40 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).

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).

The Saskatchewan Detailed Soil Survey dataset series at a scale of 1:100 000 consists of geo-referenced soil polygons with linkages to attribute data found in the associated Component File (CMP), Soil Names File (SNF) and Soil Layer File (SLF). Together, these datasets describe the spatial distribution of soils and associated landscapes for nearly all agricultural areas in southern Saskatchewan.

The “Thematic Soil Maps of Manitoba” is a revised and condensed version of the Manitoba Detailed Soils Database produced by CANSIS. It contains data relating to the soils slope, drainage, agricultural capability, erosion potential, and surface texture.

Canada Harmonized Agriculture Forest Land Cover 2015 The harmonized land cover (HLC) map is produced from Agriculture and Agri-Food Canada (AAFC) and Canadian Forest Service (CFS) data. The HLC product is exhaustive of all area from the northern edge of Canada’s forested ecosystems to the southern border. The land cover is following Intergovernmental Panel on Climate Change (IPCC) categories, represents the year 2015, and is at 30-m spatial resolution. This harmonized land cover map combines two sector-driven land cover products: the Virtual Land Cover Engine or VLCE from the CFS (Hermosilla et al., 2018), and AAFC's Annual Crop Inventory or ACI (Agriculture and Agri-Food Canada, 2018). The harmonization process was conducted using a Latent Dirichlet Allocation (LDA) model. The LDA model used regionalized class co-occurrences from multiple maps to generate a harmonized class label for each pixel by statistically characterizing land attributes from the class co-occurrences, using the information provided by the error matrices and semantic affinity scores. For a complete overview on the data, methods applied, and information on independent accuracy assessment, see Li et al. (2020). When using this data, please cite as: Li, Z., White, J.C., Wulder, M.A., Hermosilla, T., Davidson, A.M., Comber, A.J., 2020. Land cover harmonization using Latent Dirichlet Allocation. International Journal of Geographical Information Science. DOI: https://doi.org/10.1080/13658816.2020.1796131 (Open access) ( Li et al. 2020). For additional resources on the data used and methods applied, please see: Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., 2018. Disturbance-informed annual land cover classification maps of Canada’s forested ecosystems for a 29-year Landsat time series. Canadia Journal of Remote Sensing 44(1), 67-87. https://doi.org/10.1080/07038992.2018.1437719 (Open access) ( Hermosilla et al. 2018). Agriculture and Agri-Food Canada, 2018. Annual Crop Inventory [WWW Document]. URL https://open.canada.ca/data/en/dataset/ba2645d5-4458-414d-b196-6303ac06c1c9. ( AAFC, 2018. Annual Crop Inventory ).

Dynamic Habitat Index. (2000-2005) Satellite derived estimates of photosynthetically active radiation can be obtained from satellites such as MODIS. Knowledge of the land cover allows for calculation the fraction of incoming solar radiation that is absorbed by vegetation. This fraction of photosynthetically active radiation (fPAR) absorbed by vegetation describes rate at which carbon dioxide and energy from sunlight are assimilated into carbohydrates during photosynthesis of plant tissues. The summation of carbon assimilated by the vegetation canopy over time yields the landscape's gross primary productivity. Daily MODIS imagery is the basis for periodic composites and monthly data products. Over the 6 year period from 2000-2005, we calculate the annual average cumulative total of 72 monthly fPAR measurements, to describe the integrated annual vegetative production of the landscape, the integrated average annual minimum monthly fPAR measurement, which describes the annual minimum green cover of the observed landscape, and the integrated average of the annual covariance of fPAR, which describes the seasonality of the observed landscape. We also share the combination of the annual integrated values for visualization and analysis as the Dynamic Habitat Index (with additional information in Coops et al. 2008). When using this data, please cite as: Coops, N.C., Wulder, M.A., Duro, D.C., Han, T. and Berry, S., 2008. The development of a Canadian dynamic habitat index using multi-temporal satellite estimates of canopy light absorbance. Ecological Indicators, 8(5), pp.754-766. ( Coops et al. 2008).