## 1 Introduction  
Landslides, defined as \"movements of a mass of rock, earth or debris down a slope\" [1], are one of the most disastrous natural phenomena from both an economic and a social point of view. As mentioned by [PERSON] et al. [2], the occurrence of these events has been increasing over time due to climate change and human impact. For this reason, the scientific and political community recognises the need to study these phenomena in order to implement possible prevention and mitigation plans. The destructive capacity of mass movement events is a function of several parameters that are difficult to evaluate, such as the volume, the velocity, and hence, the energy of the landslide body. The latter represents an essential quantity for the landslide characterisation; indeed, the velocity is a parameter widely used in the scientific literature for the evaluation of landslide intensity [3; 4; 5; 6; 7; 8; 9] and is, consequently, a key requirement for landslide risk assessment. However, mass movement velocity is difficult, and often impossible, to measure, when referring to extremely rapid movements, such as debris flows, shallow landslides, and rockfalls; only a few examples can be found in the literature [10; 11]. Nevertheless, in the last twenty years, techniques that use images acquired by ground-based synthetic aperture radar (GB-SAR) sensors have been developed in order to monitor the kinematics of individual slopes also affected by fast-moving landslides [12]. This is an expensive technique that needs to be integrated with additional monitoring tools, and it is, therefore,mainly used for civil protection purposes [12]. Furthermore, GB-SAR, as well as other in situ instruments (e.g., total station), can be used to estimate slope deformation velocities only before the collapse of the landslides. For this reason, on a local scale, mass movement velocity is often estimated by applying numerical models that require a large amount of medium- and high-resolution data [13]. For example, for the study of shallow landslides, the SPH [14] model is widely used, while for rockfalls, RockFall Analyst [15] is often applied. Nevertheless, there are some models employed for both types of landslides (e.g., DAN-W [16], GPP [17]). Additionally to numerical models and monitoring, there are also other ways to estimate fast-moving landslide velocity on-field in the aftermath of an event, for instance, by the super-elevation of the flow, as discussed in [PERSON] et al. [18]. For slow-moving landslides, several monitoring instruments (e.g., inclinometers, ground-based radar, GPS, and total stations) installed in situ allow for the measurement of the velocity at a slope scale [19]. Nevertheless, as stated by [PERSON] et al. [20], for spatial planning and management purposes, it is necessary to obtain the components of landslide risk (i.e., intensity, vulnerability, exposure, hazard) at a regional scale [21]. Methodologies that provide these parameters at a large scale involve the expensive and time-consuming use of spatially homogeneous data that are distributed over wide areas and are often not available [22]. Satellite radar interferometry techniques are consolidated methodologies [23; 24; 25] to estimate mass movement velocity with a relatively low cost and easy application, allowing for the analysis of ground surface deformations both at a detailed scale and over large areas. Although an advantageous method for spatial planning purposes, this instrumentation does not allow for the velocity of fast-moving landslides to be detected [26]. To overcome the aforementioned issue, this study aims to introduce a method for assessing the velocity of shallow landslides and rockfalls at a regional scale--a spatialisation of the Heim Energy Line method [27]. The proposed methodology is a morphometric approach, whose application implies the knowledge several input data spatialised on the area of interest. Therefore, the two types of movement analysed in this study are shallow landslides and rockfalls.