Badlands are typical erosional landforms of the Apennines (Northern Italy) that form on Plio-Pleistocene clayey bedrock and rapidly evolve. The present study aimed at identification and assessment of the areal and temporal changes of badlands within a pilot area of the Modena Province (Emilia Apennines), where no previous detailed investigation has been carried out. For this purpose, a diachronic investigation was carried out to map the drainage basin and the drainage networks of the linear erosion features in the study area during the last 40 years, and to evaluate changes in badlands drainage basins morphometry and surface, land use and pluviometry. The investigation carried out indicated a general stabilisation trend of the badlands in the study area. In fact, a reduction in the bare surface area from 6187.1 m² in 1973 to 4214.1 m² in 2014 (31%), due to an intensified revegetation process around the badland areas, has been recorded. This trend, in line with the results of research carried out in other sector of the Northern Apennines, is mainly due to intensive land use changes, mostly the increase in forest cover and the reduction of agricultural land, that occurred in the study area from the 1970s onwards.

This paper presents a geomorphological map of the seafloor north east of the Maltese Islands (Central Mediterranean) at a scale of 1:50,000. The map was compiled following the integration, analysis and interpretation of several high-resolution seafloor bathymetry datasets. Several structural features, coastal and marine landforms and anthropogenic features have been mapped. Most of the mapped submerged landforms – including fluvial, gravity-induced and karst landforms – appear to have been formed during the sea-level lowstand of the last glacial cycle. The map provides valuable insights on the submerged landscape of the Maltese Islands and its evolution since the Last Glacial Maximum.

The relationships between hillslope and fluvial processes were studied in a mountainous area of the Northern Apennines (Italy) where intermittent landslide activity has interacted for a long time with river morphodynamics. The aim of the study was to analyse such relationships in two study sites of the Scoltenna catchment. The sites were analysed in detail and monitored through time. A long-term analysis was carried out based on multitemporal photointerpretation of aerial photos. Slope morphological changes and land use modifications since 1954 were detected and compared with the evolution of the channel morphology. A short-term analysis was also performed based on two monitoring campaigns accomplished in 2021 and 2022 in order to detect possible slope displacements and channel-bed-level changes. The techniques used are global navigation satellite systems and drone photogrammetry accompanied by geomorphological surveys and mapping. The multitemporal data collected allowed us to characterise slope surface deformations and quantify morphological changes. The combination of various techniques of remote and proximal sensing proved to be a useful tool for the analysis of the surface deformations and for the investigation of the interaction between slope and fluvial dynamics, showing the important role of fluvial processes in the remobilisation of the landslide toe causing the displacement of a significant volume of sediment into the stream.

In mountain environments, landslide activity can be assessed through a combination of remote and proximal sensing techniques performed at different scales. The complementarity of methods and the synergistic use of data can be crucial for landslide recognition and monitoring. This paper explored the potential of Multi-Temporal Differential Synthetic Aperture Radar Interferometry (MT-DInSAR) to detect and monitor slope deformations at the basin scale in a catchment area of the Northern Apennines (Italy) and verified the consistency between the landslide classification by the Inventory of Landslide Phenomena in Italy (IFFI) and displacements from the SAR data. In this research, C- and X-band SAR were considered to provide insights into the performances and suitability of sensors operating at different frequencies. This study provides clues about the state of activity of slow-moving landslides and critically assessed its contribution to the IFFI inventory update. Moreover, it demonstrated the benefits of the synergistic use of SAR and geomorphological analysis to investigate slope dynamics in clayey terrains by exemplifying the approach for a relevant case study, the Gaiato landslide. Notwithstanding the widespread use of MT-DInSAR for landslide kinematics investigations, the main limiting factors are discussed along with the expected improvements related to the upcoming new generations of L-band SAR satellites.

In mountain environments, slope and fluvial dynamics often interact, and their relationship can be investigated through an integrated methodological approach. Landslides are a source of supplying sediments into riverbeds and can interact or interrupt the water course. Water courses can trigger or re-activate slope movements. The complexity of investigating the interaction between the two dynamics needs a complementarity of methods and techniques, combining remote and proximal sensing, geotechnical in situ surveys, and repositories and catalogue datasets. This leads to a synergistic use of all the heterogeneous data from different fields and formats. The present paper provides a literature review on the approaches and surveying procedures adopted in the investigation of slope and fluvial dynamics and highlights the need to improve the integrated management of geospatial information complemented by quality information. In this regard, we outline a geodatabase structure capable of handling the variety of geoscientific data available at different spatial and temporal scales, with derived products that are useful in integrated monitoring tasks. Indeed, the future adoption of a shared physical structure would allow the merging and synergistic use of data provided by different surveyors as well as the effective storing and sharing of datasets from a monitoring perspective.