Landslide database and susceptibility map updating of a test area in the Serchio River basin (northern Tuscany, Italy)

Agostini, Margherita; Galanti, Yuri; Seppia, N. Del; Avanzi, G. D’Amato; Giannecchini, Roberto

doi:10.3301/rol.2019.34

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“…For the study area, Agostini et al (2019) derived a geomorphological inventory (as defined by Guzzetti et al, 2012) reporting the landslides, gully erosion phenomena (Figure 1c), and other geomorphological features such as detritical‐alluvial fans, classified according to the guidelines of the hydrogeological setting plan realized by the Serchio River Basin Authority (Autorità di Bacino Pilota del Fiume Serchio, 2014b). The inventory was obtained by means of visual interpretation of the 2000 and 2006 stereoscopic aerial images (1:10 000 and 1:7500 scales, respectively) and of the hillshades at 1 m resolution derived from two LiDAR surveys completed in 2006 and 2008 (hereafter denoted as visual analyses, or VAs), aided by one field survey carried out in 2017 (hereafter denoted as FS).…”

Section: Study Areamentioning

confidence: 99%

“…(b) Base map from Google Earth, captured in 2017. (c) The geomorphological inventory realized in the study area portraying gully and landslide occurrence up to May 2017: (1) gully erosion phenomena; (2) landslide event inventory (July 2014, FS + VA); (3) geomorphological inventory (pre‐June 2017, FS + VA) (Agostini et al, 2019). (d) The gully erosion phenomena triggered by the December 2017 rainfall event.…”

Section: Study Areamentioning

confidence: 99%

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Modelling seasonal variation of gully erosion at the catchment scale

Agostini

Mondini

Torri

et al. 2021

Earth Surf Processes Landf

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Geo‐hydrological phenomena, including gullies, contribute significantly to soil erosion and land degradation. To address this, proper management of basins and hillslopes should consider the mechanism, timing, and location of gully development and how gullies interact with other hillslope processes. Yet, conventional modelling techniques for such processes are rare, frequently being limited to applications of only single processes and typically requiring high‐resolution input data. Further, existing tools for characterizing basins and hillslopes tend to be based on static descriptions of geo‐environmental conditions, and thus are not effective for modelling changes such as the seasonal triggering conditions of gully phenomena over time. This study proposes a method to integrate open remote sensing data (Sentinel‐2) and an existing modelling tool (LANDPLANER) using simplified input data to better predict and forecast gullies’ spatial and temporal occurrence. The study investigates the seasonal conditions responsible for the triggering of gullies at the catchment scale using different erosion modelling schema in the Toscana region of Central Italy. Geomorphological gully inventory data were collected and used as benchmarks to test the proposed approach. The results show that the occurrence of gully erosion in the studied region changes seasonally, and the proposed method was able to effectively discriminate spatial and temporal differences of the gully phenomena. The proposed method can be applied to similar regions worldwide, allowing for the investigation of gully erosion over time, even in places with limited data availability.

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Section: Study Areamentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%