Production Of Digital Terrain Models Research Articles

Adaptive coarse-to-fine clustering and terrain feature-aware-based method for reducing LiDAR terrain point clouds

Light detection and ranging (LiDAR)-derived point cloud has become the standard spatial data for digital terrain model (DTM) construction; however, it suffers from huge data with much redundant information due to the oversampling. This often causes considerable inconvenience in the downstream data processing. To this end, an adaptive coarse-to-fine clustering and terrain feature-aware-based method is proposed to reduce data points in the context of terrain modeling in this paper. Firstly, a coarse-to-fine clustering method with the consideration of terrain complexity is developed to adaptively cluster LiDAR terrain points. Then, according to the geometric properties of terrain breaklines, a terrain feature-aware multi-strategy method is presented to pick representative points in the clusters. Finally, important boundary points including inflection point on the boundary curve and critical point on terrain features are further selected. The proposed method is compared with seven state-of-the-art point cloud simplification methods under six data reduction ratios on six plots with different terrain characteristics. Results indicate that the proposed method obtains a good balance between terrain-feature preservation and uniform distribution of data points. Compared to the state-of-the-art methods, the proposed method reduces the average root mean square errors (absolute errors) of the DTMs by 12.2%-51.7% (7.69%-83.8%) on the six plots. Moreover, the proposed method obtains the mean terrain slope and terrain roughness more reasonably approximate to the references. In short, the newly developed method can be considered as an alternative tool to select representative points from the huge remote-sensing-derived point cloud in the context of DTM production.

A critical comparison of interpolation techniques for digital terrain modelling in mining

Digital modelling of a surface is crucial for Earth science and mining applications for many reasons. These days, high-tech digital representations are used to produce a high-fidelity topographic surface in the form of a digital terrain model (DTM). DTMs are created from 2D data-points collected by a variety of techniques such as traditional ground surveying, image processing, LiDAR, radar, and global positioning systems. At the points for which data is not available, the heights need to be interpolated or extrapolated from the points with measured elevations. There are several interpolation/extrapolation techniques available, which may be categorized based on criteria such as area size, accuracy or exactness of the surface, smoothness, continuity, and preciseness. In this paper we examine these DTM production methods and highlight their distinctive characteristics. Real data from a mine site is used, as a case study, to create DTMs using various interpolation techniques in Surfer® software. The significant variation in the resulting DTMs demonstrates that developing a DTM is not straightforward and it is important to choose the method carefully because the outcomes depend on the interpolation techniques used. In mining instances, where volume estimations are based on the produced DTM, this can have a significant impact. For our data-set, the natural neighbour interpolation method made the best predictions (R2 = 0.969, β = 0.98, P < 0.0001).

Digital Deforestation: Comparing Automated Approaches to the Production of Digital Terrain Models (DTMs) in Agisoft Metashape

This paper tests the suitability of automated point cloud classification tools provided by the popular image-based modeling (IBM) software package Agisoft Metashape for the generation of digital terrain models (DTMs) at moderately-vegetated archaeological sites. DTMs are often required for various forms of archaeological mapping and analysis. The suite of tools provided by Agisoft are relatively user-friendly as compared to many point cloud classification algorithms and do not require the use of additional software. Based on a case study from the Mycenaean site of Kastrouli, Greece, the mostly-automated, geometric classification tool “Classify Ground Points” provides the best results and produces a quality DTM that is sufficient for mapping and analysis. Each of the methods tested in this paper can likely be improved through manual editing of point cloud classification.

Measurement of Soil Tillage Using UAV High-Resolution 3D Data

Remote sensing methodologies could contribute to a more sustainable agriculture, such as monitoring soil preparation for cultivation, which should be done properly, according to the topographic characteristics and the crop’s nature. The objectives of this work are to (1) demonstrate the potential of unmanned aerial vehicle (UAV) technology in the acquisition of 3D data before and after soil tillage, for the quantification of mobilised soil volume; (2) propose a methodology that enables the co-registration of multi-temporal DTMs that were obtained from UAV surveys; and (3) show the relevance of quality control and positional accuracy assessment in processing and results. An unchanged-area-matching method based on multiple linear regression analysis was implemented to reduce the deviation between the Digital Terrain Models (DTMs) to calculate a more reliable mobilised soil volume. The production of DTMs followed the usual photogrammetric-based Structure from Motion (SfM) workflow; the extraction of fill and cut areas was made through raster spatial modelling and statistical tools to support the analysis. Results highlight that the quality of the differential DTM should be ensured for a reliable estimation of areas and mobilised soil volume. This study is a contribution to the use of multi-temporal DTMs produced from different UAV surveys. Furthermore, it demonstrates the potential of UAV data in the understanding of soil variability within precision agriculture.

Evaluating Stereo Digital Terrain Model Quality at Mars Rover Landing Sites with HRSC, CTX, and HiRISE Images

We have used high-resolution digital terrain models (DTMs) of two rover landing sites based on mosaicked images from the High-Resolution Imaging Science Experiment (HiRISE) camera as a reference to evaluate DTMs based on High-Resolution Stereo Camera (HRSC) and Context Camera (CTX) images. The Next-Generation Automatic Terrain Extraction (NGATE) matcher in the SOCET SET and GXP® commercial photogrammetric systems produces DTMs with good (small) horizontal resolution but large vertical error. Somewhat surprisingly, results for NGATE are terrain dependent, with poorer resolution and smaller errors on smoother surfaces. Multiple approaches to smoothing the NGATE DTMs give similar tradeoffs between resolution and error; a 5 × 5 lowpass filter is near optimal in terms of both combined resolution-error performance and local slope estimation. Smoothing with an area-based matcher, the standard processing for U.S. Geological Survey planetary DTMs, yields similar errors to the 5 × 5 filter at slightly worse resolution. DTMs from the HRSC team processing pipeline fall within this same trade space but are less sensitive to terrain roughness. DTMs produced with the Ames Stereo Pipeline also fall in this space at resolutions intermediate between NGATE and the team pipeline. Considered individually, resolution and error each varied by approximately a factor of 2. Matching errors were 0.2–0.5 pixels but most results fell in the 0.2–0.3 pixel range that has been stated as a rule of thumb in multiple prior studies. Horizontal resolutions of 10–20 image pixels were found, consistently greater than the 3–5 pixel spacing generally used for stereo DTM production. Resolution and precision were inversely correlated; their product varied by ≤20% (4–5 pixels squared). Refinement of the stereo DTM by photoclinometry can yield quantitative improvement in resolution (more than a factor of 2), provided that albedo variations over distances smaller than the stereo DTM resolution are not too severe. We offer specific guidance for both producers and users of planetary stereo DTMs, based on our results.

PEMANFAATAN DIGITAL TERRAIN MODEL HASIL FOTOGRAMETRI UNMANED AERIAL VEHICLE UNTUK ESTIMASI KETINGGIAN GENANGAN AIR BANJIR HASIL DETEKSI CITRA SYNTHETIC APPERTURE RADAR

Utilization of The Unmanned Aerial Vehicle (UAV) or Drone has brought revolution in digital photogrammetry. The feature matching on surface reconstruction or Digital Surface Model (DSM) are quickly finished. However, DSM doesn’t represent itself as a part of topography, that is why DSM should be converted into Digital Terrain Model (DTM). This research is to investigate the accuracy of UAV photogrammetry’s DTM  for hydraulic modeling purpose. This study has produced 4 sets of DTMs; 2 sets of DTMs with different grid resolution which are 2 cm & 40 cm, also the 2 other sets of DTM with extra fine nature algorithm and set of filtering parameters adjustment; bulge, offset, spike and standard deviation. Every DTM are validated by Ground Control Point (GCP) from Real Time Kinematic-Different Global Positioning System (RTK-DGPS) measurement. According to the validation, the adjustment of filtering parameters is the most accurate method with Root Mean Square Error (RMSE) of 6,17 cm for 2 cm resolution; and 5,22 cm for 40 cm resolution. Next, DTM UAV is used to estimate the flood water level from Synthetic Aperture Radar (SAR) Image detection with 46 flood images on Glane and Losser area, east part of Overijssel, The Netherlands, since October 2014 to December 2017, then validated with the insitu water level measurement and resulted RMSE 6,72 cm for set of UAV DTM’s 40 cm resolution with the filtering parameters adjustment. Therefore, this DTM UAV can be used as a topography parameter in hydraulic modeling, especially at the similar flat-surface terrain where this research have been conducted.  Keywords: UAV photogrammetry, SAR detection, DTM production

A Fast Global Interpolation Method for Digital Terrain Model Generation from Large LiDAR-Derived Data

Airborne light detection and ranging (LiDAR) datasets with a large volume pose a great challenge to the traditional interpolation methods for the production of digital terrain models (DTMs). Thus, a fast, global interpolation method based on thin plate spline (TPS) is proposed in this paper. In the methodology, a weighted version of finite difference TPS is first developed to deal with the problem of missing data in the grid-based surface construction. Then, the interpolation matrix of the weighted TPS is deduced and found to be largely sparse. Furthermore, the values and positions of each nonzero element in the matrix are analytically determined. Finally, to make full use of the sparseness of the interpolation matrix, the linear system is solved with an iterative manner. These make the new method not only fast, but also require less random-access memory. Tests on six simulated datasets indicate that compared to recently developed discrete cosine transformation (DCT)-based TPS, the proposed method has a higher speed and accuracy, lower memory requirement, and less sensitivity to the smoothing parameter. Real-world examples on 10 public and 1 private dataset demonstrate that compared to the DCT-based TPS and the locally weighted interpolation methods, such as linear, natural neighbor (NN), inverse distance weighting (IDW), and ordinary kriging (OK), the proposed method produces visually good surfaces, which overcome the problems of peak-cutting, coarseness, and discontinuity of the aforementioned interpolators. More importantly, the proposed method has a similar performance to the simple interpolation methods (e.g., IDW and NN) with respect to computing time and memory cost, and significantly outperforms OK. Overall, the proposed method with low memory requirement and computing cost offers great potential for the derivation of DTMs from large-scale LiDAR datasets.

A Random Features-Based Method for Interpolating Digital Terrain Models with High Efficiency

Airborne light detection and ranging (lidar) is becoming a widely adopted technique for capturing elevation data, which are mainly used for creating digital terrain models (DTMs). However, the large size of lidar datasets poses a serious computational challenge to the promising radial basis function (RBF) interpolation method. In this work, to reduce the huge computational cost and improve the interpolation accuracy, random Fourier features are first introduced to approximate the Gaussian kernel of RBFs in feature space, then a random features-based weighted RBF interpolation method is developed. Based on randomized Fourier features, the nonlinear kernel-based training and evaluation of the RBF method is transformed into simple linear operations in feature space, and with the help of weighted ridge regression, the negative effect of the non-Gaussian distribution of lidar datasets on DTM production is reduced. In other words, the combination of randomized Fourier features and weighted ridge regression improves the efficiency and accuracy of the RBF interpolation method. Experiments on simulated datasets indicate that the proposed method performs better than the classical or random features-based RBF method for dealing with non-Gaussian distributed samples, with the former being slightly less accurate than the iterative RBF method due to the low-dimensional random features. However, the computational cost of the new method is much lower compared with the classical or iterative RBFs. Interpolation of airborne lidar-derived points demonstrates that the new method has a computational cost similar to the inverse distance weighting and triangulated irregular network (TIN) approaches, and is significantly faster than the ordinary kriging (OK) or thin plate spline (TPS) methods. Quantitatively, for interpolation of 644,433 points, the proposed method is approximately 833 and 21 times faster than OK and TPS, respectively. Moreover, the new method avoids the surface discontinuity artifacts presented by the OK, TPS, and TIN methods.

Photogrammetric surveying forests and woodlands with UAVs: techniques for automatic removal of vegetation and digital terrain model production for hydrological applications

The purpose of this study is the photogrammetric survey of a forested area using unmanned aerial vehicles (UAV), and the estimation of the digital terrain model (DTM) of the area, based on the photogrammetrically produced digital surface model (DSM). Furthermore, through the classification of the height difference between a DSM and a DTM, a vegetation height model is estimated, and a vegetation type map is produced. Finally, the generated DTM was used in a hydrological analysis study to determine its suitability compared to the usage of the DSM. The selected study area was the forest of Seih-Sou (Thessaloniki). The DTM extraction methodology applies classification and filtering of point clouds, and aims to produce a surface model including only terrain points (DTM). The method yielded a DTM that functioned satisfactorily as a basis for the hydrological analysis. Also, by classifying the DSM–DTM difference, a vegetation height model was generated. For the photogrammetric survey, 495 aerial images were used, taken by a UAV from a height of ∼200 m. A total of 44 ground control points were measured with an accuracy of 5 cm. The accuracy of the aerial triangulation was approximately 13 cm. The produced dense point cloud, counted 146 593 725 points.

Fusion of Swath Bathymetric Data: Application to AUV Rapid Environment Assessment

Building an accurate and large digital terrain model (DTM) of the seabed is a key issue in various applications, especially for covert rapid environment assessment using autonomous underwater vehicles (AUVs). New AUV generations are capable of acquiring bathymetry with multiple acoustic sensors: single-beam echosounder, Doppler velocity log, multibeam echosounder (MBES), interferometric sidescan sonar (ISSS), etc. As these sensors acquire the seabed with different geometries, they can be combined to produce a DTM in shorter time. For example, ISSS can reach a wide swath in shallow water but it shows an information gap at nadir, which is usually covered by completing an additional track. Simultaneously using the MBES to acquire the nadir removes the need for this additional track and reduces the energy consumption of the whole survey. This paper focuses on fusion algorithms to extract best information of the two sensors (MBES and ISSS) to feed DTM production software with optimal bathymetric information. This problem may be solved by taking into account the average information of the two sensors. However, the sensors do not always give accurate information and the average information therefore becomes biased. Another way to tackle the problem is to select a priori information given by the better of the two sensors based on a given geometric parameter (e.g., grazing angle). In this case, when the assumed best sensor fails, the information produced by the second sensor cannot be used to compensate for the erroneous information. Our approach consists in using all the available information and fuses it ahead of producing the DTM. Based on the theory of belief functions, this paper presents a framework to fuse the information coming from the two swath bathymetric sensors (MBES and ISSS). The belief theory, applied successfully to other fields, has been extended to handle the bathymetric information. The reliability and the uncertainty of each sonar are introduced in the fusion process to improve the estimation and the accuracy of the final terrain model. First, simulated sonar data, with perfectly known ground truth, are used to quantitatively assess the performance of the fusion process by comparing DTM obtained with and without fusion. Then, the experimental validation is conducted on actual data, acquired simultaneously by the two sonars systems (Klein K5000 ISSS, Reson 8125 MBES) mounted on the DAURADE AUV. Our evaluation of the fusion method shows significant quantitative and qualitative improvement in the production of the final DTM.

The effect of user-defined parameters on DTM accuracy—development of a hybrid model

User-defined parameters (point density, interpolation, and pixel size) have a great influence on the accuracy of digital terrain model (DTM). Therefore, the optimal interpolation method (IM) and appropriate pixel size should be used to create a continuous surface. Pixel size or spatial resolution tends to be a compromise between the number of samples and the size of the study area, whereby input dataset is often devaluated. In this paper, the authors propose a new methodological approach to DTM production (hybrid DTM—HDTM) from an aero-photogrammetry dataset. Two different approaches to DTM production are presented: the usual (UDTM) and the hybrid (HDTM). HDTM is based on restructuring and refining the input dataset by generating contour lines, determining the optimal interpolation method and selecting the appropriate spatial resolution. The goal is to develop a qualitative DTM while minimalizing the devaluation of input data and error propagation. The accuracy of different IMs for UDTM was examined in a comparative analysis of six statistical parameters by applying cross-validation, with the focus on the root mean square error (RMSE) parameter. Four different methods of selecting optimal spatial resolution were tested for the same model. The UDTM and HDTM generated were compared by interior, exterior, and visual accuracy assessment and by the performance success of specific hydrological parameters: (1) flow accumulation—by applying a developed DNA concept and (2) watershed—by calculating volume and 3D surface. The reference value for exterior accuracy assessment was high-resolution airborne LiDAR DTM (LDTM). It was found that ordinary kriging (OK) was the best IM (RMSE, 1.9893 m). The spatial resolution of UDTM was calculated by combining two variants of the point pattern analysis method and was found to be 19 m. On the other hand, the complexity of terrain method was used to define the spatial resolution of HDTM (3 m). HDTM achieved better results than UDTM in all aspects of accuracy assessment. The exterior accuracy of HDTM was better by 1.483 m (RMSE). Finally, the results of the applied DNA concept showed that a stream generated from HDTM had a 2.587 m lower horizontal root mean square error (HRMSE) than the stream generated from UDTM.

ON VOLUME DATA REDUCTION FOR LIDAR DATASETS

Abstract. This paper discusses a current issue for several experimental science disciplines, which is the Big Data Problem (BDP). This research study focused on light intensity and ranging (LiDAR) datasets, which are collected for modelling spatial features found on the surface of the earth. Currently, LiDAR datasets are known to be extremely redundant for many applications. Using a formula that allows for calculating the variance of the target-induced error (so-called T-error) caused by the discretisation and quantisation of a 3D surface as a criterion for the quantitative assessment of the fidelity of a model, the use of a Q-tree-based split of the surface is proposed for cells of various sizes depending on the fidelity requirements. A LiDAR dataset representing a 1 km x 1 km terrain surface tile using approximately 12 x 106 points was used during the experiments. The initial LiDAR dataset was used to produce a digital terrain model (DTM) at a 0.5 m x 0.5 m resolution, which was used as a reference model. Subsequently, the initial LiDAR dataset was decimated at various rates, and the resulting DTMs were compared with the reference model. The Q-tree based data structure was utilised to illustrate that the Q-tree approach allows for the production of DTMs at a ‘controlled’ fidelity with a considerable reduction in data volume.

Towards the Crowdsourcing of Massive Smartphone Assisted-GPS Sensor Ground Observations for the Production of Digital Terrain Models.

Digital Terrain Models (DTMs) used for the representation of the bare earth are produced from elevation data obtained using high-end mapping platforms and technologies. These require the handling of complex post-processing performed by authoritative and commercial mapping agencies. In this research, we aim to exploit user-generated data to produce DTMs by handling massive volumes of position and elevation data collected using ubiquitous smartphone devices equipped with Assisted-GPS sensors. As massive position and elevation data are collected passively and straightforwardly by pedestrians, cyclists, and drivers, it can be transformed into valuable topographic information. Specifically, in dense and concealed built and vegetated areas, where other technologies fail, handheld devices have an advantage. Still, Assisted-GPS measurements are not as accurate as high-end technologies, requiring pre- and post-processing of observations. We propose the development and implementation of a 2D Kalman filter and smoothing on the acquired crowdsourced observations for topographic representation production. When compared to an authoritative DTM, results obtained are very promising in producing good elevation values. Today, open-source mapping infrastructures, such as OpenStreetMap, rely primarily on the global authoritative SRTM (Shuttle Radar Topography Mission), which shows similar accuracy but inferior resolution when compared to the results obtained in this research. Accordingly, our crowdsourced methodology has the capacity for reliable topographic representation production that is based on ubiquitous volunteered user-generated data.

Massive stereo-based DTM production for Mars on cloud computers

Digital Terrain Model (DTM) creation is essential to improving our understanding of the formation processes of the Martian surface. Although there have been previous demonstrations of open-source or commercial planetary 3D reconstruction software, planetary scientists are still struggling with creating good quality DTMs that meet their science needs, especially when there is a requirement to produce a large number of high quality DTMs using “free” software. In this paper, we describe a new open source system to overcome many of these obstacles by demonstrating results in the context of issues found from experience with several planetary DTM pipelines. We introduce a new fully automated multi-resolution DTM processing chain for NASA Mars Reconnaissance Orbiter (MRO) Context Camera (CTX) and High Resolution Imaging Science Experiment (HiRISE) stereo processing, called the Co-registration Ames Stereo Pipeline (ASP) Gotcha Optimised (CASP-GO), based on the open source NASA ASP. CASP-GO employs tie-point based multi-resolution image co-registration, and Gotcha sub-pixel refinement and densification. CASP-GO pipeline is used to produce planet-wide CTX and HiRISE DTMs that guarantee global geo-referencing compliance with respect to High Resolution Stereo Colour imaging (HRSC), and thence to the Mars Orbiter Laser Altimeter (MOLA); providing refined stereo matching completeness and accuracy. All software and good quality products introduced in this paper are being made open-source to the planetary science community through collaboration with NASA Ames, United States Geological Survey (USGS) and the Jet Propulsion Laboratory (JPL), Advanced Multi-Mission Operations System (AMMOS) Planetary Data System (PDS) Pipeline Service (APPS-PDS4), as well as browseable and visualisable through the iMars web based Geographic Information System (webGIS) system.

A REGISTRATION METHOD OF POINT CLOUDS COLLECTED BY MOBILE LIDAR USING SOLELY STANDARD LAS FILES INFORMATION

Abstract. In the last few years, LiDAR sensors installed in terrestrial vehicles have been revealed as an efficient method to collect very dense 3D georeferenced information. The possibility of creating very dense point clouds representing the surface surrounding the sensor, at a given moment, in a very fast, detailed and easy way, shows the potential of this technology to be used for cartography and digital terrain models production in large scale. However, there are still some limitations associated with the use of this technology. When several acquisitions of the same area with the same device, are made, differences between the clouds can be observed. The range of that differences can go from few centimetres to some several tens of centimetres, mainly in urban and high vegetation areas where the occultation of the GNSS system introduces a degradation of the georeferenced trajectory. Along this article a different method point cloud registration is proposed. In addition to the efficiency and speed of execution, the main advantages of the method are related to the fact that the adjustment is continuously made over the trajectory, based on the GPS time. The process is fully automatic and only information recorded in the standard LAS files is used, without the need for any auxiliary information, in particular regarding the trajectory.

AN EFFICIENT METHOD TO CREATE DIGITAL TERRAIN MODELS FROM POINT CLOUDS COLLECTED BY MOBILE LiDAR SYSTEMS

Abstract. The digital terrain models (DTM) assume an essential role in all types of road maintenance, water supply and sanitation projects. The demand of such information is more significant in developing countries, where the lack of infrastructures is higher. In recent years, the use of Mobile LiDAR Systems (MLS) proved to be a very efficient technique in the acquisition of precise and dense point clouds. These point clouds can be a solution to obtain the data for the production of DTM in remote areas, due mainly to the safety, precision, speed of acquisition and the detail of the information gathered. However, the point clouds filtering and algorithms to separate “terrain points” from “no terrain points”, quickly and consistently, remain a challenge that has caught the interest of researchers. This work presents a method to create the DTM from point clouds collected by MLS. The method is based in two interactive steps. The first step of the process allows reducing the cloud point to a set of points that represent the terrain’s shape, being the distance between points inversely proportional to the terrain variation. The second step is based on the Delaunay triangulation of the points resulting from the first step. The achieved results encourage a wider use of this technology as a solution for large scale DTM production in remote areas.

ASSESSMENT OF THE QUALITY OF DIGITAL TERRAIN MODEL PRODUCED FROM UNMANNED AERIAL SYSTEM IMAGERY

Abstract. Production of digital terrain model (DTM) is one of the most usual tasks when processing photogrammetric point cloud generated from Unmanned Aerial System (UAS) imagery. The quality of the DTM produced in this way depends on different factors: the quality of imagery, image orientation and camera calibration, point cloud filtering, interpolation methods etc. However, the assessment of the real quality of DTM is very important for its further use and applications. In this paper we first describe the main steps of UAS imagery acquisition and processing based on practical test field survey and data. The main focus of this paper is to present the approach to DTM quality assessment and to give a practical example on the test field data. For data processing and DTM quality assessment presented in this paper mainly the in-house developed computer programs have been used. The quality of DTM comprises its accuracy, density, and completeness. Different accuracy measures like RMSE, median, normalized median absolute deviation and their confidence interval, quantiles are computed. The completeness of the DTM is very often overlooked quality parameter, but when DTM is produced from the point cloud this should not be neglected as some areas might be very sparsely covered by points. The original density is presented with density plot or map. The completeness is presented by the map of point density and the map of distances between grid points and terrain points. The results in the test area show great potential of the DTM produced from UAS imagery, in the sense of detailed representation of the terrain as well as good height accuracy.

ASSESSMENT OF THE QUALITY OF DIGITAL TERRAIN MODEL PRODUCED FROM UNMANNED AERIAL SYSTEM IMAGERY

Production of digital terrain model (DTM) is one of the most usual tasks when processing photogrammetric point cloud generated from Unmanned Aerial System (UAS) imagery. The quality of the DTM produced in this way depends on different factors: the quality of imagery, image orientation and camera calibration, point cloud filtering, interpolation methods etc. However, the assessment of the real quality of DTM is very important for its further use and applications. In this paper we first describe the main steps of UAS imagery acquisition and processing based on practical test field survey and data. The main focus of this paper is to present the approach to DTM quality assessment and to give a practical example on the test field data. For data processing and DTM quality assessment presented in this paper mainly the in-house developed computer programs have been used. The quality of DTM comprises its accuracy, density, and completeness. Different accuracy measures like RMSE, median, normalized median absolute deviation and their confidence interval, quantiles are computed. The completeness of the DTM is very often overlooked quality parameter, but when DTM is produced from the point cloud this should not be neglected as some areas might be very sparsely covered by points. The original density is presented with density plot or map. The completeness is presented by the map of point density and the map of distances between grid points and terrain points. The results in the test area show great potential of the DTM produced from UAS imagery, in the sense of detailed representation of the terrain as well as good height accuracy.

Towards the production of digital terrain models from volunteered GPS trajectories

There currently exists a wide variety of online resources providing mapping infrastructures and geographic information. Most web-based map services, such as Google Maps, Yahoo! Maps and Bing Maps, are mostly based on data that is collected by authoritative mapping agencies. Alternatively, some relatively new web-map services, such as OpenStreetMap (OSM) and Wikimapia, are mostly based on volunteered data collected by the public (e.g. crowdsourced mapping). Although such volunteered-based map services platforms show an increasing planimetric (2D) accuracy, completeness and update-rate of their mapping infrastructure, surprisingly enough, there is a lack of comparable data and accuracy measures in respect to the third dimension, i.e. height; more specifically, the topographic representation that is based on the volunteered collected data. Most of these web services still rely on existing open-source authoritative topographic infrastructures, and not on data collected by the volunteers. Moreover, topographic information that is open to the public and is free to use, e.g. advanced spaceborne thermal emission and reflection radiometer and shuttle radar topography mission, is regularly available with relatively low height accuracy (not better than 5 m) and low planimetric resolution (over 30 m). Volunteered data, on the other hand, collected by individuals that are situated ‘all over’ the globe can offer with new capabilities and data characteristics having potentially higher qualities. This research proposes to examine the feasibility of using crowdsourced volunteered geographic information working paradigm for the task of producing a reliable digital terrain model (DTM) infrastructure for general use. This is achieved by collecting GPS observations that are available from VG data sources, while applying a 2D Kalman filter-based algorithm, aimed at reducing noise and ambiguities. This paper presents this methodology, with preliminary analysis results achieved by this implementation, showing the feasibility of this working methodology, having good results and accuracy of the DTM generated.

Semi-Global Matching of Pléiades Tri-Stereo Imagery to Generate Detailed Digital Topography for High-Alpine Regions

Nowadays, several sensors are capable of acquiring very high resolution (VHR) satellite images in stereo, and even in tri-stereo mode. These data can be efficiently exploited with photogrammetric methods, such as semi-global matching (SGM), to produce high quality 2.5D digital terrain information. Regarding the production of digital terrain models, since VHR (tri-)stereo satellite images are available at high temporal frequencies for nearly the whole globe, they can be considered an attractive alternative to restricted and expensive airborne laserscanning (ALS) data. In this study, SGM is applied to a Pléiades-1A image triplet, to generate an accurate and detailed 3D point cloud and a 2.5D digital surface model (DSM) for a high-alpine study area in South Tyrol with complex, snow- and ice-covered topography. Visual and quantitative comparison to an ALS reference DSM showed that there were only small elevation errors for large parts of the study area. Errors were higher in regions that were obscured by shade. This is one of the first studies demonstrating that SGM is not only capable of delivering reliable digital surface information for flat and hilly regions, but also for high-alpine areas with complex terrain.