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Multi-Temporal Assessment of Soil Erosion After a Wildfire in Tuscany (Central Italy) Using Google Earth Engine

Author

Listed:
  • Francesco Barbadori

    (Department of Earth Sciences, University of Florence, 50121 Florence, Italy)

  • Pierluigi Confuorto

    (Department of Earth Sciences, University of Florence, 50121 Florence, Italy)

  • Bhushan Chouksey

    (Department of Civil and Environmental Engineering, University of Florence, 50139 Florence, Italy)

  • Sandro Moretti

    (Department of Earth Sciences, University of Florence, 50121 Florence, Italy)

  • Federico Raspini

    (Department of Earth Sciences, University of Florence, 50121 Florence, Italy)

Abstract

The Massarosa wildfire, which occurred in July 2022 in Northwestern Tuscany (Italy), burned over 800 hectares, leading to significant environmental and geomorphological issues, including an increase in soil erosion rates. This study applied the Revised Universal Soil Loss Equation (RUSLE) model to estimate soil erosion rates with a multi-temporal approach, investigating three main scenarios: before, immediately after, and one-year post-fire. All the analyses were carried out using the Google Earth Engine (GEE) platform with free-access geospatial data and satellite images in order to exploit the cloud computing potentialities. The results indicate a differentiated impact of the fire across the study area, whereby the central parts suffered the highest damages, both in terms of fire-related RUSLE factors and soil loss rates. A sharp increase in erosion rates immediately after the fire was detected, with an increase in maximum soil loss rate from 0.11 ton × ha −1 × yr −1 to 1.29 ton × ha −1 × yr −1 , exceeding the precautionary threshold for sustainable soil erosion. In contrast, in the mid-term analysis, the maximum soil loss rate decreased to 0.74 ton × ha −1 × yr −1 , although the behavior of the fire-related factors caused an increase in soil erosion variability. The results suggest the need to plan mitigation strategies towards reducing soil erodibility, directly and indirectly, with a continuous monitoring of erosion rates and the application of machine learning algorithms to thoroughly understand the relationships between variables.

Suggested Citation

  • Francesco Barbadori & Pierluigi Confuorto & Bhushan Chouksey & Sandro Moretti & Federico Raspini, 2024. "Multi-Temporal Assessment of Soil Erosion After a Wildfire in Tuscany (Central Italy) Using Google Earth Engine," Land, MDPI, vol. 13(11), pages 1-18, November.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:11:p:1950-:d:1524116
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    References listed on IDEAS

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    1. Alexandros Dimitrakopoulos & M. Vlahou & Ch. Anagnostopoulou & I. Mitsopoulos, 2011. "Impact of drought on wildland fires in Greece: implications of climatic change?," Climatic Change, Springer, vol. 109(3), pages 331-347, December.
    2. Olga M. Lozano & Michele Salis & Alan A. Ager & Bachisio Arca & Fermin J. Alcasena & Antonio T. Monteiro & Mark A. Finney & Liliana Del Giudice & Enrico Scoccimarro & Donatella Spano, 2017. "Assessing Climate Change Impacts on Wildfire Exposure in Mediterranean Areas," Risk Analysis, John Wiley & Sons, vol. 37(10), pages 1898-1916, October.
    3. Marco Turco & Maria-Carmen Llasat & Jost Hardenberg & Antonello Provenzale, 2014. "Climate change impacts on wildfires in a Mediterranean environment," Climatic Change, Springer, vol. 125(3), pages 369-380, August.
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