scholarly journals 3D Visualization of Large Digital Elevation Model (DEM) Data Set

2002 ◽  
pp. 975-983
Author(s):  
Min Sun ◽  
Yong Xue ◽  
Ai-Nai Ma ◽  
Shan-Jun Mao
Keyword(s):  
Digital Elevation Model ◽  
3D Visualization ◽  
Data Set ◽  
Digital Elevation ◽  
Elevation Model

scholarly journals DIGITAL ELEVATION MODEL TO VISUALIZE SMALL SPATIAL OBJECT IN 3D

MATICS ◽  
2012 ◽  
Author(s):  
Cahyo Crysdian
Keyword(s):  
Digital Elevation Model ◽  
3D Visualization ◽  
Small Scale ◽  
Data Set ◽  
Spatial Object ◽  
Population Average ◽  
Digital Elevation ◽  
Elevation Data ◽  
Elevation Model ◽  
Srtm Data

<div class="Section1"><p><strong> </strong>An effort to develop a Digital Elevation Model (DEM) for small scale spatial objects is presented in this paper. The motivation of the research is to visualize a small spatial object as detail as possible in 3D, thus the presentation of the object in term of a computer model is similar to its presentation in reality. To reach this objective, the research was conducted in four stages i.e. elevation data retrieval, 3D visualization and its enhancement, and the development of blank data removal. Elevation data is obtained from SRTM dataset that has 3 arc-second or approximately 90 meters data resolution. Data obtained from SRTM is then visualized in 3D, in which visualization is enriched with view angle setting. Results of visualization show that enhancement to SRTM data set is required to present elevation data in 3D. It is due to the existence of blank data contained in the elevation data retrieved from SRTM. Therefore, this paper proposes two methods to enhance SRTM dataset i.e. population average and neighbors average, in order to counter the existence of blank data. The first method uses the population average of statistic to eliminate blank data, while the latter relies on the average value of its neighboring pixels. Comparison among those methods is held in this research to disclose the performance of each method. Result of comparison shows that the method based on neighbors average outperform population average method to eliminate blank data contained in SRTM data set. Thus, neighbor average delivers better 3D visualization for small scale spatial object.</p></div><em> </em> <p><em> </em></p> <p><strong>Keywords:</strong> Digital Elevation Model, 3D Visualization, Spatial</p>

scholarly journals A 20-year record (1998–2017) of permafrost, active layer, and meteorological conditions at a High Arctic permafrost research site (Bayelva, Spitsbergen): an opportunity to validate remote sensing data and land surface, snow, and permafrost models

2017 ◽  
Author(s):  
Julia Boike ◽  
Inge Juszak ◽  
Stephan Lange ◽  
Sarah Chadburn ◽  
Eleanor Burke ◽  
...  
Keyword(s):  
Energy Balance ◽  
High Resolution ◽  
Observational Data ◽  
Digital Elevation Model ◽  
Positive Feedback ◽  
The Arctic ◽  
Data Sets ◽  
Data Set ◽  
Digital Elevation ◽  
Elevation Model

Abstract. Most permafrost is located in the Arctic, where frozen organic carbon makes it an important component of the global climate system. Despite the fact that the Arctic climate changes more rapidly than the rest of the globe, observational data density in the region is low. Permafrost thaw and carbon release to the atmosphere are a positive feedback mechanism that can exacerbate climate warming. This positive feedback functions via changing land-atmosphere energy and mass exchanges. There is thus a great need to understand links between the energy balance, which can vary rapidly over hourly to annual time scales, and permafrost, which changes slowly over long time periods. This understanding thus mandates long-term observational data sets. Such a data set is available from the Bayelva Site at Ny-Ålesund, Svalbard, where meteorology, energy balance components and subsurface observations have been made for the last 20 years. Additional data include a high resolution digital elevation model and a panchromatic image. This paper presents the data set produced so far, explains instrumentation, calibration, processing and data quality control, as well as the sources for various resulting data sets. The resulting data set is unique in the Arctic and serves a baseline for future studies. Since the data provide observations of temporally variable parameters that mitigate energy fluxes between permafrost and atmosphere, such as snow depth and soil moisture content, they are suitable for use in integrating, calibrating and testing permafrost as a component in Earth System Models. The data set also includes a high resolution digital elevation model that can be used together with the snow physical information for snow pack modeling. The presented data are available in the supplementary material for this paper and through the PANGAEA website ( https://doi.pangaea.de/10.1594/PANGAEA.880120).

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.

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