Quantifying spatial uncertainties in structure from motion snow depth mapping with drones in an alpine environment

Due to the heterogeneous nature of alpine snow distribution, advances in hydrological monitoring and forecasting for water resource management require an increase in the frequency, spatial resolution and coverage of field observations. Such detailed snow information is also needed to foster advances in our understanding of how snowpack affects local ecology and geomorphology. Although recent use of structure-from-motion multi-view stereo (SFM-MVS) 3D reconstruction techniques combined with aerial image collection using drones has shown promising potential to provide higher spatial and temporal resolution snow depth data for snowpack monitoring, there still remain challenges to produce high-quality data with this approach. These challenges, which include differentiating observations from noise and overcoming biases in the elevation data, are inherent in digital elevation model (DEM) differencing. A key issue to address these challenges is our ability to quantify measurement uncertainties in the SFM-MVS snow depths which can vary in space and time. The purpose of this thesis was to develop data-driven approaches for spatially quantifying, characterizing and reducing uncertainties in SFM-MVS snow depth mapping in alpine areas. Overall, this thesis provides a general framework for performing a detailed analysis of the spatial pattern of SFM-MVS snow depth uncertainties, as well as provides an approach for correction of snow depth errors due to changes in the sub-snow topography occurring between survey acquisition dates. It also contributes to the growing support of SFM-MVS combined with imagery acquired from drones as a suitable surveying technique for local scale snow distribution monitoring in alpine areas

Fusion of Interferometric SAR and Photogrammetric Elevation Data

Neurology & clinical neurophysiolog

dem extraction from archive aerial photos accuracy assessment in areas of complex topography

AbstractThe aim of this study is to analyze the accuracy of a Digital Elevation Model (DEM) created with photogrammetric techniques from stereoscopic pairs of aerial photos in areas with complex geomorphologic characteristics. The evaluation of DEM and derived geomorphometric parameters was conducted by comparison with other standard DEM products (i.e. TINITALY/01 and ASTER GDEM-V2) and by accuracy assessment based on Check Points (CPs). The validation process includes the comparison of elevation profiles, the calculation of DEM accuracies, and the evaluation of the effect of slope and aspect on the DEM accuracy.The produced DEM accurately represent complex terrain (RMSE = 4.90 m), thus providing information suitable for local-scale geomorphometric analysis. The obtained accuracy resulted slightly worse than TINITALY/01 (RMSE = 2.53 m), but significantly better than ASTER GDEM (RMSE = 12.95 m). These results confirm that photo-based DEM extraction can be a very competitive and precise methodology if other..

Airborne LiDAR for DEM generation: some critical issues

Airborne LiDAR is one of the most effective and reliable means of terrain data collection. Using LiDAR data for DEM generation is becoming a standard practice in spatial related areas. However, the effective processing of the raw LiDAR data and the generation of an efficient and high-quality DEM remain big challenges. This paper reviews the recent advances of airborne LiDAR systems and the use of LiDAR data for DEM generation, with special focus on LiDAR data filters, interpolation methods, DEM resolution, and LiDAR data reduction. Separating LiDAR points into ground and non-ground is the most critical and difficult step for DEM generation from LiDAR data. Commonly used and most recently developed LiDAR filtering methods are presented. Interpolation methods and choices of suitable interpolator and DEM resolution for LiDAR DEM generation are discussed in detail. In order to reduce the data redundancy and increase the efficiency in terms of storage and manipulation, LiDAR data reduction is required in the process of DEM generation. Feature specific elements such as breaklines contribute significantly to DEM quality. Therefore, data reduction should be conducted in such a way that critical elements are kept while less important elements are removed. Given the highdensity characteristic of LiDAR data, breaklines can be directly extracted from LiDAR data. Extraction of breaklines and integration of the breaklines into DEM generation are presented

Terrestrial structure-from-motion: spatial error analysis of roughness and morphology

Structure-from-Motion (SfM) photogrammetry is rapidly becoming a key tool for morphological characterisation and change detection of the earth surface. This paper demonstrates the use of Terrestrial Structure-from-Motion (TSfM) photogrammetry to acquire morphology and roughness data at the reach-scale in an upland gravel-bed river. We quantify 1) spatially-distributed error in TSfM derived Digital Elevation Models (DEMs) and 2) identify differences in roughness populations acquired from TSfM photogrammetry versus TLS. We identify an association between local topographic variation and error in the TSfM DEM. On flatter surfaces (e.g. bar and terrace surfaces), the difference between the TSfM and TLS DEMs are generally less than ±0.1 m. However, in areas of high topographic variability (>0.4 m) such as berm or terrace edges, differences between the TSfM and TLS DEMs can be up to ±1 m. Our results suggest that grain roughness estimates from the TSfM point cloud generate values twice those derived from the TLS point cloud on coarse berm areas, and up to four-fold those derived from the TLS point cloud over finer gravel bar surfaces. This finding has implications when using SfM data to derive roughness metrics for hydrodynamic modelling. Despite the use of standard filtering procedures, noise pertains in the SfM DEM and the time required for its reduction might partially outweigh the survey efficiency using SfM. Therefore, caution is needed when SfM surveys are employed for the assessment of surface roughness at a reach-scale

Digital Elevation Modeling of Inaccessible Slope by Using Close-range Photogrammetric Data

Digital Elevation Model (DEM) currently is extensively used extensively in various applications such as for natural hazard assessment and monitoring of high risk areas. DEM data source of inaccessible areas can be collected by using several methods, but mostly are costly and requires sophisticated instruments. Due to these conditions, close-range photogrammetry offers a low cost alternative solution. Materials presented in this thesis are based on the experiments to explain the application of close-range photogrammetry with the aid of commercial digital pocket camera as DEM data collection tools, applied on inaccessible slope areas. The analysis covers calibration of the camera and surveying instruments, DEM data collections, data processing and visualization, together with DEM quality measures. The data collections are accomplished on several study areas with different topographical characteristics by using close-range photogrammetry technique. The sampling points were selected on stereo model, by using three types of sampling methods. The DEM quality measures are assessed by following elevation interpolation error and volumetric difference error analyses. The representation of the DEM is generated using TIN-based (Triangular Irregular Network) approach. The result shows that the method is able to be applied for three dimensional (3D) modeling of potentially unstable slope areas, with accuracy of less than 15 cm in RMS for elevation error and is less than 1% in volume error. The result has indicated that topographical condition has not affected the accuracy of generated DEM. Improvement of point density radically enhances the DEM’s quality, up to a certain level of point density beyond which the increment of the accuracy is not significant. The difference setting of focal length has also influences the quality of captured images, and drastically affects the accuracy of the DEM. If the accuracy of the DEM is a matter of concern, the preferred sampling method is selective sampling, while if accuracy and DEM’s time generation are the concern the most effective sampling method is regular sampling method. Since there was no permanent points on the observed slope surface, velocity and direction of landslide could not be accurately determined. However the distribution of massmovement and elevation changed on the slope surfaces can be modeled through spatialcalculation of overlaying DEMs together with profiling of cross-section and longitudinalsection of the generated DEMs

A pipeline for automated processing of declassified Corona KH-4 (1962-1972) stereo imagery

This study was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDA20100300) and the Swiss National Science Foundation (200021E 177652/1) within the framework of the DFG Research Unit GlobalCDA (FOR2630).The Corona KH-4 reconnaissance satellite missions acquired panoramic stereo imagery with high spatial resolution of 1.8–7.5m from 1962-1972. The potential of 800,000+ declassified Corona images has not been leveraged due to the complexities arising from handling of panoramic imaging geometry, film distortions and limited availability of the metadata required for georeferencing of the Corona imagery. This paper presents the Corona Stereo Pipeline (CoSP): A pipeline for processing of Corona KH-4 stereo panoramic imagery. CoSP utilizes deep learning based feature matcher SuperGlue to automatically match features point between Corona KH-4 images and recent satellite imagery to generate Ground Control Points (GCPs). To model the imaging geometry and the scanning motion of the panoramic KH-4 cameras, a rigorous camera model consisting of modified collinearity equations with time-dependent exterior orientation parameters is employed. Using the entire frame of the Corona image, bundle adjustment with well-distributed GCPs results in an average standard deviation or σ0 of less than two pixels. We evaluate fiducial marks on the Corona films and show that pre-processing the Corona images to compensate for film bending improves the 3D reconstruction accuracy. The distortion pattern of image residuals of GCPs and y-parallax in epipolar resampled images suggest that film distortions due to long-term storage likely cause systematic deviations of up to six pixels. Compared to the SRTM DEM, the Corona DEM computed using CoSP achieved a Normalized Median Absolute Deviation of elevation differences of ≈ 4m over an area of approx. 4000km2 after a tile-based fine coregistration of the DEMs. We further assess CoSP on complex scenes involving high relief and glacierized terrain and show that the resulting DEMs can be used to compute long-term glacier elevation changes over large areas.PostprintPeer reviewe