The identification and mapping of landslides are usually performed after intense meteorological events that can activate or reactivate several gravitational phenomena. The identification and mapping of landslides can be organized into landslide event maps. Landslide event mapping is a well-known activity obtained through field surveys (Yoon et al., 2012; Santangelo et al., 2010), visual interpretation of aerial or satellite images (Brardinoni et al., 2003; Ardizzone et al., 2013) and combined analysis of lidar DTM and images (Van Den Eeckhaut et al., 2007; Haneberg et al., 2008; Giordan et al., 2013; Razak et al., 2013; Niculiţa, 2016). The use of RPASs for the identification and mapping of a landslide has been described by several authors (Niethammer et al., 2009, 2010, 2011; Rau et al., 2011; Carvajal et al., 2011; Travelletti et al., 2012; Torrero et al., 2015; Casagli et al., 2017). Niethammer et al. (2009) and Liu et al. (2015) showed how RPASs could be considered a good solution for the acquisition of ultra-high-resolution images with low-cost systems. Fiorucci et al. (2018) compared the results of the landslide limitations mapped using different techniques and found that satellite images can be considered a good solution for the identification and mapping of landslides over large areas. On the contrary, if the target of the study is the definition of the landslide's morphological features, the use of more detailed RPAS images seemed to be the better solution. As suggested by Walter et al. (2009) and Huang et al. (2017a), one of the most critical elements for correct georeferencing of acquired images is the use of GCPs. The in situ installation and positioning acquisition of GCPs can be an important challenge, in particular in dangerous areas such as active landslides. Very often, GCPs are not installed in the most active part of the slide but on stable areas. This solution can be safer for the operator, but it can also reduce the accuracy of the final reconstruction.

Another parameter that can be considered during the planning of the acquisition phase is the morphology of the studied area. According to with Giordan et al. (2015b), slope materials and gradient can affect the flight planning and the approach used for the acquisition of the RPAS images. Two possible scenarios can be identified: (i) steep to vertical areas (> 40∘) and (ii) slopes with gentle-to-moderate slopes (< 40∘). In the first case, the use of multi-copters with oblique acquisitions is often the best solution. On the contrary, with more gentle slopes, the use of fixed-wing systems can assure the acquisition of larger areas.

The second possible field of application of RPASs is the use of multi-temporal acquisitions for landslide monitoring. This topic has been described by several authors (Dewitte et al., 2008; Turner and Lucieer, 2013; Travelletti et al., 2012; Lucieer et al. 2014a; Turner et al., 2015; Marek et al., 2015; Lindner et al., 2016; Peppa et al., 2017). In these works, numerous techniques based on the multi-temporal comparison of RPAS data sets for the definition of the evolution of landslides have been presented and discussed. Niethammer et al. (2010, 2012) described how the position change of geomorphological features (in particular fissures) could be considered for a multi-temporal analysis with the aim of the characterization of the landslide evolution. Travelletti et al. (2012) introduced the possibility of a semi-automatic image correlation to improve this approach. The use of image correlation techniques has also been described by Lucieer et al. (2014a), who demonstrated that COSI-Corr (Co-registration of Optically Sensed Imaged and Correlation – Leprince et al., 2007, 2008; Ayoub et al., 2009) can be adopted for the definition of the surface movement of the studied landslide. A possible alternative solution is a multi-temporal analysis of the use of DSMs. The comparison of digital surface models can be used for the definition of volumetric changes caused by the evolution of the studied landslide. The acquisition of these digital models can be done with terrestrial laser scanners (Baldo et al., 2009) or airborne lidar (Giordan et al., 2013). Westoby et al. (2012) emphasized the advantages of RPASs concerning terrestrial laser scanners, which can suffer from line-of-sight issues, and airborne lidar, which are often cost-prohibitive for individual landslide studies. Turner et al. (2015) stressed the importance of a good co-registration of multi-temporal DSMs for good results that could decrease in accuracy. The use of benchmarks in areas not affected by morphological changes can be used for a correct calibration of rotational and translation parameters.

Landslide susceptibility and hazard assessment are often performed at basin scale (Guzzetti et al., 2005) using different remote-sensing techniques (Van Westen et al., 2008). The use of RPASs can be considered for single case study applications to help decision makers in the identification of landslide damage and the definition of residual risk (Giordan et al., 2015a). Saroglou et al. (2018) presented the use of RPASs for the definition of trajectories of rockfall-prone areas. Salvini et al. (2017, 2018) and Török et al. (2018) described the combined use of TLS and RPASs for hazard assessment of steep rock walls. All these papers considered the use of RPASs as a valid solution for the acquisition of DSM over sub-vertical areas. Török et al. (2018) and Tannant et al. (2017) also described in their papers how RPAS DSMs can be used for the evaluation of slope stability using numerical modelling. Fan et al. (2017) analysed the geometrical features and provided the disaster assessment of a landslide that occurred on 24 June 2017 in the village of Xinmo in Maoxian County (Sichuan province, south-west China). Aerial images were acquired the day after the event from a UAV (fixed-wing UAV, with a weight less than 10 kg, and flight autonomy up to 4 h), and a DEM was processed, with the purpose to analysed the main landslide geometrical features (front, rear edge elevation, accumulation area, horizontal sliding distance).

Risk assessment of flood inundation before the occurrence of a flood is crucial for the mitigation of the flood disaster damages. RPAS is capable of providing a quick and detailed analysis of the land surface information including topography, land cover and land use data, which are often incorporated into hydrological models for estimating floods (Costa et al., 2016). As a pre-flood assessment, Li et al. (2012) explored the area around an earthquake-derived barrier lake using an integrated approach of remote sensing with RPASs for hydrological analysis of the potential dam-break flood. They proposed a technical framework for real-time evacuation planning by accurately identifying the source water area of the dammed lake using an RPAS, followed by along-river hydrological computations of inundation potential. Tokarczyk et al. (2015) showed that the RPAS-derived imagery is useful for rainfall-run-off modelling for the risk assessment of floods by mapping detailed land-use information. As key input data, high-resolution imperviousness maps were generated for urban areas from RPAS imagery, which improved hydrological modelling for the flood assessment. Zazo et al. (2015) and Şerban et al. (2016) demonstrated hydrological calculations of potentially flood-prone areas using RPAS-derived 3-D models. They utilized 2-D cross profiles derived from the 3-D model for hydrological modelling.

Monitoring of the ongoing flood is potentially important for real-time evacuation planning. Le Coz et al. (2016) mentioned that videos captured by an RPAS, which can be operated not only by research specialists but also by general non-specialists, are potentially useful for quantitatively monitoring floods as well as estimating flow velocity and modelling floods. They can also contribute to the crowd-sourced data collection for flood hydrology and citizen science. In the case of flood monitoring by image-based photogrammetry, however, areas under water are often problematic because the bed is not often fully seen in aerial images. If the water is clear enough, bed images under water can be captured, and the bed morphology can be measured with additional corrections of refraction (Tamminga et al., 2015; Woodget et al., 2015), but the floodwater is often unclear because of the abundant suspended sediment and disruptive flow current. Another option is the fusion of different data sets using a sonar-based measurement for the water-covered area, which is registered with the terrestrial data sets (Flener et al., 2013; Javernick et al., 2014). Image-based topographic data of bottom water taken by an unmanned underwater vehicle (UUV, also known as an autonomous underwater vehicle, AUV) can also be another option (e.g. Pyo et al., 2015), although the application of UUV to flooding has been limited.

As well as the use of topographic data sets derived from Structure-from-Motion – Multi-View Stereo (SfM–MVS) photogrammetry, the use of orthorectified images concurrently derived from the RPAS-based aerial images is advantageous for the assessment of hydrological observation and modelling of floods. Witek et al. (2014) developed an experimental system to monitor the streamflow in real time to predict overbank flood inundation. The real-time prediction results are also visualized online with a web map service with a high-resolution image (3 cm px-1). Feng et al. (2015) reported that the accurate identification of inundated areas is feasible using RPAS-derived images. In their case, deep-learning approaches of image classification using optical images and textures by RPASs successfully extracted the inundated areas, which must be useful for flood monitoring. Erdelj et al. (2017) proposed a system that incorporates multiple RPAS devices with wireless sensor networks to perform real-time assessment of a flood disaster. They discussed the technical strategies for real-time flood disaster management including the detection, localization, segmentation and size evaluation of flooded areas from RPAS-derived aerial images.

Post-flood assessments of the land surface materials including topography, sediment and vegetation are more feasible through RPAS surveys (Izumida et al., 2017). Smith et al. (2014) proposed a methodological framework for the immediate assessment of flood magnitude and affected landforms by SfM-MVS photogrammetry using both aerial and ground-based photographs. In this case, it is recommended to carefully select appropriate platforms for SfM-MVS photogrammetry (either airborne or ground based) based on the field conditions. Tamminga et al. (2015) examined the 3-D changes in river morphology due to an extreme flood event, revealing that the changes in reach-scale channel patterns of erosion and deposition are poorly modelled by the 2-D hydrodynamics based on the initial condition before the flood. They also demonstrate that the topographic condition can be more stable after an extreme flood event. Langhammer et al. (2017) proposed a method to quantitatively evaluate the grain size distribution using optical images taken by an RPAS, which is applied to the sediment structure before and after a flash flood.

In a relatively long-term study, Dunford et al. (2009) and Hervouet et al. (2011) explored annual landscape changes after the flood using RPAS-derived images together with other data sets such as satellite image archives or a manned motor paraglider. Their work assessed the progressive development of vegetation on a braided channel at an annual scale, which appears to be controlled by local climate including rainfall, humidity and air temperature, hydrology, groundwater level, topography and seed availability. Changes in the sediment characteristics due to flooding is another key feature to be examined.

The fast deployment in the field, the ease of use and the capability to provide real-time high-resolution information of inaccessible areas to prioritize the operator's activities are the strongest features of RPASs (Boccardo et al., 2015). The use of RPASs for rescue operations started almost a decade ago (Bendea et al., 2008) but their massive adoption began only in the last few years (earthquake in Nepal 2015) thanks to the development of low-cost and easy-to-use platforms. Initiatives such as UAViators (http://uaviators.org/, last access: 6 March 2018) have further increased public awareness and acceptance of this kind of instrument. Several rescue departments have now introduced RPASs as part of the conventional equipment of their teams (Xie et al., 2014). The huge number of videos acquired by RPASs and posted by rescuers online (i.e. on YouTube) after the 2016 Italian earthquakes confirm this general trend.

The operators use RPASs to fly over the area of interest and get information through visual assessment of the streaming videos. The quality of this analysis is therefore limited to the ability of the operator to fly the RPAS over the area of interest. The lack of video georeferencing usually reduces the interpretability of the scene and the accurate localization of the collapsed parts: only small regions can be acquired in a single flight. The lack of georeferenced maps prevents the smooth sharing of collected information with other rescue teams, limiting the practical exploitation of these instruments. RPASs are mainly used in daylight conditions as night-time flights are extremely dangeous, and the use of thermal images is of limited help to the rescuers.

Many researchers have developed algorithms to automatically extract damage information from imagery (Fig. 7). The main focus of these works is to reliably detect damage in a reduced time to satisfy the time constraints of the rescuers. In Vetrivel et al. (2015) the combined use of images and photogrammetric point clouds have shown promising results thanks to a supervised approach. This work, however, highlighted how the classifier and the designed 2-D and 3-D features were hardly transferable to different data sets: each scene needed to be trained independently, strongly limiting the efficiency of this approach. In this regard, the recent developments in machine learning (i.e. convolutional neural networks, CNN) have overcome these limitations (Vetrivel et al., 2017), showing how they can correctly classify scenes even if they were trained using other data sets: a trained classifier can be directly used by rescuers on the acquired images without need for further operations. The drawback of these techniques is the computational time: the use of CNN processing such as image segmentation or point cloud generation is computationally demanding and hardly compatible with real-time needs (Brostow et al., 2008). In this regard, most recent solutions exploit only images (i.e. no need to generate point cloud) and limit the use of most expensive processes to the regions where faster classification approaches provide uncertain results to deliver almost real-time information (Duarte et al., 2017).

The damage evidence that can be captured from a UAV is not sufficient to infer the actual damage state of the building as it requires additional information such as damage to internal building elements (e.g. columns and beams) that cannot be directly defined from the images. Even though this information is limited, the images can provide useful information about the external condition of the structure, evidencing anomalies and damages and providing a first important piece of information for structural engineers. Two main types of investigation can be performed: (i) the use of images for the detection of cracks or damages on the external surfaces of the building (i.e. walls and roofs) and (ii) the use of point clouds (generated by photogrammetric approach) to detect structural anomalies such as tilted or deformed surfaces. In both cases, the automated processing can only support and ease the work of the expert, who still interprets and assesses the structural integrity of the building.

In Fernandez-Galarreta et al. (2015) a comprehensive analysis of both point clouds and images was presented to support the ambiguous classification of damages and their use for damage score. In this paper, the use of point clouds was considered efficient for more serious damages (partial or complete collapse of the building), while images were used to identify smaller damages such as cracks that can be used as the basis for the structural engineering analysis. The use of point clouds is investigated in Baiocchi et al. (2013) and Dominici et al. (2017): this contribution highlights how point clouds from UAVs can provide very useful information to detect asymmetries and small deformations of the structure.

A long-distance flight of an RPAS enables quick and safe measurements of an emerging volcanic island. Tobita et al. (2014a) successfully performed a fixed-wing RPAS one-way flight for a distance of 130 km and a total flight time of 2 h and 51 min over the sea to capture aerial images of a newly formed volcanic island next to Nishinoshima Island (Ogasawara Islands, south-west Pacific). They performed SfM-MVS photogrammetry of the aerial images taken from the RPAS to generate a 2.5 m resolution DEM of the island. The team also performed two successive measurements of Nishinoshima Island in the following 104 days, revealing that the morphological changes in the new island cover a 1600 m by 1400 m area (Nakano et al., 2014; Tobita et al., 2014b).

Since the volcanic activities often last for a long period, it is also important to connect the recent volcanic morphological changes to those in the past. Although detailed morphological data of volcanic topography are often unavailable, historical aerial photographs taken in the past decades can be utilized to generate topographic models at a certain resolution. Some case studies have used archival aerial photographs in volcanoes for periods of more than 60 years, generating DEMs with resolutions of several metres for areas of 10 km2 (Gomez, 2014; Derrien et al., 2015; Gomez et al. 2015). Although these DEMs are coarser than those derived from RPASs, they can be used as supportive data sets for modern morphological monitoring using RPASs at a higher resolution and measurement frequency.

Caltabiano et al. (2005) proposed the architecture of an RPAS for the direct monitoring of gas composition in volcanic clouds from Mt Etna in Italy. In this system, the 2 m wide fixed-wing RPASs can fly autonomously up to 4000 m altitude with a speed of 40 km h-1. Like this system, an RPAS with a payload of several kilograms can carry multiple sensors to monitor different compositions of volcanic gas. McGonigle et al. (2008) used an RPAS for volcanic gas measurements at the La Fossa crater of Mt Vulcano in Italy. The RPASs has a 3 kg payload and can host an ultraviolet spectrometer, an infrared spectrometer and an electrochemical sensor on board. The combination of these sensors enabled the estimation of fluxes of SO2 and CO2, which are crucial for revealing the geochemical condition of erupting volcanoes. The monitoring of gas composition including CO2, SO2, H2S and H2, as well as air temperature, can be used for the quantification of the degassing activities and prediction of the conduit magma convection, as suggested by the tests at several volcanoes in Japan (Shinohara, 2013; Mori et al., 2016) and in Costa Rica (Diaz et al., 2015).

An RPAS can also transport a small ground-running robot (unmanned ground vehicle, UGV) to the slope head of an active volcano, where the UGV takes close-range photographs of volcanic ash on the ground surface by running down the slope (Nagatani et al., 2013). Protocols for direct sampling of volcanic products using an RPAS have also been developed (Yajima et al., 2014).

In New Zealand, Harvey et al. (2016) and Nishar et al. (2016) carried out experimental studies on the regular monitoring of intense geothermal environments using a small RPAS. They used thermal images taken by an infrared imaging sensor together with normal RGB images for photogrammetry, mapping both the ground surface temperature with detailed topography and land cover data. Chio and Lin (2017) further assessed the use of an RPAS equipped with a thermal infrared sensor for the high-resolution geothermal image mapping in a volcanic area in Taiwan. They improved the measurement accuracies using an on-board sensor capable of post-processed kinematic GNSS positioning. This allows accurate mapping with fewer ground control points, which are hard to place on such intense geothermal fields.