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).

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).

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).

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).

Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org) Collection: - **[Canada's National Forest Inventory (NFI) 2006](https://open.canada.ca/data/en/dataset/e2fadaeb-3106-4111-9d1c-f9791d83fbf4)**

Canada’s NFI survey was designed to provide an unbiased probability sample of Canada’s forests for long-term strategic monitoring purposes. The target population is Canada’s entire non-Arctic land area. A National Terrestrial Monitoring Framework (NTMF) was created by establishing a systematic 4 km by 4 km sampling grid over all of Canada from a random offshore point. Prior to T0, NFI partners determined that the NFI program would be able to affordably achieve its mission by establishing a 2 km by 2 km (400 ha) “photo plot” at every fifth sampling point on the NTMF (i.e. every 20 km), thereby providing a one percent sample of the target population. This sampling intensity was considered sufficient for national reporting and possible to sustain over the long term with anticipated funding. Photo plots were established across Canada during 2000-2006 (T0). There are 26,139 photo plot survey locations on the 20 km by 20 km grid, of which 18,570 lie inside the target population area. For each photo plot, information is collected on land cover, land use, ownership and protection status. NFI photo plot survey data are stratified by “NFI Unit” for standard estimation and reporting purposes. NFI Units were created by the geographic intersection of Canada’s 10 provinces, 3 territories and 12 non-Arctic terrestrial ecozones. Estimates produced for NFI Units are rolled up to produce standard reports for ecozones, jurisdictions (provinces and territories) and Canada. Some NFI Units are too small to produce robust estimates for with the current sampling intensity, so NFI Unit estimates are not publicly reported. Prince Edward Island (PEI) Atlantic Maritime, for example, is PEI’s only NFI Unit and it is small (1% sampling intensity achieved with only 19 photo plots), so the NFI avoids publishing provincial reports. Information consumers are encouraged to use official statistics produced by provincial and territorial governments for the forests in their jurisdictions. Most provinces are large, however, and the current NFI sampling intensity is sufficient for producing robust NFI reports for those jurisdictions. Special estimation reports can be produced using different ecological or administrative strata, such as the Boreal Zone, or the Managed Forest. NFI photo plots are surveyed on a ten-year cycle. During first re-measurement (T1; 2008-2017), survey intensity was reduced to one photo plot every 40 km across northern Canada (Figure 3) because of budget limitations. The T2 survey (2018-2027) is currently underway.

Canada’s NFI survey was designed to provide an unbiased probability sample of Canada’s forests for long-term strategic monitoring purposes. The target population is Canada’s entire non-Arctic land area. A National Terrestrial Monitoring Framework (NTMF) was created by establishing a systematic 4 km by 4 km sampling grid over all of Canada from a random offshore point. Prior to T0, NFI partners determined that the NFI program would be able to affordably achieve its mission by establishing a 2 km by 2 km (400 ha) “photo plot” at every fifth sampling point on the NTMF (i.e. every 20 km), thereby providing a one percent sample of the target population. This sampling intensity was considered sufficient for national reporting and possible to sustain over the long term with anticipated funding. Photo plots were established across Canada during 2000-2006 (T0). There are 26,139 photo plot survey locations on the 20 km by 20 km grid, of which 18,570 lie inside the target population area. For each photo plot, information is collected on land cover, land use, ownership and protection status. NFI photo plot survey data are stratified by “NFI Unit” for standard estimation and reporting purposes. NFI Units were created by the geographic intersection of Canada’s 10 provinces, 3 territories and 12 non-Arctic terrestrial ecozones. Estimates produced for NFI Units are rolled up to produce standard reports for ecozones, jurisdictions (provinces and territories) and Canada. Some NFI Units are too small to produce robust estimates for with the current sampling intensity, so NFI Unit estimates are not publicly reported. Prince Edward Island (PEI) Atlantic Maritime, for example, is PEI’s only NFI Unit and it is small (1% sampling intensity achieved with only 19 photo plots), so the NFI avoids publishing provincial reports. Information consumers are encouraged to use official statistics produced by provincial and territorial governments for the forests in their jurisdictions. Most provinces are large, however, and the current NFI sampling intensity is sufficient for producing robust NFI reports for those jurisdictions. Special estimation reports can be produced using different ecological or administrative strata, such as the Boreal Zone, or the Managed Forest. NFI photo plots are surveyed on a ten-year cycle. During first re-measurement (T1; 2008-2017), survey intensity was reduced to one photo plot every 40 km across northern Canada (Figure 3) because of budget limitations. The T2 survey (2018-2027) is currently underway.