Vertical Error Research Articles

Modified sequential processing terrain referenced navigation considering slant range measurement

This study proposes a modified terrain referenced navigation (TRN) algorithm considering slant range measurement. The TRN estimates a vehicle's position by comparing the measured terrain information with digital elevation map data. A conventional TRN assumes that a vertical range is measured by a radar altimeter and the measurement error exists only in the vertical direction. Recently, many researches have focused on applying the slant range sensor data, such as light detection and ranging or interferometric radar altimeter data to improve the TRN performance. In such cases, not only the vertical error but also the horizontal error components are generated by slant range measurement errors and the effect of these error components increases significantly as the flight altitude and the slant range measurement errors increase. Therefore, a proper TRN algorithm should be considered to estimate the vehicle position in a stable and accurate manner. In this study, a modified extended Kalman filter [EKF]-based TRN algorithm (ETRN) considering the slant range measurement and the corresponding measurement variance is proposed. The proposed algorithm converts the ETRN measuring the slant range to the conventional ETRN measuring the vertical range. Also, the proposed algorithm provides robust and accurate TRN results by accounting for the vertical and horizontal error components generated by the slant range errors.

Estimating Coastal Digital Elevation Model Uncertainty

Amante, C.J., 2018. Estimating coastal digital elevation model uncertainty.Integrated bathymetric–topographic digital elevation models (DEMs) are representations of Earth's solid surface that extend across the coastal land–water interface. DEMs are fundamental to the modeling of coastal processes, including tsunami, storm-surge, and sea-level-rise inundation. Vertical errors in coastal DEMs are deviations in elevation values from the actual seabed or land surface, which originate from the (1) elevation measurements, (2) datum transformation that converts bathymetric and topographic measurements to a common vertical reference system, (3) spatial resolution of the DEM, and (4) interpolative gridding technique that estimates elevations in areas unconstrained by measurements. The magnitude and spatial distribution of the vertical errors are typically unknown, and a DEM uncertainty surface is a statistical assessment of the likely magnitude of these errors. The National Oceanic and Atmospheric Administration National Centers for Environmental Information develops DEMs for United States' coastal communities. This study describes a methodology to derive uncertainty surfaces that estimate coastal DEM vertical errors at the DEM cell–level. A coastal DEM south of Sarasota, Florida is the case study for deriving uncertainty surfaces. Results indicate that large vertical uncertainty exists in deeper waters offshore with sparse echo-sounder measurements, and small vertical uncertainty exists on flat terrains with dense light detection and ranging measurements. The estimated uncertainty can be propagated into the modeling of coastal processes that utilize DEMs by deriving numerous plausible DEM realizations within the uncertainty bounds. The numerous DEMs realizations can then produce an ensemble of coastal modeling results, and in turn, better-informed coastal management decisions.

Evaluation of the 3D Augmented Reality-Guided Intraoperative Positioning of Dental Implants in Edentulous Mandibular Models.

This research aimed to propose a three-dimensional (3D) augmented reality navigation method with point cloud-based image-patient registration that could merge virtual images in the real environment for dental implants using a 3D image overlay and to evaluate its feasibility. A total of 12 rapid prototyping mandibular models were fabricated using a 3D printing method and were divided into two groups: 3D augmented reality-guided group and traditional two-dimensional (2D) image-guided group. A point cloud-based preoperative image-to-patient registration method was introduced to replace the traditional point-to-point registration. After the registration, dental implant surgery was performed in the two model groups using an augmented reality-guided navigation method and a traditional two-dimensional image-guided navigation method. The planned and actual postoperative implant positions were compared for measuring positional implantation errors. The surgery time was also recorded and compared between the two groups. In the model experiment, the root-mean-square deviation of registration was 0.54 mm, and the implant surgery results showed < 1.5-mm mean linear deviation and < 5.5-degree angular deviation. The augmented reality-guided implantation showed smaller horizontal, vertical, and angular errors in the apical areas of the central incisor and the canine region. The surgery time using the augmented reality-guided navigation method was significantly shorter than that using the two-dimensional (2D) image-guided navigation method (P < .05). Moreover, the volunteer experiment demonstrated that the preoperative 3D models in situ accurately overlaid onto the surgical site. The proposed point cloud-based registration method can achieve excellent registration accuracy. Dental implant placement guided by the proposed 3D augmented reality navigation method showed better accuracy and applicability, as well as higher efficiency, than the traditional 2D image navigation method.

Retrievals of tropospheric ozone profiles from the synergism of AIRS and OMI: methodology and validation

Abstract. The Tropospheric Emission Spectrometer (TES) on the A-Train Aura satellite was designed to profile tropospheric ozone and its precursors, taking measurements from 2004 to 2018. Starting in 2008, TES global sampling of tropospheric ozone was gradually reduced in latitude, with global coverage stopping in 2011. To extend the record of TES, this work presents a multispectral approach that will provide O3 data products with vertical resolution and measurement error similar to TES by combining the single-footprint thermal infrared (TIR) hyperspectral radiances from the Aqua Atmospheric Infrared Sounder (AIRS) instrument and the ultraviolet (UV) channels from the Aura Ozone Monitoring Instrument (OMI). The joint AIRS+OMI O3 retrievals are processed through the MUlti-SpEctra, MUlti-SpEcies, MUlti-SEnsors (MUSES) retrieval algorithm. Comparisons of collocated joint AIRS+OMI and TES to ozonesonde measurements show that both systems have similar errors, with mean and standard deviation of the differences well within the estimated measurement error. AIRS+OMI and TES have slightly different biases (within 5 parts per billion) vs. the sondes. Both AIRS and OMI have wide swath widths (∼1650 km for AIRS; ∼2600 km for OMI) across satellite ground tracks. Consequently, the joint AIRS+OMI measurements have the potential to maintain TES vertical sensitivity while increasing coverage by 2 orders of magnitude, thus providing an unprecedented new data set with which to quantify the evolution of tropospheric ozone.

EVALUATION AND CALIBRATION OF FIXED-WING UAV MOBILE MAPPING SYSTEM EQUIPPED WITH LIDAR AND OPTICAL SENSORS

Abstract. In this paper, a mobile mapping system mounted on the UAV is presented and evaluated. The NEO3 UAV platform is an 11 kg fixed-wing designed by the MSP company. The UAV is equipped with a Riegl miniVUX-1UAV laser scanner, which is integrated with the GNSS/INS system of Applanix APX-15 UAV and two Sony Alfa 6000 cameras collecting images in the following spectrum: visible for the first camera and near-infrared for the second camera. The UAV mobile system presented is dedicated to the acquisition of multisource data for levee monitoring using active and passive remote sensing data. In this paper, the effectiveness of the ultralight laser scanner, which has not been mounted on the fixed-wing platforms so far, was verified in the experiment with respect to data density and accuracy. The example analyses were conducted using ground control points and surfaces measured with a terrestrial laser scanner and visible in point clouds obtained with a dense image matching algorithm. Analyses showed that the achieved accuracy is much related to trajectory accuracy. The final DTM created from the data collected during the float status of the GNSS measurements of the trajectory provided twice less accurate data than during fixed status (vertical error approximately 20 cm and 10 cm respectively).

Accuracy Assessment of Point Clouds from LiDAR and Dense Image Matching Acquired Using the UAV Platform for DTM Creation

In this paper, the results of an experiment about the vertical accuracy of generated digital terrain models were assessed. The created models were based on two techniques: LiDAR and photogrammetry. The data were acquired using an ultralight laser scanner, which was dedicated to Unmanned Aerial Vehicle (UAV) platforms that provide very dense point clouds (180 points per square meter), and an RGB digital camera that collects data at very high resolution (a ground sampling distance of 2 cm). The vertical error of the digital terrain models (DTMs) was evaluated based on the surveying data measured in the field and compared to airborne laser scanning collected with a manned plane. The data were acquired in summer during a corridor flight mission over levees and their surroundings, where various types of land cover were observed. The experiment results showed unequivocally, that the terrain models obtained using LiDAR technology were more accurate. An attempt to assess the accuracy and possibilities of penetration of the point cloud from the image-based approach, whilst referring to various types of land cover, was conducted based on Real Time Kinematic Global Navigation Satellite System (GNSS-RTK) measurements and was compared to archival airborne laser scanning data. The vertical accuracy of DTM was evaluated for uncovered and vegetation areas separately, providing information about the influence of the vegetation height on the results of the bare ground extraction and DTM generation. In uncovered and low vegetation areas (0–20 cm), the vertical accuracies of digital terrain models generated from different data sources were quite similar: for the UAV Laser Scanning (ULS) data, the RMSE was 0.11 m, and for the image-based data collected using the UAV platform, it was 0.14 m, whereas for medium vegetation (higher than 60 cm), the RMSE from these two data sources were 0.11 m and 0.36 m, respectively. A decrease in the accuracy of 0.10 m, for every 20 cm of vegetation height, was observed for photogrammetric data; and such a dependency was not noticed in the case of models created from the ULS data.

VALIDATION OF THE DIGITAL ELEVATION MODEL (SRTM) WITH GNSS SURVEYING APPLIED TO THE MIRIM LAGOON HYDROGRAPHIC BASIN

Abstract Between 2013 and 2014, a kinematic positioning based on the Global Navigation Satellite System (GNSS) was carried out for this research work. This GNSS survey resulted in 275916 points with tridimensional coordinates in the cross-border basin area of 58205 km2 called Mirim Lagoon Hydrographic Basin, located in south of Rio Grande do Sul (Brazil) and west of Uruguay. This study aims at showing the methodology firstly and, furthermore, results regarding the validation of the vertical accuracy of the DEM SRTM through kinematic positioning by GNSS, in the Mirim Lagoon Hydrographic Basin region. Also, the GNSS surveying data was post-processed with the Precise Point Positioning (PPP) method, and the ellipsoidal height was converted into orthometric height through the software INTPT geoid. During this study, the geopotential model (EGM96) was used to transform altitude differences between two countries, Brazil and Uruguay. Results showed that the vertical mean absolute error of the DEM SRTM vary from 0.07 m to ± 9.9m with average of -0.28m. This vertical accuracy is better than the absolute vertical accuracy value of ±16m published in the SRTM data specification and validates the DEM SRTM.

Holocene mangrove dynamics and relative sea-level changes along the Tanzanian coast, East Africa

There is continued uncertainty regarding the rate, timing, duration and direction of Holocene sea-level for the Indian Ocean, and indeed the wider tropical realm. We present the first synthesis, and a new chronology, for Holocene relative sea-level (RSL) using a range sediment cores retrieved from mangrove ecosystems in three locations along coastal Tanzania. This study applies the relationship of ratios between the key mangrove taxa of Sonneratia:(Bruguiera/Ceriops) (S/BC) (ranging from 0 to 22.9) and Sonneratia:Rhizophora (S/R) (ranging from 0 to 2.29), vegetation and altitude to interpret mangrove dynamics and refine the vertical errors associated with relative sea level change. The variations in mangrove taxa ratios in the sediment cores obtained from each site shows mangrove development at different periods during the Holocene from around 7900 cal yr BP. An early to mid-Holocene RSL rise occurred from ∼7900 to ∼4600 cal yr BP that may have reached a higher level than present. A lower RSL occurred after 4600 cal yr BP, resulting in mangroves retreating seaward at all three study locations, before a low magnitude RSL rise occurred between 4400 and 2000 cal yr BP. Another RSL rise is recorded at ∼500 cal yr BP before falling to a level lower than present at ∼100 cal yr BP. There is evidence of a recent RSL rise recorded from mangrove ratios during the last century. In addition, the sedimentation rates among sites are relatively different due to different altitudinal ranges with freshwater input, sediment supply and progradation having significantly more effect in the Rufiji Delta (2.1–10.9 mm cal yr−1) than at the Zanzibar sites (0.3–6.6 mm cal yr−1).

Reprojection Alignment for Trajectory Perturbation Estimation in Microtomography

For standard laboratory microtomography systems, acquired radiographs do not always adhere to the strict geometrical assumptions of the reconstruction algorithm. The consequence of this geometrical inconsistency is that the reconstructed tomogram contains motion artifacts, e.g., blurring, streaking, double-edges. To achieve a motion-artifact-free tomographic reconstruction, one must estimate, and subsequently correct for, the per-radiograph experimental geometry parameters. In this paper, we examine the use of re-projection alignment (RA) to estimate per-radiograph geometry. Our simulations evaluate how the convergence properties of RA vary with: motion-type (smooth versus random), trajectory (helical versus discrete-sampling ‘space-filling’ trajectories) and tomogram resolution. The idealized simulations demonstrate for the space-filling trajectory that RA convergence rate and accuracy is invariant with regard to the motion-type and that the per-projection motions can be estimated to less than 0.25 pixel mean absolute error by performing a single quarter-resolution RA iteration followed by a single half-resolution RA iteration. The direct impact is that, for the space-filling trajectory, one can incorporate RA in an iterative multi-grid reconstruction scheme with only a single RA iteration per multi-grid resolution step. We also find that for either trajectory, slowly varying vertical errors cannot be reliably estimated by employing the RA method alone; such errors are indistinguishable from a trajectory of different pitch. This has minimal effect in practice because RA can be combined with reference frame correction which is effective for correcting low-frequency errors.

Adaptive robust tracking control for an offshore ship-mounted crane subject to unmatched sea wave disturbances

Abstract An offshore ship-mounted crane consisting of a trolley, a wire, and a payload, is a typical nonlinear underactuated system, which suffers from unmatched disturbances mainly caused by sea waves and currents. Besides, unknown or uncertain parameters may cause vertical positioning errors or make accurate gravity compensation impossible, which may induce various risks during the transportation process. In terms of the aforementioned problems, this paper studies the adaptive robust tracking control problem for an offshore ship-mounted crane. In particular, a new adaptive robust coupling control approach, with adaptive laws included to deal with unknown parameters, and robust terms included to handle unknown disturbances, especially unmatched disturbances, is constructed in this paper, which achieves simultaneous load swing suppression and disturbances elimination. Without any approximation to the original nonlinear model, it is rigorously proven that the proposed method can ensure the stability of the overall crane system’s equilibrium point, as supported by Lyapunov techniques. Finally, some contrast simulations and experimental results are collected to verify the superior performance of the proposed controller.

Lab-built terrestrial laser scanner self-calibration using mounting angle error correction

Systematic errors generated from internal misalignments of a lab-built terrestrial laser scanner (TLS) need to be calibrated to improve the positional accuracy of point-cloud. Hence, an angle measurement error model was established by the ray-tracing method involving five types of mounting angle errors, which were estimated by two-face method and network method. Experimental results show that the two errors including mirror tilt error and vertical index offset error are regarded as main systematic errors for lab-built TLS, and consequently the positional accuracy of the point-cloud can be improved in horizontal and vertical directions. The proposed self-calibration method can be used for other TLS by adjusting the angle measurement error model.

Autonomous Adaptive Control System for Airplane Landing

AbstractThe paper presents a new autonomous adaptive system for the control of airplanes during landing in longitudinal plane. For the first stage of landing in longitudinal plane, the main variables to be controlled are the glide slope angle and longitudinal velocity; during the second main stage of landing in longitudinal plane, the vertical velocity error and the airplane's longitudinal velocity are controlled. The new robust control architecture has linear subsystems, for which the relative degrees are calculated; the new architecture will also consists of a dynamic compensator, a linear observer, and two reference models, their design being accomplished with respect to the calculated relative degrees. The signal estimated by the observer is useful for training a neural network – an adaptive subsystem of the architecture that provides the adaptive component of the control law. In the case of actuators having nonlinear dynamics, pseudo control hedging blocks are used to cancel the adapting difficulties of the neural networks. The new adaptive architecture is software implemented and validated by complex numerical simulations.

Motion Constraints and Vanishing Point Aided Land Vehicle Navigation.

In the typical Inertial Navigation System (INS)/ Global Navigation Satellite System (GNSS) setup for ground vehicle navigation, measures should be taken to maintain the performance when there are GNSS signal outages. Usually, aiding sensors are utilized to reduce the INS drift. A full motion constraint model is developed allowing the online calibration of INS frame with respect to (w.r.t) the motion frame. To obtain better heading and lateral positioning performance, we propose to use of vanishing point (VP) observations of parallel lane markings from a single forward-looking camera to aid the INS. In the VP module, the relative attitude of the camera w.r.t the road frame is derived from the VP coordinates. The state-space model is developed with augmented vertical attitude error state. Finally, the VP module is added to a modified motion constrains module in the Extended Kalman filter (EKF) framework. Simulations and real-world experiments have shown the validity of VP-based method and improved heading and cross-track position accuracy compared with the solution without VP. The proposed method can work jointly with conventional visual odometry to aid INS for better accuracy and robustness.

On the use of global DEMs in ecological modelling and the accuracy of new bare-earth DEMs

It is now widely acknowledged that the increasing availability of remotely sensed data facilitates ecological modelling. Digital elevation models (DEMs) are arguably one of the most common remote sensing products used in this context. Topographic indices (e.g. slope, orientation, rugosity) derived from DEMs are widely used as surrogates for field-measured environmental variables. Available global DEMs, such as those from the shuttle radar topography mission (SRTM), however, do not provide information on bare-earth elevation as they measure elevation of the highest objects above the ground (e.g. canopy). This affects the derived topographic indices and limits the use of global DEMs in ecological modelling. Unfortunately, most ecological studies ignore this limitation despite the fact that methods to remove the vegetation offset have been developed. We used high resolution LiDAR DTM to assess the accuracy of two newly available global bare-earth DEMs where such methods were applied and to compare them with the SRTM DEM. Furthermore, we assessed the effect of DEMs’ vertical error on species distribution models (SDMs) by calculating slope and topographic wetness index (TWI) from these different models and evaluating their suitability for SDMs by adopting a virtual species approach. We simulated virtual species based on slope and TWI derived from accurate LiDAR DTM at three resolutions (30 m, 90 m and 900 m) and developed univariate generalized models to assess the performance of the bare-earth and SRTM DEMs. Our results show that the vertical error in both newly available, vegetation-corrected global DEMs is indeed successfully reduced. The overall vertical root mean squared error (RMSE) was 10.52 m for SRTM, while it was 6.80 m and 6.25 m for the two global bare-earth DEMs. The effect of the vertical error on SDMs was most significant at finer spatial resolutions. Using SRTM DEM, as opposed to a more accurate bare-earth DEM, led to a decline in area under curve (AUC) values from 0.94 to 0.77. SDMs fitted with slope and TWI derived from new global bare-earth DTMs performed slightly better than SRTM. Since methods for vegetation-offset removal in DEMs exist and corrected DEMs are freely available, we argue that the vertical accuracy of DEMs should be more consistently considered. Local, high-accuracy DEMs should be used where available; in remaining instances, however, global DEMs where vertical bias was minimized should be used in ecological modelling. Further improvement of global DEMs at 30 m and better resolutions are needed to enhance accuracy of derived indices and ecological models.

Comparative accuracy of the AW3D30 DSM, ASTER GDEM, and SRTM1 DEM: A case study on the Zaoksky testing ground, Central European Russia

ABSTRACTQuasi-global, medium-resolution land surface models – the Shuttle Radar Topography Mission digital elevation model (SRTM1 DEM), the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM (ASTER GDEM), and the Advanced Land Observing Satellite World 3D digital surface model (AW3D30 DSM) – are widely used in geosciences. We carried out a comparative analysis of accuracy for these models exemplified by the Zaoksky testing ground, Central European Russia. First, we extracted related portions from the SRTM1 DEM, ASTER GDEM, and AW3D30 DSM for the study area. In the further analysis, we used 69 ground control points (GCPs) within the testing ground, as well as a reference DEM photogrammetrically generated from aerial stereo images. For each GCP, we determined an elevation value in four models and a related vertical error. For each model, we carried out a statistical analysis of vertical errors. To visualize the spatial distribution of vertical errors, we calculated matrices of residuals between the reference DEM and the quasi-global models. The results demonstrate that the AW3D30 DSM can be considered the most accurate quasi-global, medium-resolution land surface model. We do not recommend the use of the ASTER GDEM because it is marked by high errors with chaotically distributed hummock-like patterns.

UAS as tools for rapid detection of storm-induced morphodynamic changes at Camposoto beach, SW Spain

ABSTRACTThe increasing pressures and hazards derived from human action and climate change make the implementation of coastal monitoring plans a requirement for assessing coastal responses and evolution. For this reason, this work aims to use unmanned aerial systems (UAS) in combination with Structure from Motion algorithms for detecting morphodynamic changes caused by a storm in Camposoto beach (SW Spain). This approach provided pre- and post-storm digital elevation models of the study area with root mean square vertical errors of 6.89 and 5.54 cm, respectively, that allowed the identification of storm-induced gain and loss of sediment using Geomorphic Change Detection software. Furthermore, it also captured the response of the system against the action of specific marine processes such as storm-induced swash, collision, and overwash. The results obtained highlight the importance of an optimum flight plan design (e.g. the use of 85–75% front and side image overlap, respectively) and enhance the suitability of the UAS technology to satisfy the demands and needs in coastal monitoring studies in comparison with the traditional techniques, namely aerial photogrammetry, satellite imagery, and/or light detection and ranging.

Reducing representativeness and sampling errors in radio occultation–radiosonde comparisons

Abstract. Radio occultation (RO) and radiosonde (RS) comparisons provide a means of analyzing errors associated with both observational systems. Since RO and RS observations are not taken at the exact same time or location, temporal and spatial sampling errors resulting from atmospheric variability can be significant and inhibit error analysis of the observational systems. In addition, the vertical resolutions of RO and RS profiles vary and vertical representativeness errors may also affect the comparison. In RO–RS comparisons, RO observations are co-located with RS profiles within a fixed time window and distance, i.e. within 3–6 h and circles of radii ranging between 100 and 500 km. In this study, we first show that vertical filtering of RO and RS profiles to a common vertical resolution reduces representativeness errors. We then test two methods of reducing horizontal sampling errors during RO–RS comparisons: restricting co-location pairs to within ellipses oriented along the direction of wind flow rather than circles and applying a spatial–temporal sampling correction based on model data. Using data from 2011 to 2014, we compare RO and RS differences at four GCOS Reference Upper-Air Network (GRUAN) RS stations in different climatic locations, in which co-location pairs were constrained to a large circle (∼ 666 km radius), small circle (∼ 300 km radius), and ellipse parallel to the wind direction (∼ 666 km semi-major axis, ∼ 133 km semi-minor axis). We also apply a spatial–temporal sampling correction using European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim) gridded data. Restricting co-locations to within the ellipse reduces root mean square (RMS) refractivity, temperature, and water vapor pressure differences relative to RMS differences within the large circle and produces differences that are comparable to or less than the RMS differences within circles of similar area. Applying the sampling correction shows the most significant reduction in RMS differences, such that RMS differences are nearly identical to the sampling correction regardless of the geometric constraints. We conclude that implementing the spatial–temporal sampling correction using a reliable model will most effectively reduce sampling errors during RO–RS comparisons; however, if a reliable model is not available, restricting spatial comparisons to within an ellipse parallel to the wind flow will reduce sampling errors caused by horizontal atmospheric variability.

Quality assessment of GNSS observations from an Android N smartphone and positioning performance analysis using time-differenced filtering approach

The development of low-cost GNSS chips inspired the development of advanced positioning, navigation and timing devices. In ‘I/O of 2016’, Google announced that GNSS observations from devices running the Android version 7 operating system would be available to developers. Providing GNSS application developers the opportunity to develop advanced processing algorithms for accurate position estimation using pseudorange, Doppler and carrier phase observations. The quality of GNSS observations from Android smartphones and their accuracy in estimating position is assessed. The observed carrier-to-noise density ratio (C/N0), pseudorange noise, pseudorange rate error and phase rate error of GNSS observations are evaluated. The results demonstrate that the average (C/N0) value is approximately 10 dB-Hz lower than the representative values obtained from a geodetic-quality antenna and receiver. The station single-difference pseudorange residuals on all available signals vary from − 20 to 20 m, and the value of pseudorange rate varies within ± 10 m/s. In addition, the phase rate and Doppler reaches approximately ± 0.2 m/s. Different from the geodetic receivers, the signal noise ratio (SNR) obtained from smartphone varies more significantly, regardless of elevation angle. Therefore, the SNR-dependent weighting method is preferred during data processing. Furthermore, the results of the static data analysis show that the horizontal and vertical RMS position errors are less than 0.8 and 1.4 m, respectively, when Doppler and phase observations are incorporated into the positioning solution.

UAS &amp; SfM-based approach to Monitor Overwash Dynamics and Beach Evolution in a Sandy Spit

Talavera, L.; Del Rio, L.; Benavente, J.; Barbero, L., and Lopez-Ramirez, J.A., 2018. UAS & SfM-based approach to Monitor Overwash Dynamics and Beach Evolution in a Sandy Spit. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 221–225. Coconut Creek (Florida), ISSN 0749-0208.The role of overwash processes is of great relevance in the long-term evolution of sandy barriers, and it can also affect coastal infrastructures in the short term. In this work, a 10-month monitoring program based on the use of Unmanned Aerial Systems (UAS) in combination with Structure from Motion (SfM) algorithms was performed in order to monitor morphological changes and ulterior evolution at Camposoto beach, an overwash-prone coastal spit located in SW Spain. This UAS&SfM approach allowed the reconstruction of 6 Digital Elevation Models (DEMs) of the area with high temporal and spatial resolutions (RMS vertical errors spanning from 5 to 8 cm). The morphological changes occurred were detected using Geomorphic Change Detection Software in ArcGIS, and they were correlated with the oceanographic conditions that prevailed during the analysed period. The results obtained provided insight into the response of the system against storm-induced overwash, which caused significant erosion/accretion patterns over a pre-existing washover fan, as well as the landward migration of the system. In addition, this methodology captured the progressive partial recovery of the system, which included onshore transport of sand, fore-beach accretion, and berm reconstruction. Despite the partial recovery observed, spring tides also triggered non-storm overwash in the area during fair-weather conditions, allowing to rethink the actual vulnerability state of the spit against rollover processes.

RELATIVE ERROR EVALUATION TO TYPICAL OPEN GLOBAL DEM DATASETS IN SHANXI PLATEAU OF CHINA

Abstract. Produced by radar data or stereo remote sensing image pairs, global DEM datasets are one of the most important types for DEM data. Relative error relates to surface quality created by DEM data, so it relates to geomorphology and hydrologic applications using DEM data. Taking Shanxi Plateau of China as the study area, this research evaluated the relative error to typical open global DEM datasets including Shuttle Radar Terrain Mission (SRTM) data with 1 arc second resolution (SRTM1), SRTM data with 3 arc second resolution (SRTM3), ASTER global DEM data in the second version (GDEM-v2) and ALOS world 3D-30m (AW3D) data. Through process and selection, more than 300,000 ICESat/GLA14 points were used as the GCP data, and the vertical error was computed and compared among four typical global DEM datasets. Then, more than 2,600,000 ICESat/GLA14 point pairs were acquired using the distance threshold between 100 m and 500 m. Meanwhile, the horizontal distance between every point pair was computed, so the relative error was achieved using slope values based on vertical error difference and the horizontal distance of the point pairs. Finally, false slope ratio (FSR) index was computed through analyzing the difference between DEM and ICESat/GLA14 values for every point pair. Both relative error and FSR index were categorically compared for the four DEM datasets under different slope classes. Research results show: Overall, AW3D has the lowest relative error values in mean error, mean absolute error, root mean square error and standard deviation error; then the SRTM1 data, its values are a little higher than AW3D data; the SRTM3 and GDEM-v2 data have the highest relative error values, and the values for the two datasets are similar. Considering different slope conditions, all the four DEM data have better performance in flat areas but worse performance in sloping regions; AW3D has the best performance in all the slope classes, a litter better than SRTM1; with slope increasing, the relative error for the SRTM3 data increases faster than other DEM datasets; so SRTM3 is better than GDEM-v2 in flat regions but worse in sloping regions. As to FSR value, AW3D has the lowest value, 4.37 %; then SRTM1 data, 5.80 %, similar to AW3D data; SRTM3 has higher value, about 8.27 %; GDEM-v2 data has the highest FSR value, about 12.15 %. FSR can represent the performance of correctly creating the earth surface based on DEM data. Hence, AW3D has the best performance, which is approximate to but a little better than SRTM1. The performance of SRTM3 and GDEM-v2 is similar, which is much worse than AW3D and SRTM1, and the performance of GDEM-v2 is the worst of all. Originated from the DEM dataset with 5m resolution, AW3D is regarded as the most precise global DEM datasets up to now, so it may exerts more effect in topographic analysis and geographic research. Through analysis and comparison of the relative error for the four open global DEM datasets, this research will provide reference in open global DEM datasets selection and applications in geosciences and other relevant fields.