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

Building on the initial effort (“Database of Open Water Areas in River Ice for Provinces of Manitoba, Ontario, Quebec, and the Maritimes - Potential Locations For River Hydrokinetic Energy Extraction Identified Through Optical Satellite Imager”; weblink: https://search.open.canada.ca/openmap/d9823004-29aa-40e2-aa47-9c54cf88c309), a follow-up research project has led to the publication of this extensive analysis encompassing the entirety of Canada’s major freezing rivers. This work has applied advanced image processing and classification algorithms to the selected images, elevating the classification and analysis to a higher level of accuracy and providing a more comprehensive dataset for exploring river hydrokinetic energy prospects across the country.This dataset identifies open water areas within ice-covered major Canadian rivers, aiming to evaluate potential hydrokinetic resources. The data is derived from high-resolution optical satellite imagery obtained from Landsat and Sentinel satellites corresponding to National Research Council (NRC) 2014 flow data of river reaches with water depths of at least 10 meters and a flow velocity of a minimum of 0.5 meters per second. Using advanced image processing and machine learning algorithms, the dataset efficiently differentiates between ice-covered and open water regions, providing a reliable basis for assessing hydrokinetic energy potential in the identified areas.Disclaimer:This dataset is subject to the following limitations:• Landsat and Sentinel optical satellite images are high-resolution, but they may contain resolution errors, possible distortions, and inaccuracies in depicting on-ground conditions. • Despite advanced image processing and machine learning algorithms, errors or biases may exist. • The dataset's reliability is also influenced by the NRC flow data.Therefore, users should view this dataset as a preliminary assessment tool, not a definitive guide for decision-making or investment in hydrokinetic projects. It is strongly recommended to validate its accuracy and suitability for specific applications through additional research and studies. By accessing and using this dataset, users acknowledge and accept these disclaimers. The providers of this dataset explicitly absolve themselves of any responsibility or liability for any consequences arising from the use, reliance upon, or interpretation of this dataset. Users are advised that their use of the dataset is at their own risk, and they assume full responsibility for any actions or decisions made based on the information contained therein. This disclaimer is in accordance with applicable laws and regulations, and by accessing or utilizing the dataset, users agree to release the providers of this dataset from any legal claims, damages, or liabilities that may arise from such use.

Land Features entities are: Island, Shoreline, Wooded Area, Saturated soil, Landform Feature (esker, sand\...), and Cut Line. CanVec is a digital cartographic reference product of Natural Resources Canada (NRCan). It originates from the best available data sources covering Canadian territory, offers quality topographical information in vector format, and complies with international geomatics standards. CanVec is a multi-source product coming mainly from the National Topographic Data Base (NTDB), the Mapping the North process conducted by the Canada Center for Mapping and Earth Observation (CCMEO), the Atlas of Canada data, the GeoBase initiative, and the data update using satellite imagery coverage (e.g. Landsat 7, Spot, Radarsat, etc.).Distributed from [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps) . Discover more digital map data and interactive maps from Yukon's digital map data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)

Footprints for all imagery in the G overnment of Yukon High Resolution Satellite I magery S ervice.Distributed from [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps) . Discover more digital map data and interactive maps from Yukon's digital map data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)

In 2010 the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) continued the process of generating annual crop inventory digital maps using satellite imagery. Focusing on the Prairie Provinces, a Decision Tree (DT) based methodology was applied using both optical (AWiFS, Landsat-5, DMC) and radar (RADARSAT-2) based satellite imagery, and having a final spatial resolution of 56m. Methods were also developed to enhance the optical classification with RADARSAT-2 imagery, addressing issues associated with cloud cover. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from our regional AAFC colleagues. The overall process for Crop Inventory Map includes: satellite data acquisition; field data acquisition for classification training and accuracy assessment; and, operational implementation of the classification methodology.

Funded under DFO's Marine Conservation Targets Program, this two-year optical imagery benthic survey captured 41 drift-camera transects in the St. Anns Bank Marine Protected Area (MPA - 4364 km2) and 4 coastal transects west of the MPA, Atlantic Canada from August 15-23, 2023 and August 08-17, 2024. High-resolution still images (n=3605) were taken periodically throughout each transect, while continuous high-definition downward- and forward-facing video (~20 hours of each) was collected simultaneously along with over 14 hours of forward-facing Go-Pro video (only in 2024). Transect and image locations were collected using an acoustic positioning operating system (Kongsberg APOS) acquired in 2024; year-one of the survey relied on the vessel position. Distance travelled and distance between still images (m) was calculated using ArcGIS tools. Field of view (FOV) was estimated by measuring the length and width of a subset of still images from year-one of the survey (n=500) in ImageJ2, using 10-cm lasers for scale. FOV was standardized for each reported altitude. Transects ranged from 319 m to 2.8 km in length (~47 km surveyed in total), collecting imagery for 12 minutes to just over 1 hour at a time, surveying depths from 17 to 144 m below chart datum. Transect locations were selected based on unique bathymetric features and benthoscapes as well as areas previously surveyed from 2009-2015.Cite this data as: Lawton P, Teed L. Near-seafloor drift transect video imagery and high-resolution digital still images from a two-year survey in support of Marine Protected Area monitoring of St. Anns Bank, Atlantic Canada. Published January 2025. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, St. Andrews, N.B.

This data set shows utility points that provide services for: * power * water * communications * heating fuel They include: * fibre optic stations * hydro stations * lock pumping stations This product requires the use of geographic information system (GIS) software.

Funded under DFO's Marine Conservation Targets Program, this optical imagery benthic survey documents the occurrence and estimated percent cover of the invasive colonial tunicate, Didemnum vexillum in seven drift-camera transects in the 'Head Harbour/West Isles Archipelago/The Passages' Ecologically and Biologically Significant Area (ESBA, ~113km2) in the western Bay of Fundy, New Brunswick, Canada. Occurrence data was derived from the use of high-resolution still images (n=386) taken periodically throughout each transect, and simultaneous continuous high-definition video. Video was divided into 20-second segments (here, we report the start and end location of each segment within a transect) and when D. vexillum was present in a video segment, frequency of occurrence was classified as common (continuous coverage/patches throughout the video segment), occasional (individual colonies of various sizes encountered >5 times throughout the video segment), or rare (small, isolated colonies encountered ≤5 times throughout the video segment). A video segment was deemed unusable and removed from the dataset if there was too much turbidity, or if the camera position was too high off-bottom to reliably image the seafloor. For still images, when D. vexillum was observed in an image, colony percent cover was categorized as >50%, 26-50%, 6-25%, or ≤5% of the images field of view (FOV). Distance travelled and distance between still images (m) was calculated using ArcGIS tools. FOV was estimated by measuring the length and width of a subset of still images and video frame grabs in ImageJ2, using 10-cm lasers for scale. FOV was standardized for each reported altitude, and area sampled (m2) along a continuous video segment was estimated by multiplying the average FOV by the distance travelled in that segment. D. vexillum was found in 44% of the area sampled at depths from 34 to 118m, deeper than previous reports globally of ~80m.Cite this data as: Teed LL, Goodwin C, Lawton P, Lacoursière-Roussel A, Dinning KM (2024) Multiple perspectives on the emergence of the invasive colonial tunicate Didemnum vexillum Kott, 2002 in the western Bay of Fundy, Atlantic Canada. BioInvasions Records 13(3): 713–738, https://doi.org/10.3391/bir.2024.13.3.12

In 2009 the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) began the process of generating annual crop inventory digital maps using satellite imagery. Focusing on the Prairie Provinces, a Decision Tree (DT) based methodology was applied using both optical (AWiFS, Landsat-5) and radar (RADARSAT-2) based satellite imagery, and having a final spatial resolution of 56m. Methods were also developed to enhance the optical classification with RADARSAT-2 imagery, addressing issues associated with cloud cover. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from our regional AAFC colleagues. The overall process for Crop Inventory Map includes: satellite data acquisition; field data acquisition for classification training and accuracy assessment; and, operational implementation of the classification methodology.The initial methodology was developed in partnership with AAFC Research Branch, and supported in part by the Canadian Space Agency. The long-term objective of this endeavour is to expand from the Prairies and produce an annual crop inventory of the entire agricultural extent of Canada.

The Government of Canada acquired a national image coverage from the Systeme Pour l'Observation de la Terre (SPOT 4 - 5) satellites that includes four multispectral bands in the visible to shortwave infrared region at 20m spatial resolution. Five years from 2005 - 2010 were necessary to image all of Canada under clear-sky conditions, while acquisition anniversary dates were less important provided the data were imaged during the snow-free period. These data were downloaded from the GeoBase Orthoimage 2005 - 2010 dataset (http://www.geobase.ca/geobase/en/data/imagery/imr/description.html) and used to map 2005 - 2010 land cover south of treeline. Northern Canada has not currently been remapped since circa 2000 due to technical challenges associated with land cover variability and image acquisition dates relative to short summers. This land cover product includes 16 generic classes based on plant functional and a minimum mapping unit of 20m. Radiometric normalization was applied to balance images acquired near mid-summer during the 'peak-of-season' temporal window. The combined Enhancement and Classification by Progressive Generalization methods were used to classify large-area balanced mosaics over twenty mapping zones. Image interpretation was guided using high resolution imagery and other content in Google Earth. Knowledge of land cover spectral signatures, field experience and published reports were also used to assist interpretation in many regions. Remaining images acquired outside the peak-of-season window in early spring or late fall were subsequently classified using decision trees trained on data from overlapping classified peak-of-season images. Accuracy was assessed using ground truth data acquired during several field campaigns conducted with other government departments such as Parks Canada and the Geological Survey. This sample was enhanced using points interpreted in Google Earth as described above to provide a more even spatial coverage of Canada. Overall accuracy assessed at 71% using 1566 reference points, more than half of which were acquired in the field. When assessed using only land cover that was homogeneous within 3 by 3 pixels to account for potential geolocation errors, accuracy increased to 85% for 349 points that were biased towards easily classified classes such as water.