scholarly journals Digital Modeling Phenomenon Of Surface Ground Movement

2016 ◽  
Vol 73 (2) ◽  
pp. 342
Author(s):  
Ioan Voina ◽  
Maricel Palamariu ◽  
Iohan Neuner ◽  
Tudor Salagean ◽  
Dumitru Onose ◽  
...  
Keyword(s):  
Land Surface ◽  
Interpolation Method ◽  
Digital Terrain Model ◽  
Industrial Applications ◽  
Ground Movement ◽  
Terrain Model ◽  
Software Applications ◽  
Specialized Software ◽  
Digital Terrain ◽  
Work Area

With the development of specialized software applications it was possible to approach and resolve complex problems concerning automating and process optimization for which are being used field data. Computerized representation of the shape and dimensions of the Earth requires a detailed mathematical modeling, known as "digital terrain model". The paper aims to present the digital terrain model of Vulcan mining, Hunedoara County, Romania. Modeling consists of a set of mathematical equations that define in detail the surface of Earth and has an approximate surface rigorously and mathematical, that calculated the land area. Therefore, the digital terrain model means a digital representation of the earth's surface through a mathematical model that approximates the land surface modeling, which can be used in various civil and industrial applications in. To achieve the digital terrain model of data recorded using linear and nonlinear interpolation method based on point survey which highlights the natural surface studied. Given the complexity of this work it is absolutely necessary to know in detail of all topographic elements of work area, without the actions to be undertaken to project and manipulate would not be possible. To achieve digital terrain model, within a specialized software were set appropriate parameters required to achieve this case study. After performing all steps we obtained digital terrain model of Vulcan Mine. Digital terrain model is the complex product, which has characteristics that are equivalent to the specialists that use satellite images and information stored in a digital model, this is easier to use.

scholarly journals NEW DTM EXTRACTION APPROACH FROM AIRBORNE IMAGES DERIVED DSM

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 75-82 ◽  
Author(s):  
Y. A. Mousa ◽  
P. Helmholz ◽  
D. Belton
Keyword(s):  
Interpolation Method ◽  
Digital Terrain Model ◽  
Window Size ◽  
Group Iii ◽  
Terrain Model ◽  
Digital Terrain ◽  
Surface Models ◽  
Extraction Algorithm ◽  
Ground Point

In this work, a new filtering approach is proposed for a fully automatic Digital Terrain Model (DTM) extraction from very high resolution airborne images derived Digital Surface Models (DSMs). Our approach represents an enhancement of the existing DTM extraction algorithm <i>Multi-directional and Slope Dependent (MSD)</i> by proposing parameters that are more reliable for the selection of ground pixels and the pixelwise classification. To achieve this, four main steps are implemented: Firstly, 8 well-distributed scanlines are used to search for minima as a ground point within a pre-defined filtering window size. These selected ground points are stored with their positions on a 2D surface to create a network of ground points. Then, an initial DTM is created using an interpolation method to fill the gaps in the 2D surface. Afterwards, a pixel to pixel comparison between the initial DTM and the original DSM is performed utilising pixelwise classification of ground and non-ground pixels by applying a vertical height threshold. Finally, the pixels classified as non-ground are removed and the remaining holes are filled. The approach is evaluated using the Vaihingen benchmark dataset provided by the ISPRS working group III/4. The evaluation includes the comparison of our approach, denoted as Network of Ground Points (NGPs) algorithm, with the DTM created based on MSD as well as a reference DTM generated from LiDAR data. The results show that our proposed approach over performs the MSD approach.

scholarly journals Evaluation of GIS techniques in digital terrain model with temporal data: an approach based in the slope of the land surface

2015 ◽  
Vol 11 (1) ◽  
Author(s):  
Luiz Gilberto Bertotti ◽  
Mauricio Camargo Filho ◽  
Marcos Aurélio Pelegrina ◽  
Marquiana Freitas Vilas Boas Gomes ◽  
Bruno Henrique Costa Toledo ◽  
...  
Keyword(s):  
Land Surface ◽  
Digital Terrain Model ◽  
Temporal Data ◽  
Terrain Model ◽  
Gis Techniques ◽  
Digital Terrain

A Self-interpolation Method for Digital Terrain Model Generation

2021 ◽  
pp. 352-363
Author(s):  
Leonardo Ramos Emmendorfer ◽  
Isadora Bicho Emmendorfer ◽  
Luis Pedro Melo de Almeida ◽  
Deivid Cristian Leal Alves ◽  
Jorge Arigony Neto
Keyword(s):  
Interpolation Method ◽  
Digital Terrain Model ◽  
Model Generation ◽  
Terrain Model ◽  
Digital Terrain

scholarly journals Subaqueous geomorphology: options, tasks, needs

2019 ◽  
Vol 16 (1) ◽  
pp. 89-97
Author(s):  
Stanisław Rudowski ◽  
Radosław Wróblewski ◽  
Janusz Dworniczak ◽  
Kazimierz Szefler ◽  
Benedykt Hac ◽  
...  
Keyword(s):  
Land Surface ◽  
Digital Terrain Model ◽  
Side Scan Sonar ◽  
Multibeam Echosounder ◽  
Seismic Profiling ◽  
Terrain Model ◽  
Non Invasive ◽  
Digital Terrain ◽  
Surface Survey ◽  
Echo Sounder

Abstract The purpose of the paper is to present the potentialities of current non-invasive methods for bottom surveys, including cartometric presentation of its relief and structure in both marine and inland reservoirs. The paper presents examples of results obtained in the Maritime Institute in Gdańsk during surveys carried out at the bottom of seas, lakes and rivers with the use of the same apparatus: primarily, a multibeam echosounder (MBES) to obtain a digital terrain model (DTM); a side-scan sonar (SSS) to obtain a general image of the nature of the bottom (its “roughness”); and seismic profiling (sub-bottom profiler, sediment echo sounder [SES]) to recognise the structure of the bottom. The obtained results constitute a necessary basis for carrying out further specialist surveys (non-invasive or invasive) when needed. Current bottom survey options that use MBES, SSS and SES may be treated as subaqueous equivalents of the subaerial potentialities of a land surface survey using LiDaR and GPR (Ground Penetration Radar).

scholarly journals Effects of Structure from Motion Data Density, Interpolation Method and Grid Size on Micro Topography Digital Terrain Model Accuracy

2019 ◽  
Author(s):  
Francisco Agüera-Vega ◽  
Marta Agüera-Puntas ◽  
Francesco Mancini ◽  
Patricio Martínez-Carricondo ◽  
Fernando Carvajal-Ramírez
Keyword(s):  
Structure From Motion ◽  
Point Cloud ◽  
Interpolation Method ◽  
Digital Terrain Model ◽  
Grid Size ◽  
Ground Sample ◽  
Terrain Model ◽  
Digital Terrain ◽  
Cloud Density ◽  
Sample Distance

The objective of this study is to evaluate the effects of the 3D point cloud density derived from unmanned aerial vehicle (UAV) photogrammetry and structure from motion (SfM) and multi-view stereopsis (MVS) techniques, the interpolation method for generating a digital terrain model (DTM), and the resolution (grid size) of the derived DTM on the accuracy of estimated heights in small areas, where a very accurate high spatial resolution is required. A UAV-photogrammetry project was carried out on a bare soil of 13 &times; 13 m with a rotatory wing UAV at 10 m flight altitude (equivalent ground sample distance = 0.4 cm). The 3D point cloud was derived, and five sample replications representing 1, 2, 3, 4, 5, 10, 15, 20, 30, 40, 50, 60, 70, 80 and 90% of the original cloud were extracted to analyze the effect of cloud density on DTM accuracy. For each of these samples, DTMs were derived using four different interpolation methods (Inverse Distance Weighted (IDW), Multiquadric Radial Basis Function (MRBF), Kriging (KR), and Triangulation with Linear Interpolation (TLI)) and 15 DTM grid size (GS) values (20, 15, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0.67, 0.5, and 0.4 cm). Then, 675 DTMs were analyzed. The results showed, for each interpolation method and each density, an optimal GS value (most of the cases equal to 1 cm) for which the Root Mean Square Error (RMSE) is minimum. IDW was the interpolator which yielded best accuracies for all combination of densities and GS. Its RMSE, considering the raw cloud, was 1.054 cm. The RMSE increased 3% when a point cloud with 80% extracted from the raw cloud was used to generate the DTM. When the point cloud included the 40% of the raw cloud, RMSE increased 5%. For densities lower than 15%, RMSE increased exponentially (45% for 1% of raw cloud). The grid size minimizing RMSE for densities of 20% or higher was 1 cm, which represents 2.5 times the ground sample distance of the pictures used for developing the photogrammetry project.

scholarly journals DTM GENERATION FROM TERRASAR-X USING TIN ALGORITHM IN PAPUA ISLAND, INDONESIA

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 101-106 ◽  
Author(s):  
D. B. Susetyo ◽  
M. F. Syafiudin ◽  
Y. Prasetyo
Keyword(s):  
Quality Control ◽  
Interpolation Method ◽  
Digital Terrain Model ◽  
Quality Standard ◽  
Mass Point ◽  
Topographic Map ◽  
Geospatial Information ◽  
Terrain Model ◽  
Digital Terrain ◽  
Test Points

One of the outputs of mapping activity in Indonesia is Digital Terrain Model (DTM). DTM generated by stereo plotting with photogrammetry concept, where Indonesia Topography Map at medium scale using <i>Synthetic Aperture Radar</i> (SAR), and currently, one of SAR data that used to produce Indonesian Topographic Map is TerraSAR-X. <br><br> This paper discusses about DTM generation in Papua Island, Indonesia, using TerraSAR-X, which is part of topographic mapping activity on a scale of 1&amp;thinsp;:&amp;thinsp;25,000. We choose Triangulated Irregular Network (TIN) as the interpolation method. After TIN was build and edited, we have to check to produce good DTM. Quality control involves visual and statistic quality. <br><br> In statistic aspect, we compare Linear Error 90&amp;thinsp;% (LE90) value to map accuracy that regulated in Head of Geospatial Information Agency Rules Number 15 Year 2014. We use 50 test points for 59 map sheets in scale 1&amp;thinsp;:&amp;thinsp;25,000 (the area around 10,000&amp;thinsp;km<sup>2</sup>). To validate the elevation, we interpret test points elevation in the stereo model, then we compare to an elevation in DTM. LE90 value is 9.75&amp;thinsp;m, so we can conclude that DTM elevation still in class 3. In a visual aspect, we must edit the DTM. There are 9 parameters in visual quality control, and to meets these parameters, we can use three methods: add and reduce mass point, move mass point, and add breakline. Editing to the DTM can make we sure that it meets the quality standard in scale 1&amp;thinsp;:&amp;thinsp;25,000 data.

scholarly journals Digital Terrain Models Generated with Low-Cost UAV Photogrammetry: Methodology and Accuracy

2021 ◽  
Vol 10 (5) ◽  
pp. 285
Author(s):  
Sergio Iván Jiménez-Jiménez ◽  
Waldo Ojeda-Bustamante ◽  
Mariana Marcial-Pablo ◽  
Juan Enciso
Keyword(s):  
Land Surface ◽  
Survey Methods ◽  
Low Cost ◽  
Digital Terrain Model ◽  
High Accuracy ◽  
Digital Cameras ◽  
Ground Sample ◽  
Terrain Model ◽  
Digital Terrain ◽  
Dense Point

Digital terrain model (DTM) generation is essential to recreating terrain morphology once the external elements are removed. Traditional survey methods are still used to collect accurate geographic data on the land surface. Given the emergence of unmanned aerial vehicles (UAVs) equipped with low-cost digital cameras and better photogrammetric methods for digital mapping, efficient approaches are necessary to allow rapid land surveys with high accuracy. This paper provides a review, complemented with the authors’ experience, regarding the UAV photogrammetric process and field survey parameters for DTM generation using popular commercial photogrammetric software to process images obtained with fixed-wing or multicopter UAVs. We analyzed the quality and accuracy of the DTMs based on four categories: (i) the UAV system (UAV platforms and camera); (ii) flight planning and image acquisition (flight altitude, image overlap, UAV speed, orientation of the flight line, camera configuration, and georeferencing); (iii) photogrammetric DTM generation (software, image alignment, dense point cloud generation, and ground filtering); (iv) geomorphology and land use/cover. For flat terrain, UAV photogrammetry provided a horizontal root mean square error (RMSE) between 1 to 3 × the ground sample distance (GSD) and a vertical RMSE between 1 to 4.5 × GSD, and, for complex topography, a horizontal RMSE between 1 to 7 × GSD and a vertical RMSE between 1.5 to 5 × GSD. Finally, we stress that UAV photogrammetry can provide DTMs with high accuracy when the photogrammetric process variables are optimized.

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