Unmanned Aerial Vehicle Photogrammetry Research Articles

Application of UAV Photogrammetry on Ecological Restoration of Abandoned Open-pit mines, Northern Anhui Province, China

Mine survey is an important part of the ecological restoration design of an abandoned open-pit mine because it gives precise survey data for mine management. Unmanned aerial vehicle (UAV) photogrammetry technology is applied to mine 3D modeling and earthwork calculation to tackle the problems of high cost, low efficiency, and high labor intensity in traditional manual field mine surveys. Using the abandoned quarries in Baitu, Suzhou City, as the study area, 82 ground control points were set up, and massive elevation data of the ground was obtained using a Dajiang spirit 4 RTK UAV for aerial photography and extracted from a 3D model created with Context Capture software, while a digital orthophoto image of the study area was created. The earthwork volume is calculated using the square grid method. UAV photogrammetry has a considerably higher efficiency than typical manual field measurement results, and it meets the precision criteria of mine geological environment assessment. According to the findings, UAV photogrammetry technology has a promising future in mine geological environment monitoring and ecological environment repair.

Surface Characteristics, Elevation Change, and Velocity of High-Arctic Valley Glacier from Repeated High-Resolution UAV Photogrammetry

Unmanned Aerial Vehicles (UAVs) are being increasingly used in glaciology demonstrating their potential for the generation of high-resolution digital elevation models (DEMs) that can be further used for the evaluation of glacial processes in detail. Such investigations are especially important for the evaluation of surface changes of small valley glaciers, which are not well-represented in lower-resolution satellite-derived products. In this study, we performed two UAV surveys at the end of the ablation season in 2019 and 2021 on Waldemarbreen, a High-Arctic glacier in NW Svalbard. We derived the mean annual glacier surface velocity of 5.3 m. The estimated mean glacier surface elevation change from 2019 to 2021 was −1.46 m a−1 which corresponds to the geodetic mass balance (MB) of −1.33 m w.e. a−1. The glaciological MB for the same period was −1.61 m w.e. a−1. Our survey includes all Waldemarbreen and demonstrates the efficiency of high-resolution DEMs produced from UAV photogrammetry for the reconstruction of changes in glacier surface elevation and velocity. We suggest that glaciological and geodetic MB methods should be used complementary to each other.

Timing, volume and precursory indicators of rock‐ and cliff fall on a permafrost mountain ridge (Mattertal, Switzerland)

AbstractHigh mountain environments are increasingly affected by rockfall‐related hazards, driven by climate change. Studying rockfall in these environments is, however, challenging due to the inaccessibility of mountain ridges and the complex interaction between controlling factors. In this study, the rock wall of Grosse Grabe North Pillar in the Matter valley (Western Swiss Alps) was studied in detail over a timespan of 4 years (2017–2021). Data was collected from time‐lapse photography, terrestrial laser scanning, unmanned aerial vehicle photogrammetry and seismic measurements. The presented dataset is unique because data collection started before the onset of the rock wall destabilization, allowing us to understand precursory indicators of large‐scale events. In total, we recorded 382 rock‐ and cliff fall events (100–31 300 m3), with a total volume of 204 323 ± 8173 m3, resulting in a scar depth of ~40 m. An associated rock wall retreat rate of 71.2 ± 2.8 mm year−1 was calculated for the 1991–2021 period. Highly fractured south‐exposed gneiss lithology is viewed as the main predisposition for the observed rock‐ and cliff fall events, allowing high‐temperature oscillations to cause irreversible movements at fracture level. Cliff falls (104–106 m3) were preluded by an outward movement of the rock wall that started to increase 1.5 years before any significant collapse of the rock wall, reaching locally up to 30 cm. All cliff fall events occurred in summer, exposing ice in the clefts. This is assumed to be the base of the permafrost from the north side. Rapid permafrost degradation is viewed as a triggering factor after its exposure, causing progressive failure of the rock wall, leading to very high rock wall retreat rates on a decadal timescale.

UAV and GPR Data Integration in Glacier Geometry Reconstruction: A Case Study from Irenebreen, Svalbard

Although measurements of thickness and internal structure of glaciers are substantial for the understanding of their evolution and response to climate change, detailed data about polythermal glaciers, are scarce. Here, we present the first ground-penetrating radar (GPR) measurement data of Irenebreen, and high-resolution DEM and orthomosaic, obtained from unmanned aerial vehicle (UAV) photogrammetry. A combination of GPR and UAV data allowed for the reconstruction of the glacier geometry including thermal structure. We compare different methods of GPR signal propagation speed determination and argue that a common midpoint method (CMP) should be used if possible. Our observations reveal that Irenebreen is a polythermal glacier with a basal temperate ice layer, the volume of which volume reaches only 12% of the total glacier volume. We also observe the intense GPR signal scattering in two small zones in the ablation area and suggest that intense water percolation occurs in these places creating local areas of temperate ice. This finding emphasizes the possible formation of localised temperate ice zones in polythermal glaciers due to the coincidence of several factors. Our study demonstrates that a combination of UAV photogrammetry and GPR can be successfully applied and should be used for the high-resolution reconstruction of 3D geometries of small glaciers.

Flood Assessment and Identification of Emergency Evacuation Routes in Seti River Basin, Nepal

Sudden floods frequently occur in the Himalayas under changing climates. Rapid glacial melt has resulted in the formation of glacial lakes and associated hazards. This research aimed to (1) identify flood-prone houses, (2) determine pedestrian emergency evacuation routes, and (3) analyze their relationships to socioeconomic status in the Seti River Basin. Detailed hazard maps were created using field survey results from unmanned aerial vehicle photogrammetry and the Hydrologic Engineering Center River Analysis System. Questionnaire, focus-group, and key-informant surveys helped identify the socioeconomic situation. Inundation maps revealed that most residents are exposed to future flooding hazards without proper evacuation routes. Highly impoverished and immigrant households were at the highest risk in terms of income inequality and migration rate (p < 0.001) and were located on the riverside. The locations of 455 laborers’ houses were significantly correlated with inundation hazards (p < 0.001). Governmental and associated agencies must develop adequate plans to relocate low-income households. Group discussions revealed the need for stronger adaptive capacity-building strategies for future risk management. Pokhara requires better systematic and scientific land-use planning strategies to address this issue efficiently. A similar approach that combines flood modeling, proper evacuation route access, and socioeconomic survey is suggested for this river basin.

Estimating the cost of growth in southern right whales from drone photogrammetry data and long-term sighting histories

Animal body size and growth patterns play important roles in shaping the life history of species. Baleen whales include the largest animals on the planet, with somatic growth costs expected to be substantial. We used unmanned aerial vehicle photogrammetry and long-term individual sighting histories from photo identification (1991-2019) to estimate the cost of somatic growth for southern right whales (SRWs) Eubalaena australis. A Richards length-at-age growth model was developed, based on 161 calves, 20 yearlings, 1 juvenile and 23 adults, ranging in age from newborn to 27 yr. Predicted lengths were 4.7 m at birth, 12.5 m at minimum age of first parturition (6 yr) and an asymptotic length of 14.3 m. A volume-at-age curve was estimated from the body volume versus length relationship, and converted to a mass-at-age curve, using data on body tissue composition of North Pacific right whales E. japonica (n = 13). The energetic cost of growth was estimated using published estimates of tissue lipid and protein concentrations. The cost of growth for SRWs (in MJ d-1) was 2112 at birth, 544 at 4 mo, 314 at 1 yr (~weaning age), 108 at 5 yr (minimum age of sexual maturity), 51.5 at 10 yr and 5.2 at 30 yr. The cumulative cost to age 30 was 764.3 GJ, but varied widely (458-995 GJ) depending on the tissue energy content. Our estimates represent a healthy SRW population, and provide a baseline to investigate individual and population level impacts of anthropogenic disturbance (including climate change).

A Fusion Method Using Terrestrial Laser Scanning and Unmanned Aerial Vehicle Photogrammetry for Landslide Deformation Monitoring Under Complex Terrain Conditions

Landslides are one of the major factors threatening human life and social development. Monitoring and early warning of landslides are important parts of disaster prevention and control. However, due to complex terrain conditions, landslide monitoring in mountainous watersheds and high mountain valleys usually faces issues of high difficulty, high danger, poor data integrity and low data accuracy compared with plains or hilly areas. Based on the fusion of terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV) photogrammetry, this paper proposes a landslide deformation monitoring method that can be applied to complex terrain conditions where there are access restrictions and blind areas. The method is based on TLS to increase the number and range of ground control points (GCPs) of UAVs within the visual range and then uses assumed control points (ACPs) to reconstruct the UAV model and calculate landslide displacement in invisible areas. In addition, this paper proposes and verifies a formula based on an allometricl equation to estimate the error between the calculated and real displacements based on this method. Then, we applied this method to a real landslide case and successfully realized landslide monitoring under the conditions of access limitation and blind areas. The proposed method enables landslide monitoring to overcome terrain restrictions, and it makes it possible to perform large-scale and high-precision ground deformation measurements with limited working ranges, which can be an effective tool for landslide monitoring in emergency situations and long-term risk evaluation.

UAV-based landfill operation monitoring: A year of volume and topographic measurements.

Unmanned Aerial Vehicles (UAVs) for photogrammetry operations configures a technology capable of extracting quantitative information from land surface in a fast, accurate and safe way, reproducing it in high-resolution Digital Elevation Models (DEMs) and orthomosaics. Due to the operational efficiency of this technique, there is an interest in evaluating its quality compared to other methodologies traditionally used for monitoring procedures in infrastructure earthwork. In sanitary landfills, operational monitoring is directly linked to topographic services, as these are the main source of data for the geometric assessment of the work. In this context, the aim of the study was to verify accuracy and application range of UAV photogrammetry for geometrical and volumetric measurements, when compared to usual conventional survey procedures using total station, and how it can aggregate reliable data to landfills monitoring activities. UAV flights were carried on monthly basis, over a year. For accuracy analysis, the maximum RMSE error observed was 7.1cm for horizontal axis and 0.37cm for vertical axis for the monitoring period. Volumetric measurements were tested using Ground Control Point (GCPs) configurations distributed first at the landfill perimeter, which resulted in an average difference of 9% from that calculated by conventional topography, and measurements where GCPs were placed also in the landfill operation fronts, when a 4% average difference diverging from conventional topography was obtained. The conclusion shows that such monitoring routines, when performed periodically, provides a robust database with a high level of operational performance, covering effective information for preventive and corrective monitoring in landfill projects.

Rice Height Monitoring between Different Estimation Models Using UAV Photogrammetry and Multispectral Technology

Unmanned aerial vehicle (UAV) photogrammetry was used to monitor crop height in a flooded paddy field. Three multi-rotor UAVs were utilized to conduct flight missions in order to capture RGB (RedGreenBlue) and multispectral images, and these images were analyzed using several different models to provide the best results. Two image sets taken by two UAVs, mounted with RGB cameras of the same resolution and Global Navigation Satellite System (GNSS) receivers of different accuracies, were applied to perform photogrammetry. Two methods were then proposed for creating crop height models (CHMs), one of which was denoted as the M1 method and was based on the Digital Surface Point Cloud (DSPC) and the Digital Terrain Point Cloud (DSPT). The other was denoted as the M2 method and was based on the DSPC and a bathymetric sensor. An image set taken by another UAV mounted with a multispectral camera was used for multispectral-based photogrammetry. A Normal Differential Vegetation Index (NDVI) and a Vegetation Fraction (VF) were then extracted. A new method based on multiple linear regression (MLR) combining the NDVI, the VF, and a Soil Plant Analysis Development (SPAD) value for estimating the measured height (MH) of rice was then proposed and denoted as the M3 method. The results show that the M1 method, the UAV with a GNSS receiver with a higher accuracy, obtained more reliable estimations, while the M2 method, the UAV with a GNSS receiver of moderate accuracy, was actually slightly better. The effect on the performance of CHMs created by the M1 and M2 methods is more negligible in different plots with different treatments; however, remarkably, the more uniform the distribution of vegetation over the water surface, the better the performance. The M3 method, which was created using only a SPAD value and a canopy NDVI value, showed the highest coefficient of determination (R2) for overall MH estimation, 0.838, compared with other combinations.

Precision Evaluation and Fusion of Topographic Data Based on UAVs and TLS Surveys of a Loess Landslide

In recent years, low-cost unmanned aerial vehicles (UAVs) photogrammetry and terrestrial laser scanner (TLS) techniques have become very important non-contact measurement methods for obtaining topographic data about landslides. However, owing to the differences in the types of UAVs and whether the ground control points (GCPs) are set in the measurement, the obtained topographic data for landslides often have large precision differences. In this study, two types of UAVs (DJI Mavic Pro and DJI Phantom 4 RTK) with and without GCPs were used to survey a loess landslide. UAVs point clouds and digital surface model (DSM) data for the landslide were obtained. Based on this, we used the Geomorphic Change Detection software (GCD 7.0) and the Multiscale Model-To-Model Cloud Comparison (M3C2) algorithm in the Cloud Compare software for comparative analysis and accuracy evaluation of the different point clouds and DSM data obtained using the same and different UAVs. The experimental results show that the DJI Phantom 4 RTK obtained the highest accuracy landslide terrain data when the GCPs were set. In addition, we also used the Maptek I-Site 8,820 terrestrial laser scanner to obtain higher precision topographic point cloud data for the Beiguo landslide. However, owing to the terrain limitations, some of the point cloud data were missing in the blind area of the TLS measurement. To make up for the scanning defect of the TLS, we used the iterative closest point (ICP) algorithm in the Cloud Compare software to conduct data fusion between the point clouds obtained using the DJI Phantom 4 RTK with GCPs and the point clouds obtained using TLS. The results demonstrate that after the data fusion, the point clouds not only retained the high-precision characteristics of the original point clouds of the TLS, but also filled in the blind area of the TLS data. This study introduces a novel perspective and technical scheme for the precision evaluation of UAVs surveys and the fusion of point clouds data based on different sensors in geological hazard surveys.

Integrating terrestrial laser scanning and unmanned aerial vehicle photogrammetry to estimate individual tree attributes in managed coniferous forests in Japan

The accurate estimation of tree attributes is essential for sustainable forest management. Terrestrial Laser Scanning (TLS) is a viable remote sensing technology suitable for estimating under canopy structure. However, TLS measurements generally underestimate tree height in taller trees, which leads to the underestimation of other tree attributes (e.g., stem volume). The integration of information derived from TLS and Unmanned Aerial Vehicle (UAV) photogrammetry could potentially improve tree height estimation. This study investigated the applicability of integrating TLS and UAV photogrammetry to estimate individual tree attributes in managed coniferous forests of Japan. Diameter at breast height (DBH), tree height, and stem volume were estimated by (1) TLS data only, (2) integrating TLS and UAV data with TLS tree locations, and (3) integrating TLS and UAV data with treetop detections of the tree canopy. The TLS data only approach achieved high accuracy for DBH estimations with a root mean squared error (RMSE) of 2.36 cm (RMSE% 5.6%); however, tree height was greatly underestimated, with an RMSE of 8.87 m (28.9%) and a bias of −8.39 m. Integrating TLS and UAV photogrammetric data improved tree height estimation accuracy for both the TLS tree location (RMSE of 1.89 m and a bias of −0.46 m) and the treetop detection (RMSE of 1.77 m and a bias of 0.36 m) approaches. Integrating TLS and UAV photogrammetric data also improved the accuracy of the stem volume estimations with RMSEs of 0.21 m3 (10.8%) and 0.21 m3 (10.5%) for the TLS tree location and treetop detection approaches, respectively. Although the tree height of suppressed trees tended to be overestimated by TLS and UAV photogrammetric data integration, a good performance was obtained for dominant trees. The results of this study indicate that the integration of TLS and UAV photogrammetry is beneficial for the accurate estimation of tree attributes in coniferous forests.

Experimental Study on 3D Measurement Accuracy Detection of Low Altitude UAV for Repeated Observation of an Invariant Surface

Low-altitude unmanned aerial vehicle (UAV) photogrammetry combined with structure-from-motion (SFM) algorithms is the latest technological approach to imaging 3D stereo constructions. At present, derivative products have been widely used in landslide monitoring, landscape evolution, glacier movement, volume measurement, and landscape change detection. However, there is still a lack of research into the accuracy of 3D data positioning based on the structure-from-motion of unmanned aerial vehicle (UAV-SFM) technology, itself, which can affect the measurable effectiveness of the results in further applications of this technological approach. In this paper, validation work was carried out for the DJI Phantom 4 RTK UAV, for earth observation data related to 3D positioning accuracy. First, a test plot with a relatively stable surface was selected for repeated flight imaging observations. Specifically, three repeated flights were performed on the test plot to obtain three sorties of images; the structure from motion and multi-view stereo (SFM-MVS) key technology was used to process and construct a 3D scene model, and based on this model the digital surface model (DSM) and digital orthophoto map (DOM) data of the same plot with repeated observations were obtained. In order to check the level of 3D measurement accuracy of the UAV technology itself, a window selection-based method was used to sample the point cloud set data from the three-sortie repeat observation 3D model. The DSM and DOM data obtained from three repeated flights over the surface invariant test plots were used to calculate the repeat observation 3D point errors, taking into account the general methodology of redundant observation error analysis for topographic surveys. At the same time, to further analyze the limits of the UAV measurement technique, possible under equivalent observation conditions with the same processing environment, a difference model (DOD) was constructed for the DSM data from three sorties, to deepen the overall characterization of the differences between the DSMs obtained from repeated observations. The results of the experimental study concluded that both the analysis of the 3D point set measurements based on window sampling and the accuracy evaluation using the difference model were generally able to achieve a centimeter level of planimetric accuracy and vertical accuracy. In addition, the accuracy of the surface-stabilized hardened ground was better, overall, than the accuracy of the non-hardened ground. The results of this paper not only probe the measurement limits of this type of UAV, but also provide a quantitative reference for the accurate control and setting of an acquisition scheme of the UAV-based SfM-MVS method for geomorphological data acquisition and 3D reconstruction.

Improvement of the Soil Moisture Retrieval Procedure Based on the Integration of UAV Photogrammetry and Satellite Remote Sensing Information

In countries characterized by arid and semi-arid climates, a precise determination of soil moisture conditions on the field scale is critically important, especially in the first crop growth stages, to schedule irrigation and to avoid wasting water. The objective of this study was to apply the operative methodology that allowed surface soil moisture (SSM) content in a semi-arid environment to be estimated. SSM retrieval was carried out by combining two scattering models (IEM and WCM), supplied by backscattering coefficients at the VV polarization obtained from the C-band Synthetic Aperture Radar (SAR), a vegetation descriptor NDVI obtained from the optical sensor, among other essential parameters. The inversion of these models was performed by Neural Networks (NN). The combined models were calibrated by the Sentinel 1 and Sentinel 2 data collected on bare soil, and in cereal, pea and onion crop fields. To retrieve SSM, these scattering models need accurate measurements of the roughness surface parameters, standard deviation of the surface height (hrms) and correlation length (L). This work used a photogrammetric acquisition system carried on Unmanned Aerial Vehicles (UAV) to reconstruct digital surface models (DSM), which allowed these soil roughness parameters to be acquired in a large portion of the studied fields. The obtained results showed that the applied improved methodology effectively estimated SSM on bare and cultivated soils in the principal early growth stages. The bare soil experimentation yielded an R2 = 0.74 between the estimated and observed SSMs. For the cereal field, the relation between the estimated and measured SSMs yielded R2 = 0.71. In the experimental pea fields, the relation between the estimated and measured SSMs revealed R2 = 0.72 and 0.78, respectively, for peas 1 and peas 2. For the onion experimentation, the highest R2 equaled 0.5 in the principal growth stage (leaf development), but the crop R2 drastically decreased to 0.08 in the completed growth phase. The acquired results showed that the applied improved methodology proves to be an effective tool for estimating the SSM on bare and cultivated soils in the principal early growth stages.

OBIA-Based Extraction of Artificial Terrace Damages in the Loess Plateau of China from UAV Photogrammetry

Terraces, which are typical artificial landforms found around world, are of great importance for agricultural production and soil and water conservation. However, due to the lack of maintenance, terrace damages often occur and affect the local flow process, which will influence soil erosion. Automatic high-accuracy mapping of terrace damages is the basis of monitoring and related studies. Researchers have achieved artificial terrace damage mapping mainly via manual field investigation, but an automatic method is still lacking. In this study, given the success of high-resolution unmanned aerial vehicle (UAV) photogrammetry and object-based image analysis (OBIA) for image processing tasks, an integrated framework based on OBIA and UAV photogrammetry is proposed for terrace damage mapping. The Pujiawa terrace in the Loess Plateau of China was selected as the study area. Firstly, the segmentation process was optimised by considering the spectral features and the terrains and corresponding textures obtained from high-resolution images and digital surface models. The feature selection was implemented via correlation analysis, and the optimised segmentation parameter was achieved using the estimation of scale parameter algorithm. Then, a supervised k-nearest neighbourhood classifier was used to identify the terrace damages in the segmented objects, and additional geometric features at the object level were considered for classification. The comparison with the ground truth, as delineated by the image and field survey, showed that proposed classification can be adequately performed. The F-measures of extraction on three terrace damages were 92.07% (terrace sinkhole), 81.95% (ridge sinkhole), and 85.17% (collapse), and the Kappa coefficient was 85.34%. Finally, the potential application and spatial distribution of the terrace damages in this study were determined. We believe that this work can provide a credible framework for mapping terrace damages in the Loess Plateau of China.

Detecting and Monitoring Early Post-Fire Sliding Phenomena Using UAV–SfM Photogrammetry and t-LiDAR-Derived Point Clouds

Soil changes, including landslides and erosion, are some of the most prominent post-fire effects in Mediterranean ecosystems. Landslide detection and monitoring play an essential role in mitigation measures. We tested two different methodologies in five burned sites with different characteristics in Central Greece. We compared Unmanned Aerial Vehicles (UAV)-derived high-resolution Digital Surface Models and point clouds with terrestrial Light Detection and Ranging (LiDAR)-derived point clouds to reveal new cracks and monitor scarps of pre-existing landslides. New cracks and scarps were revealed at two sites after the wildfire, measuring up to 27 m in length and up to 25 ± 5 cm in depth. Pre-existing scarps in both Kechries sites appeared to be active, with additional vertical displacements ranging from 5–15 ± 5 cm. In addition, the pre-existing landslide in Magoula expanded by 8%. Due to vegetation regrowth, no changes could be detected in the Agios Stefanos pre-existing landslide. This high-spatial-resolution mapping of slope deformations can be used as landslide precursor, assisting prevention measures. Considering the lack of vegetation after wildfires, UAV photogrammetry has great potential for tracing such early landslide indicators and is more efficient for accurately recording soil changes.

The Spatiotemporal Relationship between Landslides and Mechanisms at the Heifangtai Terrace, Northwest China

Landslide disasters have occurred frequently in the Chinese Loess Plateau (CLP) due to increased agricultural irrigation. To explore the spatiotemporal relationship between landslides and mechanisms at the Heifangtai terrace, the HFT irrigated area was selected as a typical case study to investigate the fundamental mechanism of the irrigation-induced landslide in the CLP. Multi-temporal remote sensing images, topographic maps, and unmanned aerial vehicle (UAV) photogrammetry data were used to investigate the evolution progress of landslides. Moreover, the evolution mechanism was discussed through topographic analysis, field monitoring, and laboratory testing. The results showed that erosion, collapse, and sliding had occurred at different scales and at different locations in the past 50 years. With an average retrogressive speed of 9.6 × 103 m2 per year, the tableland decreased by 4.9 × 105 m2 from 1967 to 2018, accounting for about 4.5% of its total area. Over 20 landslides and collapses were extracted in the Dangchuan section in the past four years. More than 5.48 × 105 m3 of loess slipped with an average volume of 381 m3 per day. The evolutionary process of the irrigation-induced landslide, which features retrogression, lateral extension, and clustering, began with local failure and ended in a series of slidings. The increase of groundwater level was a slow process, which is the reason for the lagged occurrence of the landslide. The influence of rainfall and irrigation on slope stability was greater than that of the periodic change of the groundwater level. The triggering effect of irrigation and rainfall on the landslide had a time lag due to slow loess infiltration, and the time response among irrigation, rainfall, and groundwater level was 4–6 months. Our findings provide guidance, concerning the planning and controlling of landslide disasters, which is of critical value for human and construction safety.

Large-Scale Reality Modeling of a University Campus Using Combined UAV and Terrestrial Photogrammetry for Historical Preservation and Practical Use

Unmanned aerial vehicles (UAV) enable detailed historical preservation of large-scale infrastructure and contribute to cultural heritage preservation, improved maintenance, public relations, and development planning. Aerial and terrestrial photo data coupled with high accuracy GPS create hyper-realistic mesh and texture models, high resolution point clouds, orthophotos, and digital elevation models (DEMs) that preserve a snapshot of history. A case study is presented of the development of a hyper-realistic 3D model that spans the complex 1.7 km2 area of the Brigham Young University campus in Provo, Utah, USA and includes over 75 significant structures. The model leverages photos obtained during the historic COVID-19 pandemic during a mandatory and rare campus closure and details a large scale modeling workflow and best practice data acquisition and processing techniques. The model utilizes 80,384 images and high accuracy GPS surveying points to create a 1.65 trillion-pixel textured structure-from-motion (SfM) model with an average ground sampling distance (GSD) near structures of 0.5 cm and maximum of 4 cm. Separate model segments (31) taken from data gathered between April and August 2020 are combined into one cohesive final model with an average absolute error of 3.3 cm and a full model absolute error of <1 cm (relative accuracies from 0.25 cm to 1.03 cm). Optimized and automated UAV techniques complement the data acquisition of the large-scale model, and opportunities are explored to archive as-is building and campus information to enable historical building preservation, facility maintenance, campus planning, public outreach, 3D-printed miniatures, and the possibility of education through virtual reality (VR) and augmented reality (AR) tours.

Monitoring and Quantitative Human Risk Assessment of Municipal Solid Waste Landfill Using Integrated Satellite–UAV–Ground Survey Approach

Landfills are the dominant method of municipal solid waste (MSW) disposal in many developing countries, which are extremely susceptible to failure under circumstances of high pore water pressure and insufficient compaction. Catastrophic landfill failures have occurred worldwide, causing large numbers of fatalities. Tianziling landfill, one of the largest engineered sanitary landfills in China, has experienced massive deformation since January 2020, making early identification and monitoring of great significance for the purpose of risk management. The human risk posed by potential landfill failures also needs to be quantitatively evaluated. The interferometric synthetic aperture radar (InSAR) technique, unmanned aerial vehicle (UAV) photogrammetry, and ground measurements were combined to obtain landfill deformation data in this study. The integrated satellite–UAV–ground survey (ISUGS) approach ensures a comprehensive understanding of landfill deformation and evolution. The deformation characteristics obtained using the InSAR technique and UAV photogrammetry were analyzed and compared. A close relationship between the most severe mobility events, precipitation episodes, and was observed. Based on early hazard identification using ISUGS, a quantitative risk assessment (QRA) method and F-N curves were proposed, which can be applied to landfills. The comparison showed that ISUGS allowed a better understanding of the spatial and temporal evolution of the landfill and more accurate QRA results, which could be as references for local governments to take effective precautions.

Suitability of the Spanish Open Public Cartographic Resources for BIM Site Modeling

The possibility of accessing free cartographic resources on the internet instead of creating their own digital object representation is a big advantage for architecture and engineering professionals in the surveying process of a construction or restoration project. The use of these products must be carried out with the guarantee that the precision of the site representation made from them is adapted to the level of detail required by each phase of the project. This paper compares graphically and statistically the accuracy of the topographic surfaces we can get from the LIDAR point clouds and the Digital Terrain Models (DTMs) of the National Centre for Geographic Information (CNIG by its Spanish acronym) that in Spain are available on the internet free of charge. We will use as a reference surface for comparison, the topographic map of the Digital Model Elevation (DEM) from the images captured by an unmanned aerial vehicle (UAV) processed with software based on algorithms of Structure from Motion (SfM). The study case for this comparative research will be the graphical survey of an archaeological site of funerary architecture made in 1939 during the Spanish Civil War. The monument is located near the city of Burgos (Spain) in an area with difficult access and restricted airspace, which makes difficult the fieldwork. The modeling of the surface has great importance for the graphic survey of the site, because its architectural configuration is adapted to the steep slope of the hillside. The purpose of this research is to check, if the surfaces provided through the Spanish open public cartographic resources are accurate enough to replace the mapping from UAV photogrammetry. Finally, if the outcomes can be extrapolated for widespread use of these resources for the site modeling of a project, according to the requirements of the different levels of development defined in the Building Information Modeling (BIM) methodology.

Rock Mass Characterization by UAV and Close-Range Photogrammetry: A Multiscale Approach Applied along the Vallone dell’Elva Road (Italy)

Geostructural rock mass surveys and the collection of data related to discontinues provide the basis for the characterization of rock masses and the study of their stability conditions. This paper describes a multiscale approach that was carried out using both non-contact techniques and traditional support techniques to survey certain geometrical features of discontinuities, such as their orientation, spacing, and useful persistence. This information is useful in identifying the possible kinematics and stability conditions. These techniques are extremely useful in the case study of the Elva valley road (Northern Italy), in which instability phenomena are spread across 9 km in an overhanging rocky mass. A multiscale approach was applied, obtaining digital surface models (DSMs) at three different scales: large-scale DSM of the entire road, a medium-scale DSM to assess portions of the slope, and a small-scale DSM to assess single discontinuities. The georeferenced point cloud and consequent DSMs of the slopes were obtained using an unmanned aerial vehicle (UAV) and terrestrial photogrammetric technique, allowing topographic and rapid traditional geostructural surveys. This technique allowed us to take measurements along the entire road, obtaining geometrical data for the discontinuities that are statistically representative of the rock mass and useful in defining the possible kinematic mechanisms and volumes of potentially detachable blocks. The main purpose of this study was to analyse how the geostructural features of a rock mass can affect the stability slope conditions at different scales in order to identify road sectors susceptible to different potential failure mechanisms using only kinematic analysis.