The multitemporal landslide inventory map of the Collazzone study area, central Italy

Ardizzone, Francesca; Cardinali, Mauro; Fiorucci, Federica

doi:10.5194/essd-2025-684

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https://doi.org/10.5194/essd-2025-684

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31 Mar 2026

| 31 Mar 2026

Status: this preprint is currently under review for the journal ESSD.

The multitemporal landslide inventory map of the Collazzone study area, central Italy

Francesca Ardizzone, Mauro Cardinali, and Federica Fiorucci

Abstract. Multitemporal landslide inventory maps are an essential source of information for many research and applications. This type of landslide map provides information for analyzing the spatial and temporal distribution of the landslides, their potential interaction with the built environment, their contribution to landscape dynamics, and their response to the triggers over time. The acquisition of the temporal information of landslide evolution provides a fundamental step for hazard evaluation. Despite the essential value of multitemporal landslide inventory maps, their dissemination is rare. This is due to the time-consuming nature of their preparation. In this paper we present the multitemporal landslide inventory map for a 79 km²area in the Umbria region (central Italy). The inventory contains more than 3,500 landslides classified according to relative age, type of movement, and estimated depth. The landslide mapping was carried out by interpreting stereoscopic aerial photographs taken in 1941, 1954, 1977, 1985, and 1997, as well as stereo satellite images taken in 2010, 2013, and 2014. The inventory was further updated through several field checks conducted in 1999, 2004, 2005, and 2020, and by interpreting the images available on Google Earth (2011, 2012, 2015, 2015, 2015, 2019, 2021, 2022 and 2023). Landslides were mapped according to rigorous and reproducible criteria applied by a team of expert geomorphologists. The dataset consists of a digital archive including twenty-nine layers, that is available at https://doi.org/10.5281/zenodo.18980281.

Received: 12 Nov 2025 – Discussion started: 31 Mar 2026

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Francesca Ardizzone, Mauro Cardinali, and Federica Fiorucci

Status: final response (author comments only)

RC1:
'Comment on essd-2025-684', Michele Delchiaro, 24 Apr 2026
The manuscript presents a comprehensive (counting over 3,500 landslides) multi-temporal landslide inventory for the Collazzone area (central Italy), covering more than 80 years of observations derived from different datasets.
The long-term continuity of the dataset, the rigorous mapping procedures, and the integration of heterogeneous sources make this inventory a valuable contribution for hazard assessment, geomorphological analysis, and landscape evolution studies. Overall, the work is scientifically solid, methodologically robust, and highly useful for the community. Only minor revisions are needed to improve clarity, readability, and metadata completeness.
MAIN COMMENTS
The manuscript would benefit from a clearer explanation of how the different mapping scales and data sources were harmonized when delineating landslide polygons. The criteria used to define polygon boundaries in relation to the different dataset scales are not fully described, and the handling of spatial and temporal uncertainties, particularly for inter-survey periods, could be explicitly detailed.

It is not clear whether the authors explored any relationship between mapped landslides and potential meteorological triggers. Including a long-term precipitation plot (e.g., 1940-2023) would help visualise temporal correspondence, uncertainties, and possible triggering conditions associated with each mapping period.

The structure of the database and metadata could be presented in greater detail. A schematic representation of the database, a list of attribute fields with their domains would benefit the reproducibility.

Figures 3 and 4 are difficult to interpret due to the colour scheme, which does not follow a clear temporal logic. A more structured chromatic progression would improve readability.

MINOR ISSUES
LINE 8: Replace with “multi-temporal” and ensure consistency throughout the text.
LINE 10: Clarify the meaning of “the build environment”; do you refer to infrastructures or the built environment more broadly?
LINE 33: “Vintages” may not be the most appropriate term; consider rephrasing.
LINE 55: Correct “(Bhutyan et al., 2023a)”.
LINE 58: The study area should be explicitly mentioned here and also in the abstract.
LINES 75–77: Consider anticipating the age information as “Plio-Pleistocene continental gravel, sand and clay”.
EQUATION 1: Replace “con” and correct the condition to 0 ≤ E ≤ 1.
Citation: https://doi.org/10.5194/essd-2025-684-RC1
- AC1: 'Reply on RC1', Francesca Ardizzone, 12 Jun 2026
  
  Dear reviewer,
  thank you for the positive review and for the useful comments and suggestions.
  The file RC1_replay_20260612.pdf containes the responses to your comments.
  The file includes your comments in black color and the response in red color.
  I hope the response to your comments meets your needs.
  Best Regards,
  Francecsa Ardizzone
  
  Citation: https://doi.org/10.5194/essd-2025-684-AC1
RC2:
'Comment on essd-2025-684', Rita Tufano, 14 May 2026

The manuscript presents a multi-temporal landslide inventory for an area in central Italy (Collazzone). The study is very well conceived and structured. In particular, the temporal analysis is carried out by integrating a large amount of information derived from aerial photographs, satellite imagery, field checks, and the interpretation of Google Earth images, resulting in the identification of more than 3,500 landslides within the investigated area. I have only few comments:
MAIN COMMENTS:
- Despite the Italian reference database for landslides is the IdroGEO inventory, it is neither cited in the manuscript nor used for comparison with the mapped events. I consider this aspect necessary, as such a comparison could significantly enhance the scientific value and reliability of the proposed inventory.
- The manuscript only refers to rainfall-induced triggering mechanisms; however, considering the study area, I would also have expected the mapping of landslides triggered or remobilized by seismic events, such as those associated with the 1997 and 2016 Central Italy earthquakes. Furthermore, it is not sufficiently clear how the rainfall trigger was associated with the mapped events. This aspect should be described and discussed in greater detail.
MINOR COMMENTS:
- Figure 1A: the shaded relief should be replaced with the DTM in order to provide a clearer representation of the elevation variability within the investigated area.

- Figure 10 is not sufficiently clear, particularly with regard to the enlarged views, where many polygons are not clearly visible. The figure requires substantial improvement.

Citation: https://doi.org/10.5194/essd-2025-684-RC2
- AC2: 'Reply on RC2', Francesca Ardizzone, 12 Jun 2026
  
  Dear reviewer,
  thank you for the positive review and for the useful comments and suggestions.
  The file RC2_replay_20260612.pdf contains the responses to your comments.
  The file includes your comments in black color and the response in red color.
  I hope the response to your comments meets your needs.
  I have coorrect the text according to your comment except for the first one.
  In the reply file you'll find also the response to this question.
  Best Regards,
  Francesca Ardizzone
  
  Citation: https://doi.org/10.5194/essd-2025-684-AC2

Francesca Ardizzone, Mauro Cardinali, and Federica Fiorucci

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https://doi.org/10.5194/essd-2025-684-supplement

Data sets

Multitemporal Landslide Inventory Map of Collazzone study area (Italy) F. Ardizzone https://doi.org/10.5281/zenodo.18980281

Francesca Ardizzone, Mauro Cardinali, and Federica Fiorucci

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Short summary

We present the multitemporal landslide inventory map for a 79 km²area in central Italy. The inventory contains more than 3,500 landslides and was prepared by interpreting stereoscopic aerial photographs taken from 1941 to 1997, as well as stereo satellite images from 2010, 2013, and 2014. Field surveys were conducted in 1999, 2004, 2005, and 2020, and the images available on Google Earth were used. The dataset is available at https://doi.org/10.5281/zenodo.18980281.


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