Burned area mapping across the Arctic-boreal zone (1985-2020) with Landsat and Sentinel-2 imagery

Wildfires in the Arctic-boreal zone have increased in frequency over recent decades, carrying substantial ecological, social, and economic consequences. Remote sensing is crucial for mapping burned areas, monitoring wildfire dynamics, and evaluating their impacts. However, existing high-latitude burned area products suffer from significant discrepancies, particularly in Siberia, and their coarse spatial resolutions limit accuracy and utility. To address these gaps, we developed a convolutional neural network model to map burned areas at a 30-meter resolution across the Arctic-boreal zone using Landsat and Sentinel-2 imagery. Our model achieved promising results, with an Intersection Over Union (IOU) of 0.77 and an F1 score of 0.85 on unseen test data, performing better in North America (IOU=0.84) than Eurasia (IOU=0.72) due to differences in fire regimes and data quality. Predictions for six representative years showed our model’s burned area closely matched the median values of Landsat, MODIS, and VIIRS-based products, although alignment varied annually and spatially. Visual assessments indicated our approach was generally more accurate, notably in detecting unburned vegetation islands within fire perimeters missed by other products. This research has numerous potential applications, such as analyzing feedback between vegetation and burn patterns, characterizing spatial dynamics of unburned islands, and improving carbon emission estimates through detailed burn severity assessments. Here we have provided the primary series of scripts used to achieve the above results. In these scripts we use historical vector fire polygons to download imagery from Landsat 5, 7, 8, 9 and Sentinel-2 to train a deep learning model called a UNet++ in the Arctic-boreal zone. Imagery is downloaded from Google Earth Engine, while all other processing is done locally. The series of 6 scripts describes main steps from downloading training data, pre-processing it, training the model, and applying the model across the Arctic Boreal Zone. All scripting is done in python through .py scripts and Jupyter notebooks (.ipynb). Our study area includes Alaska, Canada and Eurasia, and we trained our model on all historical fire polygons from 1985-2020.

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