Abstract. For decades researchers have used the Micro Erosion Meter and it successor the Traversing Micro Erosion Meter to measure microscale rates of vertical erosion (downwearing) on rock shore platforms. Difficulties with upscaling of microscale field data in order to explain long term platform evolution have led to calls to introduce other methods which allow measurement of platform erosion at different scales. Structure from Motion Photogrammetry is fast emerging as a reliable, cost-effective tool for geomorphic change detection, providing a valuable means for detecting micro to meso-scale geomorphic change over different terrain types. Here we present the results of an experiment where we test the efficacy of Structure from Motion Photogrammetry for measuring change on rock shore platforms due to different erosion processes (sweeping abrasion, scratching and percussion). Key to this approach is the development of the Coordinate Reference System used to reference and scale the models, and which can be easily deployed in the field. Experiments were carried out on three simulated platform surfaces with low to high relative rugosity to assess the influence of surface roughness. We find that a Structure from Motion Photogrammetry can be used to reliably detect micro (sub mm) and meso (cm) scale erosion on shore platforms with a low Rugosity Index. As topographic complexity increases, the scale of detection is reduced. We also provide a detailed comparison of the two methods across a range of categories including cost, data collection, analysis and output. We find that Structure from Motion offers several advantages over the Micro Erosion Meter, most notably the ability to detect and measure erosion of shore platforms at different scales.

Stage 1: All images are loaded and reviewed in Photoscan. Data with image quality value higher than 0.5 were selected (Agisoft, 2016). The coded GCPs were detected automatically before starting batch processing of images.Local coordinates for each GCP target was entered into Photoscan. The measurement accuracy was adjusted to 0.01 mm for marker accuracy and scale bar accuracy in reference settings for experiments. This value was 0.5 mm for control target in the field. Unwanted scenes in the background or foreground of images (e.g. sky and plants) can lead to incorrect point matching. Photoscan offers a solution to mask out unwanted areas. While this can be a time-consuming process, it can be minimised with careful image acquisition.Stage 2: Images alignment and optimisation: The program matches the common point in images and determines the camera position for each photo. A 3D sparse point cloud is generated using a least square solution (Thoeni et al., 2014; Agisoft, 2016). The error between measured GCP coordinates and estimated GCP coordinates is determined through the least squares solution (Thoeni et al., 2014). Photos were aligned with the highest alignment accuracy, generic pair pre-selection and the default per-image key and tie point limits. To generate the most accurate 3D model, it is crucial to overcome systematic dishing and doming distortions in the SfM model by correcting lens distortions (Carbonneau and Dietrich, 2016). SfM workflow performs a self-calibration using Exchangeable Image File Format (EXIF) metadata from digital images. Each image is treated as unique during the self-calibration process. The GCP errors were minimised using the camera calibration parameters to refine any distortion in the model during optimisation. PhotoScan adjusts estimated point coordinates and camera parameters, during optimisation, thus reducing the sum of reprojection error and reference coordinate misalignment error (Agisoft, 2016). Although a 3D sparse point cloud is not required for DEM generation, it is required for dense point cloud reconstruction.Stage 3: Dense Point cloud generation: The dense point cloud is built, using estimated camera positions from sparse point cloud generated during image alignment process. A range of quality options are available, and we selected "High". This decision was based on the time required to achieve the required quality for our work.Choosing "Ultra high" can result in higher point density but increases processing time. We used "Mild" depth filtering as we wanted to reconstruct smaller breakdown features (Agisoft, 2016). The dense point cloud was used to generate the DEM.

We have generated sub-millimetre-resolution DEMs of weathered rock surfaces using SfM photogrammetry techniques. We apply a close-range method based on structure-from-motion (SfM) photogrammetry in the field and use it to generate high-resolution topographic data for weathered boulders and bedrock. The method was pilot tested on extensively weathered Triassic Moenkopi sandstone outcrops near Meteor Crater in Arizona. Images were taken in the field using a consumer-grade DSLR camera and were processed in commercially available software to build dense point clouds. The point clouds were registered to a local 3-D coordinate system (x, y, z), which was developed using a specially designed triangle-coded control target and then exported as digital elevation models (DEMs). The accuracy of the DEMs was validated under controlled experimental conditions. A number of checkpoints were used to calculate errors. We also evaluated the effects of image and camera parameters on the accuracy of our DEMs. We report a horizontal error of 0.5 mm and vertical error of 0.3 mm in our experiments. Our approach provides a low-cost method for obtaining very high-resolution topographic data on weathered rock surfaces (area < 10 m 2). The results from our case study confirm the efficacy of the method at this scale and show that the data acquisition equipment is sufficiently robust and portable. This is particularly important for field conditions in remote locations or steep terrain where portable and efficient methods are required.