The AAFC Land Use Time Series is a culmination and curated meta-analysis of several high-quality spatial datasets produced between 2000 and 2021 using a variety of methods by teams of researchers as techniques and capabilities have evolved. The information from the input datasets was consolidated and embedded within each 30m x 30m pixel to create consolidated pixel histories, resulting in thousands of unique combinations of evidence ready for careful consideration. Informed by many sources of high-quality evidence and visual observation of imagery in Google Earth, we apply an incremental strategy to develop a coherent best current understanding of what has happened in each pixel through the time series.

Land cover information is necessary for a large range of environmental applications related to climate impacts and adaption, emergency response, wildlife habitat, etc. In Canada, a 2008 user survey indicated that the most practical land cover data is provided in a nationwide 30 m spatial resolution format, with an update frequency of five years. In response to this need, the Canada Centre for Remote Sensing (CCRS) has generated a 30 m land cover map of Canada for the base year 2010, as well as this 2015 land cover map. This land cover dataset is also the Canadian contribution to the 30 m spatial resolution 2015 Land Cover Map of North America, which is produced by Mexican, American and Canadian government institutions 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. An accuracy assessment based on 806 randomly distributed samples shows that land cover data produced with this new approach has achieved 79.90% accuracy with no marked spatial disparities. - Land Cover of Canada - Cartographic Product Collection

The land cover classes consist of vegetation types (such as forest, wetlands and agricultural crops or pasture) and categories of non-vegetated surface (such as water bodies, bedrock outcrops or settlements). These classes reflect the nature of the land surface rather than actual or potential land use. The 2000 Edition of the Ontario Land Cover Data Base is the Second Edition of this provincial land cover classification. The coverage is derived wholly from Landsat-7 Thematic Mapper (TM) satellite data frames recorded between 1999 and 2002, most from 2000 onward. The Provincial Land Cover (2000) Data Base is divided into 4 individual Universal Transverse Mercator (UTM) grid zone tiles (15, 16, 17, and 18) and is distributed in Tagged Image File Format (TIFF) format. Documentation is provided with this database in the form of a user's guide and general use caveats.

This table contains 1392 series, with data for years 1971 - 2011 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (87 items: Abbotsford - Mission (census metropolitan area), British Columbia; Baie-Comeau (census agglomeration), Quebec; Barrie (census metropolitan area), Ontario; Bathurst (census agglomeration), New Brunswick; ...);  Area (2 items: Census metropolitan area/census agglomeration; Census metropolitan area-ecosystem/census agglomeration-ecosystem);  Land cover and land use (8 items: Total land area; Built-up area, settled; Built-up area, roads; Arable; ...).

Census of Agriculture, 1921 to date. Total farm area, land in crops, summerfallow land, tame or seeded pasture and all other land.

Land use mapping.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Canada Land Use: Total Area data was reported at 15,634,410.000 sq km in 2022. This stayed constant from the previous number of 15,634,410.000 sq km for 2021. Canada Land Use: Total Area data is updated yearly, averaging 15,640,500.000 sq km from Dec 1961 (Median) to 2022, with 62 observations. The data reached an all-time high of 15,640,500.000 sq km in 2020 and a record low of 15,634,410.000 sq km in 2022. Canada Land Use: Total Area data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Canada – Table CA.OECD.ESG: Environmental: Land Use: OECD Member: Annual.

This collection is a legacy product that is no longer supported. It may not meet current government standards. Land Cover information is the result of vectorization of raster thematic data originating from classified Landsat 5 and Landsat 7 ortho-images, for agricultural and forest areas of Canada, and for Northern Territories. The forest cover was produced by the Earth Observation for Sustainable Development (EOSD) project, an initiative of the Canadian Forest Service (CFS) with the collaboration of the Canadian Space Agency (CSA) and in partnership with the provincial and territorial governments. The agricultural coverage is produced by the National Land and Water Information Service (NLWIS) of Agriculture and Agri-Food Canada (AAFC). Northern Territories land cover was realized by the Canadian Centre of Remote Sensing (CCRS). Land Cover data are classified according to a harmonized legend build from the partner's legends. This legend is principally based on the legend described in following publication: EOSD publication: EOSD Land Cover Classification Legend Report, on which CFS and AAFC collaborated. Some classes related to Northern environments where added in order to meet the interpretation of the Northern land cover classification experts. Initially, Land Cover vector data are closest as possible to the source (original raster data). Slight differences can occur because the raster data goes through a data portrayal before being vectorized in order to enhance visual representation such as minimum size, smoothness of polygons and geometry.

The USGS Upper Midwest Environmental Sciences Center developed a Monarch Relevant Land Cover data set covering the area of Canada. We used the 2010 land cover data set produced by the tri-national North American Land Change Monitoring System (NALCMS) and supported by the Commission for Environmental Cooperation (CEC) that depicts year 2010 land cover across North America at 30-meter spatial resolution, and incorporated additional spatially-explicit information to develop this land cover map. Additional sources of information included 2004 railroad data provided by The Atlas of Canada and the CEC, 2017 roads data provided by Statistics Canada, 2017 protected areas data provided by the CEC, and 2016 Canada provincial/territory boundary file data provided by Statistics Canada.

Land cover by category, Canada, major drainage areas and sub-drainage areas (square kilometres).

Collection of Land Cover products for Canada as produced by Natural Resources Canada using Landsat satellite imagery. This collection of cartographic products offers classified Land Cover of Canada at a 30 metre scale, updated on a 5 year basis. - Landcover of Canada 2010 - Landcover of Canada 2015 - Landcover of Canada 2020

The 2020 North American Land Cover 30-meter dataset was produced as part of the North American Land Change Monitoring System (NALCMS), a trilateral effort between Natural Resources Canada, the United States Geological Survey, and three Mexican organizations including the National Institute of Statistics and Geography (Instituto Nacional de Estadística y Geografía), National Commission for the Knowledge and Use of the Biodiversity (Comisión Nacional Para el Conocimiento y Uso de la Biodiversidad), and the National Forestry Commission of Mexico (Comisión Nacional Forestal). The collaboration is facilitated by the Commission for Environmental Cooperation, an international organization created by the Canada, Mexico, and United States governments under the North American Agreement on Environmental Cooperation to promote environmental collaboration between the three countries. The general objective of NALCMS is to devise, through collective effort, a harmonized multi-scale land cover monitoring approach which ensures high accuracy and consistency in monitoring land cover changes at the North American scale and which meets each country's specific requirements. This 30-meter dataset of North American Land Cover reflects land cover information for 2020 from Mexico and Canada, 2019 over the conterminous United States and 2021 over Alaska. Each country developed its own classification method to identify Land Cover classes and then provided an input layer to produce a continental Land Cover map across North America. Canada, Mexico, and the United States developed their own 30-meter land cover products; see specific sections on data generation below. The main inputs for image classification were 30-meter Landsat 8 Collection 2 Level 1 data in the three countries (Canada, the United States and Mexico). Image selection processes and reduction to specific spectral bands varied among the countries due to study-site-specific requirements. While Canada selected most images from the year 2020 with a few from 2019 and 2021, the Conterminous United States employed mainly images from 2019, while Alaska land cover maps are mainly based on the use of images from 2021. The land cover map for Mexico was based on land cover change detection between 2015 and 2020 Mexico Landsat 8 mosaics. In order to generate a seamless and consistent land cover map of North America, national maps were generated for Canada by the CCRS; for Mexico by CONABIO, INEGI, and CONAFOR; and for the United States by the USGS. Each country chose their own approaches, ancillary data, and land cover mapping methodologies to create national datasets. This North America dataset was produced by combining the national land cover datasets. The integration of the three national products merged four Land Cover map sections, Alaska, Canada, the conterminous United States and Mexico. See also: Natural Resources Canada has North American Land Cover information available online at https://open.canada.ca/data/en/dataset/ee1580ab-a23d-4f86-a09b-79763677eb47 The National Commission for the Knowledge and Use of Biodiversity has North American Land Cover information available online at https://www.biodiversidad.gob.mx/monitoreo/cobertura-suelo The U.S. Geological Survey has North American Land Cover information available online at www.mrlc.gov

Contained within the Atlas of Canada Poster Map Series, is a poster showing the different types of land cover across the Country. The map is primarily based on AVHRR (Advanced Very High Resolution Radiometer) data, offering very precise detail. The land cover image contains 31 land cover classes. These have been grouped into 9 large land cover classes from coniferous forest to snow and ice. There are photos showing visually what each of these 9 land cover areas look like.

Canada Land Use: % of Land Area: Forest data was reported at 39.466 % in 2022. This records a decrease from the previous number of 39.470 % for 2021. Canada Land Use: % of Land Area: Forest data is updated yearly, averaging 38.772 % from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 39.470 % in 2021 and a record low of 38.696 % in 2020. Canada Land Use: % of Land Area: Forest data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Canada – Table CA.OECD.ESG: Environmental: Land Use: OECD Member: Annual.

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for nine classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2024 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2024. Key Properties Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Extent: GlobalSource imagery: Sentinel-2 L2ACell Size: 10-metersType: ThematicAttribution: Esri, Impact ObservatoryAnalysis: Optimized for analysisClass Definitions: ValueNameDescription1WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2TreesAny significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.10CloudsNo land cover information due to persistent cloud cover.11RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.NOTE: Land use focus does not provide the spatial detail of a land cover map. As such, for the built area classification, yards, parks, and groves will appear as built area rather than trees or rangeland classes.Usage Information and Best PracticesProcessing TemplatesThis layer includes a number of preconfigured processing templates (raster function templates) to provide on-the-fly data rendering and class isolation for visualization and analysis. Each processing template includes labels and descriptions to characterize the intended usage. This may include for visualization, for analysis, or for both visualization and analysis. VisualizationThe default rendering on this layer displays all classes.There are a number of on-the-fly renderings/processing templates designed specifically for data visualization.By default, the most recent year is displayed. To discover and isolate specific years for visualization in Map Viewer, try using the Image Collection Explorer. AnalysisIn order to leverage the optimization for analysis, the capability must be enabled by your ArcGIS organization administrator. More information on enabling this feature can be found in the ‘Regional data hosting’ section of this help doc.Optimized for analysis means this layer does not have size constraints for analysis and it is recommended for multisource analysis with other layers optimized for analysis. See this group for a complete list of imagery layers optimized for analysis.Prior to running analysis, users should always provide some form of data selection with either a layer filter (e.g. for a specific date range, cloud cover percent, mission, etc.) or by selecting specific images. To discover and isolate specific images for analysis in Map Viewer, try using the Image Collection Explorer.Zonal Statistics is a common tool used for understanding the composition of a specified area by reporting the total estimates for each of the classes. GeneralIf you are new to Sentinel-2 LULC, the Sentinel-2 Land Cover Explorer provides a good introductory user experience for working with this imagery layer. For more information, see this Quick Start Guide.Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. Classification ProcessThese maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.The underlying deep learning model uses 6-bands of Sentinel-2 L2A surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map for each year.The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch. CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.

Please note that this dataset is not an official City of Toronto land use dataset. It was created for personal and academic use using City of Toronto Land Use Maps (2019) found on the City of Toronto Official Plan website at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/official-plan-maps-copy, along with the City of Toronto parcel fabric (Property Boundaries) found at https://open.toronto.ca/dataset/property-boundaries/ and Statistics Canada Census Dissemination Blocks level boundary files (2016). The property boundaries used were dated November 11, 2021. Further detail about the City of Toronto's Official Plan, consolidation of the information presented in its online form, and considerations for its interpretation can be found at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/ Data Creation Documentation and Procedures Software Used The spatial vector data were created using ArcGIS Pro 2.9.0 in December 2021. PDF File Conversions Using Adobe Acrobat Pro DC software, the following downloaded PDF map images were converted to TIF format. 9028-cp-official-plan-Map-14_LandUse_AODA.pdf 9042-cp-official-plan-Map-22_LandUse_AODA.pdf 9070-cp-official-plan-Map-20_LandUse_AODA.pdf 908a-cp-official-plan-Map-13_LandUse_AODA.pdf 978e-cp-official-plan-Map-17_LandUse_AODA.pdf 97cc-cp-official-plan-Map-15_LandUse_AODA.pdf 97d4-cp-official-plan-Map-23_LandUse_AODA.pdf 97f2-cp-official-plan-Map-19_LandUse_AODA.pdf 97fe-cp-official-plan-Map-18_LandUse_AODA.pdf 9811-cp-official-plan-Map-16_LandUse_AODA.pdf 982d-cp-official-plan-Map-21_LandUse_AODA.pdf Georeferencing and Reprojecting Data Files The original projection of the PDF maps is unknown but were most likely published using MTM Zone 10 EPSG 2019 as per many of the City of Toronto's many datasets. They could also have possibly been published in UTM Zone 17 EPSG 26917 The TIF images were georeferenced in ArcGIS Pro using this projection with very good results. The images were matched against the City of Toronto's Centreline dataset found here The resulting TIF files and their supporting spatial files include: TOLandUseMap13.tfwx TOLandUseMap13.tif TOLandUseMap13.tif.aux.xml TOLandUseMap13.tif.ovr TOLandUseMap14.tfwx TOLandUseMap14.tif TOLandUseMap14.tif.aux.xml TOLandUseMap14.tif.ovr TOLandUseMap15.tfwx TOLandUseMap15.tif TOLandUseMap15.tif.aux.xml TOLandUseMap15.tif.ovr TOLandUseMap16.tfwx TOLandUseMap16.tif TOLandUseMap16.tif.aux.xml TOLandUseMap16.tif.ovr TOLandUseMap17.tfwx TOLandUseMap17.tif TOLandUseMap17.tif.aux.xml TOLandUseMap17.tif.ovr TOLandUseMap18.tfwx TOLandUseMap18.tif TOLandUseMap18.tif.aux.xml TOLandUseMap18.tif.ovr TOLandUseMap19.tif TOLandUseMap19.tif.aux.xml TOLandUseMap19.tif.ovr TOLandUseMap20.tfwx TOLandUseMap20.tif TOLandUseMap20.tif.aux.xml TOLandUseMap20.tif.ovr TOLandUseMap21.tfwx TOLandUseMap21.tif TOLandUseMap21.tif.aux.xml TOLandUseMap21.tif.ovr TOLandUseMap22.tfwx TOLandUseMap22.tif TOLandUseMap22.tif.aux.xml TOLandUseMap22.tif.ovr TOLandUseMap23.tfwx TOLandUseMap23.tif TOLandUseMap23.tif.aux.xml TOLandUseMap23.tif.ov Ground control points were saved for all georeferenced images. The files are the following: map13.txt map14.txt map15.txt map16.txt map17.txt map18.txt map19.txt map21.txt map22.txt map23.txt The City of Toronto's Property Boundaries shapefile, "property_bnds_gcc_wgs84.zip" were unzipped and also reprojected to EPSG 26917 (UTM Zone 17) into a new shapefile, "Property_Boundaries_UTM.shp" Mosaicing Images Once georeferenced, all images were then mosaiced into one image file, "LandUseMosaic20211220v01", within the project-generated Geodatabase, "Landuse.gdb" and exported TIF, "LandUseMosaic20211220.tif" Reclassifying Images Because the original images were of low quality and the conversion to TIF made the image colours even more inconsistent, a method was required to reclassify the images so that different land use classes could be identified. Using Deep learning Objects, the images were re-classified into useful consistent colours. Deep Learning Objects and Training The resulting mosaic was then prepared for reclassification using the Label Objects for Deep Learning tool in ArcGIS Pro. A training sample, "LandUseTrainingSamples20211220", was created in the geodatabase for all land use types as follows: Neighbourhoods Insitutional Natural Areas Core Employment Areas Mixed Use Areas Apartment Neighbourhoods Parks Roads Utility Corridors Other Open Spaces General Employment Areas Regeneration Areas Lettering (not a land use type, but an image colour (black), used to label streets). By identifying the letters, it then made the reclassification and vectorization results easier to clean up of unnecessary clutter caused by the labels of streets. Reclassification Once the training samples were created and saved, the raster was then reclassified using the Image Classification Wizard tool in ArcGIS Pro, using the Support...

This land cover product was produced by NBIOME to generate an up-to-date, spatially and temporally consistent land cover map of the landmass of Canada for use by scientists and other users interested in environmental information at national and regional scales. The data were acquired by CCRS and were provided to BORIS for use. This data set is gridded and was produced from 10-day composite data of surface parameters. Temporally, the 10-day compositing periods begin 11-April-95 and ends 31-Oct-95. Spatially, the data cover the entire landmass of Canada. The data are stored in binary image format files.

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.

This data tracks the review and final decisions for files where the Ministry of Municipal Affairs and Housing is the planning approval authority.

Dataset includes the following information (where available):

• Case Number
• Case Type
• Municipality
• Received Date
• Complete Date
• Decision Due Date
• Municipal Services Office (MSO) Region
• Decision Date
• Decision Type
• PIN
• Parcel Number
• Lot
• Section
• Concession
• Geographic Township
• Registered Plan Number
• Registered Plan Lot Number
• Registered Plan Lot Type
• Reference Plan Number

• Municipal Address

  *[PIN]: Property Identification Number

The dataset includes a shapefile named reference.shp, which consists of a collection of files with a common filename prefix, stored in the same directory. The shapefile stores the location, shape (point in this case), and attribute of the 467 reference points. The attribute of each point includes the land cover class among the following: bareground (refers to forb-dominated barren), mesic (refers to forb-dominated tundra), wetgrass (refers to grass-dominated wetland), wetsedge (refers to sedge-dominated wetland), wetmoss (refers to moss-dominated wetland), water, and snow. The shapefile can be opened in geographic information system (GIS) software such as QGIS (QGIS Development Team) and ArcGIS (ESRI). The geographic coordinate system is NAD 1983 (EPSG:4269) and the projected coordinate system is NAD 1983 UTM Zone 20N (EPSG:26920). The dataset also includes a file named ensemble_classifier.tif. It is a raster GIS file in GeoTIFF format with 0.5 x 0.5 meter resolution. The uncompressed s...