Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) sensors were used to generate the circa 2010 Mosaic of Canada at 30 m spatial resolution. All scenes were processed to Standard Terrain Correction Level 1T by the United States Geological Survey (USGS). Further processing performed by the Canada Centre for Remote Sensing included conversion of sensor measurements to top of atmosphere reflectance, cloud and cloud shadow detection, re-projection, selection of best measurements, mosaic generation ,noise removal and quality control. To provide a clear sky measurement for each location in Canada, data from the years 2009, 2010, and 2011 were used, but 2010 was preferentially selected. Bands 3 (0.63-0.69 µm), 4 (0.76-0.90 µm), 5 (1.55-1.75 µm), and 7 (2.08-2.35 µm) are provided in this version as significant atmosphere effects strongly limit the quality of the blue (0.45-0.52 µm) and green (0.52-0.60 µm) bands. Multi-criteria compositing was used for the selection of the most representative pixel. For ETM+ onboard Landsat 7 a scan line malfunction caused missing lines of data in all scenes collected after May 2003. Atmosphere and target variability between scenes cause these lines to have significant radiometric differences in some cases. A Fourier transformation approach was applied to correct this occurrence. This mosaic was developed for land cover and biophysical mapping applications across Canada. Other applications of these data are also possible, but should consider the temporal and spectral limitations of the product. Research to enhance the spatial, spectral and temporal aspects are in development for future versions of moderate resolution products from historical Landsat sensors, Landsat 8, and Sentinel 2 data.

Organic soils in the boreal forest commonly store as much carbon as the vegetation above ground. While recent efforts through the National Forest Inventory has yielded new spatial datasets of forest structure across the vast area of Canada’s boreal forest, organic soils are poorly mapped. In this geospatial dataset, we produce a map primarily of forested and treed peatlands, those with more than 40 cm of peat accumulation and over 10% tree canopy cover. National Forest Inventory ground plots were used to identify the range of forest structure that corresponds to the presence of over 40 cm of peat soils. Areas containing that range of forest cover were identified using the National Forest Inventory k-NN forest structure maps and assigned a probability (0-100% as integer) of being a forested or treed peatland according to a statistical model. While this mapping product captures the distribution of forested and treed peatlands at a 250 m resolution, open, completely treeless peatlands are not fully captured by this mapping product as forest cover information was used to create the maps. The methodology used in the creation of this product is described in:Thompson DK, Simpson BN, Beaudoin A. 2016. Using forest structure to predict the distribution of treed boreal peatlands in Canada. Forest Ecology and Management, 372, 19-27. https://cfs.nrcan.gc.ca/publications?id=36751 This distribution uses an updated forest attribute layer current to 2011 from:Beaudoin A, Bernier PY, Villemaire P, Guindon L, Guo XJ. 2017. Species composition, forest properties and land cover types across Canada’s forests at 250m resolution for 2001 and 2011. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/ec9e2659-1c29-4ddb-87a2-6aced147a990 Additionally, this distribution varies slightly from the original published in 2016 in that here slope data is derived from the CDEM: https://open.canada.ca/data/en/dataset/7f245e4d-76c2-4caa-951a-45d1d2051333 The above peatland probability map was further processed to delineate bogs vs fens (based on mapped Larix content via the k-NN maps), as well as an approximation of the extent of open peatlands using EOSD data. The result is a 9-type peatland map with a more complete methodology as detailed in: Webster, K. L., Bhatti, J. S., Thompson, D. K., Nelson, S. A., Shaw, C. H., Bona, K. A., Hayne, S. L., & Kurz, W. A. (2018). Spatially-integrated estimates of net ecosystem exchange and methane fluxes from Canadian peatlands. Carbon Balance and Management, 13(1), 16. https://doi.org/10.1186/s13021-018-0105-5 In plain text, the legend for the 9-class map is as follows:value="0" label="not peat" alpha="0"value="1" label="Open Bog" alpha="255" color="#0a4b32"value="2" label="Open Poor Fen" alpha="255" color="#5c5430"value="3" label="Open Rich Fen" alpha="255" color="#792652"value="4" label="Treed Bog" alpha="255" color="#6a917b"value="5" label="Treed Poor Fen" alpha="255" color="#aba476"value="6" label="Treed Rich Fen" alpha="255" color="#af7a8f"value="7" label="Forested Bog" alpha="255" color="#aad7bf"value="8" label="Forested Poor Fen" alpha="255" color="#fbfabc"value="9" label="Forested Rich Fen" alpha="255" color="#ffb6db"This colour scale is given in qml/xml format in the resources below. The 9-type peatland map from Webster et al 2018 was further refined slightly following two simple conditions: (1) any 250-m raster cell with greater than 40% pine content is classified as upland (non-peat); (2) all 250-m raster cells classified as water or agriculture via the NRCan North American Land Cover Monitoring System (https://doi.org/10.3390/rs9111098) is also classified as non-peatland (value of zero in the 9-class map. This mapping scheme was used at a regional scale in the following paper: Thompson, D. K., Simpson, B. N., Whitman, E., Barber, Q. E., & Parisien, M.-A. (2019). Peatland Hydrological Dynamics as A Driver of Landscape Connectivity and Fire Activity in the Boreal Plain of Canada. Forests, 10(7), 534. https://doi.org/10.3390/f10070534 And is reproduced here at a national scale. Note that this mapping product does not fully capture all permafrost peatland features covered by open canopy spruce woodland with lichen ground cover. Nor are treeless peatlands near the northern treeline captured in the training data, resulting in unknown mapping quality in those regions.

This dataset is provided by the Ministry of Utilities and Affordability, Rural Utilities Branch, and represents rural low-pressure pipelines (less than 700 kPa) and customer meters in Alberta that are under the jurisdiction of the Gas Distribution Act, including Gas Co-operatives, First Nations, Municipalities and Counties, Private Systems, Apex Utilities Inc., and ATCO Gas Inc. The data is based on the annual submissions of construction as-builts to Rural Utilities, Alberta Affordability and Utilities. The geodatabase contains linear pipeline data features and customer meter point features. The pipeline data features contain: Pipe material. Status. Year built, if known, and the name of the natural gas distributor and general contact information for inquiries and requests. The meter attributes include the type of meter and the contact information for each distributor. The Customer meters features contain: Meter Code. Meter Type. and the name of the natural gas distributor and general contact information for inquiries and requests.

The spatial accuracy of the lot fabric for some townships has been improved through the Ontario Parcel, Township Realignment and Township Improvement projects. Improvements to the fabric may include: * road allowance widths * spatial changes to better represent the location of lot boundaries * more consistent concession names. Data is collected on an on-going basis. The time period "end date" may be more recent than indicated here.

## GIS data containing the boundaries of Provincially Significant Employment Zones in the Greater Golden Horseshoe as identified by the Minister of Municipal Affairs and Housing As areas of high economic output, provincially significant employment zones are strategically located to provide stable, reliable employment across the Greater Golden Horseshoe region. They provide opportunities to improve coordination between land use planning, economic development, and infrastructure investments to support investment and job creation over the longer-term.

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 the first of a planned series of maps to be updated every five years, or more frequently. This land cover dataset is also the Canadian contribution to the 30 m spatial resolution 2010 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 Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) Landsat sensors. An accuracy assessment based on 2811 randomly distributed samples shows that land cover data produced with this new approach has achieved 76.60% accuracy with no marked spatial disparities.- [Land Cover of Canada - Cartographic Product Collection](https://open.canada.ca/data/en/dataset/11990a35-912e-4002-b197-d57dd88836d7)

Climate Normals and Averages are used to summarize or describe the average climatic conditions of a particular location. At the completion of each decade, Environment and Climate Change Canada updates its Climate Normals for as many locations and as many climatic characteristics as possible. The Climate Normals, Averages and Extremes offered here are based on Canadian climate stations with at least 15 years of data between 1981 to 2010.

Fisheries landings and effort mapping of the inshore lobster fishery on the DFO Maritimes Region statistical grid (2012-2014). This report describes an analysis of Maritimes Region inshore lobster logbook data reported at a grid level, including Bay of Fundy Grey Zone data reported at the coordinate level. Annual and composite (2012–2014) grid maps were produced for landings, number of license-days fished, number of trap hauls, and the same series standardized by grid area, as well as maps of catch weight per number of trap hauls as an index of catch per unit effort (CPUE). Spatial differences in fishing pressure, landings, and CPUE are indicated, and potential mapping applications are outlined. Mapping the distribution and intensity of inshore lobster fishing activity has management applications for spatial planning and related decision support. The lack of region-wide latitude and longitude coordinates for lobster effort and landings limits the utility of commercial logbook data for marine spatial planning purposes.

The Open Database of Buildings (ODB) is a collection of open data on buildings made available under the Open Government License - Canada. The ODB brings together 530 datasets originating from 107 government sources of open data. The database aims to enhance access to a harmonized collection of building features across Canada.

Knowledge of the location of Earth’s surface water in time and space is critical to inform policy decisions on environment, wildlife, and human security. Dynamic surface water maps generally represent water occurrence, also referred to as inundation frequency, depicting the percentage of valid observations when water is detected at the surface. The location and duration of surface water varies from areas of permanent water with 100% inundation frequency where water is always observed, to areas of permanent land with 0% inundation where water never occurs. Between these two extremes are areas of ephemeral water that experience periodic flooding with inundation frequencies between 0-100%. National-scale dynamic surface water information was generated for years 1984-2023 at 30m spatial resolution from the historical Landsat archive over Canada. The complete dataset consists of annual water maps and derived products including inundation frequency and inter-annual wetting and drying trends calculated using per-pixel logistic regression. The complete dataset enables an assessment of the timing, duration, and trends towards wetting or drying at regional to national scales.Associated publication:https://www.sciencedirect.com/science/article/pii/S0034425722002358