Low-budget topographic surveying comes of age: Structure from motion photogrammetry in geography and the geosciences

This is the author accepted manuscript. The final version is available from SAGE Publicarions via the DOI in this record

Optimizing UAV surveys for coastal morphodynamics: estimation of spatial uncertainty as a function of flight acquisition and post-processing factors

Recent developments in unmanned aerial vehicles (UAVs) and photogrammetry software enable the rapid collection of aerial photography and video over study areas of varying sizes, thereby providing ease of use and accessibility for studies of coastal geomorphology. However, there remains uncertainty over UAV survey techniques, with disagreement on specific flight patterns, flight altitudes, photograph amounts, ground control point (GCP) amounts, GCP spacing schemes, drone models, and which SfM software to use, amongst other study-specific parameters. A controlled field test (of 1.2 hectares) was performed to determine SfM’s sensitivity to the following flight parameters: altitude (60 m, 80 m, 120 m), photo overlap (70%, 75%, 80%), drone model (DJI Phantom quadcopter, Sensefly eBee RTK fixed-wing), SfM software (PhotoScan, Pix4D), number of GCPs (4-34), and GCP spacing scheme (even, random). Through comparisons of the root mean squared error (RMSE) relative to the GCPs, altitude affected error significantly (\u3e1 cm RMSE difference between 60 m and 120 m) while photo overlap was the least significant parameter (only 4 mm RMSE difference between 70% and 80% overlap). Different drone models, along with varying photogrammetry software, affected RMSE significantly (\u3e3 cm RMSE differences). Surprisingly, GCP spacing schemes were insignificant to error sensitivity (differences). The most efficient survey parameters were six GCPs per hectare of land surveyed, 80 m flight altitudes, and 70% photo overlap. This study can be immediately referenced in future studies for its insight on conducting efficient and low-error UAV surveys

Determining the Effect of Mission Design and Point Cloud Filtering on the Quality and Accuracy of SfM Photogrammetric Products Derived from sUAS Imagery

This research investigates the influence that various flight plan and mission design strategies for collecting small unmanned aerial system (sUAS) imagery have on the accuracy of the resulting three-dimensional models to find an optimal method to achieve a result. This research also explores the effect that using gradual selection to reduce the sparse point cloud has on product accuracy and processing details. Imagery was collected in the spring of 2018 during leaf-off conditions at six field sites along the North Fork of the White River. The aerial imagery was collected using a DJI Phantom Pro 4 sUAS. Four different image acquisition missions were flown at each of the sites. Each of the base mission imagery sets were processed individually and in various combinations. The commercial Structure-from-Motion (SfM) photogrammetry software known as Agisoft PhotoScan was used to process the data and generate the Digital Elevation Models (DEMs) and orthophotos. Due to the high number of processing iterations required in this research, a script was developed to automate the point cloud filtering gradual selection process. Profile views were used to assess the differences between each mission design and to visualize systematic errors. In this investigation, the imagery set which consistently performed with high relative accuracy and low relative processing times was the NS Oblique imagery set utilizing automated gradual selection. Imagery sets created by combining two or more of the base mission photosets generally produced results with accuracy levels similar to or worse than the results of the NS Oblique imagery set and the other base mission imagery sets. Results produced with and without gradual selection were similar in most cases, however, gradual selection reduced dense cloud processing time by an average of 37%

The forensic utility of photogrammetry in surface scene documentation

In current forensic practice, there are few standards for outdoor crime scene documentation, despite the need for such documentation to be accurate and precise in order to preserve evidence. A potential solution to this is the implementation of image-based photogrammetry. Applied Structure from Motion (SfM) reconstructs models through image point comparisons. A 3D model is produced from a reference photoset that captures a 360-degree view of the subject and the software employs triangulation to match specific points, datums, across individual photos. The datums are arranged into a point-cloud that is then transformed into the final model. Modifying the point-cloud into a final product requires algorithms that adjust the points by building a textured mesh from them. One of the disadvantages of SfM is that the point-cloud can be “noisy,” meaning that the program is unable to distinguish the features of one datum from another due to similarities, creating coverage gaps within the meshed images. To compensate for this, the software can smooth portions of the model in a best-guess process during meshing. As commercial software does not disclose the adjustment algorithms, this documentation technique, while very useful in other disciplines that regularly apply SfM such as archaeology, would fail to meet the standards of the Daubert and Kumho criteria in a forensic setting. A potential solution to this problem is to use open-source software, which discloses the adjustment algorithms to the user. It was hypothesized that the output of open-sourced software solutions would as accurate as the models produced with commercial software and with total station mapping techniques. To evaluate this hypothesis, a series of mock outdoor crime scenes were documented using SfM and traditional mapping techniques. The scenes included larger surface scatter and small surface scatter scenes. The large surface scatter scenes contained a dispersed set of plastic human remains, and various objects that might reasonably be associated with a crime scene. Ten of these scenes were laid out in 10 x 10 m units in a New England forested environment, each grid with a slightly different composition, and then documented using an electronic total station, data logger and digital camera. The small surface scatter scenes consisted of a pig mandible placed in different environments across two days of data collection. The resulting models were built using PhotoScan by AgiSoft, the commercial software, and MicMac for Mac OSX as the open-source comparison software. Accuracy is only part of the concern however; the full utility of any one of the workflows is defined additionally by the overall cost-effectiveness (affordability and accessibility) and the visual quality of the final model. Accuracy was measured by the amount of variance in fixed-datum measurements that remained consistent across scenes, whereas visual quality of the photogrammetric models were determined by cloud comparison histograms, which allows for comparison of models between software types and across different days of data collection. Histograms were generated using CloudCompare. Not all models that were rendered were useable—90% of large surface scatter models and 87.5% of small surface scatter models were useable. While there was variance in the metric outputs between the total station and photogrammetric models, the average total variance in fixed-datum lengths for individual scenes was below 0.635 cm for six of the ten scenes. However, only one of the large surface scatter scenes produced measurement that were significantly different between the total station measurements and the software measurement. The maximum differences in measurement between the total station and software measurements were 0.0917 m (PhotoScan) and 0.178 m (MicMac). The minimum difference that was found for either software was 0.000 m, indicating exact measurement. The histograms for the large scatter scenes were comparable, with the commercial and open-source software-derived models having low standard deviations and mean distances between points. For the small surface scatter scenes, the histograms between software types varied depending on the environment and the lighting conditions on the day of data collection. Conditions such as light, ground foliage and topography affect model quality significantly, as well as the amount of available computing power. No such issues of losing objects or limitations of computing power were encountered when mapping by total station and processing the data in AutoCAD. This research shows that SfM has the potential to be a rapid, accurate and low-cost resource for forensic investigation. SfM methodology for outdoor crime scene documentation can be adapted to fit within evidentiary criteria through the use of open-source software and transparent processing, but there are limitations that must be taken into consideration

Application of Low-Cost UASs and Digital Photogrammetry for High-Resolution Snow Depth Mapping in the Arctic

The repeat acquisition of high-resolution snow depth measurements has important research and civil applications in the Arctic. Currently the surveying methods for capturing the high spatial and temporal variability of the snowpack are expensive, in particular for small areal extents. An alternative methodology based on Unmanned Aerial Systems (UASs) and digital photogrammetry was tested over varying surveying conditions in the Arctic employing two diverse and low-cost UAS-camera combinations (500 and 1700 USD, respectively). Six areas, two in Svalbard and four in Greenland, were mapped covering from 1386 to 38,410 m2. The sites presented diverse snow surface types, underlying topography and light conditions in order to test the method under potentially limiting conditions. The resulting snow depth maps achieved spatial resolutions between 0.06 and 0.09 m. The average difference between UAS-estimated and measured snow depth, checked with conventional snow probing, ranged from 0.015 to 0.16 m. The impact of image pre-processing was explored, improving point cloud density and accuracy for different image qualities and snow/light conditions. Our UAS photogrammetry results are expected to be scalable to larger areal extents. While further validation is needed, with the inclusion of extra validation points, the study showcases the potential of this cost-effective methodology for high-resolution monitoring of snow dynamics in the Arctic and beyond

'Structure-from-Motion' photogrammetry: A low-cost, effective tool for geoscience applications

High-resolution topographic surveying is traditionally associated with high capital and logistical costs, so that data acquisition is often passed on to specialist third party organisations. The high costs of data collection are, for many applications in the earth sciences, exacerbated by the remoteness and inaccessibility of many field sites, rendering cheaper, more portable surveying platforms (i.e. terrestrial laser scanning or GPS) impractical. This paper outlines a revolutionary, low-cost, user-friendly photogrammetric technique for obtaining high-resolution datasets at a range of scales, termed ‘Structure-from-Motion’ (SfM). Traditional softcopy photogrammetric methods require the 3-D location and pose of the camera(s), or the 3-D location of ground control points to be known to facilitate scene triangulation and reconstruction. In contrast, the SfM method solves the camera pose and scene geometry simultaneously and automatically, using a highly redundant bundle adjustment based on matching features in multiple overlapping, offset images. A comprehensive introduction to the technique is presented, followed by an outline of the methods used to create high-resolution digital elevation models (DEMs) from extensive photosets obtained using a consumer-grade digital camera. As an initial appraisal of the technique, an SfM-derived DEM is compared directly with a similar model obtained using terrestrial laser scanning. This intercomparison reveals that decimetre-scale vertical accuracy can be achieved using SfM even for sites with complex topography and a range of land-covers. Example applications of SfM are presented for three contrasting landforms across a range of scales including; an exposed rocky coastal cliff; a breached moraine-dam complex; and a glacially-sculpted bedrock ridge. The SfM technique represents a major advancement in the field of photogrammetry for geoscience applications. Our results and experiences indicate SfM is an inexpensive, effective, and flexible approach to capturing complex topography

Determining the accuracy and repeatability of citizen-derived imagery as a source for Structure-from-Motion photogrammetry

Globally, sea levels are rising and continue to rise at an accelerating rate. Developments built near the coast are vulnerable from coastal flooding due to a direct rise in sea level and an increase in storm severity, persistence and frequency. As storm events become more prevalent and powerful they will consequently exacerbate the effects from rising sea levels and increase coastal flooding. It is therefore relevant for coastal managers to build and maintain a comprehensive understanding of the coast to predict what a future heightened sea level might bring. Building understanding at a time when resources are limited due to budget cuts is often difficult requiring cost-effective monitoring approaches. Citizen Science is a rapidly developing research method whereby scientific projects utilise public input at one or more stages of the research process. CS projects can tackle scientific research which often cannot be done by scientists alone due to human, financial, time and spatial constraints. Alongside the benefits afforded to scientific research, CS projects help in building scientific understanding within the public domain. By increasing public understanding of the coastal environment, citizens become more empowered to contribute towards coastal decisions. This project takes on the framework defined by CS by engaging a community group with data collection methods for coastal monitoring. Focus is placed on the Structure-from-Motion (SfM) photogrammetric workflow to build 3D models of the coastal environment using citizens and their personal standalone cameras or inbuilt smartphone cameras. This project aims to assess the accuracy of point clouds derived from citizen-derived imagery of a coastal environment and thus determine its potential as a source of data for coastal practitioners. It also aims to recognise the response from participating members of the public towards the SfM imaging procedure

Thermal photogrammetric imaging:a new technique for monitoring dome eruptions

Structure-from-motion (SfM) algorithms greatly facilitate the generation of 3-D topographic models from photographs and can form a valuable component of hazard monitoring at active volcanic domes. However, model generation from visible imagery can be prevented due to poor lighting conditions or surface obscuration by degassing. Here, we show that thermal images can be used in a SfM workflow to mitigate these issues and provide more continuous time-series data than visible counterparts. We demonstrate our methodology by producing georeferenced photogrammetric models from 30 near-monthly overflights of the lava dome that formed at Volcán de Colima (Mexico) between 2013 and 2015. Comparison of thermal models with equivalents generated from visible-light photographs from a consumer digital single lens reflex (DSLR) camera suggests that, despite being less detailed than their DSLR counterparts, the thermal models are more than adequate reconstructions of dome geometry, giving volume estimates within 10% of those derived using the DSLR. Significantly, we were able to construct thermal models in situations where degassing and poor lighting prevented the construction of models from DSLR imagery, providing substantially better data continuity than would have otherwise been possible. We conclude that thermal photogrammetry provides a useful new tool for monitoring effusive volcanic activity and assessing associated volcanic risks

Numerical modeling of glacial lake outburst floods using physically based dam-breach models

The instability of moraine-dammed proglacial lakes creates the potential for catastrophic glacial lake outburst floods (GLOFs) in high-mountain regions. In this research, we use a unique combination of numerical dam-breach and two-dimensional hydrodynamic modelling, employed within a generalised likelihood uncertainty estimation (GLUE) framework, to quantify predictive uncertainty in model outputs associated with a reconstruction of the Dig Tsho failure in Nepal. Monte Carlo analysis was used to sample the model parameter space, and morphological descriptors of the moraine breach were used to evaluate model performance. Multiple breach scenarios were produced by differing parameter ensembles associated with a range of breach initiation mechanisms, including overtopping waves and mechanical failure of the dam face. The material roughness coefficient was found to exert a dominant influence over model performance. The downstream routing of scenario-specific breach hydrographs revealed significant differences in the timing and extent of inundation. A GLUE-based methodology for constructing probabilistic maps of inundation extent, flow depth, and hazard is presented and provides a useful tool for communicating uncertainty in GLOF hazard assessment