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National Terrestrial Ecosystem Monitoring System for Canada

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The NFIS high resolution forest change for Canada map page is fully interactive. You can zoom in, zoom out, and pan around the map by clicking on it and dragging. The controls for zooming in and out are on the top left of the map.

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Some locations on the Map hold information on Canada's forest change. To view this information, simply click on the map and the information will be displayed below, if available.

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The National Terrestrial Ecosystem Monitoring System (NTEMS) was developed by the Canadian Forest Service to provide national-scale baseline information on Canada's forested ecosystems. Based largely on data from the Landsat series of satellites, free and open access to analysis ready data, and utilization of high performance computing, NTEMS enabled the recreation of the history of Canada's forests at a higher level of spatial and categorical detail than ever before. Through NTEMS research, methods have been developed for image compositing, change detection, and change attribution using Landsat time series data. NTEMS outputs have subsequently enabled the characterization of post-disturbance recovery, an annual land cover data cube, and national representations of forest structure. When using this data, please cite as: Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G.W. Hobart, L.B. Campbell, 2016. Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. International Journal of Digital Earth 9(11), 1035-1054 (Hermosilla et al. 2016).

(Summary of Scientific Publications from NTEMS )

World Imagery (background graphic) provided by ESRI Web services with sources from Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AeroGRID, IGN and the GIS User Community

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, partnered with the University of British Columbia, with support from the Canadian Space Agency, augmented by processing capacity from WestGrid of Compute Canada.

Download the data here:

Data Set Description Download Link
National Terrestrial Ecosystem Monitoring System for Canada (NTEMS) Products
Satellite-Based Canada Forest Inventory 2020 Satellite-Based Forest Inventory (SBFI) informing on Canada’s forested land cover, disturbance recovery, structure, species, stand age from 2020, and stand-replacing disturbances from 1985-2020. The SBFI polygons represent homogeneous forest conditions akin to those of stands delineated in a strategic forest inventory. Over 25 million SBFI polygons were delineated using a multiresolution segmentation algorithm applied to the 2020 Landsat surface-reflectance BAP image composite (30-m spatial resolution), fire year, and harvest year layers derived from Landsat with the C2C approach. A minimum map unit of 0.45 ha (5 pixels) was used to define polygons. The entirety of Canada’s forest ecosystems were mapped using the same data, attributes, and temporal representation, resulting in a common vegetation inventory system of Canada’s ~650 Mha forested ecosystems. Given the large and diverse forest area of Canada, the strength of an SBFI lies in its use of a consistent data source and methodology across jurisdictional boundaries, and across managed and unmanaged forest areas, enabling consistently generated synoptic, spatially explicit information outputs. The data included herein are based upon free and open satellite data and information products following established and communicated approaches. When using this data, please cite as: Wulder, M.A., Hermosilla, T., White, J.C., Hobart, G.W., Bater, C.W., Bronson, S.C., 2024. Development and implementation of a stand-level Satellite-Based Forest Inventory for Canada. Forestry: An International Journal of Forest Research, cpad065, https://doi.org/10.1093/forestry/cpad065 . (Wulder et al. 2024). Satellite-Based Canada Forest Inventory 2020
(GDB,7GB),
CA Forest Fires 1985-2020 Landsat-derived forest wildfire disturbances for Canada 1985-2020. The annual forest change data included in this product is national in scope (entire forested ecosystem) and represents the wall-to-wall characterization of wildfire in Canada at a 30-m spatial resolution. The information outcomes represent 36 years of wildfire change over Canada's forests, derived from a single, consistent, spatially explicit data source, derived in a fully automated manner. This demonstrated capacity to characterize forests at a resolution that captures human impacts is key to establishing a baseline for detailed monitoring of forested ecosystems from management and science perspectives. Time series of Landsat data were used to characterize national trends in stand replacing forest disturbances caused by wildfire and harvest for the period 1985-2020 for Canada's 650 Mha forested ecosystems. Landsat data has a 30 m spatial resolution, so the change information is highly detailed and informative regarding both natural and human driven changes. These data represent annual stand replacing forest changes. The stand replacing disturbance types labeled are wildfire and harvest, with lower confidence wildfire and harvest, also shared. The distinction and sharing of lower class membership likelihoods is to indicate to users that some change events were more difficult to allocate to a change type, but are generally found to be in the correct category. For an overview on the data, image processing, and time series change detection methods applied, as well as information on independent accuracy assessment of the data, see ( Hermosilla et al. 2016). The data available is Change year for Wildfire Events. When using this data, please cite as: Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G.W. Hobart, L.B. Campbell, 2016. Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. International Journal of Digital Earth 9(11), 1035-1054 ( Hermosilla et al. 2016). CA Forest Fires 1985-2020
(GeoTif,228MB),
CA Wildfire dNBR 1985-2020 Wildfire change magnitude 1985-2020. Spectral change magnitude for wildfires that occurred from 1985 and 2020. The wildfire change magnitude included in this product is expressed via differenced Normalized Burn Ratio (dNBR), computed as the variation between the spectral values before and after a given change event. The actual dNBR value is derived as follows: dNBR = value / 100. Higher dNBR values are related to higher burn severity. Time series of Landsat data with 30-m spatial resolution were used to characterize national trends in stand replacing forest disturbances caused by wildfire for the period 1985-2020 for Canada's 650 million-hectare forested ecosystems. When using this data, please cite as: Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G.W. Hobart, L.B. Campbell, 2016. Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. International Journal of Digital Earth 9(11), 1035-1054. ( Hermosilla et al. 2016). See references below for an overview on the data processing, metric calculation, change attribution and time series change detection methods applied, as well as information on independent accuracy assessment of the data. Hermosilla, T., Wulder, M. A., White, J. C., Coops, N.C., Hobart, G.W., 2015. An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites. Remote Sensing of Environment 158, 220-234. ( Hermosilla et al. 2015a).
Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., 2015. Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics. Remote Sensing of Environment 170, 121-132. ( Hermosilla et al. 2015b).
CA Wildfire dNBR 1985-2020
(GeoTif,228MB),
CA Forest Harvest 1985-2020 Landsat-derived forest harvest disturbances for Canada 1985-2020
The annual forest change data included in this product is national in scope (entire forested ecosystem) and represents the wall-to-wall characterization of harvest in Canada at a 30-m spatial resolution. The information outcomes represent 36 years of harvest change over Canada's forests, derived from a single, consistent, spatially explicit data source, derived in a fully automated manner. This demonstrated capacity to characterize forests at a resolution that captures human impacts is key to establishing a baseline for detailed monitoring of forested ecosystems from management and science perspectives. Time series of Landsat data were used to characterize national trends in stand replacing forest disturbances caused by wildfire and harvest for the period 1985-2020 for Canada's 650 Mha forested ecosystems. Landsat data has a 30 m spatial resolution, so the change information is highly detailed and informative regarding both natural and human driven changes. These data represent annual stand replacing forest changes. The stand replacing disturbance types labeled are wildfire and harvest, with lower confidence wildfire and harvest, also shared. The distinction and sharing of lower class membership likelihoods is to indicate to users that some change events were more difficult to allocate to a change type, but are generally found to be in the correct category. For an overview on the data, image processing, and time series change detection methods applied, as well as information on independent accuracy assessment of the data, see ( Hermosilla et al. 2016). The data available is Change year for Harvest Events. When using this data, please cite as: Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G.W. Hobart, L.B. Campbell, 2016. Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. International Journal of Digital Earth 9(11), 1035-1054 ( Hermosilla et al. 2016).
CA Forest Harvest 1985-2020
(GeoTif, 978MB),
CA Forest Age 2019 Landsat-derived forest age for Canada 2019
Satellite-based forest age map for 2019 across Canada’s forested ecozones at a 30-m spatial resolution. Remotely sensed data from Landsat (disturbances, surface reflectance composites, forest structure) and MODIS (Gross Primary Production) are utilized to determine age. Age can be determined where disturbance can be identified directly (disturbance approach) or inferred using spectral information (recovery approach) or using inverted allometric equations to model age where there is no evidence of disturbance (allometric approach). The disturbance approach is based upon satellite data and mapped changes and is the most accurate. The recovery approach also avails upon satellite data plus logic regarding forest succession, with an accuracy that is greater than pure modeling. Given the lack of widespread recent disturbance over Canada’s forests, the allometric approach is required over the greatest area (86.6%). Using information regarding realized heights and growth and yield modeling, ages are estimated where none are otherwise possible. Trees of all ages are mapped, with trees >150 years old combined in an “old tree” category. See Maltman et al. (2023) for an overview of the methods, data, image processing, as well as information on agreement assessment using Canada’s National Inventory (NFI). Maltman, J.C., Hermosilla, T., Wulder, M.A., Coops, N.C., White, J.C., 2023. Estimating and mapping forest age across Canada’s forested ecosystems. Remote Sensing of Environment 290, 113529. ( Maltman et al. 2023).
CA Forest Age 2019
(GeoTif, 5.8GB),
Tree Species 2019 Highly detailed (30-m spatial resolution) tree species presence and distribution maps of Canada’s forested ecosystems (2019). Products include the leading tree species as well as the class membership probabilities for 37 tree species. Also shared is the distance-to-second class (D2SC) provided as an indicator of attribution confidence. These layers were 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. For an overview on the data and image processing, as well as information on independent accuracy assessment of the data, see Hermosilla et al. (2022). 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 . ( Hermosilla et al. 2022) Leading Tree Species (GeoTif, 1.7 GB), Distance to Second Tree Species (GeoTif, 1.7 GB), Tree Species Probability (GeoTif, 1.7 GB),
Canada Forest Post-Disturbance Recovery Rate Post-disturbance forest recovery data for Canada's forested ecosystems, representing a total area of ~650 million ha, captures the return of forests following wildfire and harvest that occurred between 1986 and 2012. These spatially-explicit outputs represent the rate of spectral recovery: the rate at which a pixel returns to 80% of its pre-disturbance value (White et al. 2017) within the observation period (1985-2017) using the Y2R or Years-to-Recovery metric derived from Landsat times series data. Baseline rates of spectral recovery (Y2R) were defined for each of Canada's 12 forested ecozones. These baselines were then used to identify spatial clusters of recovering pixels on the landscape where Y2R were either significantly faster or slower than their ecozonal baseline. Finally, areas that were disturbed by wildfire and harvest (1986-2012), but which had not recovered by the end of the observation period (2017) are also provided. Note that these areas are still recovering, but they had not yet recovered according to our metric of spectral recovery, by the end of the time series in 2017. For an overview of the methods, the validation of the Y2R metric, and interpretation of the derived trends, see White et al. (2022) and White et al. (2017). White, J.C., Hermosilla, T., Wulder, M.A., Coops, N.C., 2022. Mapping, validating, and interpreting spatio-temporal trends in post-disturbance forest recovery. Remote Sensing of Environment, 271, 112904. https://doi.org/10.1016/j.rse.2022.112904 ( White et al. 2022) White, J.C., Wulder, M.A., Hermosilla, T., Coops, N.C., Hobart, G.W. 2017. A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sensing of Environment, 194, pp. 303-321. DOI: https://doi.org/10.1016/j.rse.2017.03.035 .( White et al. 2017) CA_forest_fire_recovery_rate ,(GeoTif, 73 MB)
CA_forest_harvest_recovery_rate,(GeoTif, 411 MB)
CA_forest_fire_years2recovery ,(GeoTif, 30 MB)
CA_forest_harvest_years2recovery,(GeoTif, 256 MB)
Land cover 1984-2019 Version 2 High-resolution annual forest land cover maps for Canada's forested ecosystems (1984-2019). The annual time series of forest land cover maps are national in scope (entire 650 million hectare forested ecosystem) and represent a wall-to-wall land cover characterization yearly from 1984 to 2019. These time-series land cover maps were produced from annual time-series of Landsat image composites, forest change information, and ancillary topographic and hydrologic data following the framework described in Hermosilla et al. (2022), which builds upon the approach introduced in Hermosilla et al. (2018). The methodological innovations included (i) a refined training pool derived from existing land cover products using airborne and spaceborne measures of forest structure; (ii) selection of training samples proportionally to the land cover distribution using a distance=weighted approach; and (iii) generation of regional classification models using a 150x150 km tiling system. Maps are post-processed using disturbance information to ensure logical class transitions over time using a Hidden Markov Model. Hidden Markov Models assess individual year class likelihoods to reduce variability and possible noise in year-on-year class assignments (for instances when class likelihoods are similar). Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., 2022. Land cover classification in an era of big and open data: Optimizing localized implementation and training data selection to improve mapping outcomes. Remote Sensing of Environment. Vol. 268, No. 112780. https://doi.org/10.1016/j.rse.2021.112780. ( Hermosilla et al. 2022) Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G. W. Hobart, (2018). Disturbance-Informed Annual Land Cover Classification Maps of Canada's Forested Ecosystems for a 29-Year Landsat Time Series. Canadian Journal of Remote Sensing. 44(1) 67-87.DOI: 10.1080/07038992.2018.1437719 ( Hermosilla et al. 2018). 1984 , 1985 , 1986 , 1987 , 1988 , 1989 , 1990 , 1991 , 1992 , 1993 , 1994 , 1995 , 1996 , 1997 , 1998 , 1999 , 2000 , 2001 , 2002 , 2003 , 2004 , 2005 , 2006 , 2007 , 2008 , 2009 , 2010 , 2011 , 2012 , 2013 , 2014 , 2015 , 2016 , 2017 , 2018 , 2019 ,( GeoTif, 1.7 GB),
FAO Forest 2019 Satellite-based forest area consistent with FAO definitions for Canada. The forest area is based on the Food and Agricultural Organization of the United Nations (FAO) definition. The FAO definition incorporates land use, whereby trees removed by fire and harvesting for instance, remain forest as the trees will return. The included map displays the current forest cover for year as noted (i.e. 2019), plus the satellite-based temporally informed forest area where tree cover has been temporarily lost due to stand replacing disturbances (i.e., fire, harvest). For an overview of the methods, data, image processing, as well as information on accuracy assessment see Wulder et al. (2020).
Open Access: Wulder, M.A., T. Hermosilla, G. Stinson, F.A. Gougeon, J.C. White, D.A. Hill, B.P. Smiley. (2020). Satellite-based time series land cover and change information to map forest area consistent with national and international reporting requirements. Forestry: An International Journal of Forest Research 93(3), 331-34, https://doi.org/10.1093/forestry/cpaa0063 . ( Wulder et al. 2020)
FAO forest 2019 (GeoTif, 838 MB)
Forest Elevation(Ht) Mean 2015 Mean height of lidar first returns (m). Represents the mean canopy height. Products relating the structure of Canada's forested ecosystems have been generated and made openly accessible. The shared products are based upon peer-reviewed science and relate aspects of forest structure including: (i) metrics calculated directly from the lidar point cloud with heights normalized to heights above the ground surface (e.g., canopy cover, height), and (ii) modelled inventory attributes, derived using an area-based approach generated by using co-located ground plot and ALS data (e.g., volume, biomass). Forest structure estimates were generated by combining information from "lidar-plots" (Wulder et al. 2012) with Landsat pixel-based composites (White et al. 2014; Hermosilla et al. 2016) using a nearest neighbour imputation approach with a Random Forests-based distance metric. These products were generated for strategic-level forest monitoring information needs and are not intended to support operational-level forest management. All products have a spatial resolution of 30 m. For a detailed description of the data, methods applied, and accuracy assessment results see Matasci et al. (2018). When using this data, please cite as follows: Matasci, G., Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Bolton, D.K., Tompalski, P., Bater, C.W., 2018b. Three decades of forest structural dynamics over Canada's forested ecosystems using Landsat time-series and lidar plots. Remote Sensing of Environment 216, 697-714. (Matasci et al. 2018) Forest Elevation Mean (GeoTif, 9.7 GB),
Forest Basal Area 2015 Cross-sectional area of tree stems at breast height. The sum of the cross-sectional area (i.e. basal area) of each tree in square metres in a plot, divided by the area of the plot (ha) (units = m2ha). Products relating the structure of Canada's forested ecosystems have been generated and made openly accessible. The shared products are based upon peer-reviewed science and relate aspects of forest structure including: (i) metrics calculated directly from the lidar point cloud with heights normalized to heights above the ground surface (e.g., canopy cover, height), and (ii) modelled inventory attributes, derived using an area-based approach generated by using co-located ground plot and ALS data (e.g., volume, biomass). Forest structure estimates were generated by combining information from "lidar-plots" (Wulder et al. 2012) with Landsat pixel-based composites (White et al. 2014; Hermosilla et al. 2016) using a nearest neighbour imputation approach with a Random Forests-based distance metric. These products were generated for strategic-level forest monitoring information needs and are not intended to support operational-level forest management. All products have a spatial resolution of 30 m. For a detailed description of the data, methods applied, and accuracy assessment results see Matasci et al. (2018). When using this data, please cite as follows: Matasci, G., Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Bolton, D.K., Tompalski, P., Bater, C.W., 2018b. Three decades of forest structural dynamics over Canada's forested ecosystems using Landsat time-series and lidar plots. Remote Sensing of Environment 216, 697-714. (Matasci et al. 2018) Forest Basal Area (GeoTif, 9.3 GB),
Forest Elevation(Ht) Covariance 2015 Coefficient of variation of first returns height (%). Represents the variability in canopy heights relative to the mean canopy height. Products relating the structure of Canada's forested ecosystems have been generated and made openly accessible. The shared products are based upon peer-reviewed science and relate aspects of forest structure including: (i) metrics calculated directly from the lidar point cloud with heights normalized to heights above the ground surface (e.g., canopy cover, height), and (ii) modelled inventory attributes, derived using an area-based approach generated by using co-located ground plot and ALS data (e.g., volume, biomass). Forest structure estimates were generated by combining information from "lidar-plots" (Wulder et al. 2012) with Landsat pixel-based composites (White et al. 2014; Hermosilla et al. 2016) using a nearest neighbour imputation approach with a Random Forests-based distance metric. These products were generated for strategic-level forest monitoring information needs and are not intended to support operational-level forest management. All products have a spatial resolution of 30 m. For a detailed description of the data, methods applied, and accuracy assessment results see Matasci et al. (2018). When using this data, please cite as follows: Matasci, G., Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Bolton, D.K., Tompalski, P., Bater, C.W., 2018b. Three decades of forest structural dynamics over Canada's forested ecosystems using Landsat time-series and lidar plots. Remote Sensing of Environment 216, 697-714. (Matasci et al. 2018) Forest Elevation Covariance (GeoTif, 8.1 GB),
Forest Elevation(Ht) Stddev 2015 Standard deviation of height of lidar first returns (m). Represents the variability in canopy heights. Products relating the structure of Canada's forested ecosystems have been generated and made openly accessible. The shared products are based upon peer-reviewed science and relate aspects of forest structure including: (i) metrics calculated directly from the lidar point cloud with heights normalized to heights above the ground surface (e.g., canopy cover, height), and (ii) modelled inventory attributes, derived using an area-based approach generated by using co-located ground plot and ALS data (e.g., volume, biomass). Forest structure estimates were generated by combining information from "lidar-plots" (Wulder et al. 2012) with Landsat pixel-based composites (White et al. 2014; Hermosilla et al. 2016) using a nearest neighbour imputation approach with a Random Forests-based distance metric. These products were generated for strategic-level forest monitoring information needs and are not intended to support operational-level forest management. All products have a spatial resolution of 30 m. For a detailed description of the data, methods applied, and accuracy assessment results see Matasci et al. (2018). When using this data, please cite as follows: Matasci, G., Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Bolton, D.K., Tompalski, P., Bater, C.W., 2018b. Three decades of forest structural dynamics over Canada's forested ecosystems using Landsat time-series and lidar plots. Remote Sensing of Environment 216, 697-714. (Matasci et al. 2018) Forest Elevation Stddev (GeoTif, 9.5 GB),
Forest Gross Stem Volume 2015 Gross stem volume. Individual tree gross volumes are calculated using species-specific allometric equations. In the measured ground plots, gross total volume per hectare is calculated by summing the gross total volume of all trees and dividing by the area of the plot (units = m3ha-1). Products relating the structure of Canada's forested ecosystems have been generated and made openly accessible. The shared products are based upon peer-reviewed science and relate aspects of forest structure including: (i) metrics calculated directly from the lidar point cloud with heights normalized to heights above the ground surface (e.g., canopy cover, height), and (ii) modelled inventory attributes, derived using an area-based approach generated by using co-located ground plot and ALS data (e.g., volume, biomass). Forest structure estimates were generated by combining information from "lidar-plots" (Wulder et al. 2012) with Landsat pixel-based composites (White et al. 2014; Hermosilla et al. 2016) using a nearest neighbour imputation approach with a Random Forests-based distance metric. These products were generated for strategic-level forest monitoring information needs and are not intended to support operational-level forest management. All products have a spatial resolution of 30 m. For a detailed description of the data, methods applied, and accuracy assessment results see Matasci et al. (2018). When using this data, please cite as follows: Matasci, G., Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Bolton, D.K., Tompalski, P., Bater, C.W., 2018b. Three decades of forest structural dynamics over Canada's forested ecosystems using Landsat time-series and lidar plots. Remote Sensing of Environment 216, 697-714. (Matasci et al. 2018) Forest Gross Stem Volume (GeoTif, 9.1 GB),
Forest Lorey's Height 2015 Lorey's mean height. Average height of trees weighted by their basal area (m). Products relating the structure of Canada's forested ecosystems have been generated and made openly accessible. The shared products are based upon peer-reviewed science and relate aspects of forest structure including: (i) metrics calculated directly from the lidar point cloud with heights normalized to heights above the ground surface (e.g., canopy cover, height), and (ii) modelled inventory attributes, derived using an area-based approach generated by using co-located ground plot and ALS data (e.g., volume, biomass). Forest structure estimates were generated by combining information from "lidar-plots" (Wulder et al. 2012) with Landsat pixel-based composites (White et al. 2014; Hermosilla et al. 2016) using a nearest neighbour imputation approach with a Random Forests-based distance metric. These products were generated for strategic-level forest monitoring information needs and are not intended to support operational-level forest management. All products have a spatial resolution of 30 m. For a detailed description of the data, methods applied, and accuracy assessment results see Matasci et al. (2018). When using this data, please cite as follows: Matasci, G., Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Bolton, D.K., Tompalski, P., Bater, C.W., 2018b. Three decades of forest structural dynamics over Canada's forested ecosystems using Landsat time-series and lidar plots. Remote Sensing of Environment 216, 697-714. (Matasci et al. 2018) Forest Lorey's Height (GeoTif, 9.6 GB),
Forest Percentage Above 2m 2015 Percentage of first returns above 2m (%). Represents canopy cover. Products relating the structure of Canada's forested ecosystems have been generated and made openly accessible. The shared products are based upon peer-reviewed science and relate aspects of forest structure including: (i) metrics calculated directly from the lidar point cloud with heights normalized to heights above the ground surface (e.g., canopy cover, height), and (ii) modelled inventory attributes, derived using an area-based approach generated by using co-located ground plot and ALS data (e.g., volume, biomass). Forest structure estimates were generated by combining information from "lidar-plots" (Wulder et al. 2012) with Landsat pixel-based composites (White et al. 2014; Hermosilla et al. 2016) using a nearest neighbour imputation approach with a Random Forests-based distance metric. These products were generated for strategic-level forest monitoring information needs and are not intended to support operational-level forest management. All products have a spatial resolution of 30 m. For a detailed description of the data, methods applied, and accuracy assessment results see Matasci et al. (2018). When using this data, please cite as follows: Matasci, G., Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Bolton, D.K., Tompalski, P., Bater, C.W., 2018b. Three decades of forest structural dynamics over Canada's forested ecosystems using Landsat time-series and lidar plots. Remote Sensing of Environment 216, 697-714. (Matasci et al. 2018) Forest Pecentage Above 2m (GeoTif, 4.3 GB),
Forest Percent Above Mean 2015 Percentage of first returns above the mean height (%). Represents the canopy cover above mean canopy height. Products relating the structure of Canada's forested ecosystems have been generated and made openly accessible. The shared products are based upon peer-reviewed science and relate aspects of forest structure including: (i) metrics calculated directly from the lidar point cloud with heights normalized to heights above the ground surface (e.g., canopy cover, height), and (ii) modelled inventory attributes, derived using an area-based approach generated by using co-located ground plot and ALS data (e.g., volume, biomass). Forest structure estimates were generated by combining information from "lidar-plots" (Wulder et al. 2012) with Landsat pixel-based composites (White et al. 2014; Hermosilla et al. 2016) using a nearest neighbour imputation approach with a Random Forests-based distance metric. These products were generated for strategic-level forest monitoring information needs and are not intended to support operational-level forest management. All products have a spatial resolution of 30 m. For a detailed description of the data, methods applied, and accuracy assessment results see Matasci et al. (2018). When using this data, please cite as follows: Matasci, G., Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Bolton, D.K., Tompalski, P., Bater, C.W., 2018b. Three decades of forest structural dynamics over Canada's forested ecosystems using Landsat time-series and lidar plots. Remote Sensing of Environment 216, 697-714. (Matasci et al. 2018) Forest Percent Above Mean (GeoTif, 4.0 GB),
Forest Total Aboveground Biomass 2015 Total aboveground biomass. Individual tree total aboveground biomass is calculated using species-specific equations. In the measured ground plots, aboveground biomass per hectare is calculated by summing the values of all trees within a plot and dividing by the area of the plot. Aboveground biomass may be separated into various biomass components (e.g. stem, bark, branches, foliage) (units = t/ha). Products relating the structure of Canada's forested ecosystems have been generated and made openly accessible. The shared products are based upon peer-reviewed science and relate aspects of forest structure including: (i) metrics calculated directly from the lidar point cloud with heights normalized to heights above the ground surface (e.g., canopy cover, height), and (ii) modelled inventory attributes, derived using an area-based approach generated by using co-located ground plot and ALS data (e.g., volume, biomass). Forest structure estimates were generated by combining information from "lidar-plots" (Wulder et al. 2012) with Landsat pixel-based composites (White et al. 2014; Hermosilla et al. 2016) using a nearest neighbour imputation approach with a Random Forests-based distance metric. These products were generated for strategic-level forest monitoring information needs and are not intended to support operational-level forest management. All products have a spatial resolution of 30 m. For a detailed description of the data, methods applied, and accuracy assessment results see Matasci et al. (2018). When using this data, please cite as follows: Matasci, G., Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Bolton, D.K., Tompalski, P., Bater, C.W., 2018b. Three decades of forest structural dynamics over Canada's forested ecosystems using Landsat time-series and lidar plots. Remote Sensing of Environment 216, 697-714. (Matasci et al. 2018) Forest Total Aboveground Biomass (GeoTif, 9.7 GB),
Forest 95th Percentile Elevation(Ht) 2015 95th percentile of first returns height (m). Products relating the structure of Canada's forested ecosystems have been generated and made openly accessible. The shared products are based upon peer-reviewed science and relate aspects of forest structure including: (i) metrics calculated directly from the lidar point cloud with heights normalized to heights above the ground surface (e.g., canopy cover, height), and (ii) modelled inventory attributes, derived using an area-based approach generated by using co-located ground plot and ALS data (e.g., volume, biomass). Forest structure estimates were generated by combining information from "lidar-plots" (Wulder et al. 2012) with Landsat pixel-based composites (White et al. 2014; Hermosilla et al. 2016) using a nearest neighbour imputation approach with a Random Forests-based distance metric. These products were generated for strategic-level forest monitoring information needs and are not intended to support operational-level forest management. All products have a spatial resolution of 30 m. For a detailed description of the data, methods applied, and accuracy assessment results see Matasci et al. (2018). When using this data, please cite as follows: Matasci, G., Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Bolton, D.K., Tompalski, P., Bater, C.W., 2018b. Three decades of forest structural dynamics over Canada's forested ecosystems using Landsat time-series and lidar plots. Remote Sensing of Environment 216, 697-714. (Matasci et al. 2018) Forest 95th Percentile Elevation (GeoTif, 1.7GB),
Miscellaneous Products
Wetlands 2000-2016 High-resolution binary wetland map for Canada (2000-2016). Wetland map for the forested ecosystems of Canada focused on current conditions.The binary wetland data included in this product is national in scope (entirety of forested ecosystem) and represents the wall to wall characterization for 2000-2016 (see Wulder et al. 2018). This product was generated using both annual gap free composite reflectance images and annual forest change maps following the Virtual Land Cover Engine (VLCE) process (see Hermosilla et al. 2018), over the 650 million ha forested ecosystems of Canada. Elements of the VLCE classification approach are inclusion of disturbance information in the processes as well as ensuring class transitions over time are logical. Further, a Hidden Markov Model is implemented to assess individual year class likelihoods to reduce variability and possible noise in year-on-year class assignments (for instances when class likelihoods are similar). For this product, to be considered as currently a wetland a pixel must have been classified as wetland at least 80% or 13 of the 16 years between 2000 and 2016, inclusively. For an overview on the data, image processing, and time series change detection methods applied, see Wulder et al. (2018). Wulder, M.A., Z. Li, E. Campbell, J.C. White, G.W. Hobart, T. Hermosilla, and N.C. Coops (2018). A National Assessment of Wetland Status and Trends for Canada's Forested Ecosystems Using 33 Years of Earth Observation Satellite Data. Remote Sensing. 10: 1263-1282( Wulder et al. 2018). For a detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G.W. Hobart, (2018). Disturbance-Informed Annual Land Cover Classification Maps of Canada's Forested Ecosystems for a 29-Year Landsat Time Series. Canadian Journal of Remote Sensing. 44(1), 67-87 ( Hermosilla et al. 2018). Wetlands 2000-2016 2015 (GeoTif, 607 MB),
Wetlands 84-16 High-resolution wetland year count for Canada (1984-2016). Count of number of years a pixel is classified as wetland. The wetland year count data included in this product is national in scope (entire forested ecosystem) and represents a wall to wall wetland characterization for 1984-2016 (Wulder et al. 2018). This product was generated using both annual gap free composite reflectance images and annual forest change maps following the Virtual Land Cover Engine (VLCE) process (see Hermosilla et al. 2018), over the 650 million ha forested ecosystems of Canada. Elements of the VLCE classification approach are inclusion of disturbance information in the processes as well as ensuring class transitions over time are logical. Further, a Hidden Markov Model is implemented to assess individual year class likelihoods to reduce variability and possible noise in year-on-year class assignments (for instances when class likelihoods are similar). The values can range from 0 to 33 denoting the number of years between 1984 and 2016 that a pixel was classified as wetland or wetland-treed in the VLCE data cube. For an overview on the data, image processing, and time series change detection methods applied, as well as information on independent accuracy assessment of the data, see Hermosilla et al. Hermosilla, T., M.A. Wulder, J.C.,White, N.C.,Coops, G. W.,Hobart, L.B.,Campbell, (2016). Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. International Journal of Digital Earth. 9(11), 1035-1054. ( Hermosilla et al. 2016). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G.W. Hobart, (2018). Disturbance-Informed Annual Land Cover Classification Maps of Canada's Forested Ecosystems for a 29-Year Landsat Time Series. Canadian Journal of Remote Sensing. 44. ( Hermosilla et al. 2018). The focused wetland analyses can be found described in A National Assessment of Wetland Status and Trends for Canada's Forested Ecosystems Using 33 Years of Earth Observation Satellite Data. (2018) Wulder, M.A., Z Li, E.M. Campbell, J.C. White, G.W. Hobart, T. Hermosilla and N.C. Coops.,Remote Sensing, 10, 1263-1282, Wulder et al. 2018) Wetlands Yearly Sum 1984-2016 (GeoTif, 1.7 GB),
BC Tree Species Map/Likelihoods 2015 BC Dominant Species Map 2015
The data represent dominant tree species for British Columbia forests in 2015, are based upon Landsat data and modeling, with results mapped at 30 m spatial resolution. The map was generated with the Random Forests classifier that used predictor variables derived from Landsat time series including surface reflectance, land cover, forest disturbance, and forest structure, and ancillary variables describing the topography and position. Training and validation samples were derived from the Vegetation Resources Inventory (VRI), from a pool of polygons with homogeneous internal conditions and with low discrepancies with the remotely sensed predictions. Local models were applied over 100x100 km tiles that considered training samples from the 5x5 neighbouring tiles to avoid edge effects. An overall accuracy of 72% was found for the species which occupy 80% of the forested areas. Satellite data and modeling have demonstrated the capacity for up-to-date, wall-to-wall, forest attribute maps at sub-stand level for British Columbia, Canada.
BC Species Likelihood 2015
The tree species class membership likelihood distribution data included in this product focused on the province of British Columbia, based upon Landsat data and modeling, with results mapped at 30 m spatial resolution. The data represent tree species class membership likelihood in 2015. The map was generated with the Random Forests classifier that used predictor variables derived from Landsat time series including surface reflectance, land cover, forest disturbance, and forest structure, and ancillary variables describing the topography and position. Training and validation samples were derived from the Vegetation Resources Inventory (VRI) selecting from a stratified pool of polygons with homogeneous internal conditions and with low discrepancies when related to remotely sensed information. Local models were applied over 100x100 km tiles that, to avoid edge effects, considered training samples from the 5x5 neighbouring tiles. An overall accuracy of 72% was found for the species which occupy 80% of the forested areas. As an element of the mapping process, we also obtain the votes received for each class by the Random Forest models. The votes can be understood as analogous to class membership likelihoods, providing enriched information on land cover class uncertainty for use in modeling. Tree species class membership likelihoods lower than 5% have been masked and converted to zero.
When using this data, please cite as: Shang, C., Coops, N.C., Wulder, M.A., White, J.C., Hermosilla, T., 2020. Update and spatial extension of strategic forest inventories using time series remote sensing and modeling. International Journal of Applied Earth Observation and Geoinformation 84, 101956. DOI: 10.1016/j.jag.2019.101956 ( Shang et al. 2020).
BC Tree Species Map/Likelyhoods 2015 (GeoTif, 1.5 GB),
Canada Harmonized Agriculture Forest Land Cover 2015 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. Canadian 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 . URL https://open.canada.ca/data/en/dataset/ba2645d5-4458-414d-b196-6303ac06c1c9. ( AAFC, 2018. Annual Crop Inventory ). Canada Harmonized Agriculture Forest Land Cover 2015 (GeoTif, 1.3 GB),
Canada Urban Greenness Score Canada Urban Greenness Score Urban Greenness Score (1984-2016) for 18 selected major Canadian urban area. The Urban Greenness Score data included in this product covers 33 years and all contiguous census dissemination areas of 18 selected major Canadian urban areas. The 18 urban areas represent over half of Canada's population in 2016 (Czekajlo et al. 2020). The Urban Greenness Score uses greenness fractions from an annual time series (1984-2016) of spectrally unmixed Landsat satellite image composites (White et al. 2014; https://doi.org/10.1080/07038992.2014.945827; Hermosilla et al. 2016, https://doi.org/10.1080/17538947.2016.1187673) to characterize greenness and its overall change, summarized by census dissemination area. Image Code; Urban Greenness Score; Description 1; -L; Decrease in greenness resulting in a low final greenness 2; 0L; Stable low level of greenness 3; +L; Increase in greenness resulting in a low final greenness 4; -M; Decrease in greenness resulting in a moderate final greenness 5; 0M; Stable moderate level of greenness 6; +M; Increase in greenness resulting in a moderate final greenness 7; -H; Decrease in greenness resulting in a high final greenness 8; 0H; Stable high level of greenness 9; +H; Increase in greenness resulting in a high final greenness For more information about the data, image processing and spectral unmixing methods applied, development of the urban greenness score, and information on independent accuracy assessment of the data, as well as to cite this data, please use: Czekajlo, A., Coops, N.C., Wulder, M.A., Hermosilla, T., Lu, Y., White, J.C., van den Bosch, M., 2020. The urban greenness score: A satellite-based metric for multi-decadal characterization of urban land dynamics. International Journal of Applied Earth Observation and Geoinformation. 93, 102210. https://doi.org/10.1016/j.jag.2020.102210 ( Czekajlo et al. 2020). Canada Urban Greenness Score (GeoTif, 51 MB),
Dynamic Habitat Index 2000-2006 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). Dynamic Habitat Index 2000-2006 (GeoTif, 138 MB),
Canadian Ecological Domain Classification 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). Canadian Ecosystem Regionalization (GeoTif, 42 MB),
Legacy Products
Landcover 2015 Version 1 High-resolution forest land cover for Canada (2015). The forest land cover data included in this product is the entire forested ecosystem and represents the a wall to wall land cover characterization for 2015. This product was generated using both annual gap free composite reflectance images and annual forest change maps following the Virtual Land Cover Engine (VLCE) process (see Hermosilla et al. 2018), over the 650 million ha within the forested ecosystems of Canada. Elements of the VLCE classification approach includes knowledge of disturbance information as well as ensuring class transitions over time are logical. Further, a Hidden Markov Model is implemented to assess individual year class likelihoods to reduce variability and possible noise in year-on-year class assignments (for instances when class likelihoods are similar). For an overview on the data, image processing, and time series change detection methods applied, as well as information on independent accuracy assessment of the data, see (Hermosilla et al. 2016). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see (Hermosilla et al. 2018). When using this data, please cite as: White, J.C., M.A. Wulder, T. Hermosilla, N.C. Coops, and G.W. Hobart. (2017). A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sensing of Environment. 192: 303-321. ( White et al. 2017). Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G. W. Hobart, L.B. Campbell, (2016). Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. International Journal of Digital Earth. 9(11), 1035-1054. ( Hermosilla et al. 2016). Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G. W. Hobart, (2017). Updating Landsat time series of surface-reflectance composites and forest change products with new observations. International Journal of Applied Earth Observation and Geoinformation. 63,104-111.. ( Hermosilla et al. 2017). Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G. W. Hobart, (2018). Disturbance-Informed Annual Land Cover Classification Maps of Canada's Forested Ecosystems for a 29-Year Landsat Time Series. Canadian Journal of Remote Sensing. 44(1) 67-87. ( Hermosilla et al. 2018). Forest Land Cover 2015 (GeoTif, 1.7 GB),
Wildfire Year/dNBR/Mask 1985-2015 Wildfire change magnitude 85-15. Spectral change magnitude for wildfires that occurred from 1985 and 2015. The wildfire change magnitude included in this product is expressed via differenced Normalized Burn Ratio (dNBR), computed as the variation between the spectral values before and after the change event. This dataset is composed of three layers: (1) binary wildfire mask, (2) year of greatest wildfire disturbance, and (3) differenced Normalized Burn Ratio (dNBR) transformed for data storage efficiency to the range 0-200. The actual dNBR value is derived as follows: dNBR = value / 100. Higher dNBR values are related to higher burn severity. The information outcomes represent 30 years of wildfires in Canada's forests, derived from a single, consistent spatially-explicit data source in a fully automated manner. Time series of Landsat data with 30-m spatial resolution were used to characterize national trends in stand replacing forest disturbances caused by wildfire for the period 1985-2015 for Canada's 650 million hectare forested ecosystems.
When using this data, please cite as: Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G.W. Hobart, L.B. Campbell, 2016. Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. International Journal of Digital Earth 9(11), 1035-1054. (Hermosilla et al. 2016).
See references below for an overview on the data processing, metric calculation, change attribution and time series change detection methods applied, as well as information on independent accuracy assessment of the data.
Hermosilla, T., Wulder, M. A., White, J. C., Coops, N.C., Hobart, G.W., 2015. An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites. Remote Sensing of Environment 158, 220-234. (Hermosilla et al. 2015a).
Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., 2015. Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics. Remote Sensing of Environment 170, 121-132. (Hermosilla et al. 2015b).
Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G. W. Hobart, (2017). Updating Landsat time series of surface-reflectance composites and forest change products with new observations. International Journal of Applied Earth Observation and Geoinformation. 63,104-111.. ( Hermosilla et al. 2017).
Wildfire Year/dNBR/Mask 1985-2015 (GeoTif, 1.2GB),
Harvest Year/Mask 1985-2015 Annual mapping of national level forest harvesting for Canada detected inclusive of 1985 to 2015 from Landsat satellite imagery.
This dataset is composed of two layers: (1) binary harvest mask, and (2) year of harvest disturbance detection. The information outcomes represent 31 years of harvesting activity in Canada's forests, derived from a single, consistent, spatially-explicit data source in an automated manner. Time series of Landsat data with 30-m spatial resolution were used to characterize national trends in stand replacing forest disturbances, including those attributed to harvest for the period 1985-2015 for Canada's 650 million hectare forested ecosystems (Hermosilla et al. 2016). See references below for an overview regarding the data, image processing, and time-series change detection methods applied, as well as information on independent accuracy assessment of the data. When using this data, please cite as: Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G.W. Hobart, L.B. Campbell, (2016). Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. International Journal of Digital Earth. 9(11), 1035-1054. ( Hermosilla et al. 2016) 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., (2015). An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites. Remote Sensing of Environment 158, 220-234. ( Hermosilla et al. 2015a) Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., (2015). Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics. Remote Sensing of Environment 170, 121-132. ( Hermosilla et al. 2015b) Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., 2017. Updating Landsat time series of surface-reflectance composites and forest change products with new observations. International Journal of Applied Earth Observation and Geoinformation 63, 104-111.( Hermosilla et al. 2017)
Harvest Year/Mask 1985-2015 (GeoTif, 351 MB),
Change 85-11 Forest Change and No-change bitmap for Canada. The forest change data included in this product is national in scope (entire forested ecosystem) and represents the first wall-to-wall characterization of wildfire and harvest in Canada at a spatial resolution commensurate with human impacts. The information outcomes represent 25 years of stand replacing change in Canada's forests, derived from a single, consistent spatially-explicit data source, derived in a fully automated manner. This demonstrated capacity to characterize forests at a resolution that captures human impacts is key to establishing a baseline for detailed monitoring of forested ecosystems from management and science perspectives. Time series of Landsat data were used to characterize national trends in stand replacing forest disturbances caused by wildfire and harvest for the period 1985-2011 for Canada's 650 million hectare forested ecosystems (White et al. 2017). Landsat data has a 30m spatial resolution, so the change information is highly detailed and is commensurate with that of human impacts. These data represent annual stand replacing forest changes. The stand replacing disturbances types labeled are wildfire and harvest, with lower confidence wildfire and harvest, also shared. The distinction and sharing of lower class membership likelihoods is to indicate to users that some change events were more difficult to allocate to a change type, but are generally found to be in the correct category. For an overview on the data, image processing, and time series change detection methods applied, as well as information on independent accuracy assessment of the data, see Hermosilla et al. (2016). When using this data, please cite as: White, J.C., M.A. Wulder, T. Hermosilla, N.C. Coops, and G.W. Hobart. (2017). A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sensing of Environment. 192: 303-321. (White et al. 2017). Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G.W. Hobart, L.B. Campbell, (2016). Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. International Journal of Digital Earth. 9(11), 1035-1054. ( Hermosilla et al. 2016). Binary change/no-Change 1985-2011 (GeoTif, 227 MB),
Change Type 85-11 Forest Change Type (Wildfire, Harvest, Low Confidence Wildfire, Low Confidence Harvest). The forest change data included in this product is national in scope (entire forested ecosystem) and represents the first wall-to-wall characterization of wildfire and harvest in Canada at a spatial resolution commensurate with human impacts. The information outcomes represent 25 years of stand replacing change in Canada's forests, derived from a single, consistent spatially-explicit data source, derived in a fully automated manner. This demonstrated capacity to characterize forests at a resolution that captures human impacts is key to establishing a baseline for detailed monitoring of forested ecosystems from management and science perspectives. Time series of Landsat data were used to characterize national trends in stand replacing forest disturbances caused by wildfire and harvest for the period 1985-2011 for Canada's 650 million hectare forested ecosystems (White et al. 2017). Landsat data has a 30m spatial resolution, so the change information is highly detailed and is commensurate with that of human impacts. These data represent annual stand replacing forest changes. The stand replacing disturbances types labeled are wildfire and harvest, with lower confidence wildfire and harvest, also shared. The distinction and sharing of lower class membership likelihoods is to indicate to users that some change events were more difficult to allocate to a change type, but are generally found to be in the correct category. For an overview on the data, image processing, and time series change detection methods applied, as well as information on independent accuracy assessment of the data, see Hermosilla et al. (2016). When using this data, please cite as: White, J.C., M.A. Wulder, T. Hermosilla, N.C. Coops, and G.W. Hobart. (2017). A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sensing of Environment. 192: 303-321.(White et al. 2017). Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G.W. Hobart, L.B. Campbell, (2016). Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. International Journal of Digital Earth. 9(11), 1035-1054. ( Hermosilla et al. 2016). Change type 1985-2011 (GeoTif, 249 MB),
Change Year 85-11 Forest Change Year 1985-2011. The forest change data included in this product is national in scope (entire forested ecosystem) and represents the first wall-to-wall characterization of wildfire and harvest in Canada at a spatial resolution commensurate with human impacts. The information outcomes represent 25 years of stand replacing change in Canada's forests, derived from a single, consistent spatially-explicit data source, derived in a fully automated manner. This demonstrated capacity to characterize forests at a resolution that captures human impacts is key to establishing a baseline for detailed monitoring of forested ecosystems from management and science perspectives. Time series of Landsat data were used to characterize national trends in stand replacing forest disturbances caused by wildfire and harvest for the period 1985-2011 for Canada's 650 million hectare forested ecosystems (White et al. 2017). Landsat data has a 30m spatial resolution, so the change information is highly detailed and is commensurate with that of human impacts. These data represent annual stand replacing forest changes. The stand replacing disturbances types labeled are wildfire and harvest, with lower confidence wildfire and harvest, also shared. The distinction and sharing of lower class membership likelihoods is to indicate to users that some change events were more difficult to allocate to a change type, but are generally found to be in the correct category. For an overview on the data, image processing, and time series change detection methods applied, as well as information on independent accuracy assessment of the data, see Hermosilla et al. (2016). When using this data, please cite as: White, J.C., M.A. Wulder, T. Hermosilla, N.C. Coops, and G.W. Hobart. (2017). A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sensing of Environment. 192, 303-321.(White et al. 2017). Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G.W. Hobart, L.B. Campbell, (2016). Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. International Journal of Digital Earth. 9(11), 1035-1054. ( Hermosilla et al. 2016). Change year 1985-2011 (GeoTif, 280 MB),
Change 12-15 Forest Change and Nochange bit map for Canada. The Forest Change/No-change data described here is an update to previously posted open data. The date range for this data is 2012 to 2015. The forest change data included in this product is national in scope (entire forested ecosystem) and represents the first wall-to-wall characterization of wildfire and harvest in Canada at a spatial resolution commensurate with human impacts. The information outcomes represent 4 years of stand replacing change in Canada's forests, derived from a single, consistent spatially-explicit data source, derived in a fully automated manner. Hermosilla et al. (2016) This demonstrated capacity to characterize forests at a resolution that captures human impacts is key to establishing a baseline for detailed monitoring of forested ecosystems from management and science perspectives. Time series of Landsat data were used to characterize national trends in stand replacing forest disturbances caused by wildfire and harvest for the period 21012-2015 for Canada's 650 million hectare forested ecosystems (White et al, 2017). Landsat data has a 30m spatial resolution, so the change information is highly detailed and is commensurate with that of human impacts. These data represent annual stand replacing forest changes. The stand replacing disturbances types labeled are wildfire and harvest, with lower confidence wildfire and harvest, also shared. The distinction and sharing of lower class membership likelihoods is to indicate to users that some change events were more difficult to allocate to a change type, but are generally found to be in the correct category. For an overview on the data, image processing, and time series change detection methods applied, as well as information on independent accuracy assessment of the data. When using this data, please cite as: White, J.C., M.A. Wulder, T. Hermosilla, N.C. Coops, and G.W. Hobart. (2017). A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sensing of Environment. 192: 303-321.(White et al. 2017).Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G.W. Hobart, L.B. Campbell, (2016). Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. International Journal of Digital Earth. 9(11), 1035-1054. ( Hermosilla et al. 2016). Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G.W. Hobart, (2017). Updating Landsat time series of surface-reflectance composites and forest change products with new observations. International Journal of Applied Earth Observation and Geoinformation. 63,104-111. ( Hermosilla et al. 2017). Binary change/no-Change 2012-2015 (GeoTif, 82 MB),
Change Type 12-15 Forest Change Type (Wildfire, Harvest, Low Confidence Wildfire, Low Confidence Harvest).The Forest Change Type data described here is an update to previously posted open data. The date range for this data is 2012 to 2015. The forest change data included in this product is national in scope (entire forested ecosystem) and represents the first wall-to-wall characterization of wildfire and harvest in Canada at a spatial resolution commensurate with human impacts. The information outcomes represent 25 years of stand replacing change in Canada's forests, derived from a single, consistent spatially-explicit data source, derived in a fully automated manner. This demonstrated capacity to characterize forests at a resolution that captures human impacts is key to establishing a baseline for detailed monitoring of forested ecosystems from management and science perspectives. Time series of Landsat data were used to characterize national trends in stand replacing forest disturbances caused by wildfire and harvest for the period 1985-2010 for Canada's 650 million hectare forested ecosystems (Hermosilla et al. 2017). Landsat data has a 30m spatial resolution, so the change information is highly detailed and is commensurate with that of human impacts. These data represent annual stand replacing forest changes. The stand replacing disturbances types labeled are wildfire and harvest, with lower confidence wildfire and harvest, also shared. The distinction and sharing of lower class membership likelihoods is to indicate to users that some change events were more difficult to allocate to a change type, but are generally found to be in the correct category. For an overview on the data, image processing, and time series change detection methods applied, as well as information on independent accuracy assessment of the data. When using this data, please cite as: White, J.C., M.A. Wulder, T. Hermosilla, N.C. Coops, and G.W. Hobart. (2017). A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sensing of Environment. 192: 303-321.(White et al. 2017). Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G.W. Hobart, L.B. Campbell, (2016). Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. International Journal of Digital Earth. 9(11), 1035-1054. ( Hermosilla et al. 2016). Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G.W. Hobart, (2017). Updating Landsat time series of surface-reflectance composites and forest change products with new observations. International Journal of Applied Earth Observation and Geoinformation. 63,104-111. ( Hermosilla et al. 2017). Change type 2012-2015 (GeoTif, 90 MB),
Change Year 12-15 The Forest Change Year data described here is an update to previously posted open data. The date range for this data is 2012 to 2015. The forest change data included in this product is national in scope (entire forested ecosystem) and represents the first wall-to-wall characterization of wildfire and harvest in Canada at a spatial resolution commensurate with human impacts. The information outcomes represent 4 years of stand replacing change in Canada's forests, derived from a single, consistent spatially-explicit data source, derived in a fully automated manner. This demonstrated capacity to characterize forests at a resolution that captures human impacts is key to establishing a baseline for detailed monitoring of forested ecosystems from management and science perspectives. Time series of Landsat data were used to characterize national trends in stand replacing forest disturbances caused by wildfire and harvest for the period 2012-2015 for Canada's 650 million hectare forested ecosystems (Hermosilla et al. 2017). Landsat data has a 30m spatial resolution, so the change information is highly detailed and is commensurate with that of human impacts. These data represent annual stand replacing forest changes. The stand replacing disturbances types labeled are wildfire and harvest, with lower confidence wildfire and harvest, also shared. The distinction and sharing of lower class membership likelihoods is to indicate to users that some change events were more difficult to allocate to a change type, but are generally found to be in the correct category. For an overview on the data, image processing, and time series change detection methods applied, as well as information on independent accuracy assessment of the data. When using this data, please cite as: White, J.C., M.A. Wulder, T. Hermosilla, N.C. Coops, and G. Hobart. (2017). ( White et al. 2017). Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G.W. Hobart, L.B. Campbell, (2016). Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. International Journal of Digital Earth. 9(11), 1035-1054. ( Hermosilla et al. 2016). Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G.W. Hobart, (2017). Updating Landsat time series of surface-reflectance composites and forest change products with new observations. International Journal of Applied Earth Observation and Geoinformation, 63,104-111.. ( Hermosilla et al. 2017). Change year 2012-2015 (GeoTif, 92 MB),