Home /Search
Search datasets
We have found 307 datasets for the keyword " classification". You can continue exploring the search results in the list below.
Datasets: 103,380
Contributors: 42
Results
307 Datasets, Page 1 of 31
Soil Great Group taxonomy - Soil Landscape Grids of Canada, 100m
Predicted Soil Great Group class as defined by the The Canadian System of Soil Classification (third edition).
14 Class - Canadian Ecological Domain Classification from Satellite Data
14 Class - Canadian Ecological Domain Classification from Satellite Data. Satellite derived data including 1) topography, 2) landscape productivity based on photosynthetic activity, and 3) land cover were used as inputs to create an environmental regionalization of the over 10 million km2 of Canada’s terrestrial land base. The outcomes of this clustering consists of three main outputs. An initial clustering of 100 classes was generated using a two-stage multivariate classification process. Next, an agglomerative hierarchy using a log-likelihood distance measure was applied to create a 40 and then a 14 class regionalization, aimed to meaningfully group ecologically similar components of Canada's terrestrial landscape. For more information (including a graphical illustration of the cluster hierarchy) and to cite this data please use: Coops, N.C., Wulder, M.A., Iwanicka, D. 2009. An environmental domain classification of Canada using earth observation data for biodiversity assessment. Ecological Informatics, Vol. 4, No. 1, Pp. 8-22, DOI: https://doi.org/10.1016/j.ecoinf.2008.09.005. ( Coops et al. 2009).
100 Class - Canadian Ecological Domain Classification from Satellite Data
100 Class - Canadian Ecological Domain Classification from Satellite Data. Satellite derived data including 1) topography, 2) landscape productivity based on photosynthetic activity, and 3) land cover were used as inputs to create an environmental regionalization of the over 10 million km2 of Canada’s terrestrial land base. The outcomes of this clustering consists of three main outputs. An initial clustering of 100 classes was generated using a two-stage multivariate classification process. Next, an agglomerative hierarchy using a log-likelihood distance measure was applied to create a 40 and then a 14 class regionalization, aimed to meaningfully group ecologically similar components of Canada's terrestrial landscape. For more information (including a graphical illustration of the cluster hierarchy) and to cite this data please use: Coops, N.C., Wulder, M.A., Iwanicka, D. 2009. An environmental domain classification of Canada using earth observation data for biodiversity assessment. Ecological Informatics, Vol. 4, No. 1, Pp. 8-22, DOI: https://doi.org/10.1016/j.ecoinf.2008.09.005. ( Coops et al. 2009).
Tree Species (2019)
High-resolution map of leading tree species distribution for Canada’s forested ecosystems (2019). Leading tree species map produced from a 2019 Landsat image composite, geographic and climate data, elevation derivatives, and remote sensing derived phenology following the framework described in Hermosilla et al. (2022). Regional classification models were generated based on Canada’s National Forest Inventory using a 150x150 km tiling system. The leading tree species are defined by representing the most voted tree species from the Random Forests classification models (i.e. the class with the highest class membership probability).The data represents leading tree species of Canada's forested ecosystems in 2019. An image compositing window of August 1 ± 30 days was used to generate the best-available-pixel (BAP) image composites utilized as source data for the classification.The science and methods developed to generate the information outcomes shown here, that track and characterize the history of Canada’s forests, were led by Canadian Forest Service of Natural Resources Canada, developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS), partnered with the University of British Columbia, augmented by processing capacity from Digital Research Alliance of Canada.For an overview on the data, image processing, and methods applied, as well as information on independent accuracy assessment of the data, see Hermosilla et al. (2022) https://doi.org/10.1016/j.rse.2022.113276When using this data, please cite as: Hermosilla, T., Bastyr, A., Coops, N.C., White, J.C., Wulder, M.A., 2022. Mapping the presence and distribution of tree species in Canada’s forested ecosystems. Remote Sensing of Environment 282, 113276.
Agriculture Land Capability Class - Field Analysis
The land classification shown is derived from in-field investigation and is only undertaken for project investigation purposes. These polygons represent the most accurate and up-to-date information regarding agriculture land conditions at those specific locations.Distributed from [[GeoYukon](.underline)](https://yukon.ca/geoyukon) by the [[Government of Yukon](.underline)](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](.underline)](mailto:geomatics.help@yukon.ca)
Rural Road Classification Map
Rural Road Classification MapA map of rural road classification of provincial highways
Soil Landscapes of Canada V.2.2/V.3.1 - Soil Order
The “Soil Landscapes of Canada V.2.2/V.3.1 - Soil Order” displays the highest (most general) level of soil classification. Within the Canadian System of Soil Classification there are ten recognized soil orders (Soil Classification Working Group 1998). This system is hierarchical (from general to specific). Soil orders are further subdivided to great groups, subgroups, families, and series.
Physical Land Classification (PLC)
This dataset is produced for the Government of Alberta and is available to the general public. Please consult the Distribution Information of this metadata for the appropriate contact to acquire this dataset. Physical Land Classification (PLC) is a mapping system that was designed to describe the landscape in terms of landform, soils, drainage and slope. It is a hierarchical system that captures physiographic information at the following levels: Region - 1:3 000 000 or smaller Section - 1:1 000 000 to 1:3 000 000 District - 1:500 000 to 1:1 000 000 Geomorphic System - 1:100 000 (can range from 1:50 000 to 1:250 000) Geomorphic Unit - 1:10 000 to 1:50 000 There are some variations in this hierarchy for individual study areas. The Land Classification Group (Resource Inventory Section), Alberta Energy and Natural Resources, adopted the initial Physical Land Classification methodology in 1977 to meet the needs of resource planning and management agencies. Many aspects of the methodology were developed from landform mapping schemes used by the System of Soil Classification for Canada (1976). The PLC system is essentially a geomorphic interpretation and classification system based on the principles of the inherent properties of the land and its forms. Physical Land Classification (PLC) maps have been created largely during the 1980s and 1990s as part of a program to acquire background information for Integrated Resource Plans along the eastern slopes and across northern Alberta. The data were generally mapped at the geomorphic unit level using the 1:50 000 scale National Topographic System maps as a base. The PLC hardcopy maps were scanned, georeferenced, rectified, cleaned, vectorized, merged and attributed to form GIS polygons. The polygons are attributed for parent geologic material, landform / surface expression, modifying process, slope, texture, soil taxonomy and soil drainage. This classification system was designed to enhance and replace the Canada Land Inventory (CLI) and Alberta Landform Inventory (ALI) Landform classification systems. There is more attribution associated with PLC mapping than with ALI / CLI Landform mapping. There is some overlap with the ALI / CLI Landform maps but much of the PLC mapping was conducted in areas not covered by ALI / CLI Landform maps. PLC mapping is considered to be more reliable than ALI / CLI Landform mapping as field checking was more extensive.
Prairie Soil Zones of Canada
The Prairie Soil Zones file shows the general distribution of major soil zones across the Prairie region of Canada. Soil zones (based on the Canadian System of Soil Classification) are named based on the dominant soil classification of the soils in each zone. Data extent is limited to the Agricultural Zone as defined in Soil Landscapes of Canada v 3.0 (Lefebvre et al. 2005).
Marine Ecosections - Coastal Resource Information Management System (CRIMS)
Marine Ecosection classification for coastal and offshore British Columbia. The Marine Ecosections are: Johnstone Strait; Continental Slope; Dixon Entrance; Hecate Strait; Queen Charlotte Strait; Juan de Fuca Strait; North Coast Fjords; Queen Charlotte Sound; Strait of Georgia; Subarctic Pacific; Transitional Pacific; and Vancouver Island Shelf. The British Columbia Marine Ecological Classification (BCMEC) is a hierarchical classification that delineates Provincial marine areas into Ecozones, Ecoprovinces, Ecoregions and Ecosections. The classification was developed from previous Federal and Provincial marine ecological classifications which were based on 1:2,000,000 scale information. The BCMEC has been developed for marine and coastal planning, resource management and a Provincial marine protected areas strategy. A new, smaller level of classification termed ecounits developed using 1:250,000 scale depth, current, exposure, subsurface relief and substrate was created to verify the larger ecosections, and to delineate their boundaries. CRIMS is a legacy dataset of BC coastal resource data that was acquired in a systematic and synoptic manner from 1979 and was intermittently updated throughout the years. Resource information was collected in nine study areas using a peer-reviewed provincial Resource Information Standards Committee consisting of DFO Fishery Officers, First Nations, and other subject matter experts. There are currently no plans to update this legacy data.
Tell us what you think!
GEO.ca is committed to open dialogue and community building around location-based issues and topics that matter to you.
Please send us your feedback