Gully erosion is one of the main processes of soil degradation, representing 50%–90% of total erosion at basin scales. Thus, its precise characterization has received growing attention in recent years. Geomatics techniques, mainly photogrammetry and LiDAR, can support the quantitative analysis of gully development. This paper deals with the application of these techniques using aerial photographs and airborne LiDAR data available from public database servers to identify and quantify gully erosion through a long period (1980–2016) in an area of 7.5 km2 in olive groves. Several historical flights (1980, 1996, 2001, 2005, 2009, 2011, 2013 and 2016) were aligned in a common coordinate reference system with the LiDAR point cloud, and then, digital surface models (DSMs) and orthophotographs were obtained. Next, the analysis of the DSM of differences (DoDs) allowed the identification of gullies, the calculation of the affected areas as well as the estimation of height differences and volumes between models. These analyses result in an average depletion of 0.50 m and volume loss of 85000 m3 in the gully area, with some periods (2009–2011 and 2011–2013) showing rates of 10,000–20,000 m3/year (20–40 t/ha*year). The manual edition of DSMs in order to obtain digital elevation models (DTMs) in a detailed sector has facilitated an analysis of the influence of this operation on the erosion calculations, finding that it is not significant except in gully areas with a very steep shape.

In the present work, the case of the Cármenes del Mar resort (Granada, Spain) is shown. It can be considered one of the most extreme examples on the Mediterranean coast of severe pathologies associated with urban development on coastal landslides. The resort, with 416 dwellings, was partially built on a deepseated landslide which affects a soft formation composed of dark graphite schists. In November 2015, the City Council officially declared a state of emergency in the resort and 24 dwellings have already been evacuated. We have used two remote sensing techniques to monitor the landslide with the aim of identifying and measuring a wide range of displacements rates (from mm/year to m/year): (1) PSInSAR, exploiting 25 ENVISAT SAR images acquired from May 2003 to December 2009, and (2) photogrammetry, considering the output from two Unmanned Aerial Vehicle (UAV) flights made in June 2015 and January 2016 and the outdated photos from a conventional flight in 2008. The relationship between the geology of the site, data from PS deformation measurements, building displacements, rainfall and damage observed and their temporal occurrence allows a better understanding of the landslide kinematics and both the spatial and temporal evolution of the instability. Results indicate building displacements of up to 1.92 m in 8 years, a clear lithological control in the spatial distribution of damage and a close relationship between the most damaging events and water recharge episodes (rainy events and leaks from swimming pools and the water supply network). This work emphasises the need to incorporate geohazards into urban planning, including policies to predict, prepare for and prevent this type of phenomenon.

This study describes a new approach to Remotely Piloted Aerial Systems (RPAS) photogrammetric mission flight planning. In this context, we have identified different issues appearing in complex scenes or difficulties caused by the project requirements in order to establish those functions or tools useful for resolving them. This approach includes the improvement of some common photogrammetric flight operations and the proposal of new flight schemas for some scenarios and practical cases. Some examples of these specific schemas are the combined flight (which includes characteristics of a classical block flight and a corridor flight in only one mission) and a polygon extrusion mode to be used for buildings and vertical objects, according to the International Committee of Architectural Photogrammetry (CIPA) recommendations. In all cases, it is very important to allow a detailed control of the flight and image parameters, such as the ground sample distance (GSD) variation, scale, footprints, coverage, and overlaps, according to the Digital Elevation Models (DEMs) available for the area. In addition, the application could be useful for quality control of other flights (or flight planning). All these new functions and improvements have been implemented in a software developed in order to make RPAS photogrammetric mission planning easier. The inclusion of new flight typologies supposes a novelty with respect to other available applications. The application has been tested using several cases including different types of flights. The results obtained in the quality parameters of flights (coverage and GSD variation) have demonstrated the viability of our new approach in supporting other photogrammetric procedures.

This paper presents a methodology for measuring road surface deformation due to terrain instability processes. The methodology is based on ultra-high resolution images acquired from unmanned aerial vehicles (UAVs). Flights are georeferenced by means of Structure from Motion (SfM) techniques. Dense point clouds, obtained using the multiple-view stereo (MVS) approach, are used to generate digital surface models (DSM) and high resolution orthophotographs (0.02 m GSD). The methodology has been applied to an unstable area located in La Guardia (Jaen, Southern Spain), where an active landslide was identified. This landslide affected some roads and accesses to a highway at the landslide foot. The detailed road deformation was monitored between 2012 and 2015 by means of eleven UAV flights of ultrahigh resolution covering an area of about 260 m × 90 m. The accuracy of the analysis has been established in 0.02 ± 0.01 m in XY and 0.04 ± 0.02 m in Z. Large deformations in the order of two meters were registered in the total period analyzed that resulted in maximum average rates of 0.62 m/month in the unstable area. Some boundary conditions were considered because of the low required flying height (<50 m above ground level) in order to achieve a suitable image GSD, the fast landslide dynamic, continuous maintenance works on the affected roads and dramatic seasonal vegetation changes throughout the monitoring period. Finally, we have analyzed the relation of displacements to rainfalls in the area, finding a significant correlation between the two variables, as well as two different reactivation episodes.

This paper deals with the use of aerial photogrammetry and LiDAR techniques to analyze landslide activity over a long time span—just over 32 years. The data correspond to several aerial surveys (1984, 1996, 2001, 2005, 2009, 2010, 2011, 2013 and 2016) covering an area of about 50 km2 along highway A-44, near Jaén (Southern Spain). An ad hoc combined photogrammetric and LiDAR aerial survey of 2010 was established as the reference flight. This flight was processed by means of direct orientation methods and iterative adjustments between both data sets. Meanwhile, historical flights available in public geographical data servers were oriented by transferring ground control points from the reference flight. Then, digital surface models (DSMs) and orthophotographs were generated, as well as the corresponding differential models (DoDs), which, after the application of filters and taking into account the estimated uncertainty of ± 1 m, allowed us to identify true changes on the ground surface. This analysis, complemented by photointerpretation, led us to obtain a landslide multitemporal inventory in the study area that was analyzed in order to characterize the landslide type, morphology and activity. Three basic typologies were identified: rock falls–collapses, slides and flows. These types present different morphometric properties (area, perimeter and height interval) and are associated with different conditions (height, slope, orientation and lithology). Moreover, a set of monitoring areas, common for the different flights, was also used to analyze the activity throughout the study period. Thus, some more active periods were identified (2009–2010, 2010–2011, 2011–2013 and 1996–2001) among other less active ones (1984–1996, 2001–2005, 2005–2009 and 2013–2016), which are related to rainy events and dry years, respectively.