Structure-from-motion Multi-view Stereo Research Articles

All-around 3D plant modeling system using multiple images and its composition.

In this study, we developed an all-around 3D plant modeling system that operates using images and is capable of measuring plants non-destructively without any contact. During the fabrication of this device, we selected a method capable of performing 3D model reconstruction from multiple images. We then developed an improved SfM-MVS (Structure from Motion / Multi-View-Stereo) method that enables 3D reconstruction by simply capturing images with a camera. The resulting image-based method offers a high degree of freedom because the hardware and software can comprise commercially available products, and it permits the use of one or more cameras according to the shape and size of the plant. The advantages of the image-based method are that 3D reconstruction can be conducted at any time as long as the images are already taken, and that the desired locations can be observed, measured, and analyzed from 2D images and a 3D point cloud. The device we developed is capable of 3D measurements and modeling of plants from a few millimeters to 2.4 m of height using this method. This article explains this device, the principles of its composition, and the accuracy of the models obtained from it.

Developing a deep learning-based layer-3 solution for thermal infrared large-scale photovoltaic module inspection from orthorectified big UAV imagery data

The increasing adoption of photovoltaic(PV) technology highlights the need for efficient and large-scale deployment-ready inspection solutions. In the thermal infrared imagery-based inspection framework, we develop a robust and versatile deep learning model for the classification of defect-related patterns on PV modules. The model is developed from big UAV imagery data, and designed as a layer-3 building block that can be implemented on top of any two-stage PV inspection workflow comprising: (1)An aerial Structure from Motion– MultiView Stereo (SfM-MVS) photogrammetric acquisition/processing stage, at which a georeferenced thermal orthomosaic of an inspected PV site is generated, and which enables to locate precisely defective modules on field; then (2)an instance segmentation stage that extracts the images of modules. Orthomosaics from 28 different PV sites were produced, comprising 93220 modules with various types, layouts and thermal patterns. Modules were extracted through a developed semi-automatic workflow, then labeled into six classes. Data augmentation and balancing techniques were used to prepare a highly representative and balanced deep learning-ready dataset. The dataset was used to train, cross-validate and test the developed classifier, as well as benchmarking with the VGG16 architecture. The developed model achieves the state-of-art performance and versatility on the addressed classification problem, with a mean F1-score of94.52%. The proposed three-layer solution resolves the issues of conventional imagery-based workflows. It ensures highly accurate and versatile defect detection, and can be efficiently deployed to real-world large-scale applications.

Assessment of the trueness and precision of smartphone photogrammetry for rock joint roughness measurement

The roughness of rock joint surfaces plays a vital role in the hydrological and mechanical properties of discontinuous rock masses. Applicable and reliable measurements are important for investigating the roughness characterization of rock joints. This research comprehensively evaluates the performance of smartphone photogrammetry for rock joints through a roughness characterization method. Five laboratory-sized rock joint specimens were selected and operated by 24 experimenters to assess the trueness and precision of smartphone photogrammetry based on Structure from Motion- Multi View Stereo (SfM-MVS) photogrammetry technique. Benchmark data from a structure light scanner were used to assess the usability of smartphone photogrammetry through the directional roughness metric method. The results show that it can be applied to achieve acceptable trueness and precision of joint roughness measurements for engraving rock and replica rock joints with no reflective or translucent materials. In addition, the roughness errors of the subsampled models with point intervals of 0.25 mm, 0.5 mm, and 1.0 mm are close to each other.

Application of SfM-MVS for mining geology: Capture set-up and automated processing using the Dugald River Zn-Pb-Ag mine as a case study

The use of structure-from-motion, multi-view stereo (SfM-MVS) in the mining industry is well-established for capturing digital data on surface. However, the application of SfM-MVS in active underground mining has received less attention as there are unique challenges that need to be overcome, especially if a procedure is to be applied daily in active mining. Unique challenges include preparation time, camera positioning, illumination and useability. The faces of active development drives are generally only available for a short period before prepared and blasted, thus information is lost if not mapped immediately. Furthermore, due to safety concerns, unsupported faces cannot be approached for physical mapping. SfM-MVS allows these faces to be viewed and mapped in the form of a virtual outcrop, thus, allowing for remote mapping of underground development, which at the Dugald River mine, located in Queensland, Australia, allowed for mapping continuity as staff worked remotely during COVID-19. This contribution describes how to set-up a capture in an underground mine to produce high-quality SfM-MVS 3D reconstructions of development faces. The methodology can be readily incorporated into a standard operating procedure. While the procedure can be used with most photogrammetry software packages that utilise SfM-MVS algorithms, it is best utilised using the provided Python script and Agisoft Metashape Professional v1.6. The script allows for automation of capture processing, which can free up several hours per day compared to user-interacted processing.

Introduction of All-Around 3D Modeling Methods for Investigation of Plants

Digital image phenotyping has become popular in plant research. Plants are complex in shape, and occlusion can often occur. Three-dimensional (3D) data are expected to measure the morphological traits of plants with higher accuracy. Plants have organs with flat and/or narrow shapes and similar component structures are repeated. Therefore, it is difficult to construct an accurate 3D model by applying methods developed for industrial materials and architecture. Here, we review noncontact and all-around 3D modeling and configuration of camera systems to measure the morphological traits of plants in terms of system composition, accuracy, cost, and usability. Typical noncontact 3D measurement methods can be roughly classified into active and passive methods. We describe their advantages and disadvantages. Structure-from-motion/multi-view stereo (SfM/MVS), a passive method, is the most frequently used measurement method for plants. It is described in terms of “forward intersection” and “backward resection.” We recently developed a novel SfM/MVS approach by mixing the forward and backward methods, and we provide a brief overview of our approach in this paper. While various fields are adopting 3D model construction, nonexpert users struggle to use them and end up selecting inadequate methods, which lead to model failure. We hope that this review will help users who are considering starting to construct and measure 3D models.

Structure-from-Motion on shallow reefs and beaches: potential and limitations of consumer-grade drones to reconstruct topography and bathymetry

Reconstructing the topography of shallow underwater environments using Structure-from-Motion—Multi View Stereo (SfM-MVS) techniques applied to aerial imagery from Unmanned Aerial Vehicles (UAVs) is challenging, as it involves nonlinear distortions caused by water refraction. This study presents an experiment with aerial photographs collected with a consumer-grade UAV on the shallow-water reef of Fuvahmulah, the Maldives. Under conditions of rising tide, we surveyed the same portion of the reef in ten successive flights. For each flight, we used SfM-MVS to reconstruct the Digital Elevation Model (DEM) of the reef and used the flight at low tide (where the reef is almost entirely dry) to compare the performance of DEM reconstruction under increasing water levels. Our results show that differences with the reference DEM increase with increasing depth, but are substantially larger if no underwater ground control points are taken into account in the processing. Correcting our imagery with algorithms that account for refraction did not improve the overall accuracy of reconstruction. We conclude that reconstructing shallow-water reefs (less than 1 m depth) with consumer-grade UAVs and SfM-MVS is possible, but its precision is limited and strongly correlated with water depth. In our case, the best results are achieved when ground control points were placed underwater and no refraction correction is used.

Multispectral aerial imagery-based 3D digitisation, segmentation and annotation of large scale urban areas of significant cultural value

• Urban areas 3D digitisation using multispectral aerial imagery • Experimental low-cost multispectral camera for drone development • Supervised machine learning methods for 3D mesh segmentation using multispectral data • Performance evaluation of the proposed methods on a large scale case study • Web-based visualisation and annotation tool for segmentation fine-tuning Disaster risk management of movable and immovable cultural heritage is a highly significant research topic. In this work, we present a pipeline for 3D digitisation, segmentation and annotation of large scale urban areas in order to produce data that can be exploited in disaster management simulators (e.g fire spreading, crowd movement, firefighting training, evacuation planning, etc.). We have selected the old town of Xanthi (Greece) as a challenging case study. We developed a custom multispectral camera to be carried by a commercial drone. Using the structure from motion / multiview stereo (SFM/MVS) approach, we produced a 3D model of the urban area covering 0.5 k m 2 that is followed by a multilayer texture map which carries information from visible and near-infrared regions of the electromagnetic spectrum. We developed a set of machine learning approaches based on logistic regression, support vector machines and artificial neural networks that allow 3D model segmentation by exploiting not only morphological and structural features but also the multispectral behaviour of different material surfaces. We objectively evaluate the performance of the proposed segmentation approaches on six significant material-based classes (cobbled-roads granite kilns, building walls, ceramic roof-tiles, low-vegetation, high-vegetation and metal surfaces) that are used in simulating fire propagation and crowd movement. The experiments revealed that the segmentation accuracy can be enhanced by taking into consideration surface material multispectral properties as well as morphological features. A Web-based multi-user annotation tool complements our proposed pipeline by enabling further 3D model segmentation, fine tuning and semantics annotation (e.g. usage-based building classification and evacuation priorities, escape paths and gathering points).

Operability of Point Cloud Data in an Architectural Heritage Information Model

ABSTRACT 3D point cloud data from data acquisition techniques such as terrestrial photogrammetry, close range photogrammetry (CRP) and light detection and ranging (LiDAR) play a major role in heritage because of the large amount of information they provide. This data includes the metric accuracy of the objects surface, reflectivity information and materials texture. The point clouds can be exported to Building Information Models (BIM) to simplify the 3D modelling processes, thus becoming part of the restoration and reconstruction project. Currently, most scientific works in the field implement insertion procedures of point clouds into BIM, whose interoperability should be analysed in order to reveal its usefulness in heritage buildings modelling. This paper reviews the scientific literature on the integration of Terrestrial Laser Scanning (TLS) and Structure-from-Motion/Multi-View Stereo (SfM/MVS) data into HBIM. Despite having been investigated, this field lacks a methodological structure regarding the integration of point cloud data into HBIM.

SfM‐MVS Photogrammetry for Splash Erosion Monitoring under Natural Rainfall

AbstractAn understanding of splash erosion is the basis to describe the impact of rain characteristics on soil disturbance. In typical splash cup experiments, splashed soil is collected, filtered, and weighed. As a way to collect additional data, our experiments have been supplemented by a photogrammetric approach. A total of three soils were tested across three sites, one in the Czech Republic and two in Austria, all equipped with rain gauges and disdrometers to measure rainfall parameters. The structure from motion multiview stereo (SfM‐MVS) photogrammetric method was used to measure the raindrops impact on the soil surface. The images were processed using Agisoft PhotoScan, resulting in orthophotos and digital elevation models (DEMs) with a resolution of 0.1 mm/pix. The surface statistics included the mean surface height (whose standard deviation was used as a measure of surface roughness), slope, and other parameters. These parameters were evaluated depending on soil texture and rainfall parameters. The results show a linear correlation between consolidation and splash erosion with a coefficient of determination (R2) of approximately 0.65 for all three soils. When comparing the change in soil volume with rainfall parameters, the best correlation was found with the maximum 30‐minintensity (I30), resulting in R2 values of 0.48 (soil A, silt loam, 26% clay), 0.59 (soil B, silt loam, 18% clay), and 0.68 (soil C, loamy sand, 12% clay). The initial increase in the sample volume for the lowest splashed mass corresponds with the increase in the clay content of each of the soils. Soil A swells the most. Soil B swells less. Soil C does not swell at all and consolidates the most. We derived the relationship between the photogrammetrically measured change in surface height and the splash erosion (measured by weight) by accounting for the effect of the clay content.

DIGITAL 3D MODELS OF MINERALS AND ROCKS IN A NUTSHELL: ENHANCING SCIENTIFIC, LEARNING, AND CULTURAL HERITAGE ENVIRONMENTS IN GEOSCIENCES BY USING CROSS-POLARIZED LIGHT PHOTOGRAMMETRY

Three-dimensional (3D) minerals and rocks in the form of interactive, engaging, and immersive experiences are of paramount importance to the geoscience community, researchers, students, and philomaths. Moreover, the SARS-CoV-2 (COVID-19) crisis affecting our society in the spring of 2020 highlighted the importance of 3D material in geoscience education — compared to 2D images, the three-dimensional models provide a better way to learn and to recognize different minerals and rocks, properties, textures, etc. This paper seeks to provide a comprehensive method to create an interactive scientific, learning, and cultural heritage environment in the field of Geosciences. In this paper, we overcome most of the Structure-from-Motion - Multi-View Stereo (SfM-MVS) photogrammetry limitations, where samples with a transparent, translucent, or glossy surface are a real challenge for the feature detection algorithms of the SfM workflow. Correct lighting setup, the usage of cross-polarized light photogrammetry workflow, anti-reflection coating spray and post-processing steps are the essential ingredients for an enhanced photogrammetric study. The main output of this research consists of a comprehensive virtual 3D collection of minerals and rocks which are available online via the Sketchfab repository of the Museum of Mineralogy and Petrography “Grigore Cobălcescu” (https://sketchfab.com/MineralogyPetrographyMuseum).

A revised orientation-based correction method for SfM-MVS point clouds of outcrops using ground control planes with marks

Structure-from-Motion/Multiview Stereo (SfM-MVS) point cloud has the potential to display objects and to generate corresponding digital elevation model (DEM) and orthomosaic, from which geometric parameters such as the orientations, shapes, and sizes can be derived, thus making it a promising tool for structural geology. High-precision coordinates of ground control points (GCP) are commonly used for SfM-MVS point cloud correction, but the coordinate measurement depends on expensive and heavy equipment, and more importantly, there is uncertainty of the Global Position System (GPS) signal in some environments. A potential alternative correction method is to use orientations from outcrops, although this method still requires further optimization. This study aims to improve this orientation-based method using ground control planes (GCPL) and test its effectiveness through geometric analysis and simulation experiments, and the improved schemes are as follows: using a single pair of nonparallel planes, using a single horizontal plane with linear orientation marks, and using a single inclined plane with a strike mark. The GCPL method requires a few orientations, and its accuracy is sufficient for fieldwork. It greatly simplifies the field investigation and improves the applicability of SfM–MVS in environments with unavailable GPS signals or with rigid body rotations for GPS imprecision.

Constraining coseismic earthquake slip using Structure from Motion from fault scarp mapping (East Helanshan Fault, China)

This study uses fault scarps to investigate past earthquakes and the potential seismic hazard of the East Helanshan Fault (EHF). The EHF is a Holocene active normal fault located at the complex transition from compression in the northeastern corner of the Tibetan Plateau to apparently east-west extension in the west margin of Ordos Block, as implied by the Yinchuan Faulted Basin and its boundary normal faults. Repeated normal faulting events, including the 1739 CE event on the EHF, offset alluvial fans of Late Pleistocene and Holocene age and formed remarkable fault scarps, especially along its Suyukou and Hongguozi segments. We selected three fault sections in the Suyukou and Hongguozi segments, where fault scarps of different heights are well preserved on different terrace surfaces. By using Structure-from-Motion-Multiview Stereo (SfM-MVS) techniques on Unmanned Aerial System (UAS) imagery, we acquired high-quality topography of the selected fault sections. Detailed mapping and surveying of the offset geomorphic features and their associated fault scarps, with distinct free faces and multiple slope breaks, suggest that surface-rupturing earthquakes occurred repeatedly along the EHF. Densely spaced cross scarp topographic profiles were extracted to evaluate the vertical slip distributions using newly acquired SfM-derived topography. Through statistical analysis of the displacement dataset, we constrain multiple earthquake events and their cumulative offsets on the Suyukou and Hongguozi segments. Large earthquakes on the EHF produced different characteristic co-seismic slips on the Suyukou segment and the Hongguozi segment of ~2.6 m and ~1.7 m, respectively. The scaling relationships between the moment magnitude, surface rupture length, and the average co-seismic slip indicate possible magnitudes within a range of Mw 6.8–7.2, and a potential magnitude of approximately Mw 7.4 as an upper bound in the case of a multi-fault rupture along the EHF. This study demonstrates the UAS-based SfM photogrammetry as an effective means for mapping fault zone topography in areas of sparse vegetation, as well as its potential for identifying and measuring subtle geomorphic features associated with past earthquakes.

Suitability Study of Structure-from-Motion for the Digitisation of Architectural (Heritage) Spaces to Apply Divergent Photograph Collection

The digitisation of architectural heritage has experienced a great development of low-cost and high-definition data capture technologies, thus enabling the accurate and effective modelling of complex heritage assets. Accordingly, research has identified the best methods to survey historic buildings, but the suitability of Structure-from-Motion/Multi-view-Stereo (SfM/MVS) for interior square symmetrical architectural spaces is unexplored. In contrast to the traditional SfM surveying for which the camera surrounds the object, the photograph collection approach is divergent in courtyards. This paper evaluates the accuracy of SfM point clouds against Terrestrial Laser Scanning (TLS) for these large architectural spaces with a symmetrical configuration, with the main courtyard of Casa de Pilatos in Seville, Spain, as a case study. Two different SfM surveys were conducted: (1) Without control points, and (2) referenced using a total station. The first survey yielded unacceptable results: A standard deviation of 0.0576 m was achieved in the northwest sector of the case study, mainly because of the difficulty of aligning the SfM and TLS data due to the way they are produced. This value could be admissible depending on the purpose of the photogrammetric model.

Combining UAV-Based SfM-MVS Photogrammetry with Conventional Monitoring to Set Environmental Flows: Modifying Dam Flushing Flows to Improve Alpine Stream Habitat

Setting environmental flows downstream of hydropower dams is widely recognized as important, particularly in Alpine regions. However, the required flows are strongly influenced by the effects of the physical environment of the downstream river. Here, we show how unmanned aerial vehicle (UAV)-based structure-from-motion multiview stereo (SfM-MVS) photogrammetry allows for incorporation of such effects through determination of spatially distributed patterns of key physical parameters (e.g., bed shear stress, bed grain size) and how they condition available stream habitat. This is illustrated for a dam-impacted Alpine stream, testing whether modification of the dam’s annual flushing flow could achieve the desired downstream environmental improvement. In detail, we found that (1) flood peaks in the pilot study were larger than needed, (2) only a single flood peak was necessary, (3) sediment coarsening was likely being impacted by supply from nonregulated tributaries, often overlooked, and (4) a lower-magnitude but longer-duration rinsing flow after flushing is valuable for the system. These findings were enabled by the spatially rich geospatial datasets produced by UAV-based SfM-MVS photogrammetry. Both modeling of river erosion and deposition and river habitat may be revolutionized by these developments in remote sensing. However, it is combination with more traditional and temporarily rich monitoring that allows their full potential to be realized.

A photogrammetry-based approach for soil bulk density measurements with an emphasis on applications to cosmogenic nuclide analysis

Abstract. The quantification of soil bulk density (ρB) is a cumbersome and time-consuming task when traditional soil density sampling techniques are applied. However, it can be important for terrestrial cosmogenic nuclide (TCN) production rate scaling when deriving ages or surface process rates from buried samples, in particular when short-lived TCNs such as in situ 14C are applied. Here, we show that soil density determinations can be made using structure-from-motion multi-view stereo (SfM-MVS) photogrammetry-based volume reconstructions of sampling pits. Accuracy and precision tests as found in the literature and as conducted in this study clearly indicate that photographs taken from both a consumer-grade digital single-lens mirrorless (DSLM) and a smartphone camera are of sufficient quality to produce accurate and precise modelling results, i.e. to regularly reproduce the “true” volume and/or density by >95 %. This finding holds also if a freeware-based computing workflow is applied. The technique has been used to measure ρB along three small-scale (<1 km) N–S transects located in the semi-arid to arid Altos de Talinay, northern central Chile (∼30.5∘ S, ∼71.7∘ W), during a TCN sampling campaign. Here, long-term differences in microclimatic conditions between south-facing and north-facing slopes (SFSs and NFSs, respectively) explain a sharp contrast in vegetation cover, slope gradient and general soil condition patterns. These contrasts are also reflected by the soil density data, generally coinciding with lower densities on SFSs. The largest differences between NFSs and SFSs are evident in the lower portion of the respective slopes, close to the thalwegs. In general, field-state soil bulk densities were found to vary by about 0.6 g cm−3 over a few tens of metres along the same slope. As such, the dataset that was mainly generated to derive more accurate TCN-based process rates and ages can be used to characterise the present-day condition of soils in the study area, which in turn can give insight into the long-term soil formation and prevailing environmental conditions. This implies that the method tested in this study may also being applied in other fields of research and work, such as soil science, agriculture or the construction sector.

Photogrammetric 3D Model via Smartphone GNSS Sensor: Workflow, Error Estimate, and Best Practices

Geotagged smartphone photos can be employed to build digital terrain models using structure from motion-multiview stereo (SfM-MVS) photogrammetry. Accelerometer, magnetometer, and gyroscope sensors integrated within consumer-grade smartphones can be used to record the orientation of images, which can be combined with location information provided by inbuilt global navigation satellite system (GNSS) sensors to geo-register the SfM-MVS model. The accuracy of these sensors is, however, highly variable. In this work, we use a 200 m-wide natural rocky cliff as a test case to evaluate the impact of consumer-grade smartphone GNSS sensor accuracy on the registration of SfM-MVS models. We built a high-resolution 3D model of the cliff, using an unmanned aerial vehicle (UAV) for image acquisition and ground control points (GCPs) located using a differential GNSS survey for georeferencing. This 3D model provides the benchmark against which terrestrial SfM-MVS photogrammetry models, built using smartphone images and registered using built-in accelerometer/gyroscope and GNSS sensors, are compared. Results show that satisfactory post-processing registrations of the smartphone models can be attained, requiring: (1) wide acquisition areas (scaling with GNSS error) and (2) the progressive removal of misaligned images, via an iterative process of model building and error estimation.

PLAUSIBLE RECONSTRUCTION OF AN APPROXIMATED MESH MODEL FOR NEXT-BEST VIEW PLANNING OF SFM-MVS

Abstract. Structure-from-Motion (SfM) and Multi-View Stereo (MVS) are widely used methods in three dimensional (3D) model reconstruction for an infrastructure maintenance purpose. However, if a set of images is not captured from well-placed positions, the final dense model can contain low-quality regions. Since MVS requires a much longer processing time than SfM as larger amounts of images are provided, it is impossible for surveyors to wait for the SfM–MVS process to complete and evaluate the geometric quality of a final dense model on-site. This challenge results in response inefficiency and the deterioration of dense models in 3D model reconstruction. If the quality of the final dense model can be predicted immediately after SfM, it will be possible to revalidate the images much earlier and to obtain the dense model with better quality than the existing SfM–MVS process. Therefore, we propose a method for reconstructing a more plausible 3D mesh model that accurately approximates the geometry of the final dense model only from sparse point clouds generated from SfM. This approximated mesh model can be generated using Delaunay triangulation for the sparse point clouds and triangle as well as tetrahedron filtering. The approximated model can be used to predict the geometric quality of the final dense model and for an optimization-based view planning. Some experimental results showed that our method is effective in predicting the quality of the final dense model and finding the potentially degraded regions. Moreover, it was confirmed that the average reconstruction errors of the dense model generated by the optimization-based view planning went below tens of millimeters and falls within an acceptable range for an infrastructure maintenance purpose.

Dune migration and volume change from airborne LiDAR, terrestrial LiDAR and Structure from Motion-Multi View Stereo

Sand dunes are commonly regarded as a challenge to traditional photogrammetry due their homogeneous texture and spectral response. In this work we present an evaluation of Structure from Motion-Multi View Stereo (SfM-MVS) to obtain high-resolution elevation data of coastal sand dunes based on images acquired by Remotely Piloted Aircraft (RPA). A Digital Elevation Model (DEM) of a dunefield in Southern Brazil was generated from 810 photos captured by an RPA at 100 m above the takeoff point in February 2019. Image matching was successful in all areas of the survey due the presence of superficial features (footprints and sandboard tracks) and visibility of the sedimentary stratification, highlighted by heavy minerals. Altimetric accuracy of the SfM-MVS DEM was validated by comparison with Terrestrial LiDAR (TLS) data collected during the same fieldwork campaign of the RPA flights. The SfM-MVS DEM was then compared to an Airborne LiDAR (ALS) DEM from October 2010. While the SfM-MVS and TLS DEMs are very similar, without any major difference in elevation or in the reconstruction of topographic features, the SfM-MVS DEM presents a small scale surface roughness not visible in the TLS DEM. The Feature Preserving DEM Smoothing (FPD) algorithm was applied to the SfM-MVS DEM with good results in terms of surface smoothing, but without any significant changes in descriptive statistics and error metrics, with an RMSE of 0.08 m and MAE of 0.06 m for both the original and the FPD-filtered DEM. Displacement of dune crest lines from the ALS and SfM-MVS DEMs resulted in a migration rate of ≈5 m/year between 2010 and 2019, in good agreement with rates derived from satellite images and historical aerial photographs of the same area. Sand volume change in the same period showed a decrease of only 0.2%, which can be related to the installation of sand fences to promote dune stabilization and sand removal from the front of the dune field to keep a road open to vehicles. ALS can cover large areas in little time but its high cost still remains a barrier to wider usage, especially by researchers in developing countries. TLS has an intermediate cost but demands more fieldwork and more processing time. In our case we needed three days for the TLS survey and around three weeks to produce a DEM of ≈80 400m2. On the other hand, we were able to cover ≈740 900m2 with six flight missions in under three hours, with ≈13 h processing time in a medium-range workstation. This makes SfM-MVS a low-cost solution with fast and reliable results for 3D modelling and continuous monitoring of coastal dunes.

Where did the soil go? Quantifying one year of soil erosion on a steep tile-drained agricultural field

Distributed measurements of agricultural erosion at the farm-scale are needed to evaluate both the on and off-site impacts of sediment redistribution. While best management practices have been shown to reduce surface erosion rates and improve water quality, their farm-scale effects can be difficult to quantify. In this study we use imagery from an unmanned aerial vehicle (UAV) and structure-from-motion multi-view stereo (SfM-MVS) to quantify erosion rates and their effects on crop yield across a 15.9-ha agricultural field. Our results highlight that the installation of catch basins were able to stop 159.52 t of sediment and associated nutrients from entering the waterway adjacent to the study site over the course of one year, corresponding to an erosion rate of 18.83 t ha−1 yr−1 across six study basins. Poor soil structure resulting from downslope tillage reduced crop yields on topographic shoulders of the study site, while accelerated water erosion processes were responsible for large areas of washout that caused highly variable crop growth in footslopes. The highest crop yields were associated with backslopes and topographically flat regions of the field which experienced minimal erosion. Change-detection results showed that UAV imagery was able to reliably quantify depositional plumes and was comparable to that of a terrestrial laser scanner (TLS) using a ± 0.04 m confidence interval.

Refining the spatial and temporal signatures of creep and co-seismic slip along the southern San Andreas Fault using very high resolution UAS imagery and SfM-derived topography, Coachella Valley, California

The current interseismic period for an event on the Coachella Valley segment of the San Andreas Fault (SAF) is ~300 years, much greater than the best-estimated average recurrence interval of ~180 years, prompting questions regarding earthquake recurrence and fault behavior. Digital Surface Models (DSMs) generated using Structure-from-Motion-Multiview Stereo (SfM-MVS) techniques on unmanned aerial systems (UAS) imagery and very-high resolution UAS orthomosaics were used to identify and measure fault-related offsets of small geomorphic features and develop a model of late Holocene slip-per-event for the southern SAF (sSAF) in the Coachella Valley. We aim to better constrain slip estimates from recent earthquakes along the sSAF and combine new slip data with ages of past earthquakes from paleoseismic studies to understand earthquake recurrence. Slip-per-event for paleoearthquakes, which ruptured this segment of the sSAF, and the relationship between co-seismic slip events, creep or afterslip, and triggered slip were evaluated. Examination of offsets suggests that five (5) large, surface rupturing events produced average displacements of 2.6–3.1 m per event. Gaussian distribution analysis revealed small-scale offset clusters that we attribute to climate-modulated channel incision across the fault and subsequent creep. These offset clusters support a creep rate of ~3 mm/yr in the Mecca Hills averaged over the past ~160 years.