Unmanned Aerial Systems Photogrammetry Research Articles

Applications of ArcticDEM for measuring volcanic dynamics, landslides, retrogressive thaw slumps, snowdrifts, and vegetation heights

Topographical changes are of fundamental interest to a wide range of Arctic science disciplines faced with the need to anticipate, monitor, and respond to the effects of climate change, including geohazard management, glaciology, hydrology, permafrost, and ecology. This study demonstrates several geomorphological, cryospheric, and biophysical applications of ArcticDEM – a large collection of publicly available, time-dependent digital elevation models (DEMs) of the Arctic. Our study illustrates ArcticDEM's applicability across different disciplines and five orders of magnitude of elevation derivatives, including measuring volcanic lava flows, ice cauldrons, post-failure landslides, retrogressive thaw slumps, snowdrifts, and tundra vegetation heights. We quantified surface elevation changes in different geological settings and conditions using the time series of ArcticDEM. Following the 2014–2015 Bárðarbunga eruption in Iceland, ArcticDEM analysis mapped the lava flow field, and revealed the post-eruptive ice flows and ice cauldron dynamics. The total dense-rock equivalent (DRE) volume of lava flows is estimated to be (1431 ± 2) million m3. Then, we present the aftermath of a landslide in Kinnikinnick, Alaska, yielding a total landslide volume of (400 ± 8) × 103 m3 and a total area of 0.025 km2. ArcticDEM is further proven useful for studying retrogressive thaw slumps (RTS). The ArcticDEM-mapped RTS profile is validated by ICESat-2 and drone photogrammetry resulting in a standard deviation of 0.5 m. Volume estimates for lake-side and hillslope RTSs range between 40,000 ± 9000 m3 and 1,160,000 ± 85,000 m3, highlighting applicability across a range of RTS magnitudes. A case study for mapping tundra snow demonstrates ArcticDEM's potential for identifying high-accumulation, late-lying snow areas. The approach proves effective in quantifying relative snow accumulation rather than absolute values (standard deviation of 0.25 m, bias of −0.41 m, and a correlation coefficient of 0.69 with snow depth estimated by unmanned aerial systems photogrammetry). Furthermore, ArcticDEM data show its feasibility for estimating tundra vegetation heights with a standard deviation of 0.3 m (no bias) and a correlation up to 0.8 compared to the light detection and ranging (LiDAR). The demonstrated capabilities of ArcticDEM will pave the way for the broad and pan-Arctic use of this new data source for many disciplines, especially when combined with other imagery products. The wide range of signals embedded in ArcticDEM underscores the potential challenges in deciphering signals in regions affected by various geological processes and environmental influences.

Assessment of UAS Photogrammetry and Planet Imagery for Monitoring Water Levels around Railway Tracks

High water levels near railway tracks can be a major factor affecting the safety of train passage. Water conditions near the tracks are normally monitored through visual inspections. However, this method is limited in spatial coverage and may not provide comparable information over time. We evaluated the utility of satellite imagery (Planet Dove constellation at 3 m pixel size) at the landscape level to assess overall water surface area along railway tracks. Comparatively, we evaluated the use of Structure- from-Motion 3D point clouds and high spatial detail orthomosaics (3 cm) generated from a commercial off-the-shelf Unmanned Aerial System (UAS) (DJI M300 RTK) for measuring vertical water level changes and extent of surface water, respectively, within the right-of-way of a railway line in Ontario, Canada, in areas prone to high water level and flooding. Test sites of varied lengths (~180 m to 500 m), were assessed four times between June and October 2021. Our results indicate that the satellite imagery provides a large-scale overview regarding the extent of open water in wetlands at long distances from the railway tracks. Analysis of the UAS derived 3D point cloud indicates that changes in water level can be determined at the centimeter scale. Furthermore, the spatial error (horizontal and vertical alignments) between the multi-temporal UAS data collections between sites was less than 3 cm. Our research highlights the importance of using consistent UAS data collection protocols, and the significant potential of commercial off-the-shelf UAS systems for water level monitoring along railway tracks.

SINGLE BUILDING POINT CLOUD SEGMENTATION: TOWARDS URBAN DATA MODELING AND MANAGEMENT

Abstract. To manage urban areas, a key step is the development of a geometric survey and its subsequent analysis and processing in order to provide useful information, and to become a good basis for urban modeling. Surveys of urban areas can be developed with various technologies, such as Aerial Laser Scanning, Unmanned Aerial Systems photogrammetry, and Mobile Mapping Systems. To make the resulting point clouds useful for subsequent steps, it is necessary to segment them into classes representing urban elements. On the other hand, there are 2D land representations that provide a variety of information related to the elements in the urban environment, which are linked to databases that have information content related to them. In this context, the element identified as interesting for urban management of the built heritage is the individual building unit. This paper presents an automated method for using map datasets to segment individual building units on a point cloud of an urban area. A unique number is then assigned to the segmented points, linking them directly to the corresponding element in the map database. The resulting point cloud thus becomes a container of the information in the map database, and a basis for possible city modeling. The method was successfully tested on the historic city of Sabbioneta (northern Italy), using two point clouds, one obtained through the use of a Mobile Mapping System and one obtained with Unmanned Aerial System photogrammetry. Two cartographic databases were used, one opensource (OpenStreetMap) and one provided by the regional authorities (regional cartographic database).

Efficiency Study of Combined UAS Photogrammetry and Terrestrial LiDAR in 3D Modeling for Maintenance and Management of Fill Dams

Low-cost unmanned aerial system (UAS) photogrammetry and terrestrial laser scanner (TLS, terrestrial LiDAR) technologies are being used as noncontact measurement methods for collecting unstructured data for the maintenance of construction infrastructure facilities. This study investigated the possibility of settlement, which is a maintenance condition evaluation item for fill-dam bodies, using point clouds based on the UAS (unmanned aerial system) structure from motion (UAS-SfM) and TLS (terrestrial laser scanner) point clouds. Specifically, the Z-axis RMSE of the point cloud improved to 0.012 m and the shape reproducibility rate to 98.53% by complementing the heterogeneous data of the UAS and TLS by combining the two systems with block coordination and ICP algorithms. The maximum settlement height and volume (heaving) of the dam crest and upstream and downstream slopes were derived from the combined UAS/TLS point-cloud-based 3D model. The quantitative values for the settlement of the fill-dam body were derived using the combined 3D model with high accuracy and density. This result verified the possibility of using the combined 3D model for evaluation of the maintenance condition.

The Spatial Analysis of Vegetation Cover and Permafrost Degradation for a Subarctic Palsa Mire Based on UAS Photogrammetry and GPR Data in the Kola Peninsula

Subarctic palsa mires undergo substantial transformation under climate impacts, and today a reliable marker of their degradation is the vegetation cover. We studied the correspondence between the surface traits of palsa degradation, as expressed in the vegetation composition, and the interior condition of permafrost within subarctic palsa mires in the central part of the Kola Peninsula. We have employed a set of methods to collect the data, including geobotanical relevés, unmanned aerial system (UAS) photogrammetry, and ground-penetrating radar (GPR) survey. Based on RGB orthophoto values and morphometric variables, we produced a land cover classification (LCC) consistent with the vegetation classes identified during field measurements. The outcome proves that the additional morphometric predictors improve the accuracy of classification algorithms. We identified three major patterns in GPR cross-sections defining (i) permafrost in palsas, (ii) water saturated peat, and (iii) the regular peat layer. As a result, our GPR data demonstrated a high correlation with land cover classes and pointed to some vegetation features controlled by the peat deposit inner structure. Under our results, palsas with thawing permafrost can be appraised using sequences of LCC. This is primarily the lichen hummock—tall shrub—carpet vegetation (LH–TSh–C) sequence from palsa top to foot. We have also detected an asymmetric configuration of permafrost in some palsas in the west-to-east direction and hypothesized that it can relate to the wind regime of the area and snow accumulation on the eastern slopes. Our results highlight that the combined application of the remote UAS photogrammetry and GPR survey enables a more precise delineation of the lateral degradation of palsas.

Deriving First Floor Elevations within Residential Communities Located in Galveston Using UAS Based Data

Flood damages occur when just one inch of water enters a residential household and models of flood damage estimation are sensitive to first-floor elevation (FFE). The current sources for FFEs consist of costly survey-based elevation certificates (ECs) or assumptions based on year built, foundation type, and flood zone. We sought to address these limitations by establishing the role of an Unmanned Aerial System (UAS) to efficiently derive accurate FFEs. Four residential communities within Galveston Island, Texas were selected to assess efficient flight parameters required for UAS photogrammetry within the built environment. A real-time kinematic positioning enabled (RTK) UAS was then used to gather georeferenced aerial imagery and create detailed 3D photogrammetric models with ±0.02 m horizontal and ±0.05 m vertical accuracies. From these residential models, FFEs and other structural measurements present in traditional ECs were obtained. Comparative statistical analyses were performed using the UAS-based measurements and traditional EC measurements. UAS based FFE measurements achieved 0.16 m mean absolute error (MAE) across all comparative observations and were not statistically different from traditional EC measures. We conclude the RTK enabled UAS approach is an efficient, cost-effective method in establishing accurate FFEs and other flood-sensitive measures in residential communities.

Innovative methods to monitor rock and mountain slope deformation

AbstractDisplacement rates of mountain slope deformations that can affect entire valley mountain flanks are often measured spatially distributed in‐situ without spatial significance. The spatially explicit measurement and recording of time series of slope deformations is a challenge, as the unstable slopes are often disintegrated into several subdomains, which move with different deformation rates. The current state‐of‐the‐art monitoring systems detect slow to very slow deformation rates between mm/a and several m/a. Using the examples of slope deformations in Saalbach‐Hinterglemm and the deep rock slide Marzellkamm in Austria this paper presents the results of terrestrial laser scans, extensometer measurements, Spaceborne InSAR data, unmanned Aerial System Photogrammetry (UAS‐P), and fixed‐point measurements. The different measurements complement each other and are optimally aligned for different application areas. InSAR data can help to identify hot spots on regional and local scale, while UAS‐P enables for spatially high level accuracy in the detection of subdomains moving at different speeds. For local warning systems TLS, extensometers and GBInSAR deliver higher accuracy.

Resolving Fine-Scale Surface Features on Polar Sea Ice: A First Assessment of UAS Photogrammetry Without Ground Control

Mapping landfast sea ice at a fine spatial scale is not only meaningful for geophysical study, but is also of benefit for providing information about human activities upon it. The combination of unmanned aerial systems (UAS) with structure from motion (SfM) methods have already revolutionized the current close-range Earth observation paradigm. To test their feasibility in characterizing the properties and dynamics of fast ice, three flights were carried out in the 2016–2017 austral summer during the 33rd Chinese National Antarctic Expedition (CHINARE), focusing on the area of the Prydz Bay in East Antarctica. Three-dimensional models and orthomosaics from three sorties were constructed from a total of 205 photos using Agisoft PhotoScan software. Logistical challenges presented by the terrain precluded the deployment of a dedicated ground control network; however, it was still possible to indirectly assess the performance of the photogrammetric products through an analysis of the statistics of the matching network, bundle adjustment, and Monte-Carlo simulation. Our results show that the matching networks are quite strong, given a sufficient number of feature points (mostly > 20,000) or valid matches (mostly > 1000). The largest contribution to the total error using our direct georeferencing approach is attributed to inaccuracies in the onboard position and orientation system (POS) records, especially in the vehicle height and yaw angle. On one hand, the 3D precision map reveals that planimetric precision is usually about one-third of the vertical estimate (typically 20 cm in the network centre). On the other hand, shape-only errors account for less than 5% for the X and Y dimensions and 20% for the Z dimension. To further illustrate the UAS’s capability, six representative surface features are selected and interpreted by sea ice experts. Finally, we offer pragmatic suggestions and guidelines for planning future UAS-SfM surveys without the use of ground control. The work represents a pioneering attempt to comprehensively assess UAS-SfM survey capability in fast ice environments, and could serve as a reference for future improvements.

Augmentation of Traditional Forest Inventory and Airborne Laser Scanning with Unmanned Aerial Systems and Photogrammetry for Forest Monitoring

Forest inventories are constrained by resource-intensive fieldwork, while unmanned aerial systems (UASs) offer rapid, reliable, and replicable data collection and processing. This research leverages advancements in photogrammetry and market sensors and platforms to incorporate a UAS-based approach into existing forestry monitoring schemes. Digital imagery from a UAS was collected, photogrammetrically processed, and compared to in situ and aerial laser scanning (ALS)-derived plot tree counts and heights on a subsample of national forest plots in Oregon. UAS- and ALS-estimated tree counts agreed with each other (r2 = 0.96) and with field data (ALS r2 = 0.93, UAS r2 = 0.84). UAS photogrammetry also reasonably approximated mean plot tree height achieved by the field inventory (r2 = 0.82, RMSE = 2.92 m) and by ALS (r2 = 0.97, RMSE = 1.04 m). The use of both nadir-oriented and oblique UAS imagery as well as the availability of ALS-derived terrain descriptions likely sustain a robust performance of our approach across classes of canopy cover and tree height. It is possible to draw similar conclusions from any of the methods, suggesting that the efficient and responsive UAS method can enhance field measurement and ALS in longitudinal inventories. Additionally, advancing UAS technology and photogrammetry allows diverse users access to forest data and integrates updated methodologies with traditional forest monitoring.

Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation

Performing two independent surveys in 2016 and 2017 over a flat sample plot (6700 m 2 ), we compare snow-depth measurements from Unmanned-Aerial-System (UAS) photogrammetry and from a new high-resolution laser-scanning device (MultiStation) with manual probing, the standard technique used by operational services around the world. While previous comparisons already used laser scanners, we tested for the first time a MultiStation, which has a different measurement principle and is thus capable of millimetric accuracy. Both remote-sensing techniques measured point clouds with centimetric resolution, while we manually collected a relatively dense amount of manual data (135 pt in 2016 and 115 pt in 2017). UAS photogrammetry and the MultiStation showed repeatable, centimetric agreement in measuring the spatial distribution of seasonal, dense snowpack under optimal illumination and topographic conditions (maximum RMSE of 0.036 m between point clouds on snow). A large fraction of this difference could be due to simultaneous snowmelt, as the RMSE between UAS photogrammetry and the MultiStation on bare soil is equal to 0.02 m. The RMSE between UAS data and manual probing is in the order of 0.20–0.30 m, but decreases to 0.06–0.17 m when areas of potential outliers like vegetation or river beds are excluded. Compact and portable remote-sensing devices like UASs or a MultiStation can thus be successfully deployed during operational manual snow courses to capture spatial snapshots of snow-depth distribution with a repeatable, vertical centimetric accuracy.

Detecting Surface Changes from an Underground Explosion in Granite Using Unmanned Aerial System Photogrammetry

Efficient detection and high-fidelity quantification of surface changes resulting from underground activities are important national and global security efforts. In this investigation, a team performed field-based topographic characterization by gathering high-quality photographs at very low altitudes from an unmanned aerial system (UAS)-borne camera platform. The data collection occurred shortly before and after a controlled underground chemical explosion as part of the United States Department of Energy’s Source Physics Experiments (SPE-5) series. The high-resolution overlapping photographs were used to create 3D photogrammetric models of the site, which then served to map changes in the landscape down to 1-cm-scale. Separate models were created for two areas, herein referred to as the test table grid region and the nearfield grid region. The test table grid includes the region within ~40 m from surface ground zero, with photographs collected at a flight altitude of 8.5 m above ground level (AGL). The near-field grid area covered a broader area, 90–130 m from surface ground zero, and collected at a flight altitude of 22 m AGL. The photographs, processed using Agisoft Photoscan® in conjunction with 125 surveyed ground control point targets, yielded a 6-mm pixel-size digital elevation model (DEM) for the test table grid region. This provided the ≤3 cm resolution in the topographic data to map in fine detail a suite of features related to the underground explosion: uplift, subsidence, surface fractures, and morphological change detection. The near-field grid region data collection resulted in a 2-cm pixel-size DEM, enabling mapping of a broader range of features related to the explosion, including: uplift and subsidence, rock fall, and slope sloughing. This study represents one of the first works to constrain, both temporally and spatially, explosion-related surface damage using a UAS photogrammetric platform; these data will help to advance the science of underground explosion detection.

Landslide Fissure Inference Assessment by ANFIS and Logistic Regression Using UAS-Based Photogrammetry

Unmanned Aerial Systems (UAS) are now capable of gathering high-resolution data, therefore, landslides can be explored in detail at larger scales. In this research, 132 aerial photographs were captured, and 85,456 features were detected and matched automatically using UAS photogrammetry. The root mean square (RMS) values of the image coordinates of the Ground Control Points (GPCs) varied from 0.521 to 2.293 pixels, whereas maximum RMS values of automatically matched features was calculated as 2.921 pixels. Using the 3D point cloud, which was acquired by aerial photogrammetry, the raster datasets of the aspect, slope, and maximally stable extremal regions (MSER) detecting visual uniformity, were defined as three variables, in order to reason fissure structures on the landslide surface. In this research, an Adaptive Neuro Fuzzy Inference System (ANFIS) and a Logistic Regression (LR) were implemented using training datasets to infer fissure data appropriately. The accuracy of the predictive models was evaluated by drawing receiver operating characteristic (ROC) curves and by calculating the area under the ROC curve (AUC). The experiments exposed that high-resolution imagery is an indispensable data source to model and validate landslide fissures appropriately.

Multitemporal Monitoring of the Morphodynamics of a Mid-Mountain Stream Using UAS Photogrammetry

This paper explores the potential of Unmanned Aerial Systems (UASs) for the analysis of variations in the fluvial dynamics of a mid-mountain stream. The UAS photogrammetry was employed to acquire a multitemporal set of high-precision digital terrain models (DTMs) and orthoimages, thereby enabling the reconstruction of variations in riverbed and quantitative analysis of volumetric changes. A hexacopter UAS platform was used for the repeated acquisition of data for the photogrammetric analysis of a stretch of mid-mountain streams with elevated fluvial dynamics. Photogrammetric reconstruction enabled the development of accurate DTMs and orthoimages with spatial resolutions of 2 cm per pixel. These were identified and used to quantitatively assess the segments of channels with active lateral erosion. The UAS-derived data facilitated an analysis of the shifts of stream banks and the calculation of the areal extent of changes and volumetric extent of bank erosion. Comparison of UAS-derived point clouds with aerial LiDAR scanning data demonstrated the high spatial accuracy and precision of the UAS data. The accuracy and high operability of the imaging provide spatial data of a new qualitative level and the potential for the detailed analysis of experimental areas where spatial information is of limited availability.