This collection is a legacy product that is no longer supported. It may not meet current government standards. This inventory presents chronologically the satellite images acquired, orthorectified and published over time by Natural Resources Canada. It is composed of imagery from the Landsat7 (1999-2003) and RADARSAT-1 (2001-2002) satellites, as well as the CanImage by-product and the control points used to process the images. Landsat7 Orthorectified Imagery: The orthoimage dataset is a complete set of cloud-free (less than 10%) orthoimages covering the Canadian landmass and created with the most accurate control data available at the time of creation. RADARSAT-1 Orthorectified Imagery: The 5 RADARSAT-1 images (processed and distributed by RADARSAT International (RSI) complete the landsat 7 orthoimagery coverage. They are stored as raster data produced from SAR Standard 7 (S7) beam mode with a pixel size of 15 m. They have been produced in accordance with NAD83 (North American Datum of 1983) using the Universal Transverse Mercator (UTM) projection. RADARSAT-1 orthoimagery were produced with the 1:250 000 Canadian Digital Elevation Data (CDED) and photogrammetric control points generated from the Aerial Survey Data Base (ASDB). CanImage -Landsat7 Orthoimages of Canada,1:50 000: CanImage is a raster image containing information from Landsat7 orthoimages that have been resampled and based on the National Topographic System (NTS) at the 1:50 000 scale in the UTM projection. The product is distributed in datasets in GeoTIFF format. The resolution of this product is 15 metres. Landsat7 Imagery Control Points: the control points were used for the geometric correction of Landsat7 satellite imagery. They can also be used to correct vector data and for simultaneously displaying data from several sources prepared at different scales or resolutions.

The link: Access the data directory is available in the section*Dataset Description Sheets; Additional Information*. Satellite image mosaics are products designed by combining several adjacent tiles of satellite images from the Landsat or Sentinel sensor. The coverage of the mosaics varies according to the years of acquisition, ranging from southern Quebec to all of Quebec. These mosaics are designed to identify land use classes, including forest environments, agricultural environments, wetlands, and environments modified by humans. They also offer an overview of the various natural disturbances that occur on the territory. In the end, they offer easy monitoring of the evolution of forest cover and natural disturbances across territory and time. These mosaics are primarily used to support planning, monitoring, and land use planning. The mosaics have a spatial resolution of between 10 and 30 meters. This third party metadata element was translated using an automated translation tool (Amazon Translate).

The Canada Geospatial Imagery Analytics Market report segments the industry into Type (Imagery Analytics, Video Analytics), Deployment Mode (On-premise, Cloud), Organization Size (SMEs, Large Enterprises), and Verticals (Insurance, Agriculture, Defense and Security, Environmental Monitoring, Engineering and Construction, Government, Other Verticals). Five years of historic data and five-year forecasts are provided.

The Canada Geospatial Imagery Analytics market is experiencing robust growth, driven by increasing government investments in infrastructure development, precision agriculture adoption, and a heightened focus on environmental monitoring and urban planning. The market's Compound Annual Growth Rate (CAGR) of 21.76% from 2019 to 2024 suggests a significant expansion, projected to continue over the forecast period (2025-2033). Key market drivers include the rising availability of high-resolution satellite imagery, advancements in AI-powered image processing and analysis techniques, and the growing demand for real-time geospatial intelligence across various sectors. The cloud-based deployment model is gaining traction due to its scalability and cost-effectiveness, while large enterprises are the primary consumers, owing to their greater resources and need for sophisticated analytics. Significant growth is observed in verticals such as agriculture, where imagery analytics optimizes crop management and yield prediction, and in the defense and security sectors, which leverage it for surveillance and strategic planning. While data limitations prevent precise market sizing for Canada, extrapolating from the global market and considering Canada's robust technology sector, a reasonable estimate for the 2025 market size could be in the range of $150-200 million USD, with continued growth expected throughout the forecast period. The market segmentation reveals crucial insights into growth dynamics. The imagery analytics segment (including satellite, aerial and drone imagery) is likely to dominate due to its versatility. Within deployment modes, the cloud segment shows high potential, facilitated by the increasing affordability and accessibility of cloud computing resources. Large enterprises' greater financial capacity drives adoption of advanced analytics solutions, contributing significantly to market size. The insurance sector benefits from risk assessment and claims management using geospatial data, while the environmental monitoring sector utilizes it for resource management and impact assessment. Competition is expected to intensify among established players like Satellite Imaging Corporation, BAE Systems, and Google LLC, alongside emerging technology firms, spurring innovation and driving down costs. Potential restraints include data privacy concerns, the high initial investment costs associated with advanced analytics systems, and the need for skilled professionals to interpret the complex data generated. Recent developments include: October 2023: Canada will invest CAD 1.01 billion (USD 740.90 million) in satellite technology over the next 15 years to boost the Earth observation data it uses to track wildfires and other environmental crises. The new initiative called Radarsat+ will gather information about Earth's oceans, land, climate, and populated areas. Data collected from earth observation technologies allows scientists to see how the planet changes and make decisions for emergencies like wildfires or longer-term issues like climate change., December 2022: Carl Data Solutions, a player in predictive analytics for environmental monitoring as a service (EMaaS) and smart city applications driven by compliance, signed a strategic partnership agreement with K2 Geospatial, a Montreal-based company. Over 350 cities and municipalities, seaports, airports, road authorities, and utilities across North America and Europe are among the 500 organizations using K2 Geospatial. JMap, a spatial analysis mapping integration platform made to connect silos in a fully integrated IT ecosystem, is used by K2 users., In September 2022, CAPE Analytics, a provider of AI-powered geospatial property intelligence, partnered with weather technology firm Canopy Weather to launch a product for storm-related damage. Canopy Weather applies an application-first approach to deliver weather data products with specific, real-world business applications. Powered by machine learning, the rating indicates the likelihood of post-storm damage to roofs after severe weather. It can predict the subset of claims eligible for automated, straight-through processing with over 96% accuracy.. Key drivers for this market are: Increasing Demand for Location-based Services, Technological Innovations in Geospatial Imagery Services. Potential restraints include: Lack of Awareness About Benefits of Geospatial Imagery Services. Notable trends are: Increasing Adoption of 5G in Canada is Boosting the Market Growth.

This map includes a variety of Landsat services which have been time enabled and can be explored using the Time Slider in the ArcGIS.COM Map Viewer or Explorer. Each layer has a predefined useful band combination already set on the services. For more information about each layer, click on the hyperlink bellow. Data Source: This map includes image services compiled from the following Global Land Survey (GLS) datasets: GLS 2005, GLS 2000, GLS 1990, and GLS 1975. GLS datasets are created by the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) using Landsat images. These global minimal-cloud cover, orthorectified Landsat data products support global assessments of land-cover, land cover-change, and ecosystem dynamics such as disturbance and vegetation health.

The satellite image of Canada is a composite of several individual satellite images form the Advanced Very High Resolution Radiometre (AVHRR) sensor on board various NOAA Satellites. The colours reflect differences in the density of vegetation cover: bright green for dense vegetation in humid southern regions; yellow for semi-arid and for mountainous regions; brown for the north where vegetation cover is very sparse; and white for snow and ice. An inset map shows a satellite image mosaic of North America with 35 land cover classes, based on data from the SPOT satellite VGT (vegetation) sensor.

This dataset contains georeferenced three-band orthomosaics of green, red, and near-infrared (NIR) digital imagery at 1m resolution collected over selected surface waters across Alaska and Canada between July 9 and August 17, 2017. The orthomosaics were generated from individual images collected by a Cirrus Designs Digital Camera System (DCS) mounted on a Beechcraft Super King Air B200 aircraft from approximately 8-11 km altitude. Flights were over the following areas: Saskatchewan River, Saskatoon, Inuvik, Yukon River including Yukon Flats, Sagavanirktok River, Arctic Coastal Plain, Old Crow Flats, Peace-Athabasca Delta, Slave River, Athabasca River, Yellowknife, Great Slave Lake, Mackenzie River and Delta, Daring Lake, and other selected locations. Most locations were imaged twice during two flight campaigns in Canada and Alaska extending roughly SE-NW then NW-SE up to a month apart. The data were georeferenced using 303 ground control points (GCPs) across the study region.

This dataset provides 1) a conservative open water mask for future water surface elevation (WSE) extraction from the co-registered AirSWOT Ka-band interferometry data, and 2) high-resolution (1 m) water body distribution maps for water bodies greater than 40 m2 along the NASA Arctic-Boreal Vulnerability Experiment (ABoVE) foundational flight lines. The masks and maps were derived from georeferenced three-band orthomosaics generated from individual images collected during the flights and a semi-automated water classification algorithm based on the Normalized Difference Water Index (NDWI). In total, 3,167 km2 of open water were mapped from 23,380 km2 of flight lines spanning 23 degrees of latitude. Detected water body sizes range from 40 m2 to 15 km2. The image tiles were georeferenced using manually selected ground control points (GCPs). Comparison with manually digitized open water boundaries yields an overall open-water classification accuracy of 98.0%.

Vegetation biophysical parameters correspond to physical properties of vegetation structure (e.g. density, height, biomass), biochemistry (e.g. chlorophyll and water content) or energy exchange (e.g. albedo, temperature). These parameters have been identified by the Global Climate Observing System as an essential climate variable required for ecosystem, weather and climate modelling and monitoring. The Canada wide products are derived from systematically acquired satellite imagery with spatial resolution from 10m to 30m and provided as monthly temporal or peak-season composites due to cloud cover. Products are derived applying algorithms developed at Canada Centre for Remote Sensing (NRCan) to Copernicus Sentinel 2 satellite imagery. Select a related product first to view content.

Discover the booming Canadian Geospatial Imagery Analytics market! This in-depth analysis reveals a 21.76% CAGR, driven by government investment, AI adoption, and diverse sector demands (agriculture, defense, environment). Explore market size, segmentation, key players, and future trends through 2033. Recent developments include: October 2023: Canada will invest CAD 1.01 billion (USD 740.90 million) in satellite technology over the next 15 years to boost the Earth observation data it uses to track wildfires and other environmental crises. The new initiative called Radarsat+ will gather information about Earth's oceans, land, climate, and populated areas. Data collected from earth observation technologies allows scientists to see how the planet changes and make decisions for emergencies like wildfires or longer-term issues like climate change., December 2022: Carl Data Solutions, a player in predictive analytics for environmental monitoring as a service (EMaaS) and smart city applications driven by compliance, signed a strategic partnership agreement with K2 Geospatial, a Montreal-based company. Over 350 cities and municipalities, seaports, airports, road authorities, and utilities across North America and Europe are among the 500 organizations using K2 Geospatial. JMap, a spatial analysis mapping integration platform made to connect silos in a fully integrated IT ecosystem, is used by K2 users., In September 2022, CAPE Analytics, a provider of AI-powered geospatial property intelligence, partnered with weather technology firm Canopy Weather to launch a product for storm-related damage. Canopy Weather applies an application-first approach to deliver weather data products with specific, real-world business applications. Powered by machine learning, the rating indicates the likelihood of post-storm damage to roofs after severe weather. It can predict the subset of claims eligible for automated, straight-through processing with over 96% accuracy.. Key drivers for this market are: Increasing Demand for Location-based Services, Technological Innovations in Geospatial Imagery Services. Potential restraints include: Increasing Demand for Location-based Services, Technological Innovations in Geospatial Imagery Services. Notable trends are: Increasing Adoption of 5G in Canada is Boosting the Market Growth.

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.

Leaf area index (LAI) quantified the density of vegetation irrespective of land cover. LAI quantifies the total foliage surface area per ground surface area. LAI has been identified by the Global Climate Observing System as an essential climate variable required for ecosystem, weather and climate modelling and monitoring. This product consists of a national scale coverage (Canada) of monthly maps of the maximum LAI during a growing season (May-June-july-August-September) at 20m. References: L. Brown, R. Fernandes, N. Djamai, C. Meier, N. Gobron, H. Morris, C. Canisius, G. Bai, C. Lerebourg, C. Lanconelli, M. Clerici, J. Dash. Validation of baseline and modified Sentinel-2 Level 2 Prototype Processor leaf area index retrievals over the United States IISPRS J. Photogramm. Remote Sens., 175 (2021), pp. 71-87, https://doi.org/10.1016/j.isprsjprs.2021.02.020. https://www.sciencedirect.com/science/article/pii/S0924271621000617 Richard Fernandes, Luke Brown, Francis Canisius, Jadu Dash, Liming He, Gang Hong, Lucy Huang, Nhu Quynh Le, Camryn MacDougall, Courtney Meier, Patrick Osei Darko, Hemit Shah, Lynsay Spafford, Lixin Sun, 2023. Validation of Simplified Level 2 Prototype Processor Sentinel-2 fraction of canopy cover, fraction of absorbed photosynthetically active radiation and leaf area index products over North American forests, Remote Sensing of Environment, Volume 293, https://doi.org/10.1016/j.rse.2023.113600. https://www.sciencedirect.com/science/article/pii/S0034425723001517

Discover the booming Canadian satellite-based Earth observation market! This report reveals a CAGR of 8.14%, driven by agriculture, urban planning, and resource management needs. Learn about key players, market segments, and future growth projections for 2025-2033. Recent developments include: May 2023: The University of Saskatchewan Canadian CubeSat Project used the spaceX CRS-28 rocket to launch the RADSAT-SK satellite. The satellite will collect radiation data for a year while in orbit., October 2022: The Canadian government announced that it would fund the development of a satellite and instruments for a NASA-led Earth science program. The Canadian government will fund the manufacturing of the satellite and other sensors for a NASA-led Earth research program.. Key drivers for this market are: New Technologies Implementation in earth observation satellite technologies, Increasing demand of high-resolution imaging services. Potential restraints include: New Technologies Implementation in earth observation satellite technologies, Increasing demand of high-resolution imaging services. Notable trends are: New Technologies Implementation in Earth Observation Satellite Technologies.

This data set contains several forecast image products from the 20 member Canadian ensemble forecast system over North America. The products are available from the 00 and 12 UTC runs every 12 hours out to 204 hours. The products include 12 hour precipitation mean, 500 mb spaghetti plots of the 534 and 594 dam heights, 500 mb spaghetti plots of the 546 dam height, 500 mb spaghetti plots of the 558 dam height, and mean SLP and SLP centers. The imagery were developed by Environment Canada.

Leaf area index (LAI) quantified the density of vegetation irrespective of land cover. LAI quantifies the total foliage surface area per groud surface area. LAI has been identified by the Global Climate Observing System as an essential climate variable required for ecosystem,weather and climate modelling and monitoring. This product consists of annual maps of the maximum LAI during a grownig season (June-July-August) at 100m resolution covering Canada's land mass.

Contained within the Atlas of Canada's Various Map Series, 1965 to 2006, is the first of a planned series of regional satellite image maps of all of Canada. It was one of the first satellite image maps to combine imagery and other map components such as boundaries, roads, railways and place names. The imagery is a composite of many images from the United States National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) satellites. The imagery was captured between August 11 to 20, 1990 to obtain cloud-free coverage. This was the only map in this planned series that was produced.

Contained within the 5th Edition (1978 to 1995) of the National Atlas of Canada is a map that shows Canada as seen from space in August, 1990 using uninterrupted 1.1 kilometer resolution imagery; final colors are adjusted to approximate those of the land cover portrayed.

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

Contained within the Atlas of Canada's Various Map Series, 1965 to 2006, is the first of a planned series of regional satellite image maps of all of Canada. It was one of the first satellite image maps to combine imagery and other map components such as boundaries, roads, railways and place names. The imagery is a composite of many images from the United States National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) satellites. The imagery was captured between August 11 to 20, 1990 to obtain cloud-free coverage. This was the only map in this planned series that was produced.

As part of the MOSES airborne campaign led by the Alfred Wegener Institute in 2018, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 48.03 px/m³ and 17.85 px/m³ respectively. This dataset covers approximately 1.38 km² of Herschel Island on the Yukon Coast, Canada, with all images collected on 15 August 2018. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.