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<p>This study presents a UAV-based methodology for monitoring slow-moving landslides using high-resolution data. The case study is the Pietrapertosa earthflow in the Southern Apennines, which showed intermittent activity and impacted a critical road network. The investigation involved nine drone surveys from 2022 to 2025, enabling the development of high-resolution orthoimages and DEMs, analysed to quantify the spatial and temporal evolution of the landslide. The data comparison showed that landslide activity concentrated within the central area, with minimal changes in its perimeter. Within the active area, the complex evolution of the scarp-terrace systems was investigated, enabling the estimation of displacement rates ranging from 7.28&#xa0;m/month after reactivation to 0.08&#xa0;m/month during the current resting phase. Additionally, the different DEM models were quantitatively compared to identify the main erosion and deposition areas within the landslide, thus allowing the estimation of the total mobilised volume. The applied methodology provides an example of large, slow-moving landslide monitoring, transferable to similar cases where the velocities are too high for satellite images and field instruments. The high-resolution monitoring of such complex processes can offer critical information to prevent their impact on vulnerable infrastructure and to optimise the planning of the mitigation strategies.</p>

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Analysis of slow-moving landslide evolution using high-resolution UAV monitoring: The Pietrapertosa earthflow (Southern Apennines, Italy)

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月30日

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  • A. Santo,
  • E. Marino,
  • E. Valente,
  • L. Massaro

摘要

This study presents a UAV-based methodology for monitoring slow-moving landslides using high-resolution data. The case study is the Pietrapertosa earthflow in the Southern Apennines, which showed intermittent activity and impacted a critical road network. The investigation involved nine drone surveys from 2022 to 2025, enabling the development of high-resolution orthoimages and DEMs, analysed to quantify the spatial and temporal evolution of the landslide. The data comparison showed that landslide activity concentrated within the central area, with minimal changes in its perimeter. Within the active area, the complex evolution of the scarp-terrace systems was investigated, enabling the estimation of displacement rates ranging from 7.28 m/month after reactivation to 0.08 m/month during the current resting phase. Additionally, the different DEM models were quantitatively compared to identify the main erosion and deposition areas within the landslide, thus allowing the estimation of the total mobilised volume. The applied methodology provides an example of large, slow-moving landslide monitoring, transferable to similar cases where the velocities are too high for satellite images and field instruments. The high-resolution monitoring of such complex processes can offer critical information to prevent their impact on vulnerable infrastructure and to optimise the planning of the mitigation strategies.