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Monitoring Post-Fire Deciduous Shrub Cover Using Machine Learning and Multiscale Remote Sensing

Author

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  • Hannah Trommer

    (Department of Geography, Kent State University, Kent, OH 44242, USA)

  • Timothy Assal

    (Department of Geography, Kent State University, Kent, OH 44242, USA
    Bureau of Land Management, National Operations Center, Denver, CO 80225, USA)

Abstract

Wildfire and drought are key drivers of shrubland expansion in southwestern US landscapes. Stand-replacing fires in conifer forests induce shrub-dominated stages, and changing climatic patterns may cause a long-term shift to deciduous shrubland. We assessed change in deciduous fractional shrub cover (DFSC) in the eastern Jemez Mountains from 2019 to 2023 using topographic and Sentinel-2 satellite data and evaluated the impact of spatial scale on model performance. First, we built a 10 m and a 20 m random forest model. The 20 m model outperformed the 10 m model, achieving an R-squared value of 0.82 and an RMSE of 7.85, compared to the 10 m model (0.76 and 9.99, respectively). We projected the 20 m model to the other years of the study using imagery from the respective years, yielding yearly DFSC predictions. DFSC decreased from 2019 to 2022, coinciding with severe drought and a 2022 fire, followed by an increase in 2023, particularly within the 2022 fire footprint. Overall, DFSC trends showed an increase, with elevation being a key variable influencing these trends. This framework revealed vegetation dynamics in a semi-arid system and provided a close look at post-fire regeneration in deciduous resprouting shrubs and could be applied to similar systems.

Suggested Citation

  • Hannah Trommer & Timothy Assal, 2025. "Monitoring Post-Fire Deciduous Shrub Cover Using Machine Learning and Multiscale Remote Sensing," Land, MDPI, vol. 14(8), pages 1-23, August.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:8:p:1603-:d:1718766
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