Level curves with an equidistance of 1 m derived from a lidar survey conducted in 2024.attributes:ID - Unique IDSubtype - Master (1) or secondary (2) level curve SCORE - Elevation value (m) The High Resolution Digital Elevation Model (m) product The High Resolution Digital Elevation Model (HRDM) product is available on the Open Government website.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

List of Businesses / Persons licensed to operate as direct sellers in Nova Scotia

Summer, Spring, Early Winter, and Late Winter multi-scale habitat model for mountain caribou in the Western Cariboo Region / Columbia Highlands / Northern Columbia Mountains. [Season] field should be used to split the data out into separate summer, spring, early winter, and late winter habitat models. [Model development is detailed in _Apps, C. D. and T. A. Kinley. 2000. Multiscale Habitat Modeling for Mountain Caribou in the Columbia Highlands and Northern Columbia Mountains Ecoregions, British Columbia.Wildlife Section, Ministry of Water, Land and Air Protection, Williams Lake, British Columbia, Canada](http://www.env.gov.bc.ca/cariboo/env_stewardship/wildlife/inventory/caribou/mtncar/hmi/habitatmod04-00.pdf).

Fraction of absorbed photosynthetically active radiation (fAPAR) quantified the absorbed by green foliage. fAPAR 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 fAPAR during a growing season (May-June-July-August-September) at 20m resolution.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/S0924271621000617Richard 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

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/S0924271621000617Richard 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

Level below which soil or rock is saturated with water, in the well and at the time the level has been measured, expressed in m above the sea level. Groundwater depth is measured on the field, using a water level meters. The depth is then subtracted from the elevation of the measurement site to obtain the water level elevation. The dataset is a general description of the measurement site including location and well elevation. It features a series of points of the surface elevation of the groundwater body.

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 a national scale coverage (Canada) of monthly maps of FCOVER indicator during a growing season (May-June-July-August-September) at 20m resolution.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/S0924271621000617Richard 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

Temporal analysis of changes in Calgary, Alberta, based on GeoAI features automatically extracted from satellite images acquired in 2011 and 2021. Simple geospatial analysis intersecting Statistics Canada's Open Database of Buildings, version 3 (ODB v3) with GeoAI multidate building features enables the detection of buildings observed in 2021 that were not detected in 2011. The addition of new buildings is a good indicator of urban development and/or sprawl. Using the same approach, GeoAI multidate roads enable the detection of Statistics Canada's National Roads Network (NRN) segments present in 2021 and/or in 2011. The development of new roads is also indicator of urban development and/or sprawl. Complementarily, an analysis of changes in the forest coverage from the GeoAI datasets is done. This analysis reflects the gains and losses between both dates..‌GeoAI enables temporal coverage of various areas in Canada, thus providing a useful tool for change detection and trend analysis at high resolution. While the series is still fairly new, and such examples are limited for the time being, NRCan strives to gradually increase its GeoAI data offering for both spatial and temporal coverage.For more information about the GeoAI - GeoBase Series, please visit the following link: https://open.canada.ca/data/en/dataset/74738ff5-5367-5958-9aee-98fffdcd1876

Temporal analysis of changes for Victoria, British Columbia, based on GeoAI features automatically extracted from satellite images acquired in the 2000s, 2010s and 2020s. Simple geospatial analysis intersecting Statistics Canada's Open Database of Buildings, version 3 (ODB v3) with GeoAI multidate building features enables the detection of buildings observed in the 2000s, 2010s and 2020s. The addition of new buildings is a good indicator of urban development and/or sprawl. Using the same approach, GeoAI multidate roads enable the detection of Statistics Canada's National Roads Network (NRN) segments present in the 2000s, 2010s and 2020s. The development of new roads is also indicator of urban development and/or sprawl. Complementarily, an analysis of changes in the forest coverage from the GeoAI datasets is done between the 2000s and the 2020s. This analysis reflects the gains and losses between both dates.‌GeoAI enables temporal coverage of various areas in Canada, thus providing a useful tool for change detection and trend analysis at high resolution. While the series is still fairly new, and such examples are limited for the time being, NRCan strives to gradually increase its GeoAI data offering for both spatial and temporal coverage.For more information about the GeoAI - GeoBase Series, please visit the following link: https://open.canada.ca/data/en/dataset/74738ff5-5367-5958-9aee-98fffdcd1876

This is the 5 year investment plan for Manitoba Highway and Infrastructure Capital Projects.This dataset will show all the proposed infrastructure projects for the next 5 years. This Feature layer is similar to the "Manitoba Capital Plans Project Status" and will be updated once a year.This dataset contains points showing the locations of the capital projects such as asphalt surface treatment, grading, bituminous rehabilitation, concrete reconstruction, culvert jacking, intersection improvement, structures improvement, traffic signal improvements, and traffic safety improvements. This dataset was created by digitizing high-resolution imagery of Manitoba road network, bridge sites, and culvert locations. The details of the capital projects are available as attributes of the dataset.Fields included (Alias (Field Name): Field description.)Hwy (Hwy): The highway number where the project is located.  Location Description (Location_Description): A detailed description of the project location. Project Distance in km (km): The physical length of the project Project Type (Project_Type): A particular category assigned to the project based on the nature of work. Investment Category : (Investment_Category): Category of future investment for the project. (Ex: Renewal, Economic Development, Connectivity and Innovation, Climate Resiliency)  Municipality Name (MUNI_NAME): The name of the municipality where the project is located. Department Program (Department_Program): The name of the MI department responsible for the project.This dataset is used in Manitoba Infrastructure Projects an interactive map that shows Manitoba infrastructure's new, approved, and continuing capital project locations.