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Authors: Mandla, Venkata Ravibabu
Issue Date: 2008
Abstract: A Digital Elevation Model (DEM) is a digital representation of a portion of the earth's surface. In a DEM, earth's surface is represented as spatially referenced regular grid points where each grid point represents a ground elevation value. It has long been known that DEMs have a potential for solving theoretical and applied problems in earth science. DEMs also have a major role to play in Geographic Information Systems (GIS), hydrological modeling, flood mapping, analysis of visibility and hazard mapping, Geomorphometry and the physiographic correction of digital satellite imagery. Errors in DEMs can occur in either the elevation or vertical (Z) and planimetric or horizontal (XY) coordinates, but the focus is usually on the former because planimetric error will produce elevation error. Many commercial data suppliers only report elevation error. Errors in DEMs are usually (Cooper, 1998; Wise, 2000) categorized into three groups: gross errors or blunders, systematic errors due to deterministic bias in the data collection or processing, and random errors. n Three main sources of error in DEMs are usually identified (Shearer, 1990; Li, 1992; Li and Chen, 1999; Gong et al., 2000); a) those derived from variations in the 'accuracy, density and distribution' (Li and Chen, 1999) of the measured source data as determined by the specific method of data generation; b) those derived from the processing and interpolation used to derive the DEM from the source data; c) those resulting from the characteristics of the terrain surface being modelled in relation to the representation of the DEM. The first two are quite clearly errors. The third, however, should be considered a matter of uncertainty. Alternatively, one can think of a) as data-based, being strictly concerned with the source data, while b) and c) are model-based being concerned with how well the resulting DEM approximates the real physiography (Theobald, 1989; Shortridge, 2001). Historically, DEMs encountered by the scientist/academics have most frequently been sourced by digitizing contour lines and spot heights from paper maps. Other sourcesmay have included imagery such as stereo aerial photographs using various types of photogrammetry, or less frequently point measurements derived direct from land survey. The errors and its correction is modeled in details in case of DEM generated form Land Survey method, Phtogrammetric method. With the GIS application in varios engineering projects increasing exponentially in last few years the use of automatic DEM generation from stereo satellite data and digitization of contours from the available map has increased in the same ratio. The GIS software provider has included DEM extraction module in their software for making it more versatile and applicable in different engineering problems. However, they could not match the accuracy ofDEM provided by professional DEM software. Also, number of tools iii required forDEMaccuracy checking and correction are missing in their software. The GIS user without much background in DEM starts using the module without caring the DEM accuracy aspect and produce inferior results from his study. The present study is an effort to analyze the shortcoming of these add-on DEM modules in GIS software and make recommendation to improve accuracy by either making some change in the procedure for extracting DEM from stereo satellite data and digitization of contours from the available map and also to suggest some important features to be added in DEM add-on module in GIS software. A web survey was conducted to find the concern of GIS user in DEM accuracy. This survey suggests that about half of the DEM users recognize that their work is affected by errors. The need for educating users who are currently not aware of the problems of errors (uncertainty) in the DEMdata is the conclusion from this survey. In DEM accuracy from contour data the study suggest to constrain the surface by adding morphological lines such as ridges and valley; the heights of these points must naturally verify the regular slope condition between contour lines. For this the study recommends facility of including additional surface information e.g. ridge, valley, channels etc. in GIS software having DEM generation module. DEM from stereo satellite data using automatic, manual tip points was conducted. The study conclude that automatic tie points alone can't generate best quality DEM and a semiautomatic approach combining manual tie points with automatic tie points gives better DEM. For doing this the study recommends that the editing of automatic tie points and adding manual tie points should be an inherent feature in DEM generating GIS software IV DEM generation using satellite data without RPC was done to check the possibility of using xparallax information for DEM generation. The study shows linear relation between x-parallax and height in stereo satellite data.. Google Earth has brought a dynamic change in use of maps and satellite imagery in Geomatics field. Google image is a well known source of information for preliminary planning of and engineering project. With the addition of height information the Google image may be taken a substitute of well known method of extracting DEM from old maps. The present study analyze DEM accuracy issue from Google Earth, readymade DEM used from Google Earth and its acceptability in different engineering applications. The height variation from DEM generated DEM at different scale was found to be within the range of 10-12 m. Which almost matches with the accuracy expected from 1:50,000 scale Survey of India map of ±10 m. Therefore the study concluded that DEM available from Google Earth using software like AutoCAD 3D map is a breakthrough in GIS application for engineering projects. Automatic tie point selection in stereo satellite data is not perfect as per the study conducted above. The present study on Texture filter in DEM generation from satellite stereo data covers the DEM quality improvement using statistical texture filter. The Texture filter and entropy filter used in different stage of stereo DEM generation and produced high quality DEM avoiding manual editing. The recommendation suggested in the study can be summarizes as that the GIS software providing DEM modules should have DEM editing feature, texture filter and provision of including topographical feature other than height e.g. ridge, valley, vertical cut etc. in developing DEM. Lastly finding DEM from Google Earth sufficient for large engineering projects.
Other Identifiers: Ph.D
Appears in Collections:DOCTORAL THESES (Civil Engg)

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