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Monitoring post-fire ecohydrological recovery through integrated remote sensing and ecological modeling

Formally Refereed
Download (PDF 14.80 MB): https://research.fs.usda.gov/download/treesearch/80970.pdf

Abstract

Wildfire simultaneously disrupts ecosystem carbon, water, and soil processes, yet these coupled effects and postfire recovery trajectories cannot be reliably assessed from spectral vegetation indices alone. Here, we developed a fine-scale, multi-dimensional framework to quantify wildfire impacts and post-fire ecohydrological recovery, using the 2016 megafire in Great Smoky Mountains National Park in the eastern United States as a testbed. This framework integrated gap-filled 30 m NASA Harmonized Landsat and Sentinel-2 surface reflectance with an expanded diagnostic ecosystem model (Coupled Carbon and Water Model) to generate spatially consistent estimation of gross primary productivity (GPP), evapotranspiration (ET), water yield (WY), and soil erosion (SE) from 2014 to 2024 within Google Earth Engine. Fire effects were isolated from climate variability using pixellevel counterfactual simulations that produced no-fire baselines under identical meteorological forcing. We found that GPP declined by 20.7%, ET by 20.0%, WY increased by 19.2%, and soil erosion increased 12.5 times in the first post-fire year. Recovery trajectories varied strongly with burn severity, with high-severity patches retaining persistent modeled functional deficits (􀀀 13% GPP, 􀀀 9% ET, +9% WY, and 5× SE) after eight years despite full spectral recovery (NDVI +2%). Our findings reveal a distinct decoupling between spectral recovery and the modeled carbon, water, and erosion responses, driven by incomplete ecological succession, where highseverity areas remain in herb- and shrub-dominated stages rather than recovering to forest. The severitystratified diagnostic framework we provide offers a directly applicable tool for post-fire vegetation assessment, hydrological response monitoring, and long-term restoration planning in fire-affected ecosystems.

Citation

Zhang, Yulong; Li, Wenhong; Caldwell, Peter V.; Norman, Steven P.; Song, Conghe; Sun, Ge. 2026. Monitoring post-fire ecohydrological recovery through integrated remote sensing and ecological modeling. Science of Remote Sensing, 14: 100482. https://doi.org/10.1016/j.srs.2026.100482
Citations