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 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 Application of 30m Resolution SRTM DEM in Nepal

2018 ◽  
Vol 14 (1) ◽  
pp. 235-240 ◽  
Author(s):  
Raghunath Jha
Keyword(s):  
High Resolution ◽  
Digital Terrain Model ◽  
Topographic Map ◽  
Srtm Dem ◽  
Terrain Model ◽  
Digital Terrain ◽  
Digital Elevation ◽  
Raster Analysis ◽  
Elevation Model ◽  
Almost All

 Digital Terrain Model (DTM) or Digital Elevation Model (DEM) is an important data for Raster Analysis in modern GIS. Its use is extremely important for almost all fields of engineering, especially Water Resources Engineering. In Nepal, high-resolution DTM is not available, and often funds are limited to generate high-resolution DTM using modern day technology such as LiDAR or Aerial Photography. As a result most of the works are based on SRTM DEM which is available free of cost. Presently, 1arc second DTM is available in SRTM for Nepalese Territories. In this study, the applicability of 1arc second or 30m resolution SRTM is checked in comparison with the Department of Survey Digital Topographic Map. It is found that SRTM DEM performs better than DEM generated from Data available with Survey Department.Journal of the Institute of Engineering, 2018, 14(1): 235-240

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.

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 Internet-based Land Valuation System Powered by a GIS of 1:10,000 Soil Maps

2006 ◽  
Vol 55 (1) ◽  
pp. 109-116 ◽  
Author(s):  
Tibor Tóth ◽  
T. Németh ◽  
T. Fábián ◽  
T. Hermann ◽  
E. Horváth ◽  
...  
Keyword(s):  
Digital Terrain Model ◽  
Geographical Information ◽  
Aerial Photographs ◽  
Topographic Map ◽  
Soil Maps ◽  
Terrain Model ◽  
Land Taxation ◽  
Digital Terrain ◽  
Land Valuation ◽  
Automated Algorithms

An internet-based land valuation system is being developed to replace the scientifically obsolete Hungarian land valuation system, the so-called AK (“Gold Crown”) ratings. The new system is supported by a GIS and it is unique in its capability of providing an up-to-date index of soil quality and land value. The geographical information is provided by national map databases on genetic soil maps and soil attributes at the scale of 1:10.000, combined with cadastral maps, digital terrain model, topographic map, orthophotos of aerial photographs and agronomic field records. The automated algorithms are easy to update, can be made legally binding and can provide a transparent system for land taxation, calculation of subsidies, appropriation. Given that detailed (1:10,000 or finer) soil map coverage will be completed for all lands of Hungary (at date only 60% of the croplands have soil maps), this way a multifunctional system will be available that promotes an optimum use of land resources. 

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 Utilization of Airborne Topo-Bathymetric LiDAR Technology for Coastline Determination in Western Part of Java Island

2021 ◽  
Vol 925 (1) ◽  
pp. 012065
Author(s):  
B Soeksmantono ◽  
Y. Prita Utama ◽  
F Syaifudin
Keyword(s):  
Tide Gauge ◽  
Digital Terrain Model ◽  
Time Cost ◽  
Geospatial Information ◽  
Terrain Model ◽  
Marine Management ◽  
Astronomical Tide ◽  
Digital Terrain ◽  
Management Area ◽  
The Mean

Abstract Indonesia is the largest archipelagic country consisting of 17,504 islands which have 99,093 km of coastline. From the total, approximately only 10% had mapped. The coastline is essential for several applications such as topographic height reference, a reference in the delimitation of the marine management area, coastal boundaries, etc. Law number 4 of 2011 (UUIG), in article 13 paragraph 2 concerning Geospatial Information, mentioned three types of coastlines, namely: (a) the lowest astronomical tide, (b) the highest astronomical tide, and (c) the mean sea level. The existing method for determining the coastlines is observing a tide gauge over a long period at several places, then densify the point height by levelling method. This method is less effective due to time, cost, and amount of sample points. This paper presents our experience on coastlines determination by extracting it from a digital terrain model (DTM). The Airborne Topo-Bathymetric LiDAR technology is utilized to provide DTM that covers land and seabed. The points cloud, which is the output of this technology, was transformed to the geoid and corrected by tidal datum before those three types of coastlines were determined and delineated. The Western Part of Java Island is a study area. The project covers 1,000 km of coastline. The DTM quality was validated using several independent check-points along the coastline and hundreds of shorelines transect points at two locations. The result shows that vertical accuracy within the decimeter level.

scholarly journals The Effect of LiDAR Sampling Density on DTM Accuracy for Areas with Heavy Forest Cover

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 265
Author(s):  
Mihnea Cățeanu ◽  
Arcadie Ciubotaru
Keyword(s):  
Forest Cover ◽  
Initial Point ◽  
Ground Surface ◽  
Digital Terrain Model ◽  
Morphological Data ◽  
Sampling Density ◽  
Terrain Model ◽  
Digital Terrain ◽  
Point Density ◽  
The Impact

Laser scanning via LiDAR is a powerful technique for collecting data necessary for Digital Terrain Model (DTM) generation, even in densely forested areas. LiDAR observations located at the ground level can be separated from the initial point cloud and used as input for the generation of a Digital Terrain Model (DTM) via interpolation. This paper proposes a quantitative analysis of the accuracy of DTMs (and derived slope maps) obtained from LiDAR data and is focused on conditions common to most forestry activities (rough, steep terrain with forest cover). Three interpolation algorithms were tested: Inverse Distance Weighted (IDW), Natural Neighbour (NN) and Thin-Plate Spline (TPS). Research was mainly focused on the issue of point data density. To analyze its impact on the quality of ground surface modelling, the density of the filtered data set was artificially lowered (from 0.89 to 0.09 points/m2) by randomly removing point observations in 10% increments. This provides a comprehensive method of evaluating the impact of LiDAR ground point density on DTM accuracy. While the reduction of point density leads to a less accurate DTM in all cases (as expected), the exact pattern varies by algorithm. The accuracy of the LiDAR-derived DTMs is relatively good even when LiDAR sampling density is reduced to 0.40–0.50 points/m2 (50–60 % of the initial point density), as long as a suitable interpolation algorithm is used (as IDW proved to be less resilient to density reductions below approximately 0.60 points/m2). In the case of slope estimation, the pattern is relatively similar, except the difference in accuracy between IDW and the other two algorithms is even more pronounced than in the case of DTM accuracy. Based on this research, we conclude that LiDAR is an adequate method for collecting morphological data necessary for modelling the ground surface, even when the sampling density is significantly reduced.

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