Canada Landsat Disturbance (CanLaD) – 2025 Disturbance including Forest Insect Pest 

  Please keep this “readme” file with the dataset for reference.

  Updated metadata and dataset updates can be found at this link: 
https://doi.org/10.23687/902801fd-4d9d-4df4-9e95-319e429545cc

  If you use this data in your research or publication, please use these citations:
    
    Dataset
      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 1985. https://doi.org/10.23687/902801fd-4d9d-4df4-9e95-319e429545cc

    Scientific publication  
      Pauline P., L. Guindon, D. Correia, O. Reisi Gahrouei, J-F. Côté and M. Béland, Remote Sensing for Annual Monitoring of Insect Disturbance in Canadian Boreal Forests. (to be published)


- Description
  This repository contains a set of raster files predicting disturbance in Canada from 1985 to 2024. The data was generated using deep learning algorithms applied to remote sensing data. The detailed methodology and data validation analyses are described in the scientific publication.

 The data consists of a set of raster files in GeoTIFF format, covering the entire non-arctic land mass of Canada. 
  
The rasters have a spatial resolution of 30 meters. 

Cartographic projection definition (WKT format) can be found in this file:
_projectionDefinitionWKT_lcccan83.prj

- The data is split into three sets of rasters:
  1)  Latest Disturbances
	* canlad_1985_2024_latest_type_v1.tif 
	0= Undisturbed
	1= Wildfire
	2= Harvesting
	3= Windthrows
	4= Water extension
	5= Defoliation and Harvesting
	6= Low severity defoliation
	7= Medium severity defoliation	
	8= High severity defoliation

	* canlad_1985_2024_latest_year_v1.tif 
	Value from 1985 to 2024
  
     

  2)  Annual disturbance maps. 40 rasters, one for each year.
	canlad_annual_*_v1.tif
	0= Undisturbed
	1= Wildfire
	2= Harvesting
	3= Windthrows
	4= Water extension
	5= Defoliation and Harvesting
	6= Low severity defoliation
	7= Medium severity defoliation	
	8= High severity defoliation
    
 3) Last Julian day of the time series.
	canlad_JJ_max.tif
	




- Data limitations
(1) For pest-related disturbances, 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 error and commission. 
(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.
(6) The windthrow class is effective at detecting large-scale events but has a high false detection rate, particularly along the edges of harvested areas, where mixed pixels create spectral similarities with windthrow. Among all disturbance classes, windthrow has the highest error rate. 
(7) The new water body class was not formally validated in this study, though visual assessments were conducted. Users are advised to perform their own validation before use.
(8) 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 Julian days raster could be used to better interpret the predicted year of disturbance. 
(9) The most recent years in the time series may have a higher commission error. These errors will be addressed in future annual updates.
(10) The minimum detectable disturbance size is 1.08 hectares (12 pixels), which may limit the detection of linear disturbances such as roads. 




- Data Access and Usage
  The data is freely available for download and use. 
  
  You may use this data for research or commercial purposes, but you must cite the original source of the data. 
  
  The data is licensed under the Creative Commons Attribution 4.0 International license.
  
  To access and download the data, please navigate to https://doi.org/10.23687/902801fd-4d9d-4df4-9e95-319e429545cc


- Contact
  If you have any questions or feedback about this data, please contact the authors at luc.guindon@nrcan-rncan.gc.ca