Francesca Ceccato’s scientific contributions

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Publications (1)


Schematic diagram of LSM framework and specific processing steps for each part (Section 2.1, Section 2.2 and Section 2.3).
(a) Landslide susceptibility map for all, R, F, SH, and C landslide types. (b) Landslide susceptibility map for RT, DF, SL, DSGSD, and TR landslide type.
Areas (km²) susceptible to different classifications of landslide types obtained from landslide susceptibility maps.
AUC-ROC for all, R, F, SH, C, RT, DF, SL, DSGSD, and TR landslide types.
Feature importance for all, R, F, SH, C, RT, DF, SL, DSGSD, and TR landslide types.

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Exploring the Interaction Between Landslides and Carbon Stocks in Italy
  • Article
  • Full-text available

December 2024

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38 Reads

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1 Citation

Jibran Qadri

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Francesca Ceccato

Landslides, as natural hazards, have far-reaching impacts beyond their immediate effects on human lives and infrastructure; landslides disrupt both carbon storage and ecosystem stability, and their role in the global carbon cycle cannot be underestimated. This study delves into the complex relationship between landslides and carbon stocks such as, in particular, soil organic carbon (SOC) and above-ground biomass (AGB), and outlines the spatial relationship between different types of landslides, soil organic carbon (SOC), and the carbon cycle, underscoring the importance of understanding these interconnections for environmental sustainability and climate change mitigation efforts. By employing machine learning algorithms on the Google Earth Engine platform, landslide susceptibility maps were created for different landslide types across Italy, and their spatial patterns with SOC accumulation were analyzed using the Python environment. The findings reveal a nuanced relationship between landslide hazard levels and SOC dynamics, with varying trends observed for different landslide types. In addition, this study investigates the potential impact of large-scale landslide events on carbon sequestration in the short term via a case study of the May 2023 landslide event in the Emilia Romagna region of Italy. The analysis reveals a substantial reduction in above-ground biomass by 35%, which approximately accounts for the loss of 0.133 MtC, and a decrease in SOC accumulation in 72% of the affected areas, indicating that landslides can transform carbon sinks into carbon sources, at least in the short term, and suggested that carbon released from extreme landslide events at a larger scale needs to be accounted for in regional or national carbon emissions. This research underscores the importance of considering landslides in carbon cycle assessments and emphasizes the need for sustainable land management strategies to protect and enhance carbon sinks, such as forests and healthy soils, in the face of increasing natural hazards and climate change impacts.

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Citations (1)


... A significant contribution to the understanding of carbon cycle instability is presented in [89], where machine learning algorithms were applied via the Google Earth Engine platform to evaluate landslide susceptibility in Italy. The findings indicated that largescale landslides can shift ecosystems from functioning as carbon sinks to becoming carbon sources, thereby undermining the stability of biogeochemical cycles. ...

Reference:

The Impact of Artificial Intelligence on the Sustainability of Regional Ecosystems: Current Challenges and Future Prospects
Exploring the Interaction Between Landslides and Carbon Stocks in Italy