Wildfire change year 1985-2022. Wildfire changes occurred from 1985 to 2022 displaying the year of greatest wildfire disturbance. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). The information outcomes represent 38 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 1985-2022 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. https://doi.org/10.1080/17538947.2016.1187673 (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. https://doi.org/10.1016/j.rse.2014.11.005 (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. https://doi.org/10.1016/j.rse.2015.09.004 (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. https://doi.org/10.1016/j.jag.2017.07.013 (Hermosilla et al. 2017).

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 Type data for the prior period from 1985 to 2011 can be found here: https://opendata.nfis.org/mapserver/nfis-change_eng.html or https://gcgeo.gc.ca/geonetwork/search/eng search for “Forest Change” but you must be logged in to see the data. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). 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 (https://authors.elsevier.com/sd/article/S0034425717301360 ). 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; http://www.tandfonline.com/doi/full/10.1080/17538947.2016.1187673). The data available is, 1. a binary change/no-change; 2. Change year; and, 3. Change type. When using this data, please cite as: 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. DOI: 10.1016/j.jag.2017.07.013 White, J.C., M.A. Wulder, T. Hermosilla, N.C. Coops, and G. 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. DOI: 10.1016/j.rse.2017.03.035.

Map of burned area in Canada's forested ecosystems for the 2023 fire session at 30-m spatial resolution mapped from time-series data from Sentinel-2A and -2B, and Landsat-8 and -9 using the Tracking Intra- and Inter-year Change (TIIC) algorithm (Pelletier et al. 2024). It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). Fires are grouped into two classes based on detection period: summer fires and fall fires. Summer burned pixels were detected between May 30 and September 17, and fall burned pixels were detected between September 17 and October 25. For summer fires, burned pixels were identified by TIIC as changed and typed as fire. For the fall period, TIIC only detected changes within a 4-km buffer of the NRCan fire perimeters (https://cwfis.cfs.nrcan.gc.ca/datamart). This approach was used to limit commission errors that can occur due to known limitations of mapping with optical data in the fall due to phenology, snow cover, or low sun angles. For the 2023 fire season, the TIIC algorithm detected 12.74 Mha of burned area in Canada's forested ecozones, representing 1.8% of the total forest-dominated ecozone area. Of the 12.74 Mha, 11.57 Mha (90.9%) was burned by summer fires and 1.16 Mha (9.1%) by fall fires (Pelletier et al, 2024). When using this data, please cite as: Pelletier, F., Cardille, J.A., Wulder, M.A., White, J.C., Hermosilla, T., 2024. Revisiting the 2023 wildfire season in Canada. Science of Remote Sensing. 10, 100145. (Pelletier et al. 2024).

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 Type data for the prior period from 1985 to 2011 can be found here: https://opendata.nfis.org/mapserver/nfis-change_eng.html or https://gcgeo.gc.ca/geonetwork/search/eng search for “Forest Change” but you must be logged in to see the data. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). 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 (https://authors.elsevier.com/sd/article/S0034425717301360). 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; http://www.tandfonline.com/doi/full/10.1080/17538947.2016.1187673). The data available is, 1. a binary change/no-change; 2. Change year; and, 3. Change type. When using this data, please cite as: 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. DOI: 10.1016/j.jag.2017.07.013 White, J.C., M.A. Wulder, T. Hermosilla, N.C. Coops, and G. 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. DOI: 10.1016/j.rse.2017.03.035.

The dataset contains spectral indices values calculated from Landsat imagery, normalized difference vegetation index (NDVI) and delta normalized burn ratio (NBR, deltaNBR). The spectral indices were computed for 1472 forested sites in the Glacier National Park, Montana, over the 1984-2021 period, in areas that were burned or not burned during the Robert Fire of 2003.

High resolution forest change for Canada (Binary Change/No-change) 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. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). 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 (https://authors.elsevier.com/sd/article/S0034425717301360 ). 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; http://www.tandfonline.com/doi/full/10.1080/17538947.2016.1187673). The data available is, 1. a binary change/no-change; 2. Change year; and, 3. Change type. When using this data, please cite as: White, J.C., M.A. Wulder, T. Hermosilla, N.C. Coops, and G. 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. DOI: 10.1016/j.rse.2017.03.035. https://authors.elsevier.com/sd/article/S0034425717301360 Geographic extent: Canada's forested ecosystems (~ 650 Mha) Time period: 1985–2010

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)