Document Type
Article
Publication Date
8-4-2026
Department
Michigan Tech Research Institute
Abstract
Background: Although soil burn severity (SBS) is traditionally mapped post-fire, pre-fire predictive approaches remain limited despite their potential to identify areas at risk of soil degradation, runoff and erosion. Aim: To develop a large-scale model for predicting post-fire SBS from pre-fire conditions at ~30 m spatial resolution. Methods: A harmonized dataset of 62 wildfires (2018–2024) was used to build machine learning (ML) frameworks based on Random Forest, eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), using pre-fire vegetation, climate, soil–terrain and anthropogenic predictors. Performance was evaluated using five-fold cross-validation (CV), leave-one-fire-out (LOFO) validation and independent testing on 2025 fires. Key Results: All models showed similar CV overall accuracy (OA ~0.58), but LOFO performance varied. LightGBM maintained more consistent OA (0.51–0.58) across fire size classes, whereas XGBoost and Random Forest exhibited comparable performance. Key predictors included vegetation biomass (Normalized Difference Moisture Index, Enhanced Vegetation Index), fuel stress (Evaporative Stress Index, Evapotranspiration), elevation and soil moisture. High SBS was associated with dry conditions and high biomass availability, with region-specific controls modulating these patterns. Conclusion: ML models using pre-fire environmental data enable proactive SBS prediction. Implications: This framework supports fuel management planning, potential post-fire hydrological impact assessment and the prediction of SBS from pre-fire conditions.
Publication Title
International Journal of Wildland Fire
Recommended Citation
Das, S.,
Dobre, M.,
Lew, R.,
Lewis, S.,
Brooks, E.,
Robichaud, P.,
Srivastava, A.,
Miller, M. E.,
Watanabe, A.,
&
Basso, M.
(2026).
An interpretable remote sensing–machine learning framework for wildfire soil burn severity prediction in the northwestern United States.
International Journal of Wildland Fire,
35(8).
http://doi.org/10.1071/WF25295
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p2/2906
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
© 2026 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF. Publisher’s version of record: https://doi.org/10.1071/WF25295