Unmanned Aerial Vehicle-Based Structure from Motion Technique for Precise Snow Depth Retrieval—Implication for Optimal Ground Control Point Deployment Strategy

Fully-automated estimation of snow depth in near real time with the use of unmanned aerial vehicles without utilizing ground control points

Snow depth from ICESat laser altimetry — A test study in southern Norway

Accurate snow depth estimation is of significant importance, particularly for preventing avalanche disasters and predicting flood seasons. The predominant approaches for such snow depth estimation, based on deep learning methods, typically rely on passive microwave remote sensing data. However, due to the low resolution of passive microwave remote sensing data, it often results in low-accuracy outcomes, posing considerable limitations in application. To further improve the accuracy of snow depth estimation, in this paper, we used active microwave remote sensing data. We fused multi-spectral optical satellite images, synthetic aperture radar (SAR) images and land cover distribution images to generate a snow remote sensing dataset (SRSD). It is a first-of-its-kind dataset that includes active microwave remote sensing images in high-latitude regions of Asia. Using these novel data, we proposed a multi-scale feature perception and aggregation neural network (MFPANet) that focuses on improving feature extraction from multi-source images. Our systematic analysis reveals that the proposed approach is not only robust but also achieves high accuracy in snow depth estimation compared to existing state-of-the-art methods, with RMSE of 0.360 and with MAE of 0.128. Finally, we selected several representative areas in our study region and applied our method to map snow depth distribution, demonstrating its broad application prospects.

Recent technological innovations have led to the available of lightweight Unmanned Aerial Vehicle (UAV) and Structure-from-Motion (SfM) photogrammetry that are successfully applied for 3D topographic surveys. However, application of UAV and SfM for complex topographic areas i.e. open-pit mine areas is still poorly understood. This paper aims to investigate and verify potential application of these techniques for generating Digital Surface Model (DSM) at open-pit coal mine area and assessing its accuracy. For this purpose, the Nui Beo open-pit coal mine located in northeast Vietnam is selected as a case study. Accordingly, a total of 206 photos were captured using DJI Phantom 3 Professional. In addition, 19 ground control points (GCPs) were established using Leica TS09 total station. The accuracy of DSM was assessed using root-mean-square error (RMSE) in X, Y, Z, XY, and XYZ components. The result showed that the DSM model has high accuracy, RMSE on the 12 calibrated GCPs for X, Y, Z, XY, XYZ is 1.1 cm, 1.9 cm, 0.8 cm, 2.2 cm, and 2.3 cm, respectively, whereas RMSE on the 7 checked GCPs is 1.8 cm, 2.4 cm, 3.2 cm, 3.0 cm, and 4.4 cm for X, Y, Z, XY, XYZ components, respectively. We concluded that small UAV and SfM are feasible and valid tools for 3D topographic mapping in complex terrains such as open-pit coal mine areas.

Recent advances in structure from motion (SfM) and dense matching algorithms enable surface reconstruction from unmanned aerial vehicle (UAV) images with high spatial resolution, allowing for new insights into earth surface processes. However, accuracy issues are inherent in parallel‐axes UAV image configurations. In this study, the quality of digital elevation models (DEMs) is assessed using images from a simulated UAV flight. Five different SfM tools and three different cameras are compared. If ground control points (GCPs) are not integrated into the adjustment process with parallel‐axes image configurations, significant dome‐effect systematic errors are observed, which can be reduced based on calibration parameters retrieved from a testfield captured with convergent images immediately before or after the UAV flight. A comparison between DEMs of a soil surface generated from UAV images and terrestrial laser‐scanning data show that natural surfaces can be very accurately reconstructed from UAV images, even when GCPs are missing and simple geometric camera models are considered.

Abstract. This paper looks into the latest achievement in the low-cost Unmanned Aerial Vehicle (UAV) technology in their capacity to map the semi-development areas. The objectives of this study are to establish a new methodology or a new algorithm in image registration during interior orientation process and to determine the accuracy of the photogrammetric products by using UAV images. Recently, UAV technology has been used in several applications such as mapping, agriculture and surveillance. The aim of this study is to scrutinize the usage of UAV to map the semi-development areas. The performance of the low cost UAV mapping study was established on a study area with two image processing methods so that the results could be comparable. A non-metric camera was attached at the bottom of UAV and it was used to capture images at both sites after it went through several calibration steps. Calibration processes were carried out to determine focal length, principal distance, radial lens distortion, tangential lens distortion and affinity. A new method in image registration for a non-metric camera is discussed in this paper as a part of new methodology of this study. This method used the UAV Global Positioning System (GPS) onboard to register the UAV image for interior orientation process. Check points were established randomly at both sites using rapid static Global Positioning System. Ground control points are used for exterior orientation process, and check point is used for accuracy assessment of photogrammetric product. All acquired images were processed in a photogrammetric software. Two methods of image registration were applied in this study, namely, GPS onboard registration and ground control point registration. Both registrations were processed by using photogrammetric software and the result is discussed. Two results were produced in this study, which are the digital orthophoto and the digital terrain model. These results were analyzed by using the root mean square errors and mean absolute error to determine the level of accuracy and the precision of photogrammetric products. It can be concluded that the new method of image registration by using the GPS onboard of the UAV produces medium accuracy result compared to the method that uses the ground control point. This new method can be used for the medium accuracy requirements. Unmanned Aerial Vehicle can be used for several applications, which requires a medium accuracy.

The present study evaluates the accuracy of Triangulation, Digital Elevation Model (DEM) and orthoimage generated by Cartosat-1 data by using Rational Polynomial Coefficient’s (RPC’s) provided with the Cartosat-1 satellite stereo (PAN) data. The study area is taken as Jaipur city, Rajasthan. The data acquired over Jaipur, on 18 May’ 2005 was analysed. The stereopair comprises of terrain ranging from plain area, agriculture fields, urban area, waterbody and Aravalli hill range. A Differential Global Positioning System (DGPS) survey was conducted to collect ground control points (GCPs) for satellite triangulation, DEM generation, orthoimage generation and subsequent analysis for accuracy assessment. Eighteen GCPs were measured through DGPS survey. Twelve GCPs are used for triangulation and six GCPs were used as check points. The overall RMSE for satellite triangulation is 0.753 pixel. Horizontal accuracy of 2.08 m and 1.74 m is achieved in X and Y direction, respectively. The vertical accuracy of 3.72 m has been assessed separately also by using independent check point in highly varying terrain.

Combined with the structure from motion (SfM) technique, unmanned aerial vehicles (UAVs) are powerful tools for generating high-resolution digital elevation models (DEMs) for application in hazard assessments. During our field observations in October 2015 at Langtang Village, which was destroyed by the Gorkha earthquake in April 2015, three different UAVs with mounted cameras were operated to evaluate the volume of the avalanche deposit covering the village. This study evaluated the performance of DEMs created from the different cameras on board those UAVs. Multiple DEMs for the different cameras, including Sony-α7R (PA7), Ricoh-GR (XGR), and Canon-IXUS (EIX), were created using SfM software. All DEMs were compared with a base DEM created from differential global positioning system survey data, which was obtained simultaneously with the UAV campaigns. The results show that the elevation difference of PA7-, XGR-, and EIX-DEMs are within ±0.14 m; the standard deviations of elevation difference range from 0.33 to 0.40 m. Although there were slightly larger differences in elevation on the southwest-to-west sides of the XGR- and EIX-DEMs, which can be attributed mainly to the flight paths and ground control point network, our DEMs are still of high enough quality to be used in hazard assessments.

<p>Nowadays Unmanned Aerial Vehicle (UAV) is one of the most utilized tools in the field of coastal geomorphology studies due to its efficiency and cost-effectiveness to carry out high-spatial and temporal resolution topographic surveys. The images produced by UAV surveys can be processed using Structure from Motion (SfM) photogrammetry, which allows 3D reconstruction of the terrain from the series of overlapping images. This research aimed to utilize UAV topographic surveys to characterize the geomorphological evolution of a portion of the dune belt that was subjected to a restoration in 2016. The study site is located in the protected natural area of the Bevano River mouth in Ravenna, (Northern Adriatic Coast, Italy). The reinforcement of the dune system was initiated since the zone is primarily characterized as a low-lying coastal area and is subjected to increasing environmental risks, such as coastal erosion, storm surge, groundwater and soil salinization. The restoration measure included two windbreak wooden fences, which were installed in front of the dune foot and parallel to the coast to stop wind and facilitate sand deposition and accumulation, to favor embryo dune formation and prevent sand loss toward the inland, out of the sedimentary cell. UAV topographic survey, coupled with GPS ground survey using Real-time Kinematic (RTK) positioning, were carried out from 2016 to 2021 in order to assess the geomorphological evolution of the area over time. SfM photogrammetry was carried out to generate the point cloud and orthomosaic images for each survey year using Agisoft Metashape Professional. Point cloud data were interpolated in ArcMap to create Digital Elevation Models (DEMs), while the orthomosaic images were utilized to confirm the possible sources of data noise in the model and assess vegetation changes. The collected GPS data points, including Ground Control Points (GCPs) and several dune profiles, were used to validate the DEMs. Then, the volumetric changes in sediment storage over time were calculated by using the DEM of Difference (DoD) approach under the Geomorphic Change Detection (GCD) extension toolbar in ArcMap. Probabilistic thresholding was used as the uncertainty analysis method for the volume calculation. The changes in dune height and slope were assessed using both the GCD and 2D profiler toolbar as well. The results show that the windbreak fence has proven to be an effective intervention to prevent dune erosion since significant geomorphological changes and vegetation colonization have occurred. Based on volume calculations, main sand accumulation was observed along the dune foot where the fences were established. The sand deposition has also reduced the slope steepness of the dune and some profiles exhibit embryo dune development. Erosion has only been evident in the northern beach portion towards the end of the fence. An increase in both pioneer species and stabilizing plants were also noticed on the dune front and crest, respectively.</p>

In this study, we generated Digital Elevation Model (DEM) from Unmanned Aerial Vehicle (UAV) to confirm the suitability of UAV to the tidal flat study. The DEMs were generated from aerial triangulation method using rotary-wing UAV. For the accurate generation of mosaic images and DEM, the distorted images occurred by interior and exterior orientation were corrected using camera calibration. In addition, we set up a Ground Control Points (GCPs) in order to correct of the UAV position error. Therefore, the DEM was obtained with geometric error less than 30 cm. The height of generated DEM by UAV was compared with the levelled elevation by RTK-GPS and TanDEM-X radar satellite DEM. From this study, we could confirm that accurate DEM of the tidal flat can be generated using UAV and these detailed spatial information about tidal flat will be widely used for tidal flat management.

To accurately assess landslide susceptibility and model debris flow paths, acquisition of high-resolution elevation data is essential. These data enable precise topographical analysis, considering factors like slope and curvature. But most of the time such high-resolution data will have inaccuracies due to vegetation, especially in tropical region. This study proposes a comprehensive approach that integrates field data and Structure from Motion (SfM) technology to rectify such DEM inaccuracies caused by dense vegetation. The goal is to enhance the accuracy of landslide simulations and volumetric analysis for effective post-disaster management. The Western Ghats, running parallel to India's western coast, has experienced a surge in rainfall-induced landslides, resulting in significant casualties in recent years. Notably, the Pettimudi village landslide in 2020 caused 70 deaths. The unique geomorphological features of the Western Ghats, such as concave curvature, colluvium deposits, and evidence of paleo landslides, contribute to the area's susceptibility. The study emphasizes the need for detailed assessment and mitigation strategies. The proposed method aims to improve the post-event high-resolution DEM accuracy by integrating field-collected elevation values and utilizing Agisoft Metashape software with the SfM algorithm. The rectification process involves combining elevation differences measured during a field study with photogrammetric elevation data. The field-collected elevation differences are crucial for rectifying these points, and enhancing the Digital Elevation Model (DEM) accuracy. The inaccessible source region is improved using a SfM-created DEM from drone footage, resulting in a more accurate post-event DEM. Correcting the 1 m DEM using an SfM-generated DEM proves challenging but significantly improves detail and accuracy, especially over the landslide source area. The impact of the rectification on accuracy is assessed by comparing volumes calculated from the initial DEM and the newly corrected DEM. The difference in volumes of debris depletion and accumulation, computed using initial and corrected DEMs, highlights variations, with depleted volumes being significantly larger due to the extensive depth increase over the landslide source region. These volumes are then utilized in Rapid Mass Movement Simulation (RAMMS) to validate the rectification process and enhance landslide impact predictions. Debris flow simulations in RAMMS, utilizing the rectified 1 m DEM, show specific outcomes at the landslide source region and toe of the landslide. The study emphasizes the importance of integrating SfM technology with field data to improve DEM accuracy, acknowledging the significance of additional field data for further refinement. The potential adoption of highly precise aerial imagery from Unmanned Aerial Vehicle (UAV) surveys is suggested to further enhance the SfM DEM. Debris flow modeling with RAMMS serves as a vital step in validating the accuracy and reliability of the rectified elevation model. Keywords: Landslides, Western Ghats, Pettimudi, Digital elevation model (DEM), Structure-from-Motion (SfM) technology, Rapid Mass Movement Simulation (RAMMS)

Precisely measuring the Earth’s changing surface on decadal to centennial time scales is critical for many science and engineering applications, yet long-term records of quantitative landscape change are often temporally and geographically sparse. Archives of scanned historical aerial photographs provide an opportunity to augment these records with accurate elevation measurements that capture the historical state of the Earth surface. Structure from Motion (SfM) photogrammetry workflows produce high-quality digital elevation models (DEMs) and orthoimage mosaics from these historical images, but time-intensive tasks like manual image preprocessing (e.g., fiducial marker identification) and ground control point (GCP) selection impede processing at scale. We developed an automated method to process historical images and generate self-consistent time series of high-resolution (0.5–2 m) DEMs and orthomosaics, without manual GCP selection. The method relies on SfM to correct camera interior and exterior orientation and a robust multi-stage co-registration approach using modern reference terrain datasets for geolocation refinement. We demonstrate the method using scanned images from the North American Glacier Aerial Photography (NAGAP) archive collected between 1967 and 1997. We present results for two sites with variable photo acquisition geometry and overlap — Mount Baker and South Cascade Glacier in Washington State, USA. The automated method corrects initial camera position errors of several kilometers and produces accurately georeferenced, high-resolution DEMs and orthoimages, regardless of camera configuration, acquisition geometry, terrain characteristics, and reference DEM properties. The average RMS reprojection error following bundle adjustment optimization was 0.67 px (0.15 m) for the 261 images contributing to 10 final DEM mosaics between 1970 and 1992 at Mount Baker, and 0.65 px (0.13 m) for the 243 images contributing to 18 individual DEMs between 1967 and 1997 at South Cascade Glacier. The relative accuracy of elevation values in the historical time series stacks was 0.68 m at Mount Baker and 0.37 m at South Cascade Glacier. Our products have reduced systematic error and improved accuracy compared to DEM products generated using SfM with manual GCP selection. Final elevation change measurement precision was ∼0.7–1.0 m over a 30-year period, enabling the study of processes with rates as low as ∼1-3 cm/yr. Our results demonstrate the potential of this scalable method to rapidly process archives of historical imagery and deliver new quantitative insights on long-term geodetic change and Earth surface processes.

Snow depth is a general input variable in many models of agriculture, hydrology, climate, and ecology. However, there are some uncertainties in the retrieval of snow depth by remote sensing. Errors occurred in snow depth evaluation under the D-InSAR methods will affect the accuracy of snow depth inversion to a certain extent. This study proposes a scheme to estimate spatial snow depth that combines remote sensing with site observation. On the one hand, this scheme adopts the Sentinel-1 C-band of the European Space Agency (ESA), making use of the two-pass method of differential interferometry for inversion of spatial snow depth. On the other hand, the 3DVAR (three dimensional variational) fusion algorithm is used to integrate actual snow depth data of virtual stations and real-world observation stations into the snow depth inversion results. Thus, the accuracy of snow inversion will be improved. This scheme is applied in the study area of Bayanbulak Basin, which is located in the central hinterland of Tianshan Mountains in Xinjiang, China. Observation data from stations in different altitudes are selected to test the fusion method. According to the results, most of the obtained snow depth values using interferometry are lower than the observed ones. However, after the fusion using the 3DVAR algorithm, the snow depth accuracy is slightly higher than it was in the inversion results (R2 = 0.31 vs. R2 = 0.50, RMSE = 2.51 cm vs. RMSE = 1.96 cm; R2 = 0.27 vs. R2 = 0.46, RMSE = 4.04 cm vs. RMSE = 3.65 cm). When compared with the inversion results, the relative error (RE) improved by 6.97% and 3.59%, respectively. This study shows that the scheme can effectively improve the accuracy of regional snow depth estimation. Therefore, its future application is of great potential.

Ground control points (GCPs) are used in forest surveys employing unmanned aerial vehicle (UAV)-based structure from motion (SfM). In that context, the influence of the surrounding environment on GCP placement requires further analysis. This study investigated the effects of GCP placement and the surrounding environment on the estimation of forest information by UAV-SfM. Forest resource estimation was performed using UAV (Inspire2) aerial images and SfM analysis (via Pix4Dmapper) under varying environmental conditions around GCPs within the same forest stand. The results indicated that GCP placement had no significant effect on SfM processing, tree top extraction (the number of extracted target trees was 151 or 150), or tree crown area estimation (RMSEs ranged from approximately 5 to 6.5 m2). However, when GCPs were placed in open areas, the tree height estimation accuracy improved, without significant differences between estimated and measured values (patterns A, B, D and E, had RMSEs of 1.60 to 3.09 m; patterns C and D had RMSEs of 5.69 to 7.92 m). These findings suggest that in UAV-SfM-based forest resource surveys, particularly for tree height estimation, both the number and placement of GCPs, as well as the surrounding environment, are crucial in enhancing estimation accuracy.

Seasonal snowpack is one of the primary sources of freshwater for rivers in the Indian Himalaya. It plays a vital role in regional hydrology, climate variability, and water resource management. To understand these processes and their impact on the community, spatial and temporal monitoring of snow is essential. Snow depth is a key parameter for monitoring snow. However, in the Himalayas, due to accessibility challenges and logistical constraints, limited snow depth observations are available. To address this gap and estimate snow depth at high spatial and temporal resolution, we develop a model using polarimetric parameters derived from Sentinel-1 SAR data, topographic and auxiliary data, integrated with field-based observations in the European Alps and Grand Mesa, USA. Field observations are filtered to match the Sentinel-1 pass, ensuring consistency between field-based observations and satellite acquisition. Our model employs topographic data (e.g., elevation, slope, and aspect) from the Copernicus 30 m digital elevation model, auxiliary parameters (such as day of the season (DoS)), Forest cover fraction from MODIS, and Sentinel-1 SAR-based polarimetric parameters (cross-ratio, entropy, Stokes parameters, alpha), ensuring a topographically dependent snow depth distribution. Sensitivity analysis is performed using SHAP (SHapley Additive Explanations) to identify the most critical parameters for estimating snow depth. The model shows a Mean Absolute Error (MAE) of 0.04m, a root mean square error (RMSE) of 0.15m, with a test R-squared (R2) of 0.95 and a cross-validation correlation coefficient (R) of 0.98 in the European Alps. We transfer the model to the mountains in the Chandra Bagha basin (33°01′N°, 76°40′E) of the Indian Himalayas. Our transferred model highlights the potential of estimating snow depth in data-scarce regions while resolving the spatial and temporal details.