The "Starter Kits" of the Canadian Geospatial Data Infrastructure (CGDI) are curated bundles of geospatial data delivered as a service. These kits are the result of a user needs assessment commissioned by Natural Resources Canada (NRCan), with a primary focus on addressing the specific requirements of Indigenous Peoples. Within these kits, you'll discover a selection of data provided as a service from a collection of over 71,000 datasets falling under the "Nature and Environment" category sourced from CGDI's Federal and Provincial/Territorial partners. These Starter Kits have been thoughtfully crafted into four thematic or geographic areas of Canada:a) British Columbia,b) Northwest Territories,c) Climate Change across Canada,d) And Water Resources across Canada.Each of these kits represents diverse areas and data categories, offering new users a compact, comprehensive, and user-friendly gateway to explore a wide array of information provided by the CGDI.

This layer contains polygon features that represent geological units in New Brunswick. The associated Bedrock Geology Relate Table contains details about each map unit, including: a brief legend description, Group name, Formation name, and rock age. This layer is the result of nearly 50 years of bedrock mapping and it continues to change as new interpretations are made and more data is collected through field work and analysis.

The Canada Centre for Mapping and Earth Observation (CCMEO) has created a 30m resolution radar mosaic of Canada's landmass from the RADARSAT Constellation Mission (RCM). This product highlights different types of radar interaction with the surface, which can assist the interpretation and study of land cover on a national scale. The national mosaic is made up of 3222 RCM images acquired between August 2023 and February 2024. (Credit: RADARSAT Constellation Mission imagery © Government of Canada [2024]. RADARSAT is an official mark of the CSA.)

“Biomass Inventory Mapping and Analysis – Business Data” provides a number of datasets related to the yield and production of residues from the agricultural and forestry industry, agricultural crops, and municipal solid wastes across Canada. The datasets contain agricultural residue production information (i.e., straw or stover) for barley, wheat, flax, oats and corn, and crop production information for barley, wheat, flax, oats, corn, canola and soybean. They also include information about amounts of straw required for cattle bedding and feeding, the type of tillage used in an area, and the amount of residue needed for soil conservation purposes. Datasets in the series provide the yield, production and other information for the median year and 1-in-10 year and 1-in-20 year lows. The forestry inventory dataset provides information about the location and quantity of residues from the forestry industry, as well as urban wood waste and potential sites and productivity of plantations of fast-growing trees that are grown as feedstock. Forestry residues include material left at the roadside after harvesting and excess and waste materials from mills. The municipal solid waste inventory dataset provides information about the approximate location and quantity of different types of municipal solid wastes, such as organics (including food and yard), paper and total. A transportation network dataset and datasets that are used to calculate cost to harvest and transport biomass are also included in this series.

The three-satellite RADARSAT Constellation Mission (RCM) acquires synthetic aperture radar data of the Earth's surface. Canadian lands and waters are tracked daily. The standardized RCM acquisition plan uses ScanSAR 30m beams with a compact polarimetric configuration and enables bimonthly monitoring of Canadian land cover. The Canada Centre for Remote Sensing (CCRS) has created a 30m resolution land cover map of Canada, representing the decomposition of information from the data collected according to the type of interaction of the radar wave with the earth's surface. **This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**

The three-satellite RADARSAT Constellation Mission (RCM) acquires synthetic aperture radar data of the Earth's surface. Canadian lands and waters are tracked daily. The standardized RCM acquisition plan uses ScanSAR 30m beams with a compact polarimetric configuration and enables bimonthly monitoring of Canadian land cover. The Canada Centre for Remote Sensing (CCRS) has created a 30m resolution land cover map of Canada, representing the decomposition of information from the data collected according to the type of interaction of the radar wave with the earth's surface. **This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**

CanVec contains more than 60 topographic features classes organized into 8 themes: Transport Features, Administrative Features, Hydro Features, Land Features, Manmade Features, Elevation Features, Resource Management Features and Toponymic Features.This multiscale product originates from the best available geospatial data sources covering Canadian territory. It offers quality topographic information in vector format complying with international geomatics standards.CanVec can be used in Web Map Services (WMS) and geographic information systems (GIS) applications and used to produce thematic maps. Because of its many attributes, CanVec allows for extensive spatial analysis.Related Products (Open Maps Links):**[Constructions and Land Use in Canada - CanVec Series - Manmade Features](https://open.canada.ca/data/en/dataset/fd4369a4-21fe-4070-914a-067474da0fd6)****[Lakes, Rivers and Glaciers in Canada - CanVec Series - Hydrographic Features](https://open.canada.ca/data/en/dataset/9d96e8c9-22fe-4ad2-b5e8-94a6991b744b)****[Administrative Boundaries in Canada - CanVec Series - Administrative Features](https://open.canada.ca/data/en/dataset/306e5004-534b-4110-9feb-58e3a5c3fd97)****[Mines, Energy and Communication Networks in Canada - CanVec Series - Resources Management Features](https://open.canada.ca/data/en/dataset/92dbea79-f644-4a62-b25e-8eb993ca0264)****[Wooded Areas, Saturated Soils and Landscape in Canada - CanVec Series - Land Features](https://open.canada.ca/data/en/dataset/80aa8ec6-4947-48de-bc9c-7d09d48b4cad)****[Transport Networks in Canada - CanVec Series - Transport Features](https://open.canada.ca/data/en/dataset/2dac78ba-8543-48a6-8f07-faeef56f9895)****[Elevation in Canada - CanVec Series - Elevation Features](https://open.canada.ca/data/en/dataset/64aad38d-f692-4ab6-bf2c-f938586c1249)****[Map Labels - CanVec Series - Toponymic Features](https://open.canada.ca/data/en/dataset/b3fdcd34-4533-415f-8f83-68f17f9d5d68)**

The RVI/CVI database is derived from the CanEcumene 3.0 GDB (Eddy, et. al. 2023) using a selection of socio-economic variables identified in Eddy and Dort (2011) that aim to capture the overall state of socio-economic conditions of communities as ‘human habitats’. This dataset was developed primarily for application in mapping socio-economic conditions of communities and regions for environmental and natural resource management, climate change adaptation, Impact Assessments (IAs) and Regional Assessments (RAs), and Cumulative Effects Assessment (CEA).The RVI/CVI is comprised of five sub-indicators: 1) population change, 2) age structure, 3) education levels, 4) employment levels, and 5) real estate values. Index values are based on percentile ranks of each sub-indicator, and averaged for each community, and for three ranked groups: 1) all of Canada, 2) by province, and 3) by population size. The data covers the Census periods of 2001, 2006, 2011 (NHS), 2016, and 2021.The index is mapped in two ways: 1) as ‘points’ for individual communities (CVI), and 2) as ‘rasters’ for spatial interpolation of point data (RVI). These formats provide an alternative spatial framework to conventional StatsCan CSD framework. (For more information on this approach see Eddy, et. al. 2020).============================================================================================Eddy, B.G., Muggridge, M., LeBlanc, R., Osmond, J., Kean, C., and Boyd, E. 2023. The CanEcumene 3.0 GIS Database. Federal Geospatial Platform (FGP), Natural Resources Canada. https://gcgeo.gc.ca/viz/index-en.html?keys=draft-3f599fcb-8d77-4dbb-8b1e-d3f27f932a4bEddy B.G., Muggridge M, LeBlanc R, Osmond J, Kean C, Boyd E. 2020. An Ecological Approach for Mapping Socio-Economic Data in Support of Ecosystems Analysis: Examples in Mapping Canada’s Forest Ecumene. One Ecosystem 5: e55881. https://doi.org/10.3897/oneeco.5.e55881Eddy, B.G.; Dort, A. 2011. Integrating Socio-Economic Data for Integrated Land Management (ILM): Examples from the Humber River Basin, western Newfoundland. Geomatica, Vol. 65, No. 3, p. 283-291. doi:10.5623/cig2011-044.

Mapping Resources on energy infrastructure and potential implemented as part of the North American Cooperation on Energy Information (NACEI) between the Department of Energy of the United States of America, the Department of Natural Resources of Canada, and the Ministry of Energy of the United Mexican States.[Natural Gas Processing Plants](https://open.canada.ca/data/en/dataset/636b9550-3700-4e66-8259-5cfc8159a784): Facilities designed to recover natural gas liquids from a stream of natural gas. These facilities control the quality of the natural gas to be marketed.[Refineries](https://open.canada.ca/data/en/dataset/57e7bc4c-680b-4640-9fa1-ded7ce186fab): Facilities that separate and convert crude oil or other feedstock into liquid petroleum products, including upgraders and asphalt refineries.[Liquefied Natural Gas Terminals](https://open.canada.ca/data/en/dataset/e08eec16-7c7a-4253-9bee-ea640d400a54): Natural gas onshore facilities used to receive, unload, load, store, gasify, liquefy, process and transport by ship, natural gas that is imported from a foreign country, exported to a foreign country, or for interior commerce.[Power Plants, 100 MW or more](https://open.canada.ca/data/en/dataset/40fbe40c-01cd-49d3-8add-0d20ed64c90d): Stations containing prime movers, electric generators, and auxiliary equipment for converting mechanical, chemical, and/or fission energy into electric energy with an installed capacity of 100 megawatts or more.[Renewable Power Plants, 1 MW or more](https://open.canada.ca/data/en/dataset/490db619-ab58-4a2a-a245-2376ce1840de): Stations containing prime movers, electric generators, and auxiliary equipment for converting mechanical, chemical into electric energy with an installed capacity of 1 Megawatt or more generated from renewable energy, including biomass, hydroelectric, pumped-storage hydroelectric, geothermal, solar, and wind. [Natural Gas Underground Storage](https://open.canada.ca/data/en/dataset/07b63e0e-09bb-4c4f-b057-63a58d40553a): Sub-surface facilities used for storing natural gas. The facilities are usually hollowed-out salt domes, geological reservoirs (depleted oil or gas field) or water bearing sands (called aquifers) topped by an impermeable cap rock.Border Crossings: [Electric transmission lines](https://open.canada.ca/data/en/dataset/35c51098-5cd8-44b0-b1cd-c8ce2f5dae89), [liquids pipelines](https://open.canada.ca/data/en/dataset/82053097-6b4f-44d9-a750-549df44b36b4) and [gas pipelines](https://open.canada.ca/data/en/dataset/e313db89-4219-4a5e-a543-772a86068710).[Solar Resource, NSRDB PSM Global Horizontal Irradiance (GHI)](https://open.canada.ca/data/en/dataset/9554ed18-6ab2-477f-9545-da091eba762f): Average of the hourly Global Horizontal Irradiance (GHI) over 17 years (1998-2014). Data extracted from the National Solar Radiation Database (NSRDB) developed using the Physical Solar Model (PSM) by National Renewable Energy Laboratory ("NREL"), Alliance for Sustainable Energy, LLC, U.S. Department of Energy ("DOE").[Solar Resource, NSRDB PSM Direct Normal Irradiance (DNI)](https://open.canada.ca/data/en/dataset/a2dd0554-03f8-4edc-a3b3-67b47c5c9d6d): Average of the hourly Direct Normal Irradiance (DNI) over 17 years (1998-2014). Data extracted from the National Solar Radiation Database (NSRDB) developed using the Physical Solar Model (PSM) by National Renewable Energy Laboratory ("NREL"), Alliance for Sustainable Energy, LLC, U.S. Department of Energy ("DOE").The participating Agencies and Institutions shall not be held liable for improper or incorrect use of the data described and/or contained herein. These data and related graphics, if available, are not legal documents and are not intended to be used as such. The information contained in these data is dynamic and may change over time and may differ from other official information. The Agencies and Institutions participants give no warranty, expressed or implied, as to the accuracy, reliability, or completeness of these data.

Average of the hourly Direct Normal Irradiance (DNI) over 17 years (1998-2014). Data extracted from the National Solar Radiation Database (NSRDB) developed using the Physical Solar Model (PSM) by National Renewable Energy Laboratory ("NREL"), Alliance for Sustainable Energy, LLC, U.S. Department of Energy ("DOE").The current version of the National Solar Radiation Database (NSRDB) (v2.0.1) was developed using the Physical Solar Model (PSM), and offers users the solar resource datasets from 1998 to 2014). The NSRDB comprises 30-minute solar and meteorological data for approximately 2 million 0.038-degree latitude by 0.038-degree longitude surface pixels (nominally 4 km2). The area covered is bordered by longitudes 25° W on the east and 175° W on the west, and by latitudes -20° S on the south and 60° N on the north. The solar radiation values represent the resource available to solar energy systems. The AVHRR Pathfinder Atmospheres-Extended (PATMOS-x) model uses half-hourly radiance images in visible and infrared channels from the GOES series of geostationary weather satellites, a climatological albedo database and mixing ratio, temperature and pressure profiles from Modern Era-Retrospective Analysis (MERRA) to generate cloud masking and cloud properties. Cloud properties generated using PATMOS-x are used in fast radiative transfer models along with aerosol optical depth (AOD) and precipitable water vapor (PWV) from ancillary sources to estimate Direct Normal Irradiance (DNI) and Global Horizontal Irradiance (GHI). A daily AOD is retrieved by combining information from the MODIS and MISR satellites and ground-based AERONET stations. Water vapor and other inputs are obtained from MERRA. For clear sky scenes the direct normal irradiance (DNI) and GHI are computed using the REST2 radiative transfer model. For cloud scenes identified by the cloud mask, Fast All-sky Radiation Model for Solar applications (FARMS) is used to compute the GHI. The DNI for cloud scenes is then computed using the DISC model. The data in this layer is an average of the hourly GHI over 17 years (1998-2014). NOTE: The Geographical Information System (GIS) data and maps for solar resources for Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) were developed by the U.S. National Renewable Energy Laboratory (NREL) and provided for Canada as an estimate. At present, neither the NREL data, nor the Physical Solar Model (PSM) on which the NREL data is based, have been either assessed or validated for the particular Canadian weather applications. A Canadian GHI map developed by the department of Natural Resources Canada (NRCan) is based on the State University of New York (SUNY) model and has been assessed and validated for the particular Canadian weather applications. The Canadian GHI map is available at http://atlas.gc.ca/cerp-rpep/en/.