###### Abstract  
Owing to the combination of technological progress in Unmanned Aerial Vehicles (UAVs) and recent advances in photogrammetry processing with the development of the Structure-from-Motion (SfM) approach, UAV photogrammetry enables the rapid acquisition of high resolution topographic data at low cost. This method is particularly widely used for geomorphological surveys of linear coastal landforms. However, linear surveys are generally pointed out as problematic cases because of geometric distortions creating a \"bowl effect\" in the computed Digital Elevation Model (DEM). Secondly, the survey of linear coastal landforms is associated with peculiar constraints for Ground Control Points (GCPs) measurements and for the spatial distribution of the tie points. This article aims to assess the extent of the bowl effects affecting the DEM generated above a linear beach with a restricted distribution of GCPs, using different acquisition scenarios and different processing procedures, both with PhotoScan(r) software tool and MicMac(r) software tool. It appears that, with a poor distribution of the GCPs, a flight scenario that favors viewing angles diversity can limit DEM's bowl effect. Moreover, the quality of the resulting DEM also depends on the good match between the flight plan strategy and the software tool via the choice of a relevant camera distortion model. U  
A 2018 21 December 2018 23  
As small Unmanned Aerial Vehicles (UAVs) allow the rapid acquisition of high resolution (<5 cm) topographic data at low cost, they are now widely used for geomorphological surveys [2; 3; 4; 5]. The use of UAVs for civil or research purposes notably increases with the development of Structure-from-Motion (SfM) algorithms [6]. In comparison with classic digital photogrammetry, the SfM workflow allows more automation and is therefore more straightforward for users [7; 8; 9]. The status of SfM photogrammetry among other topographic survey techniques is fully described in reference [10]. A literature review addressing performance assessment of methods and software solutions for georeferenced point clouds or DEMs production is proposed in reference [11]. As reported in reference [7], SfM photogrammetry methods offer the opportunity to extract high resolution and accurate spatial data at very low-cost using consumer grade digital cameras that can be embedded on small UAVs. Nevertheless, several articles highlight the fact that the reconstructed results may be affected by systematic broad-scale errors restricting their use [1; 12; 13; 14]. These reconstruction artefacts may be sometimes difficult to detect if no validation dataset is available or if the user is not aware that such artefacts may appear. It is therefore helpful to propose some practical guidance to limit such geometric distortions. Possible error sources of SfM photogrammetry are thoroughly reviewed in reference [1], distinguishing local errors due to surface quality or lighting conditions and more systematic errors due to referencing and image network geometry. In particular, image acquisition along a linear axis is a critical configuration [9; 15; 16]. As reported in references [13; 15], inappropriate modelling of lens distortion results in systematic deformations. Weak network geometries, common in linear surveys, result in errors when recovering distortion parameters [13]. But changing the distortion model can reduce the sensitivity to distortion parameters [15], limiting \"doming\" deformation or the \"bowl effect\". They suggest two strategies to correct this drift: to density GCPs distribution (which is not always possible depending on field configurations or implies additional field work), or to improve the estimation of exterior orientation of each image. Use of an adequate spatial distribution of Ground Control Points (GCPs) can limit these effects [17]. One common method for GCPs consists in putting targets whose position is measured by DGPS.