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

Francesco Barbadori (), Pierluigi Confuorto, Bhushan Chouksey, Sandro Moretti and Federico Raspini
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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

Land, 2024, vol. 13, issue 11, 1-18

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.

Keywords: soil erosion; wildfire impact; Google Earth Engine; RUSLE (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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