WMS
High Resolution Satellite Forest Information for Canada
NFIS Project Office. This Web services are for forest change products that represents the first wall-to-wall characterization of wildfire and harvest in Canada at a spatial resolution commensurate with human impacts. The information outcomes represents 25 years of stand replacing change in Canada's forests derived from a single consistent spatially-explicit data source and derived in a fully automated manner.
ecology
CCFM
NFIS
National
Forest
Change
Support
National Forest Information System Project Office
Support Officer
postal
506 West Burnside Rd.
Victoria
British Columbia
V8Z 1M5
CANADA
(250) 298-2414
(250) 363-0775
support@nfis.org
none
WMS image only
4096
4096
text/xml
image/png
image/jpeg
image/png; mode=8bit
image/tiff
text/html
text/plain
gml
text/xml
image/png
image/jpeg
image/png; mode=8bit
text/xml
XML
INIMAGE
BLANK
NFIS_High_Resolution_Forest_Data
High Resolution Satellite Forest Information for Canada
NFIS Project Office. This Web services are for forest change products that represents the first wall-to-wall characterization of wildfire and harvest in Canada at a spatial resolution commensurate with human impacts. The information outcomes represents 25 years of stand replacing change in Canada's forests derived from a single consistent spatially-explicit data source and derived in a fully automated manner.
ecology
CCFM
NFIS
National
Forest
Change
EPSG:4617
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-170
0
40
60
image/png
1000
5e+07
prov_bound
ca_prov_r
A simple representation of Canada's Provincial Boundaries
Boundaries
political
province
provincial
territory
territorial
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:3857
EPSG:3978
EPSG:3979
-149.927
-52.6363
41.3806
86.4421
changenochange
ca_change_nochange_r1984
Forest Change and Nochange bit map 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)). 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
ecology
forest management
Forest
Change
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-176.412
-10.8073
34.3112
83.977
faoforest
ca_faoforest
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). https://doi.org/10.1093/forestry/cpaa006 (open access).
ecology
forest management
Forest
Change
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-159.515
-25.8034
34.3112
76.484
changeyear
ca_change_year_r1984
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)). 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
ecology
Forest
Year
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-176.412
-10.8073
34.3112
83.977
changetype
ca_change_type_r
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)). 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
ecology
Forest
Change
Type
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-176.412
-10.8073
34.3112
83.977
changenochange2
ca_change_nochange_r2012
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. 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 (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).
ecology
forest management
Forest
Change
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-176.412
-10.8073
34.3112
83.977
changeyear2
ca_change_year_r2012
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 (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).
ecology
Forest
Year
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-176.412
-10.8073
34.3112
83.977
changetype2
ca_change_type_r2012
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 (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).
ecology
Forest
Change
Type
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-176.412
-10.8073
34.3112
83.977
landsat_composite_2015
ca_rgb_2015_wkg_r
High-resolution RGB Landsat image composite of Canada (2015). This national image product represents the Composite to Change (C2C) proxy composite image derived from thousands of Landsat images acquired between July 1 and August 30, 2015. The overall process followed is described in Hermosilla et al. (2016; http://www.tandfonline.com/doi/full/10.1080/17538947.2016.1187673) with the details regarding the generation of gap-free surface reflectance composites found in Hermosilla et al. (2015; https://www.sciencedirect.com/science/article/pii/S0034425714004453). Following the motivation and rationale presented in White et al. (2014), Landsat imagery is subject to a series of processing stages to remove cloud and shadow as well as haze and other unwanted atmospheric effects. Year-on-year time series of Landsat imagery is interrogated to avoid having locations with missing values to ensure exhaustive spatial coverage of the national surface reflectance composites. False colour 3 Channel RBG image (Landsat-8 Bands 6-5-4; Landsat 7, Bands 5-4-3 ).Cubic Convolution (CC) resampling for reprojection from UTM to Lambert Conformal Conic (LCC)
Landsat
cloud free
composite
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-176.412
-10.8073
34.3112
83.977
land_cover_1984
ca_vlce_1984_wkg_r
High-resolution forest land cover for Canada (1984). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 1984. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_1985
ca_vlce_1985_wkg_r
High-resolution forest land cover for Canada (1985). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 1985. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_1986
ca_vlce_1986_wkg_r
High-resolution forest land cover for Canada (1986). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 1986. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_1987
ca_vlce_1987_wkg_r
High-resolution forest land cover for Canada (1987). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 1987. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_1988
ca_vlce_1988_wkg_r
High-resolution forest land cover for Canada (1988). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 1988. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_1989
ca_vlce_1989_wkg_r
High-resolution forest land cover for Canada (1989). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 1989. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_1990
ca_vlce_1990_wkg_r
High-resolution forest land cover for Canada (1990). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 1990. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_1991
ca_vlce_1991_wkg_r
High-resolution forest land cover for Canada (1991). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 1991. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_1992
ca_vlce_1992_wkg_r
High-resolution forest land cover for Canada (1992). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 1992. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_1993
ca_vlce_1993_wkg_r
High-resolution forest land cover for Canada (1993). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 1993. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_1994
ca_vlce_1994_wkg_r
High-resolution forest land cover for Canada (1994). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 1994. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_1995
ca_vlce_1995_wkg_r
High-resolution forest land cover for Canada (1995). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 1995. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_1996
ca_vlce_1996_wkg_r
High-resolution forest land cover for Canada (1996). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 1996. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_1997
ca_vlce_1997_wkg_r
High-resolution forest land cover for Canada (1997). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 1997. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_1998
ca_vlce_1998_wkg_r
High-resolution forest land cover for Canada (1998). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 1998. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_1999
ca_vlce_1999_wkg_r
High-resolution forest land cover for Canada (1999). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 1999. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_2000
ca_vlce_2000_wkg_r
High-resolution forest land cover for Canada (2000). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 2000. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_2001
ca_vlce_2001_wkg_r
High-resolution forest land cover for Canada (2001). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 2001. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_2002
ca_vlce_2002_wkg_r
High-resolution forest land cover for Canada (2002). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 2002. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_2003
ca_vlce_2003_wkg_r
High-resolution forest land cover for Canada (2003). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 2003. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_2004
ca_vlce_2004_wkg_r
High-resolution forest land cover for Canada (2004). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 2004. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_2005
ca_vlce_2005_wkg_r
High-resolution forest land cover for Canada (2005). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 2005. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_2006
ca_vlce_2006_wkg_r
High-resolution forest land cover for Canada (2006). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 2006. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_2007
ca_vlce_2007_wkg_r
High-resolution forest land cover for Canada (2007). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 2007. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_2008
ca_vlce_2008_wkg_r
High-resolution forest land cover for Canada (2008). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 2008. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_2009
ca_vlce_2009_wkg_r
High-resolution forest land cover for Canada (2009). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 2009. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_2010
ca_vlce_2010_wkg_r
High-resolution forest land cover for Canada (2010). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 2010. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_2011
ca_vlce_2011_wkg_r
High-resolution forest land cover for Canada (2011). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 2011. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_2012
ca_vlce_2012_wkg_r
High-resolution forest land cover for Canada (2012). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 2012. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_2013
ca_vlce_2013_wkg_r
High-resolution forest land cover for Canada (2013). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 2013. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_2014
ca_vlce_2014_wkg_r
High-resolution forest land cover for Canada (2014). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 2014. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_2015
ca_vlce_2015_wkg_r
High-resolution forest land cover for Canada (2015). The forest land cover data included in this product is national in scope (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 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
-176.412
-10.8073
34.3112
83.977
land_cover_2016
ca_vlce_2016_wkg_r
High-resolution forest land cover for Canada (2016). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 2016. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_2017
ca_vlce_2017_wkg_r
High-resolution forest land cover for Canada (2017). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 2017. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_2018
ca_vlce_2018_wkg_r
High-resolution forest land cover for Canada (2018). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_2019
ca_vlce_2019_wkg_r
High-resolution forest land cover for Canada (2019). The forest land cover data included in this product is national in scope (entire forested ecosystem) and represents the a wall to wall land cover characterization for 2019. 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
land_cover_2015_v1
ca_vlce_2015_v1_wkg_r
High-resolution forest land cover for Canada (2015). The forest land cover data included in this product is national in scope (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 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 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). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018; https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719).
Landsat
land cover
forest
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
-176.412
-10.8073
34.3112
83.977
wetlands_post2000
wetlands_post2000
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. 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. https://doi.org/10.3390/rs10101623. For a detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018)
Landsat
wetlands
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-176.412
-10.8073
34.3112
83.977
wetlands_year_sum
wetlands_year_sum
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. (2016; http://www.tandfonline.com/doi/full/10.1080/17538947.2016.1187673). A detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see
Hermosilla et al. (2018). The focused wetland analyses can be found described in Wulder et al (2018).
Landsat
wetlands
ecology
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-176.412
-10.8073
34.3112
83.977
elev_mean
elev_mean_r
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. https://doi.org/10.1016/j.rse.2018.07.024
Landsat
forest height
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-176.412
-10.8073
34.3112
83.977
elev_cv
elev_cv_r
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. https://doi.org/10.1016/j.rse.2018.07.024
Landsat
forest height
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-176.412
-10.8073
34.3112
83.977
elev_stddev
elev_stddev_r
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. https://doi.org/10.1016/j.rse.2018.07.024
Landsat
forest height
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-176.412
-10.8073
34.3112
83.977
stem_vol
stem_vol_r
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. https://doi.org/10.1016/j.rse.2018.07.024
Landsat
forest biomass
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-176.412
-10.8073
34.3112
83.977
tot_bio
tot_bio_r
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. https://doi.org/10.1016/j.rse.2018.07.024
Landsat
forest biomass
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-176.412
-10.8073
34.3112
83.977
pct_2m
pct_2m_r
Percentage of first returns above 2 m (%). 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. https://doi.org/10.1016/j.rse.2018.07.024
Landsat
forest biomass
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-176.412
-10.8073
34.3112
83.977
pct_mean
pct_mean_r
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. https://doi.org/10.1016/j.rse.2018.07.024
Landsat
forest structure
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-176.412
-10.8073
34.3112
83.977
95pct_mean
95pct_mean_r
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. https://doi.org/10.1016/j.rse.2018.07.024
Landsat
forest structure
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-176.412
-10.8073
34.3112
83.977
lorey_hgt
lorey_hgt_r
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. https://doi.org/10.1016/j.rse.2018.07.024
Landsat
forest structure
Lorey height
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-176.412
-10.8073
34.3112
83.977
basal_area
basal_area
Basal area. 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. https://doi.org/10.1016/j.rse.2018.07.024
Landsat
forest structure
basal area
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-176.412
-10.8073
34.3112
83.977
FireRec
Fire 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).
wildfire
Forest Recovery
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-159.515
-25.8034
34.3112
76.484
HarvRec
Harvest 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).
Harvest
Forest Recovery
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-159.515
-25.8034
34.3112
76.484
FireY2R
Fire Years To Recovery
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).
wildfire
Forest Recovery
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-159.515
-25.8034
34.3112
76.484
HarvY2R
Harvest Years To Recovery
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).
Harvest
Forest
Recovery
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-159.515
-25.8034
34.3112
76.484
wildfire
wildfire_r
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 a binary wildfire mask. 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 (Hermosilla et al. 2017).
wildfire
Forest
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-159.516
-25.8029
34.3112
76.4842
wildfire_year
wildfire_year_r
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 year of greatest wildfire disturbance. 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 (Hermosilla et al. 2017).
wildfire
Forest
Year
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-159.516
-25.8029
34.3112
76.4842
wildfire_dnbr
dnbr_r
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 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 (Hermosilla et al. 2017).
Landsat
forest
wildfire
dnbr
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-159.516
-25.8029
34.3112
76.4842
harvest
harvest_r
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.
Forest
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-176.412
-10.8073
34.3112
83.977
harvest_year
harvest_year_r
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.
Forest
Year
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-176.412
-10.8073
34.3112
83.977
DHI_lyr
DHI_lyr_r
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).
Landsat
cloud free
composite
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-158.271
-25.6575
34.6327
76.6727
eco14
eco14_r
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-158.271
-25.6575
34.6327
76.6727
eco40
eco40_r
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-158.271
-25.6575
34.6328
76.6728
eco100
eco100_r
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-158.271
-25.6575
34.6327
76.6727
bctreespec
bc_tree_spec_r
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.
map
landsat
forest
tree species
British Columbia
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-141.662
-111.763
45.1517
64.947
HLC
hlc_r
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; https://doi.org/10.1080/13658816.2020.1796131).
map
landsat
forest
tree species
Canada
agriculture
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-176.412
-10.8073
34.3112
83.977
Greenness
green_r
Urban Greenness Score (1984-2016) for 18 selected major Canadian urban areas 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.2020.10221
map
landsat
forest
tree species
Canada
agriculture
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-176.412
-10.8073
34.3112
83.977
Distance2SecondSpecies
dist2nd
map
landsat
forest
tree species
Canada
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-159.515
-25.8034
34.3112
76.484
LeadingTreeSpecies
LeadTreeSpec
map
landsat
forest
tree species
Canada
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-159.515
-25.8034
34.3112
76.484
wildfireTo2020
ca_change_year_r1984
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; https://doi.org/10.1080/17538947.2016.1187673).
ecology
Forest
Year
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-159.515
-25.8034
34.3112
76.484
harvestTo2020
ca_change_year_r1984
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; https://doi.org/10.1080/17538947.2016.1187673).
ecology
Forest
Year
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-159.515
-25.8034
34.3112
76.484
dNBR_2020
dnbr_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).
Landsat
forest height
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-159.515
-25.8034
34.3112
76.484
Age_2019
age_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.
Landsat
forest height
age
EPSG:42101
EPSG:4269
EPSG:4326
EPSG:42304
EPSG:3857
EPSG:3978
EPSG:3979
-159.515
-25.8034
34.3112
76.484