Extending Canadian forest disturbance history maps prior to 1985

Abstract

AbstractAn accurate depiction of wildfire, harvesting, and insect outbreak disturbances is essential for sustainable ecosystem management of forests in Canada. Even though the advent of temporally consistent 30‐m resolution Landsat data has enabled the detailed mapping of forest disturbances in Canada from 1985 onward, the disturbance record prior to 1985 remains sparse. This study aimed to extend the existing pre‐1985 disturbance history record by mapping wildfire, harvest, and insect outbreaks in Canadian forests between 1965 and 1984. Our geospatial data processing methodology relied on multilayer perceptrons (MLP) trained on spectral recovery signatures to map and age these disturbances. Our model detected approximately 4.8, 7.3, and 3.8 million ha of burnt, harvested, and insect‐ravaged forest areas, respectively, that were absent from national and provincial disturbance databases and forest inventories. Results were validated using both internal and external validation datasets. Our disturbance detection methodology was highly effective, with an internal validation kappa score of 0.91 and an external score of 0.81. The fire and harvest age disturbance MLPs, whose predictions can also be used as a proxy of forest stand age, performed adequately on the internal (fire R2 = 0.675; root mean squared error [RMSE] = 4.42; harvest R2 = 0.723; RMSE = 3.17) and external validation datasets (fire R2 = 0.242; RMSE = 4.69; harvest R2 = 0.257; RMSE = 5.46), outperforming existing forest age disturbance products. Finally, we relied on several open data products, such as provincial forest inventories, to correct our disturbance type and year prediction whenever these more reliable, but incomplete, data sources were available. Specific years were not assigned to insect outbreaks due to the lack of dependable training and validation data. We also illustrate how extending the existing forest disturbance record by 20 years may provide a more in‐depth understanding of landscape‐disturbance dynamics with a case study of the 2023 Canadian wildfire season.