Dem Imagery for Investigation and Verification Najaf-Karbala Hill Formation by Using Remote Sensing Techniques

The development of the techniques of remote sensing and the software used to deal with data packets or digital technologies, to control vast areas in describing and diagnosing the formologies of the earth, which have become necessary in studying natural phenomena on the earth, knowing the origin of the earth and the interaction of factors that affect it and predicting future changes through Monitor, analyze and compare. The purpose of the presented study was to investigate and verify on Hill of Najaf-Karbala location, by applying remote sensing techniques and GIS template. To fulfill this goal in search, applied Digital Elevation Models (DEM) images were used in the study. The generation of DEM images applied for a geographic database is important in many applications. ArcGIS V10.5 is used to achieve this study and analysis of the study area to verify the goal. It is one of the major processing including in elevation assignment systems, and the traditional approach is manually identify elevation of contours from existing topographic map. It is the physical make-up of the land, including natural and manufactured physical features. Moreover, the DEM Image aid thought to verify and identify all features (series of hills, or small hills, knoll, Gullies, Spurs, and a gully is a small valley). Additionally, a spur is a sloping ridge jutting out from the side of a hill, and in some time in terrain, hillsides have been eroding to form a series of gullies separated by spurs. In this study, every bend in a contour either indicates gullies or spurs. In addition, it is crucial to be able to tell which gully or spur. Finally, The study verified that most regions of Najaf and Karbala lie on terrain formed big hills

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

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Effect of DEM Interpolation Neighbourhood on Terrain Factors

Topographic factors such as slope and aspect are essential parameters in depicting the structure and morphology of a terrain surface. We study the effect of the number of points in the neighbourhood of a digital elevation model (DEM) interpolation method on mean slope, mean aspect, and RMSEs of slope and aspect from the interpolated DEM. As the moving least squares (MLS) method can maintain the inherent properties and other characteristics of a surface, this method is chosen for DEM interpolation. Three areas containing different types of topographic features are selected for study. Simulated data from a Gauss surface is also used for comparison. First, the impact of the number of points on the DEM root mean square error (RMSE) is analysed. The DEM RMSE in the three study areas decreases gradually with the number of points in the neighbourhood. In addition, the effect of the number of points in the neighbourhood on mean slope and mean aspect was studied across varying topographies through regression analysis. The two variables respond differently to changes in terrain. However, the RMSEs of the slope and aspect in all study areas are logarithmically related to the number of points in the neighbourhood and the values decrease uniformly as the number of points in the neighbourhood increases. With more points in the neighbourhood, the RMSEs of the slope and aspect are not sensitive to topography differences and the same trends are observed for the three studied quantities. Results for the Gauss surface are similar. Finally, this study analyses the spatial distribution of slope and aspect errors. The slope error is concentrated in ridges, valleys, steep-slope areas, and ditch edges while the aspect error is concentrated in ridges, valleys, and flat regions. With more points in the neighbourhood, the number of grid cells in which the slope error is greater than 15° is gradually reduced. With similar terrain types and data sources, if the calculation efficiency is not a concern, sufficient points in the spatial autocorrelation range should be analysed in the neighbourhood to maximize the accuracy of the slope and aspect. However, selecting between 10 and 12 points in the neighbourhood is economical