial reports. Information consumers are encouraged to use official statistics produced by provincial and territorial governments for the forests in their jurisdictions. Most provinces are large, however, and the current NFI sampling intensity is sufficient for producing robust NFI reports for those jurisdictions. Special estimation reports can be produced using different ecological or administrative strata, such as the Boreal Zone, or the Managed Forest.NFI photo plots are surveyed on a ten-year cycle. During first re-measurement (T1; 2008-2017), survey intensity was reduced to one photo plot every 40 km across northern Canada (Figure 3) because of budget limitations. The T2 survey (2018-2027) is currently underway.

Land cover information is necessary for a large range of environmental applications related to climate impacts and adaption, emergency response, wildlife habitat, etc. The Canada Centre for Remote Sensing (CCRS) has generated a 30 m land cover map of Canada in response to this need. This land cover dataset is also the Canadian contribution to the 30 m spatial resolution Land Cover Map of North America under a collaboration called the North American Land Change Monitoring System (NALCMS). This land cover dataset for Canada is produced using observation from Operational Land Imager (OLI) Landsat sensor. **This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**

Land cover information is necessary for a large range of environmental applications related to climate impacts and adaption, emergency response, wildlife habitat, etc. The Canada Centre for Remote Sensing (CCRS) has generated a 30 m land cover map of Canada in response to this need. This land cover dataset is also the Canadian contribution to the 30 m spatial resolution Land Cover Map of North America under a collaboration called the North American Land Change Monitoring System (NALCMS). This land cover dataset for Canada is produced using observation from Operational Land Imager (OLI) Landsat sensor. **This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**