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Abstract Peatlands in the Canadian boreal forest are being negatively impacted by anthropogenic climate change, the effects of which are expected to worsen. Peatland types and sub‐classes vary in their ecohydrological characteristics and are expected to have different responses to climate change. Large‐scale modelling frameworks such as the Canadian Model for Peatlands, the Canadian Fire Behaviour Prediction System and the Canadian Land Data Assimilation System require peatland maps including information on sub‐types and vegetation as critical inputs. Additionally, peatland class and vegetation height are critical variables for wildlife habitat management and are related to the carbon cycle and wildfire fuel loading. This research aimed to create a map of peatland sub‐classes (bog, poor fen, rich fen permafrost peat complex) for the Canadian boreal forest and create an inventory of peatland vegetation height characteristics using ICESat‐2. A three‐stage hierarchical classification framework was developed to map peatland sub‐classes within the Canadian boreal forest circa 2020. Training and validation data consisted of peatland locations derived from various sources (field data, aerial photo interpretation, measurements documented in literature). A combination of multispectral data, L‐band SAR backscatter and C‐Band interferometric SAR coherence, forest structure and ancillary variables was used as model predictors. Ancillary data were used to mask agricultural areas and urban regions and account for regions that may exhibit permafrost. In the first stage of the classification, wetlands, uplands and water were classified with 86.5% accuracy. In the second stage, within the wetland areas only, peatland and mineral wetlands were differentiated with 93.3% accuracy. In the third stage, constrained to only the peatland areas, bogs, rich fens, poor fens and permafrost peat complexes were classified with 71.5% accuracy. Then, ICESat‐2 ATL08 spaceborne lidar data were used to describe regional variations in peatland vegetation height characteristics and regional and class‐wise variations based on a boreal forest wide sample. This research introduced a comprehensive large‐scale peatland sub‐class mapping framework for the Canadian boreal forest, presenting the first moderate resolution map of its kind.

Abstract A recent global study reported a net difference between areas of forest cover loss and of forest cover gain of about 3.6% of total forest area across the boreal biome, and of 5.6% for Canada, over a 12‐yr period. Net losses of this magnitude should be of concern given the importance of this biome in global biogeochemical cycles linked to climate change. Our analysis for Canada fails to support these results and suggests that post‐harvest recovery of tree cover is generally strong, while post‐fire recovery of tree cover is weaker but nevertheless prevalent. We find that current large area remote sensing methodologies can fail to properly recognize post‐disturbance recovery from non‐forest to forest status in low‐productivity boreal forests when using short time series. With climate change and human impacts intensifying around the world, it is urgently important to be able to reliably distinguish temporary forest cover loss followed by naturally slow recovery from forest decline requiring policy action. The analysis was in large part based on the new Canada Landsat Disturbance product in which fires and harvest since 1984 are mapped at 30‐m resolution (https://doi.org/10.23687/add1346b-f632-4eb9-a83d-a662b38655ad).