scholarly journals Combination of UAV and terrestrial photogrammetry to assess rapid glacier evolution and conditions of glacier hazards

2017 ◽  
Author(s):  
Davide Fugazza ◽  
Marco Scaioni ◽  
Manuel Corti ◽  
Carlo D'Agata ◽  
Roberto Sergio Azzoni ◽  
...  
Keyword(s):  
Point Clouds ◽  
Melting Rate ◽  
Aerial Survey ◽  
Thickness Change ◽  
Glacier Tongue ◽  
Glacier Terminus ◽  
Digital Elevation ◽  
Glacier Melting ◽  
Uav Images ◽  
Terrestrial Photogrammetry

Abstract. Tourists and hikers visiting glaciers all year round face hazards such as the rapid formation of collapses at the terminus, typical of such a dynamically evolving environment. In this study, we analysed potential hazards of the Forni glacier, an important geo-site located in Stelvio Park (Italian Alps), by describing local surface features and evaluating the glacier melting rate. The analyses were based on point clouds and digital elevation models (DEMs) from two separate surveys of the glacier tongue carried out in 2014 and 2016 with Unmanned Aerial Vehicles (UAVs), terrestrial photogrammetry (only in 2016) and a DEM obtained in 2007 from an aerial survey. On the area covered by the 2016 survey, average glacier thinning rates of −4.15 ma−1 were found in 2007-2016, while the mean thickness change of the glacier tongue in 2014–2016 was −10.40 ± 2.60 m. UAV-based DEMs were thus found to be sufficiently accurate with respect to the rates of glacier down-wasting, while terrestrial photogrammetry allowed the reconstruction of the glacier terminus, presenting several vertical and sub-vertical surfaces whose modelling was difficult to obtain from airborne UAV images. The integration of UAV and terrestrial photogrammetry provided a detailed and accurate 3D model of the glacier tongue, which we used to identify hazard areas.

scholarly journals APPLICATION OF RAIL TRACK GEOMETRY MEASURING TROLLEYS FOR GEOREFERENCING OF UAV IMAGES

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 101-107
Author(s):  
V. V. Shcherbakov ◽  
M. A. Altyntsev ◽  
M. A. Altyntseva
Keyword(s):  
Low Cost ◽  
Control Point ◽  
Point Clouds ◽  
Aerial Survey ◽  
Control Points ◽  
Data Types ◽  
Track Geometry ◽  
Rail Track ◽  
Uav Images ◽  
The Cost

Abstract. Rail track geometry measuring trolleys are widely used in the railway industry. They can collect information about the state of rails with high accuracy. Nowadays there are a lot of trolleys. Principles of measurements in different trolleys may vary greatly. The trolleys that can use the absolute method of measuring coordinates have advantages. Coordinates of rails and rail track axis can be used as control points for georeferencing of any other surveying data. UAV images are one of these data types. In railways aerial survey using UAVs is mostly used for mapping, gathering data for creation of profiles and some other measurements. UAVs allow reducing the volume of field surveying works. The cost of UAVs is very different. Application of low-cost UAVs imposes increased requirements to distribution of control points. As distribution of control points taken from a trolley trajectory is poor, the issue of such control point application emerges. The study of opportunity to use the trolley trajectory for georeferencing of UAV images is carried out. Accuracy estimation of generating photogrammetric models and image-based point clouds using control point coordinates measured with the trolley is given. Accuracy of measuring obstruction clearances with the help of image-based point clouds is estimated.

scholarly journals PERFORMANCE EVALUATION OF DIFFERENT GROUND FILTERING ALGORITHMS FOR UAV-BASED POINT CLOUDS

2016 ◽  
Vol XLI-B1 ◽  
pp. 245-251 ◽  
Author(s):  
C. Serifoglu ◽  
O. Gungor ◽  
V. Yilmaz
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Good Alternative ◽  
Lidar Data ◽  
Filtering Algorithms ◽  
Aerial Photos ◽  
Digital Elevation ◽  
Aerial Vehicle ◽  
Elevation Model ◽  
Uav Images

Digital Elevation Model (DEM) generation is one of the leading application areas in geomatics. Since a DEM represents the bare earth surface, the very first step of generating a DEM is to separate the ground and non-ground points, which is called ground filtering. Once the point cloud is filtered, the ground points are interpolated to generate the DEM. LiDAR (Light Detection and Ranging) point clouds have been used in many applications thanks to their success in representing the objects they belong to. Hence, in the literature, various ground filtering algorithms have been reported to filter the LiDAR data. Since the LiDAR data acquisition is still a costly process, using point clouds generated from the UAV images to produce DEMs is a reasonable alternative. In this study, point clouds with three different densities were generated from the aerial photos taken from a UAV (Unmanned Aerial Vehicle) to examine the effect of point density on filtering performance. The point clouds were then filtered by means of five different ground filtering algorithms as Progressive Morphological 1D (PM1D), Progressive Morphological 2D (PM2D), Maximum Local Slope (MLS), Elevation Threshold with Expand Window (ETEW) and Adaptive TIN (ATIN). The filtering performance of each algorithm was investigated qualitatively and quantitatively. The results indicated that the ATIN and PM2D algorithms showed the best overall ground filtering performances. The MLS and ETEW algorithms were found as the least successful ones. It was concluded that the point clouds generated from the UAVs can be a good alternative for LiDAR data.

scholarly journals A multiscale approach to assess geomorphological processes in a semiarid badland area (Ebro Depression, Spain)

2017 ◽  
Vol 43 (1) ◽  
pp. 41 ◽  
Author(s):  
V. Ferrer ◽  
P. Errea ◽  
E. Alonso ◽  
E. Nadal-Romero ◽  
A. Gómez-Gutiérrez
Keyword(s):  
High Resolution ◽  
Spatial Scales ◽  
Laser Scanner ◽  
Point Clouds ◽  
Multiscale Approach ◽  
Erosion Rates ◽  
Preliminary Identification ◽  
Digital Elevation ◽  
Geomorphological Processes ◽  
Uav Images

In this paper, three methods (Terrestrial Laser Scanner (TLS), terrestrial Structure from Motion photogrammetry (SfM) and aerial SfM photogrammetry with an Unmanned Aerial Vehicle (UAV)) were evaluated and compared to produce high resolution point clouds and Digital Elevation Models (DEMs) in a semiarid, complex badland area (Los Aguarales) with tourism activities. Geomorphological processes and dynamics were studied at different spatial scales. The preliminary results showed the possibilities of a multiscale approach, using various non-invasive techniques, to assess geomorphological processes. The high resolution of the point clouds, obtained with TLS and terrestrial SfM photogrammetry, allowed preliminary identification of numerous spatial details, although no relevant topographical changes were detected during a short, wet spring period (with rainfall of 200 mm). UAV images allowed work at larger scales (catchment), mapping piping features, and could be seen as a worthwhile tool for time-effective data acquisition from larger areas. The application of different technologies and a multiscale approach to generate high resolution DEMs is a useful technique when carrying out geomorphological studies in semiarid badland areas. However, long term studies will be necessary to verify the suitability of these techniques in such complex landscapes, and quantify topographical changes and erosion rates. Finally, the information obtained with these tools could be used to promote the study area as an interesting geomorphosite with opportunities for tourism.

scholarly journals PERFORMANCE EVALUATION OF DIFFERENT GROUND FILTERING ALGORITHMS FOR UAV-BASED POINT CLOUDS

2016 ◽  
Vol XLI-B1 ◽  
pp. 245-251 ◽  
Author(s):  
C. Serifoglu ◽  
O. Gungor ◽  
V. Yilmaz
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Good Alternative ◽  
Lidar Data ◽  
Filtering Algorithms ◽  
Aerial Photos ◽  
Digital Elevation ◽  
Aerial Vehicle ◽  
Elevation Model ◽  
Uav Images

Digital Elevation Model (DEM) generation is one of the leading application areas in geomatics. Since a DEM represents the bare earth surface, the very first step of generating a DEM is to separate the ground and non-ground points, which is called ground filtering. Once the point cloud is filtered, the ground points are interpolated to generate the DEM. LiDAR (Light Detection and Ranging) point clouds have been used in many applications thanks to their success in representing the objects they belong to. Hence, in the literature, various ground filtering algorithms have been reported to filter the LiDAR data. Since the LiDAR data acquisition is still a costly process, using point clouds generated from the UAV images to produce DEMs is a reasonable alternative. In this study, point clouds with three different densities were generated from the aerial photos taken from a UAV (Unmanned Aerial Vehicle) to examine the effect of point density on filtering performance. The point clouds were then filtered by means of five different ground filtering algorithms as Progressive Morphological 1D (PM1D), Progressive Morphological 2D (PM2D), Maximum Local Slope (MLS), Elevation Threshold with Expand Window (ETEW) and Adaptive TIN (ATIN). The filtering performance of each algorithm was investigated qualitatively and quantitatively. The results indicated that the ATIN and PM2D algorithms showed the best overall ground filtering performances. The MLS and ETEW algorithms were found as the least successful ones. It was concluded that the point clouds generated from the UAVs can be a good alternative for LiDAR data.

scholarly journals INTEGRATION BETWEEN UNMANNED AERIAL VEHICLE AND TERRESTRIAL LASER SCANNER IN PRODUCING 3D MODEL

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 391-398 ◽  
Author(s):  
A. Mat Adnan ◽  
N. Darwin ◽  
M. F. M. Ariff ◽  
Z. Majid ◽  
K. M. Idris
Keyword(s):  
Data Acquisition ◽  
3D Model ◽  
Accuracy Assessment ◽  
Laser Scanner ◽  
Point Clouds ◽  
Aerial Survey ◽  
3D Data ◽  
Ground Survey ◽  
Aerial Vehicle ◽  
Uav Images

Abstract. Unmanned Aerial Vehicles (UAV) frequently used for obtaining 2D or 3D data acquisition. Meanwhile, Terrestrial Laser Scanners (TLS) are used for obtaining only 3D data acquisition. However if both are integrated, they were able to produce a more accurate data. The purpose of this study is to investigate the possible integration of point clouds obtained by TLS with UAV images at T06 FBES building through the aerial survey where the roof is scanned and ground survey which scans the facades‟ building. Topcon GLS 2000 and DJI Inspire 1 UAV were used to acquire the data at the field. The aerial data and ground data were processed using Pix4D and Scanmaster respectively. The data integration process is done by converting both point clouds into the same coordinate system and then by aligning the same points of both points clouds in Cloud Compare. For verification purposes, dimensional survey was done and there are several distances were taken from the study area to validate the accuracy assessment. The result of residuals between the dimension survey and integration is 0.183 m which is below 1 meter. The result of this study is a 3D model of UTM T06 FBES building based on the point cloud accuracy in cm level. To conclude, the integration between these two methods can be implemented to produce an accurate 3D model.

scholarly journals CROSS VALIDATION ON THE EQUALITY OF UAV-BASED AND CONTOUR-BASED DEMS

2018 ◽  
Vol XLII-3 ◽  
pp. 1245-1248
Author(s):  
R. Ma ◽  
Z. Xu ◽  
L. Wu ◽  
S. Liu
Keyword(s):  
Flight Control ◽  
Point Clouds ◽  
Time Saving ◽  
Ground Control Points ◽  
Digital Elevation ◽  
Irregular Networks ◽  
Feature Based ◽  
Elevation Model ◽  
Uav Images

Unmanned Aerial Vehicles (UAV) have been widely used for Digital Elevation Model (DEM) generation in geographic applications. This paper proposes a novel framework of generating DEM from UAV images. It starts with the generation of the point clouds by image matching, where the flight control data are used as reference for searching for the corresponding images, leading to a significant time saving. Besides, a set of ground control points (GCP) obtained from field surveying are used to transform the point clouds to the user’s coordinate system. Following that, we use a multi-feature based supervised classification method for discriminating non-ground points from ground ones. In the end, we generate DEM by constructing triangular irregular networks and rasterization. The experiments are conducted in the east of Jilin province in China, which has been suffered from soil erosion for several years. The quality of UAV based DEM (UAV-DEM) is compared with that generated from contour interpolation (Contour-DEM). The comparison shows a higher resolution, as well as higher accuracy of UAV-DEMs, which contains more geographic information. In addition, the RMSE errors of the UAV-DEMs generated from point clouds with and without GCPs are ±0.5 m and ±20 m, respectively.

STUDY OF THE LANDSLIDE MORPHOLOGY BASED ON GNSS DATA AND AIRBORNE SOUNDING (ON THE EXAMPLE OF A SECTION OF THE PROTVA RIVER VALLEY)

2018 ◽  
Vol 12 (5-6) ◽  
pp. 50-57 ◽  
Author(s):  
I. S. Voskresensky ◽  
A. A. Suchilin ◽  
L. A. Ushakova ◽  
V. M. Shaforostov ◽  
A. L. Entin ◽  
...  
Keyword(s):  
Digital Elevation Model ◽  
Geodetic Network ◽  
Aerial Survey ◽  
River Valley ◽  
Ease Of Use ◽  
Digital Elevation ◽  
Loose Deposits ◽  
Positioning Method ◽  
Landslide Body ◽  
Elevation Model

To use unmanned aerial vehicles (UAVs) for obtaining digital elevation models (DEM) and digital terrain models (DTM) is currently actively practiced in scientific and practical purposes. This technology has many advantages: efficiency, ease of use, and the possibility of application on relatively small area. This allows us to perform qualitative and quantitative studies of the progress of dangerous relief-forming processes and to assess their consequences quickly. In this paper, we describe the process of obtaining a digital elevation model (DEM) of the relief of the slope located on the bank of the Protva River (Satino training site of the Faculty of Geography, Lomonosov Moscow State University). To obtain the digital elevation model, we created a temporary geodetic network. The coordinates of the points were measured by the satellite positioning method using a highprecision mobile complex. The aerial survey was carried out using an unmanned aerial vehicle from a low altitude (about 40–45 m). The processing of survey materials was performed via automatic photogrammetry (Structure-from-Motion method), and the digital elevation model of the landslide surface on the Protva River valley section was created. Remote sensing was supplemented by studying archival materials of aerial photography, as well as field survey conducted immediately after the landslide. The total amount of research results made it possible to establish the causes and character of the landslide process on the study site. According to the geomorphological conditions of formation, the landslide refers to a variety of landslideslides, which are formed when water is saturated with loose deposits. The landslide body was formed with the "collapse" of the blocks of turf and deluvial loams and their "destruction" as they shifted and accumulated at the foot of the slope.

scholarly journals Terrain-Based Shadow Correction Method for Assessing Supraglacial Features on the Greenland Ice Sheet

2021 ◽  
Vol 2 ◽  
Author(s):  
Sasha. Z. Leidman ◽  
Åsa K. Rennermalm ◽  
Richard G. Lathrop ◽  
Matthew. G. Cooper
Keyword(s):  
Land Surface ◽  
Greenland Ice Sheet ◽  
Correction Method ◽  
Correction Coefficient ◽  
Digital Elevation ◽  
Aerial Vehicle ◽  
Sediment Cover ◽  
Elevation Model ◽  
Uav Images ◽  
Gis Software

The presence of shadows in remotely sensed images can reduce the accuracy of land surface classifications. Commonly used methods for removing shadows often use multi-spectral image analysis techniques that perform poorly for dark objects, complex geometric models, or shaded relief methods that do not account for shadows cast on adjacent terrain. Here we present a new method of removing topographic shadows using readily available GIS software. The method corrects for cast shadows, reduces the amount of over-correction, and can be performed on imagery of any spectral resolution. We demonstrate this method using imagery collected with an uncrewed aerial vehicle (UAV) over a supraglacial stream catchment in southwest Greenland. The structure-from-motion digital elevation model showed highly variable topography resulting in substantial shadowing and variable reflectance values for similar surface types. The distribution of bare ice, sediment, and water within the catchment was determined using a supervised classification scheme applied to the corrected and original UAV images. The correction resulted in an insignificant change in overall classification accuracy, however, visual inspection showed that the corrected classification more closely followed the expected distribution of classes indicating that shadow correction can aid in identification of glaciological features hidden within shadowed regions. Shadow correction also caused a substantial decrease in the areal coverage of dark sediment. Sediment cover was highly dependent on the degree of shadow correction (k coefficient), yet, for a correction coefficient optimized to maximize shadow brightness without over-exposing illuminated surfaces, terrain correction resulted in a 49% decrease in the area covered by sediment and a 29% increase in the area covered by water. Shadow correction therefore reduces the overestimation of the dark surface coverage due to shadowing and is a useful tool for investigating supraglacial processes and land cover change over a wide variety of complex terrain.

scholarly journals VALIDATION OF A UAV-DERIVED POINT CLOUD BY SEMANTIC CLASSIFICATION AND COMPARISON WITH TLS DATA

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 83-90
Author(s):  
M. Franzini ◽  
V. Casella ◽  
P. Marchese ◽  
M. Marini ◽  
G. Della Porta ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Point Clouds ◽  
Aerial Survey ◽  
Gradual Transition ◽  
Support Vector ◽  
Uniform Density ◽  
Mutual Distance ◽  
Semantic Classification ◽  
Cost Efficient

Abstract. Recent years showed a gradual transition from terrestrial to aerial survey thanks to the development of UAV and sensors for it. Many sectors benefited by this change among which geological one; drones are flexible, cost-efficient and can support outcrops surveying in many difficult situations such as inaccessible steep and high rock faces. The experiences acquired in terrestrial survey, with total stations, GNSS or terrestrial laser scanner (TLS), are not yet completely transferred to UAV acquisition. Hence, quality comparisons are still needed. The present paper is framed in this perspective aiming to evaluate the quality of the point clouds generated by an UAV in a geological context; data analysis was conducted comparing the UAV product with the homologous acquired with a TLS system. Exploiting modern semantic classification, based on eigenfeatures and support vector machine (SVM), the two point clouds were compared in terms of density and mutual distance. The UAV survey proves its usefulness in this situation with a uniform density distribution in the whole area and producing a point cloud with a quality comparable with the more traditional TLS systems.

scholarly journals DENSE 3D POINT CLOUD GENERATION FROM UAV IMAGES FROM IMAGE MATCHING AND GLOBAL OPTIMAZATION

2016 ◽  
Vol XLI-B1 ◽  
pp. 1005-1009 ◽  
Author(s):  
S. Rhee ◽  
T. Kim
Keyword(s):  
Global Optimization ◽  
Image Matching ◽  
Point Clouds ◽  
Image Space ◽  
Object Space ◽  
3D Point Clouds ◽  
Local Point ◽  
Matching Technique ◽  
Uav Images ◽  
Orientation Parameters

3D spatial information from unmanned aerial vehicles (UAV) images is usually provided in the form of 3D point clouds. For various UAV applications, it is important to generate dense 3D point clouds automatically from over the entire extent of UAV images. In this paper, we aim to apply image matching for generation of local point clouds over a pair or group of images and global optimization to combine local point clouds over the whole region of interest. We tried to apply two types of image matching, an object space-based matching technique and an image space-based matching technique, and to compare the performance of the two techniques. The object space-based matching used here sets a list of candidate height values for a fixed horizontal position in the object space. For each height, its corresponding image point is calculated and similarity is measured by grey-level correlation. The image space-based matching used here is a modified relaxation matching. We devised a global optimization scheme for finding optimal pairs (or groups) to apply image matching, defining local match region in image- or object- space, and merging local point clouds into a global one. For optimal pair selection, tiepoints among images were extracted and stereo coverage network was defined by forming a maximum spanning tree using the tiepoints. From experiments, we confirmed that through image matching and global optimization, 3D point clouds were generated successfully. However, results also revealed some limitations. In case of image-based matching results, we observed some blanks in 3D point clouds. In case of object space-based matching results, we observed more blunders than image-based matching ones and noisy local height variations. We suspect these might be due to inaccurate orientation parameters. The work in this paper is still ongoing. We will further test our approach with more precise orientation parameters.

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