Tile index for BC historical Forest Cover circa 1970 and download locations

A simple representation of Canada's Provincial Boundaries

Images for BC historical Forest Cover circa 1970 scanned from di-positives 105mm from the Pacific Forestry Centre's map archives

Tile index for BC historical Interim Forest Cover circa 1960 and download locations

Images for BC historical Forest Cover circa 1960 scanned from paper copies of the Interim Forest Cover from the Pacific Forestry Centre's map archives

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

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).

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).

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)