This data publication includes three map datasets: 1. Detailed annual disturbance maps – Forty raster layers mapping Canadian forest disturbance types from 1985 to 2024 at a 30m resolution. 2. Latest disturbance type and latest disturbance year maps – A simplified version for enhanced usability, consisting of three rasters that represent the most recent disturbance type and year (starting and ending). 3. Latest Landsat time series Julian day map – A raster capturing the latest Julian day for each pixel in the time series, where the Julian day represents the number of days elapsed since January 1, 1970. 2025 Update Changes: For the update of 2025, an additional post-processing step was performed to clean the annual data. The post-processing includes: (1) To limit false positives in defoliation classes, additional satellite data sources were used (Sentinel-2). (2) Correction of certain fire pixels in areas known to be heavily impacted by the hemlock looper. (3) Correction of pixels at disturbance boundaries. (4) In areas without forestry operations (the non-commercial northern part of Canada and national parks), visual inspection and correction as needed of pixels classified as forest harvesting. (5) Validation of forest fires using NBAC and visual validation across all of Canada. The forest disturbance types include wildfire, harvesting, pest outbreaks, other disturbances (windthrow, partial harvesting, landslide, wildfire not confirmed by NBAC), and new water bodies. The method is based on the summer composite Landsat time series (Guindon et al 2024). Disturbance breaks in the time series are first detected using the LandTrendr approach (Kennedy et al., 2010). Next, a one-dimensional convolutional neural network model (TempCNN; Pelletier et al., 2019) is applied with a 10-year window to classify disturbance types. The resulting maps achieve an overall accuracy of 81%. For aggregated pest/no-pest classes, the overall accuracy is 88.0% ±1.2%, with a commission error of 25.4% ±5.7% and an omission error of 63.2% ±4.3%. ## Data use constraints: 1. For pest-related defoliation, the proposed defoliation severity classes do not directly correspond to annual aerial survey classifications. Instead, they represent the intensity of cumulative spectral change at the end of the epidemic or, for ongoing outbreaks, the most recent observed year. Contrary to aerial survey classifications, only moderate to severe cumulative defoliation levels are detected, representing a good compromise between omission and commission errors. 2. The models are aimed at insect pests that primarily affect conifers. However, they may also capture severe defoliation in mixed or deciduous forests. 3. The analysis is based on a 10-year time window to adequately capture the effects of progressive defoliation. Therefore, to properly detect a pest causing this type of defoliation, such as the spruce budworm, historical data only becomes truly relevant starting around 1995. For insects with faster defoliation, the 1990s might be considered a good starting point. 4. The wood harvesting class refers to the removal of trees, regardless of the underlying intention. It primarily includes areas intended to remain forested or to be reforested but may also encompass certain sectors converted to other uses, such as road construction, mining, or various infrastructure projects. 5. The 'other disturbances' class includes windthrow, partial harvesting, landslides, as well as fires not confirmed by the NBAC (National Burned Area Composite) database. 6. The new water body class was not formally validated in this study, though visual assessments were conducted. 7. Since the summer composite considers only July and August imagery, disturbances occurring in the fall are detected the following year. For example, a wildfire that occurred in August 2023 might only become visible in the 2024 composite if the 2023 composite used images from early July, before the disturbance occurred. Additionally, cloud or shadow masking can create gaps in the time series, causing some disturbance events to appear delayed by one or two years. Users can use the national fire database (NBAC Canadian Wildland Fire Information System) for validation and year adjustments of wildfires. Moreover, the last Julians days raster could be used to better interpret the predicted year of disturbance. 8. The most recent years in the time series may have a higher commission error. These errors will be addressed in future annual updates. 9. The minimum detectable disturbance size is 1.08 hectares (12 pixels), which may limit the detection of linear disturbances such as roads. 10. Class 5 corresponds to the observation of defoliation followed by harvesting, for example, salvage logging. The severity of the defoliation is not associated with this class. For Class 5, the date indicated in the layer canlad_1985_2025_latest_start_year corresponds to the year when the defoliation began, and canlad_1985_2025_latest_ending_year corresponds to the year of the harvest. ## More details in the scientific publication: Perbet, P., Guindon, L., Correia, D.L.P. et al. Historical insect disturbance maps from 1985 onwards for Canadian forests derived using earth observation data. Sci Data 12, 2012 (2025). https://doi.org/10.1038/s41597-025-06269-x ## Please cite this dataset as: Perbet, P., Guindon, L., Correia D.L.P., P. Villemaire, O., Reisi Gahrouei R. St-Amant, Canada Landsat Disturbance with pest (CanLaD): a Canada-wide Landsat-based 30-m resolution product of fire, harvest and pest outbreak detection and attribution since 1987. https://doi.org/10.23687/902801fd-4d9d-4df4-9e95-319e429545cc ## Cited references: Guindon, Luc, Francis Manka, David L.P. Correia, Philippe Villemaire, Byron Smiley, Pierre Bernier, Sylvie Gauthier, Andre Beaudoin, Jonathan Boucher, et Yan Boulanger. 2024. « A New Approach for Spatializing the CAnadian National Forest Inventory (SCANFI) Using Landsat Dense Time Series ». Canadian Journal of Forest Research, février, cjfr-2023-0118. https://doi.org/10.1139/cjfr-2023-0118. Kennedy, Robert E., Zhiqiang Yang, et Warren B. Cohen. 2010. « Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms ». Remote Sensing of Environment 114 (12): 2897 2910. https://doi.org/10.1016/j.rse.2010.07.008. Pelletier, Charlotte, Geoffrey I. Webb, et François Petitjean. 2019. « Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series ». Remote Sensing 11 (5): 523. https://doi.org/10.3390/rs11050523.