Francesca Ardizzone

Multi-temporal 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 structure and infrastructure, 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 multi-temporal landslide inventory maps, their dissemination is rare. This is due to the time-consuming nature of their preparation. In this paper we present the multi-temporal landslide inventory map for a 79 km ² area, Collazzone study area, in the Umbria region (central Italy). The inventory contains more than 3500 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.21531647 (Ardizzone, 2026).

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 km² area 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.

Rockfalls represent a substantial threat to railway routes, due to their rapidity, destructive potential and high probability of occurrence on steep topographies. Approaches for the assessment of rockfall susceptibility range from statistical methods, for modeling large areas, to deterministic ones, for application in local analyses. A common requirement is the need to locate the source areas, often found uphill on cliffs, and the subsequent assessment of the runout areas of rockfalls stemming from such areas. Modelling rockfall phenomena is complex and requires various inputs, including: accurate location of the source areas, geomorphological and geological setting, and other geo-environmental factors.We present an application conducted along the Rocca San Zenone - Giuncano Scalo railway line, in a study area (26 km2) in Central Italy, where rockfall are abundant. The activity consisted in creating rockfall trajectory maps, in geotiff format, starting from possible source areas, and in classifying and validating the maps. The following software was used to create the trajectory map: i) rockyfor3D (https://www.ecorisq.org/ecorisq-tools); and ii) STONE (Guzzetti et al., 2002). The classification and validation phase was carried out using the R RF-Tools software (Rossi, 2023). Methodology for the Creation of Landslide Susceptibility Maps to produce rockfall susceptibility zoning, considering three scenarios: i) plausible scenario, ii) best case scenario; and iii) worst case scenario.The rockfall modeling procedure was developed as part of a national project dedicated to the preparation of an operational methodology for assessing landslide susceptibility along the entire Italian railway network.

The quality of a landslide inventory depends on its accuracy and the type and certainty of information shown on the map. Defining the accuracy of a landslide inventory is not straightforward, and there are no standards. Accuracy depends on the completeness of the map and the geographic and thematic correctness of the information displayed on the map.In this study, an expert, semi-quantitative evaluation approach was developed to assess mapping errors and consequently determine the quality of a geomorphological historical landslide inventory map for a 2,000 km2 area in the Southern Italian Apennines (Daunia, Puglia region) prepared through aerial photo-interpretation.Quality control aims to quantify parameters, even in terms of binary choices. Whenever involving expert estimates, the assessment was carried out through a reference grid to limit inconsistencies. Furthermore, the expert evaluations were carried out by a team of evaluators working collegially, and who had not previously worked simultaneously on the same areas.The general approach involves a systematic evaluation within 5 sample areas covering a total of 202 km2 deemed a choice enabling the identification of areas that are sufficiently extensive (i.e., 10% of the total area and 20% of the total number of landslides) and adequately represent the diverse morphological and litho-structural characteristics of the study area. The assumption of representativeness of these areas forms the basis for extrapolating error data to the entire investigated area.Geographic accuracy gauges the degree of correspondence between morphological and photographic evidence of landslides and their portrayal on the map. This correspondence was decomposed in terms of position, shape, and size and was evaluated, for each landslide portrayed in the 5 sites, at the declared scale of the final map (1:5,000). Each component was considered acceptable if at least 2/3 of the landslide was mapped correctly, and the overall accuracy satisfactory if at least two components were sufficient.To assess the completeness of the inventory, the authors of the map were preliminarily asked to state the minimum size of landslides consistently mapped (ACM) in the inventory. Then, within the sample areas, the ratio between the number of landslides exceeding ACM and not represented in the map and the number of the landslides mapped expresses the degree of completeness of the inventory for the declared ACM.To evaluate the thematic accuracy, a percentage score was assigned representing the proportion of landslides with specific thematic errors within the inventory, considering the correct classification (type and relative age) of each landslide represented in the map and compared to its photographic and morphological evidence.Considering the absence of qualitative standards in the scientific literature for landslide inventory maps, and, consequently, the lack of evaluative standards for the accuracy of such cartographic products, this work can be considered an attempt to define a procedure to evaluate the informative content of landslide inventory maps.

Remote images are useful tools for detecting and monitoring landslides, including shallow landslides in agricultural environments. However, the use of non-commercial satellite images to detect the latter is limited because their spatial resolution is often comparable to or greater than landslide sizes, and the spectral characteristics of the pixels within the landslide body (LPs) are often comparable to those of the surrounding pixels (SPs). The buried archaeological remains are also often characterized by sizes that are comparable to image spatial resolutions and the spectral characteristics of the pixels overlying them (O_BARPs) are often comparable to those of the pixels surrounding them (S_BARPs). Despite these limitations, satellite images have been used successfully to detect many buried archaeological remains since the late 19th century. In this research context, some methodologies, which examined the values of O_BARPs and S_BARPs, were developed to rank images according to their capability to detect them. Based on these previous works, this paper presents an updated methodology to detect shallow landslides in agricultural environments. Sentinel-2 and Google Earth (GE) images were utilized to test and validate the methodology. The landslides were mapped using GE images acquired simultaneously or nearly simultaneously with the Sentinel-2 data. A total of 52 reference data were identified by monitoring 14 landslides over time. Since remote sensing indices are widely used to detect landslides, 20 indices were retrieved from Sentinel-2 images to evaluate their capability to detect shallow landslides. The frequency distributions of LPs and SPs were examined, and their differences were evaluated. The results demonstrated that each index could detect shallow landslides with sizes comparable to or smaller than the spatial resolution of Sentinel-2 data. However, the overall accuracy values of the indices varied from 1 to 0.56 and two indices (SAVI and RDVI) achieved overall accuracy values equal to 1. Therefore, to effectively distinguish areas where shallow landslides are present from those where they are absent, it is recommended to apply the methodology to many image processing products. In conclusion, given the significant impact of these landslides on agricultural activity and surrounding infrastructures, this methodology provides a valuable tool for detecting and monitoring landslide presence in such environments.

Timely and systematic collection of landslide information after a triggering event is pivotal for the definition of landslide trends in response to climate change. On September 15, 2022, a large part of central Italy, particularly Marche and Umbria regions, was struck by an anomalous rainfall event that showed characteristics of a persistent convective system. An extraordinary cumulated rainfall of 419 mm was recorded by a rain gauge in the area in only 9 h. The rainfall triggered 1687 landslides in the area affected by the peak rainfall intensity and caused widespread flash floods and floods in the central and lower parts of the catchments. In this work, we describe the characteristics of the landslides identified during a field survey started immediately after the event. Most of the mass movements are shallow, and many are rapid (i.e., debris flows, earth flows) and widely affecting the road network. Landslide area spans from a few tens of square meters to 10⁵ m², with a median value of 87 m². Field evidence revealed diffuse residual risk conditions, being a large proportion of landslides located in the immediate vicinity of infrastructures. Besides reporting the spatial distribution of landslides triggered by an extreme rainfall event, the data collected on landslides can be used to make comparisons with the distribution of landslides in the past, validation of landslide susceptibility models, and definition of the general interaction between landslides and structures/infrastructures.

Systematic and timely documentation of triggered (i.e. event) landslides is fundamental to build extensive datasets worldwide that may help define and/or validate trends in response to climate change. More in general, preparation of landslide inventories is a crucial activity since it provides the basic data for any subsequent analysis. In this work we present an event landslide inventory map (E-LIM) that was prepared through a systematic reconnaissance field survey in about 1 month after an extreme rainfall event hit an area of about 5000 km² in the Marche-Umbria regions (central Italy). The inventory reports evidence of 1687 triggered landslides in an area of ~550 km². All slope failures were classified according to type of movement and involved material, and documented with field pictures, wherever possible. The database of the inventory described in this paper as well as the collection of selected field pictures associated with each feature is publicly available at figshare.

Timely and systematic collection of landslide information after a triggering event is essential for the definition of landslide trends in response to climate change. On September 15, 2022 Marche and Umbria regions, in Central Italy, were struck by an anomalous rainfall event that showed characteristics of a persistent convective system. An extraordinary cumulated rainfall of 419 mm was recorded by a rain gauge in the area in only 9 hours. It was carried out a systematic reconnaissance field survey to prepare an event landslide inventory map in an area of 550 km2 that includes a large neighbourhood of the area that recorded the highest rainfall intensity. The rainfall triggered 1687 landslides in the area affected by the peak rainfall intensity. Landslide area spans from a few tens of square meters to 105 m2, with a median value of 87 m2. We describe the characteristics of the landslides identified during a field survey conducted immediately after the event. Most of the mass movements are shallow, many are rapid (i.e., debris flows, earth flows) and widely affecting the road network. Many national and local roads were interrupted, mostly by earth and rock slides; national and local railways were interrupted at several points; extensive damage was registered to structures and infrastructures. Furthermore, field evidence revealed that a vast proportion of landslides occurred in the immediate vicinity of roads, mostly affecting road embankments and that a large number of landslides initiated within natural and semi-natural areas and hit the road network and, locally, affected houses and activities. Field surveys also revealed diffuse residual risk conditions, being a large proportion of landslides located in the immediate vicinity of infrastructures. Besides reporting the spatial distribution of landslides triggered by an extreme rainfall event, the data collected on landslides can be used to make comparisons with the distribution of landslides in the past, validation of landslide susceptibility models, definition of the general interaction between landslides and structures/infrastructures.

Earth observation data are helpful in analyzing how climate-related variables affect geomorphological processes. This work aims at evaluating the influence of rainfall on slow-moving landslides, by means of a quantitative procedure for (i) identifying clusters of pixels measuring satellite-based surface displacements indicating landslide activity, (ii) comparing them with rainfall series, and (iii) applying statistical tests to assess their relationships at the regional scale. The methodology was developed within the framework of the OT4CLIMA project (Development of Innovative Earth Observation Technologies for the Study of Climate Change and Its Impacts on the Environment), funded by the Italian Ministry of Education, University and Research. The project aimed to develop advanced Earth observation technologies and methodologies for improving the capabilities to understand the effects of climate change at regional and sub-regional scale.The procedure presented here was applied in the Basento river basin, within the Basilicata region, southern Italy. The Basento catchment, with an area of 1535 km2 and a NW-SE trend, falls within the domain of the Apennine chain in the western part, and within the Bradanic Trough in the eastern part. The stratigraphic and structural setting of the basin plays a significant role in determining landslide occurrence and distribution.Rainfall series were gathered from rain gauges (http://www.centrofunzionalebasilicata.it/it/) and analyzed to evaluate the presence of temporal trends. Ground displacements were obtained by applying the P-SBAS (Parallel Small BAseline Subset) technique to three datasets of Sentinel-1 images: T146 ascending orbit, and T51 and T124 descending orbits, for the period 2015–2020. The displacement series of the pixels located in areas mapped as landslides by the Italian Landslide Inventory (IFFI database, https://idrogeo.isprambiente.it/app/) and sited within rain gauge influence regions (defined as 10 km circular buffers) were studied.Two slow-moving landslides were selected and investigated in detail. The average displacement series of the landslides were analyzed and compared to the rainfall series to search for relations, by employing statistical and non-parametric tests. More in detail, the Kendall rank correlation coefficient and the Maximal Information Coefficient were adopted in the analysis. Significant results were obtained for the T124 descending orbit for both landslides, for a 3-day cumulative rainfall and a 7-day delay of the slope response. Given the procedure’s replicability it can be applied to study areas with different physiographic and climatic features. Other applications might involve satellite- or radar-based rainfall estimates.

Gully erosion is one of the most extreme land degradation processes that exhibit spatial and temporal variation depending on topography, soil, climate, and land use and management characteristics. This study investigated the impact of changes in rainfall, land use/land cover (LULC), and land use management (LUM) practices on gully erosion in two midland watersheds (treated Kecha and untreated Laguna) in the Upper Blue Nile basin of Ethiopia by using the LANDPLANER model in combination with intensive field measurements and remote sensing products. We simulated gully erosion under past (in 2005), present (in 2021), and three potential future curve number conditions, each time under four rainfall scenarios (10, 30, 60, and 100 mm) using the dynamic erosion index (e), static topographic (esp), and erosion channel (esp_channel) thresholds. Density plot analyses showed that gullies frequently occur in low-lying gentle slope areas with relatively higher curve number values. The best predictions of gullies identified through true positive rates (TPR) and true negative rates (TNR) were achieved considering the static esp_channel > 1 for Kecha (TPR = 0.667 and TNR = 0.544) and the dynamic e > 0.1 for 60 mm of rainfall in Laguna (TPR = 0.769 and TNR = 0.516). Despite the 10 mm rainfall having negligible erosion-triggering potential in both watersheds, the 60 and 100 mm rainfall scenarios were 4–5 and 10–17 times, respectively, higher than the 30 mm rainfall scenario. While the LULC change in the untreated Laguna watershed increased the impact of rainfall on gully initiation by only 0–2% between 2005 and 2021, the combination of LULC and LUM significantly reduced the impact of rainfall in the treated Kecha watershed by 64–79%. Similarly, the gully initiation area in Kecha was reduced by 28% (from 33% in 2005 to 5% in 2021) due to changes in LULC and LUM practices, whereas Laguna showed little increment by only 1% (from 42% in 2005 to 43% in 2021) due to LULC change. In addition, the future predicted alternative land use planning options showed that gully initiation areas in Laguna could be reduced by 1% with only LULC conversion; 39% when only LUM practices were implemented; and 37% when both were combined. These results indicate that LUM practices outweigh the impact of LULC on gully erosion in the studied paired watersheds. Overall, LANDPLANER successfully simulated the spatio-temporal variation of gully erosion with scenario-based analyses and hence can be used to predict gullies in the study area and other data-scarce regions with similar agro-ecological settings.