scholarly journals A Parallel Computing Approach to Spatial Neighboring Analysis of Large Amounts of Terrain Data Using Spark

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 365
Author(s):  
Jianbo Zhang ◽  
Zhuangzhuang Ye ◽  
Kai Zheng
Keyword(s):  
Parallel Computing ◽  
Spatial Data ◽  
Parallel Performance ◽  
Digital Elevation ◽  
Hadoop Distributed File System ◽  
Elevation Data ◽  
Calculation Results ◽  
Digital Elevation Data ◽  
Elevation Model ◽  
Computing Approach

Spatial neighboring analysis is an indispensable part of geo-raster spatial analysis. In the big data era, high-resolution raster data offer us abundant and valuable information, and also bring enormous computational challenges to the existing focal statistics algorithms. Simply employing the in-memory computing framework Spark to serve such applications might incur performance issues due to its lack of native support for spatial data. In this article, we present a Spark-based parallel computing approach for the focal algorithms of neighboring analysis. This approach implements efficient manipulation of large amounts of terrain data through three steps: (1) partitioning a raster digital elevation model (DEM) file into multiple square tile files by adopting a tile-based multifile storing strategy suitable for the Hadoop Distributed File System (HDFS), (2) performing the quintessential slope algorithm on these tile files using a dynamic calculation window (DCW) computing strategy, and (3) writing back and merging the calculation results into a whole raster file. Experiments with the digital elevation data of Australia show that the proposed computing approach can effectively improve the parallel performance of focal statistics algorithms. The results also show that the approach has almost the same calculation accuracy as that of ArcGIS. The proposed approach also exhibits good scalability when the number of Spark executors in clusters is increased.

scholarly journals Environmental classification of administrative units of the Ukrainian Carpathians

2014 ◽  
pp. 245-253
Author(s):  
Ivan Kruhlov
Keyword(s):  
Cluster Analysis ◽  
Standard Deviation ◽  
Satellite Images ◽  
Environmental Classification ◽  
Digital Elevation ◽  
Elevation Data ◽  
Digital Elevation Data ◽  
Administrative Units ◽  
Elevation Model

Boundaries of 43 administrative units (raions and oblast towns) were digitized and manually rectified using official schemes and satellite images. SRTM digital elevation data were used to calculate mean relative elevation and its standard deviation for each unit, as well as to delineate altitudinal bioclimatic belts and their portions within the units. These parameters were used to classify the units via agglomerative cluster analysis into nine environmental classes. Key words: cluster analysis, digital elevation model, geoecosystem, geo-spatial analysis.

scholarly journals Automated Conflation of Digital Elevation Model with Reference Hydrographic Lines

2020 ◽  
Vol 9 (5) ◽  
pp. 334
Author(s):  
Timofey E. Samsonov
Keyword(s):  
Spatial Analysis ◽  
Digital Elevation Model ◽  
Spatial Data ◽  
Drainage Network ◽  
Map Generalization ◽  
Data Types ◽  
Digital Elevation ◽  
Elevation Data ◽  
Elevation Model ◽  
Different Sources

Combining misaligned spatial data from different sources complicates spatial analysis and creation of maps. Conflation is a process that solves the misalignment problem through spatial adjustment or attribute transfer between similar features in two datasets. Even though a combination of digital elevation model (DEM) and vector hydrographic lines is a common practice in spatial analysis and mapping, no method for automated conflation between these spatial data types has been developed so far. The problem of DEM and hydrography misalignment arises not only in map compilation, but also during the production of generalized datasets. There is a lack of automated solutions which can ensure that the drainage network represented in the surface of generalized DEM is spatially adjusted with independently generalized vector hydrography. We propose a new method that performs the conflation of DEM with linear hydrographic data and is embeddable into DEM generalization process. Given a set of reference hydrographic lines, our method automatically recognizes the most similar paths on DEM surface called counterpart streams. The elevation data extracted from DEM is then rubbersheeted locally using the links between counterpart streams and reference lines, and the conflated DEM is reconstructed from the rubbersheeted elevation data. The algorithm developed for extraction of counterpart streams ensures that the resulting set of lines comprises the network similar to the network of ordered reference lines. We also show how our approach can be seamlessly integrated into a TIN-based structural DEM generalization process with spatial adjustment to pre-generalized hydrographic lines as additional requirement. The combination of the GEBCO_2019 DEM and the Natural Earth 10M vector dataset is used to illustrate the effectiveness of DEM conflation both in map compilation and map generalization workflows. Resulting maps are geographically correct and are aesthetically more pleasing in comparison to a straightforward combination of misaligned DEM and hydrographic lines without conflation.

scholarly journals Integration von hochauflösenden Fernerkundungs- und Geländemodelldaten für die Landschaftsvisualisierung

1997 ◽  
Vol 52 (1) ◽  
pp. 21-26
Author(s):  
P. Pirchl ◽  
P. Hirtz ◽  
M. Suter ◽  
D. Nüesch
Keyword(s):  
Land Cover ◽  
Aerial Photographs ◽  
Surface Model ◽  
Northern Switzerland ◽  
Transition Zones ◽  
Gis Tools ◽  
Digital Elevation ◽  
Elevation Data ◽  
Digital Elevation Data ◽  
Elevation Model

Abstract. This paper presents experiments for realistic landscape visualization using high resolution digital aerial photographs and elevation modeis. The natural environment of a river in northern Switzerland is visualized. Photogrammetrically measured digital elevation data and ortho-rectified remote sensing imagery (Landsat Thematic Mapper/TM and aerial photographs) are combined to compute realistic 3D views of the landscape. To renderthe landscape more realistically, the digital elevation model (DEM) is transformed to a digital surface model (DSM), representing the surface and including objects like forests or bushes. For this transformation land cover information and GIS tools were used. Unnaturally looking vertical borders between different land cover classes were suppressed by interpolating transition zones. Additionally, 3D objects (trees) are used in the foreground to increase the realism of the views.

Geomorphometry of the Glacial Cirques in Zardkuh Mountain, Iran

2016 ◽  
Vol 16 (2) ◽  
pp. 94-101
Author(s):  
Ali Ahmadabadi ◽  
Varduhi Sargsyan
Keyword(s):  
Environmental Science ◽  
Shape Features ◽  
Topographic Map ◽  
Quaternary Period ◽  
Digital Elevation ◽  
Second Derivatives ◽  
Curvature Index ◽  
Elevation Data ◽  
Digital Elevation Data ◽  
Elevation Model

AbstractCirques as one of the glacial erosional forms are suitable indicators to recognize the environmental conditions of the Quaternary period. Therefore, considering the importance of glacial cirque landforms, identifying and mapping the distribution of the circus with their shape features meets the need of environmental science, especially geomorphology. In this paper, in order to identify the quantitative features of cirques in Zardkuh region, the second derivatives, including second-degree curvature of the plan, profile and general curvature along with slope as a primarily derivative were used from geomorphometry indices. To this end, 20 meter resolution digital elevation model was generated from 1: 25,000 topographic map which was used in the geomorphometric analysis. The result shows that secondary derivatives had higher performance in identifying the feature shapes of glacial cirques. Likewise, the plan curvature Index could truly present the headwall around the circus as well as profile curvature clearly showed the avalanche path. In conclusion, it seems that the second derivative indicators, including curvature’s family, have high capability to extract and detect different natural shapes from digital elevation data.

Comparison of Late Pleistocene and Modern Glacier Extents in Central Nepal Based on Digital Elevation Data and Satellite Imagery

1998 ◽  
Vol 49 (3) ◽  
pp. 241-254 ◽  
Author(s):  
Christopher C. Duncan ◽  
Andrew J. Klein ◽  
Jeffrey G. Masek ◽  
Bryan L. Isacks
Keyword(s):  
Late Pleistocene ◽  
Equilibrium Line Altitude ◽  
Small Temperature ◽  
New Techniques ◽  
Central Nepal ◽  
Digital Elevation ◽  
Elevation Data ◽  
Digital Elevation Data ◽  
Elevation Model ◽  
Temperature Depression

Late Pleistocene and modern ice extents in central Nepal are compared to estimate equilibrium line altitude (ELA) depressions. New techniques are used for determining the former extent of glaciers based on quantitative, objective geomorphic analyses of a ∼90-m resolution digital elevation model (DEM). For every link of the drainage network, valley form is classified as glacial or fluvial based on cross-valley shape and slope statistics. Down-valley transitions from glacial to fluvial form indicate the former limits of glaciation in each valley. Landsat Multispectral Scanner imagery for the same region is used to map current glacier extents. For both full-glacial and modern cases, ELAs are computed from the glacier limits using the DEM and a toe-to-headwall altitude ratio of 0.5. Computed ELA depressions range from 100–900 m with a modal value of ∼650 m and a mean of ∼500 m, values consistent with previously published estimates for the central Himalaya but markedly smaller than estimates for many other regions. We suggest that this reflects reduced precipitation, rather than a small temperature depression, consistent with other evidence for a weaker monsoon under full-glacial conditions.

Urban Flood Mapping

2017 ◽  
Author(s):  
Indra Riyanto ◽  
Lestari Margatama
Keyword(s):  
Natural Disasters ◽  
Satisfying Condition ◽  
Three Dimensions ◽  
Urban Flood ◽  
Digital Elevation ◽  
Elevation Data ◽  
Recent Occurrence ◽  
Elevation Model ◽  
Digital Elevation Model Data ◽  
Potential Area

The recent degradation of environment quality becomes the prime cause of the recent occurrence of natural disasters. It also contributes in the increase of the area that is prone to natural disasters. Flood history data in Jakarta shows that flood occurred mainly during rainy season around January – February each year, but the flood area varies each year. This research is intended to map the flood potential area in DKI Jakarta by segmenting the Digital Elevation Model data. The data used in this research is contour data obtained from DPP–DKI with the resolution of 1 m. The data processing involved in this research is extracting the surface elevation data from the DEM, overlaying the river map of Jakarta with the elevation data. Subsequently, the data is then segmented using watershed segmentation method. The concept of watersheds is based on visualizing an image in three dimensions: two spatial coordinates versus gray levels, in which there are two specific points; that are points belonging to a regional minimum and points at which a drop of water, if placed at the location of any of those points, would fall with certainty to a single minimum. For a particular regional minimum, the set of points satisfying the latter condition is called the catchments basin or watershed of that minimum, while the points satisfying condition form more than one minima are termed divide lines or watershed lines. The objective of this segmentation is to find the watershed lines of the DEM image. The expected result of the research is the flood potential area information, especially along the Ciliwung river in DKI Jakarta.

scholarly journals MAT: GIS-Based Morphometry Assessment Tools for Concave Landforms

2021 ◽  
Vol 13 (14) ◽  
pp. 2810
Author(s):  
Joanna Gudowicz ◽  
Renata Paluszkiewicz
Keyword(s):  
Rapid Development ◽  
Assessment Tools ◽  
Lidar Data ◽  
Automatic Extraction ◽  
Sensing Technology ◽  
Digital Elevation ◽  
Elevation Data ◽  
Elevation Model ◽  
Morphometric Assessment ◽  
Morphometric Evaluation

The rapid development of remote sensing technology for obtaining high-resolution digital elevation models (DEMs) in recent years has made them more and more widely available and has allowed them to be used for morphometric assessment of concave landforms, such as valleys, gullies, glacial cirques, sinkholes, craters, and others. The aim of this study was to develop a geographic information systems (GIS) toolbox for the automatic extraction of 26 morphometric characteristics, which include the geometry, hypsometry, and volume of concave landforms. The Morphometry Assessment Tools (MAT) toolbox in the ArcGIS software was developed. The required input data are a digital elevation model and the form boundary as a vector layer. The method was successfully tested on an example of 21 erosion-denudation valleys located in the young glacial area of northwest Poland. Calculations were based on elevation data collected in the field and LiDAR data. The results obtained with the tool showed differences in the assessment of the volume parameter at the average level of 12%, when comparing the field data and LiDAR data. The algorithm can also be applied to other types of concave forms, as well as being based on other DEM data sources, which makes it a universal tool for morphometric evaluation.

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