scholarly journals What should a bare earth digital terrain model (DTM) portray?

2018 ◽  
Author(s):  
Peter L Guth
Keyword(s):  
Point Cloud ◽  
Western Europe ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Noise Removal ◽  
Free Software ◽  
Surface Generation ◽  
Classification Algorithms ◽  
Terrain Model ◽  
Digital Terrain

National mapping agencies in North American and western Europe have released free lidar point clouds with densities of 2-23 points/m², and derived terrain grids. Geomorphometric processing uses a bare earth digital terrain model (DTM), which can be acquired from mapping agencies or created from the point cloud to better control its characteristics. Free software provides tools for noise removal, ground classification, surface generation, void filling, surface smoothing, and hydraulic conditioning. Tests with three ground classification algorithms, and four surface generation algorithms show that they produced very similar results. The main issues for geomorphometric operations on DTMs involve whether the highest and lowest ground points should be in the DTM if they are not on a grid node, how water, buildings, and roads should be treated, if using a DTM of lower resolution will effectively filter out noise and allow much faster processing, and if lower resolution DTMs should be created directly from the point cloud or by processing a higher resolution DTM.

scholarly journals What should a bare earth digital terrain model (DTM) portray?

2018 ◽  
Author(s):  
Peter L Guth
Keyword(s):  
Point Cloud ◽  
Western Europe ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Noise Removal ◽  
Free Software ◽  
Surface Generation ◽  
Classification Algorithms ◽  
Terrain Model ◽  
Digital Terrain

National mapping agencies in North American and western Europe have released free lidar point clouds with densities of 2-23 points/m², and derived terrain grids. Geomorphometric processing uses a bare earth digital terrain model (DTM), which can be acquired from mapping agencies or created from the point cloud to better control its characteristics. Free software provides tools for noise removal, ground classification, surface generation, void filling, surface smoothing, and hydraulic conditioning. Tests with three ground classification algorithms, and four surface generation algorithms show that they produced very similar results. The main issues for geomorphometric operations on DTMs involve whether the highest and lowest ground points should be in the DTM if they are not on a grid node, how water, buildings, and roads should be treated, if using a DTM of lower resolution will effectively filter out noise and allow much faster processing, and if lower resolution DTMs should be created directly from the point cloud or by processing a higher resolution DTM.

scholarly journals Modeling Streamflow and Sediment Loads with a Photogrammetrically Derived UAS Digital Terrain Model: Empirical Evaluation from a Fluvial Aggregate Excavation Operation

Drones ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 20
Author(s):  
Joseph P. Hupy ◽  
Cyril O. Wilson
Keyword(s):  
Soil Erosion ◽  
Point Cloud ◽  
Empirical Evaluation ◽  
Digital Terrain Model ◽  
Surface Flow ◽  
Sediment Loading ◽  
Terrain Model ◽  
Digital Terrain ◽  
Statistical Measures ◽  
Geospatial Datasets

Soil erosion monitoring is a pivotal exercise at macro through micro landscape levels, which directly informs environmental management at diverse spatial and temporal scales. The monitoring of soil erosion can be an arduous task when completed through ground-based surveys and there are uncertainties associated with the use of large-scale medium resolution image-based digital elevation models for estimating erosion rates. LiDAR derived elevation models have proven effective in modeling erosion, but such data proves costly to obtain, process, and analyze. The proliferation of images and other geospatial datasets generated by unmanned aerial systems (UAS) is increasingly able to reveal additional nuances that traditional geospatial datasets were not able to obtain due to the former’s higher spatial resolution. This study evaluated the efficacy of a UAS derived digital terrain model (DTM) to estimate surface flow and sediment loading in a fluvial aggregate excavation operation in Waukesha County, Wisconsin. A nested scale distributed hydrologic flow and sediment loading model was constructed for the UAS point cloud derived DTM. To evaluate the effectiveness of flow and sediment loading generated by the UAS point cloud derived DTM, a LiDAR derived DTM was used for comparison in consonance with several statistical measures of model efficiency. Results demonstrate that the UAS derived DTM can be used in modeling flow and sediment erosion estimation across space in the absence of a LiDAR-based derived DTM.

scholarly journals Automated Generation of Digital Terrain Model using Point Clouds of Digital Surface Model in Forest Area

2011 ◽  
Vol 3 (5) ◽  
pp. 845-858 ◽  
Author(s):  
Kande R.M.U. Bandara ◽  
Lal Samarakoon ◽  
Rajendra P. Shrestha ◽  
Yoshikazu Kamiya
Keyword(s):  
Digital Terrain Model ◽  
Point Clouds ◽  
Forest Area ◽  
Surface Model ◽  
Digital Surface Model ◽  
Automated Generation ◽  
Terrain Model ◽  
Digital Terrain

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

2016 ◽  
Vol XLI-B1 ◽  
pp. 893-899
Author(s):  
M. Kosmatin Fras ◽  
A. Kerin ◽  
M. Mesarič ◽  
V. Peterman ◽  
D. Grigillo
Keyword(s):  
Quality Assessment ◽  
Point Cloud ◽  
Digital Terrain Model ◽  
Quality Parameter ◽  
Unmanned Aerial System ◽  
Test Field ◽  
Absolute Deviation ◽  
Terrain Model ◽  
Digital Terrain

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.

scholarly journals Presenting a Morphological Based Approach for Filtering The Point Cloud to Extract the Digital Terrain Model

2019 ◽  
Vol 6 (4) ◽  
pp. 73-96
Author(s):  
Parham Pahlavani ◽  
Hamid Reza Sahraiian ◽  
Behnaz Bigdeli ◽  
◽  
◽  
...  
Keyword(s):  
Point Cloud ◽  
Digital Terrain Model ◽  
Terrain Model ◽  
Digital Terrain

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

2019 ◽  
Vol 7 (1) ◽  
pp. 1-20
Author(s):  
Fotis Giagkas ◽  
Petros Patias ◽  
Charalampos Georgiadis
Keyword(s):  
Vegetation Type ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Aerial Images ◽  
Surface Model ◽  
Terrain Model ◽  
Vegetation Height ◽  
Digital Terrain ◽  
Dense Point ◽  
Height Model

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.

scholarly journals Reduction Method for Mobile Laser Scanning Data

2018 ◽  
Vol 7 (7) ◽  
pp. 285 ◽  
Author(s):  
Wioleta Błaszczak-Bąk ◽  
Zoltan Koppanyi ◽  
Charles Toth
Keyword(s):  
Data Reduction ◽  
Point Cloud ◽  
Laser Scanning ◽  
Digital Terrain Model ◽  
New Approach ◽  
Terrain Model ◽  
Digital Terrain ◽  
Airborne Laser ◽  
Short Time ◽  
Mls Method

Mobile Laser Scanning (MLS) technology acquires a huge volume of data in a very short time. In many cases, it is reasonable to reduce the size of the dataset with eliminating points in such a way that the datasets, after reduction, meet specific optimization criteria. Various methods exist to decrease the size of point cloud, such as raw data reduction, Digital Terrain Model (DTM) generalization or generation of regular grid. These methods have been successfully applied on data captured from Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS), however, they have not been fully analyzed on data captured by an MLS system. The paper presents our new approach, called the Optimum Single MLS Dataset method (OptD-single-MLS), which is an algorithm for MLS data reduction. The tests were carried out in two variants: (1) for raw sensory measurements and (2) for a georeferenced 3D point cloud. We found that the OptD-single-MLS method provides a good solution in both variants; therefore, the choice of the reduction variant depends only on the user.

scholarly journals BADANIE MOŻLIWOŚCI WYKORZYSTANIA ZOBRAZOWAŃ DYNAMICZNYCH (WIDEO) Z NISKIEGO PUŁAPU LOTNICZEGO (BSP) DO OPRACOWAŃ FOTOGRAMETRYCZNYCH

1970 ◽  
pp. 53-64
Author(s):  
ADAM MŁYNARCZYK ◽  
SŁAWOMIR KRÓLEWICZ ◽  
PAWEŁ RUTKOWSKI
Keyword(s):  
High Altitude ◽  
Unmanned Aerial Vehicles ◽  
Point Cloud ◽  
Digital Terrain Model ◽  
Image Data ◽  
Terrain Model ◽  
Digital Terrain ◽  
Aerial Vehicles ◽  
The Creation ◽  
Recorded Image

The use of unmanned aerial vehicles is becoming more and more popular for making high-altitude and orthophotomap models. In this process, series of images are taken at specific intervals, usually lasting several seconds. This article demonstrates the ability to make models and orthophotomaps from dynamic images – video recorded from UAV. The best mutual coverage of photographs was indicated (95–96%) and the photogrammetric process for joining images was presented, through the creation of a point cloud to obtain a digital terrain model and the orotfotomap. The data was processed in 150 different variants and the usefulness of this method was demonstrated. Problems and errors that may occur during the processing of recorded image data are also described.

scholarly journals Aerial Laser Scanning Data as a Source of Terrain Modeling in a Fluvial Environment: Biasing Factors of Terrain Height Accuracy

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2063 ◽  
Author(s):  
Zsuzsanna Szabó ◽  
Csaba Albert Tóth ◽  
Imre Holb ◽  
Szilárd Szabó
Keyword(s):  
Laser Scanning ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Threshold Value ◽  
Noise Filtering ◽  
Terrain Model ◽  
Digital Terrain ◽  
Natural Neighbor ◽  
Wide Range ◽  
Ground Point

Airborne light detection and ranging (LiDAR) scanning is a commonly used technology for representing the topographic terrain. As LiDAR point clouds include all surface features present in the terrain, one of the key elements for generating a digital terrain model (DTM) is the separation of the ground points. In this study, we intended to reveal the efficiency of different denoising approaches and an easy-to-use ground point classification technique in a floodplain with fluvial forms. We analyzed a point cloud from the perspective of the efficiency of noise reduction, parametrizing a ground point classifier (cloth simulation filter, CSF), interpolation methods and resolutions. Noise filtering resulted a wide range of point numbers in the models, and the number of points had moderate correlation with the mean accuracies (r = −0.65, p < 0.05), indicating that greater numbers of points had larger errors. The smallest differences belonged to the neighborhood-based noise filtering and the larger cloth size (5) and the smaller threshold value (0.2). The most accurate model was generated with the natural neighbor interpolation with the cloth size of 5 and the threshold of 0.2. These results can serve as a guide for researchers using point clouds when considering the steps of data preparation, classification, or interpolation in a flat terrain.

scholarly journals TERRAIN EXTRACTION BY INTEGRATING TERRESTRIAL LASER SCANNER DATA AND SPECTRAL INFORMATION

2015 ◽  
Vol XL-2/W4 ◽  
pp. 45-51 ◽  
Author(s):  
C. L. Lau ◽  
S. Halim ◽  
M. Zulkepli ◽  
A. M. Azwan ◽  
W. L. Tang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Digital Camera ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Spectral Information ◽  
Terrestrial Laser Scanner ◽  
Cloud Data ◽  
Terrain Model ◽  
Colour Image

The extraction of true terrain points from unstructured laser point cloud data is an important process in order to produce an accurate digital terrain model (DTM). However, most of these spatial filtering methods just utilizing the geometrical data to discriminate the terrain points from nonterrain points. The point cloud filtering method also can be improved by using the spectral information available with some scanners. Therefore, the objective of this study is to investigate the effectiveness of using the three-channel (red, green and blue) of the colour image captured from built-in digital camera which is available in some Terrestrial Laser Scanner (TLS) for terrain extraction. In this study, the data acquisition was conducted at a mini replica landscape in Universiti Teknologi Malaysia (UTM), Skudai campus using Leica ScanStation C10. The spectral information of the coloured point clouds from selected sample classes are extracted for spectral analysis. The coloured point clouds which within the corresponding preset spectral threshold are identified as that specific feature point from the dataset. This process of terrain extraction is done through using developed Matlab coding. Result demonstrates that a higher spectral resolution passive image is required in order to improve the output. This is because low quality of the colour images captured by the sensor contributes to the low separability in spectral reflectance. In conclusion, this study shows that, spectral information is capable to be used as a parameter for terrain extraction.

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