state of art of digital elevation...

Quaíni, Karina STATE OF ART OF DIGITAL ELEVATION MODEL

STATE OF ART OF DIGITAL ELEVATION MODELS

QUAÍNI, Karina

CONAE

Master of Space Applications on Early Worning and Response for Emergencies

August, 2010

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CONTENTS INDEX page

1. What is a DEM? - Origins - Specifications

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2. DEM applications 5

3. DEM production Sources for DEM generation

- Contour lines - Photogrammetry - SAR interferometry - Laser scanning

Techniques to generate a DEM Interpolation Matching

(1) Area Based Matching 1.a. Cross-correlation 1.b. Minimum Square Correlation

(2) Objects Based Matching (3) Relations Based Matching

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4. DEM acquisition and missions Aerial photographic images Space images

GTOPO30 model SRTM GLOBE project ASTER Global DEM (ASTER GDEM) ASTER RADARSAT-1 and TerraSAR-X SPOT IKONOS QuickBird LIDAR altimeter ALTM

Specific missions SRTM V2 released SRTM 90m CGIAR HYDRO1k ASTER Global DEM (ASTER-DEM)

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5. DEM errors and corrections 6. DEM accuracy and validation 7. Conclusions

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References 39

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1- Introduction: What is a DEM? A Digital Elevation Model (DEM) is a numeric structure of data which

represent the spatial distribution of the quantitative and continuous high variable; in other words, is a set of data defining the spatial distribution in the terrain of its superficial high.

Each point is characterized by three values: X, Y and Z. ‘Model’ is a simplify representation of a complex reality. Hence DEM

means a simplify representation of the real terrain land.

Figure 1 DEM raster

Although the nominations DEM (Digital Elevation Model) and DTM (Digital

Terrain Model) or DSM (Digital Surface Model) are sometimes used as synonymous, the term ‘terrain’ implies attributes of a landscape other than the

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altitude of the landsurface. The term DEM is preferred for models containing only elevation data. In this sense, DTM is a wider definition because includes also any object overlapped to it like buildings, trees, etc, meanwhile the term DEM is only use when Z variable represents the height of the terrain. Although DEMs were originally developed for modeling relief, they can be used to model the continuous variation of any other attribute Z over a two-dimensional surface, and so Z represent any variable from the terrain (Felicísimo, 1994; Jacobsen, 2003).

The decrease in DEM accuracy is dependent on altitude and slope

inclination (Gerstenecker et al, 2005).

A. DEM origin With the first objective on the highways design, the origin of DTM seems to

have taken place at the Massachussets Technological Institute Photogrametry Lab on the ‘50s. In its pioneer work, Miller and Laflamme (1859) had established the principles of the uses of the digital models for technological, scientific and military problems treatment.

From now on and gradually, DEMs applications have been increasing, especially in SIG environmental and USA digital cartography (Arcila Garrido, 2003).

B. Specifications of a DEM: Formats In relation to how is the data represented, DEMs can be classified in two

groups: raster and vectorial. (a) Raster format: Altitude matrices or regular rectangular grid are the most

common form of DEM, obtained from quantitative measurements from stereoscopic images. Because of the ease with which matrices can be handled in the computer, in particular in raster formats, the altitude matrices has became the most available form of DEM. Although altitude matrices are useful as topographic data, the regular grid system has its disadvantages: the large amount of data redundancy in areas of uniform terrain; the inability to adapt to areas of differing relief complexity without changing the grid size (Burrough, 1986).

In the raster format, each pixel contains the altitude of the center of the pixel. This is the more appropriate format to integer the high in a GIS because allows the use of different tools to obtain new maps from it (Alonso Sarría, 2006). Increasing the pixel size will therefore result in more general DEMs.

(b) Vector format: The Triangulated Irregular Networks (TIN) are series of polygons in the form of a triangle, in which each triangle has a uniform slope steepness and slop direction. When the terrain is complex, the number of triangles increases, and in more flattened areas, the triangles are less and bigger (Alonso Sarría, 2006). TIN is a system deigned for digital elevation modeling that avoids the redundancies of the altitude matrix and which at the same time would also be more efficient for many types of computation (such as

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slope) than systems that are based only on digitized contours. Unlike the altitude matrices, the TIN allows extra information to be gathered in areas of complex relief without the need for huge amounts of redundant data to be gathered from areas of simple relief. Consequently, the data capture process for TIN can specifically follows ridges, stream lines, and other important topological features that can be digitized to the accuracy required (Burrough, 1986).

Figure 2 TIN (left) and regular net or raster (right)

Figure 3 DEM in raster format

2. DEM applications: uses and products associated

Digital Elevation Models (DEMs) are used when there is a need to analyze space and environmental processes. DEMs have become an important source of topographical data for many fields of study, e.g. civil engineering, earth sciences (hydrological, geological, flood planning, infrastructure planning, environmental applications like erosion control and agriculture), military applications as well as for natural resources management.

In general, the use of DEMs allows measuring, planning, analyzing ground, visualizing, simulating and making decisions (Cea Roa, 2005).

In most applications DEMs replace or complement conventional data sources and formats such as paper or hardcopy maps. Where topographical

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data is simply unavailable, global coverage elevation data sets, typically DEMs from remotely sensed data can be the main source of information.

DEM applications include its use in difficult environments, such as pre- or post-earthquake events, also with volcanoes, floods and fires. In this sense, DEM has a cost-effective means of acquiring current and accurate land cover and topographic information (Yu et al, 2009).

DEMs have many uses. Among the most important are the following: 1. Generation of contour lines 2. 3D views 3. Orthorectification 4. In GIS, as a background for displaying thematic information or for

combing relief data with thematic data such as soils, land use or vegetation 5. DEM derived maps (as: travel time, cost, population, indices, levels of

pollution, groundwater levels, slope steepness maps, slope direction maps, slope convexity maps)

6. For several military uses and in general: to establish dangerous zone points in the terrain, and analize the inaccessible zones

7. Risk Probability Models: DEM and its derived maps are inputs in several simulation models of environmental processes.

8. Some other applications (Burrough, 1986; Jackobsen, 2003)

1- Generation of contour lines

Figure 4 Contour lines from DEM

A few methods for the generalization of DEM (reducing the resolution to the original data) have been suggested responding to the need of reducing the data volume and accelerating the calculation or transmission of the relief data. The Douglas and Peucker algorithm has become one of the most commonly used methods for simplification of curves in the software of GIS and digital cartography because of its excellent capability of always selecting the relatively important feature points out from the source data (Lifan et al, 2008).

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In ArcGIS software, the ‘contour tool’ creates isolines or contours, representing constant cell values in the input raster. Using height values of DEM it would create contour lines, while using another variable values would create isolines of that variable (ESRI-ArcGIS, 2004).

2- 3D-views

Stereoscopic visualization can be generated by the combined use of DEM data with satellite or map data.

3- Orthorectification

One of the most important uses of DEMs is the image orthorectification, because it corrects the distortion produced by means of the terrain relief on an image. The product of this process is and orthophoto or orthophotograph. It can be used to measure true distances, because it is an accurate representation of the Earth's surface, having been adjusted for topographic relief, lens distortion, and camera tilt.

Today the most often used photogrammetric product are orthoimages generated by means of a single image and a DEM. The very high resolution space sensors are mainly operating in a single image mode; stereo pairs are not taken very often. A correct geo-referencing is only possible based on a DEM. But these DEMs have to be created. The existing and not classified world wide DEMs usually do not have a sufficient accuracy and reliability for more precise applications or they may be too expensive. The height information can be generated by means of optical images, used in a stereo configuration but also with interferometric SAR (InSAR). The optical images are dependent upon a cloud free view and sufficient light conditions, but they do have the advantage of a high resolution.

If a DEM shall be used also for other purposes like a generation of orthoimages, orthographic heights are required to be related to the geoid. Space data are often available in WGS84 ellipsoidal heights which have to be corrected by the geoid undulation. Not in all areas of the world, the geoid undulations are known with a sufficient accuracy, so a local fit to vertical control points, defined with orthographic heights, is required in this case (Jackobsen, 2003).

4- In GIS

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DEM is the perfect complement for GIS (Geographical Information Systems) combining relief data with thematic data such as soils, land-use or vegetation. Increasingly, a variety of SIG software has been incorporating tools that allow building digital models. The incorporation of both (GIS and DEM) enhanced the degree of depth and accuracy in the analysis of the environmental problematic issues (Arcila Garrido, 2003).

5- DEM derived maps

A DEM not only contains explicit information about the altitude in various points of sampled area but also provides information on the relationships (distance and proximity) between the different values of altitude. This allows the calculation, based on different map algebra procedures, of new topographic variables.

In this sense, by replacing altitude by any other continuously varying attribute, the DEM became a thematic map which can represent surfaces of travel time, cost, population, indices, levels of pollution, groundwater levels, slope maps (in degrees, percentages or radians for each pixel), slope direction maps (or slope aspect maps, shows the compass direction of the slope, between 0 – 360 degrees), slope convexity maps (mapas de pendiente), hillshade and so on.

* Disaster 1: volcanic hazard map By a simulation using DEM as a platform, possible lava flow can be

estimated Local authorities can provide more advanced "volcanic hazard maps" that are crucial to ensure the safety of the residents. Examples of DTM used in this area are: flow direction map, slope maps.

* Disaster 2: flood hazard map

Flood risk areas can be estimated by combining the rainfall simulation process with the topographic data basin using DEM as a platform. In this sense, local authorities can provide advanced "flood hazard maps" to the residents.

* Hydrology and water resource management

Melted water is a valuable water resource. By calculating water catchment area using DEM, a potential water supply can be estimated even for unmapped areas. The obtained result can be utilized for water resource management.

Obtaining thematic maps from ArcHydro toolset from ArcGIS The Arc Hydro Toolset is a suite of tools which facilitate the creation,

manipulation and display of hydro features and objects within the ArcMap environment, allows to identify sinks, determine flow direction, calculate flow accumulation, delineate watersheds and create stream networks. The image below is an example of a resulting stream network derived from an elevation model.

Using a DEM as input, it is possible to automatically delineate a drainage

system and quantify the characteristics of the system. The following graphics

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illustrate the steps involved in calculating a watershed and stream network from a DEM with theTerrain Processing tool of ArcHydro.

(see also http://www.geog.ubc.ca/courses/klink/gis.notes/ncgia/u38.html#SEC38.1.4)

Some of the ArgHydro tools are the followings: Fill Sinks: With the Sink function, any sinks in the original DEM are

identified. A sink is usually an incorrect value lower than the values of its surroundings. The depressions are problematic because any water that flows into them cannot flow out. To ensure proper drainage mapping, these depressions can be filled using the Fill tool. water is assumed to flow from each cell to the lowest of its neighbors if no neighbor is lower, the cell is a "pit" and gets code "0".

Flow Direction: Using the DEM as

input into the Flow Direction tool, the direction in which water would flow out of each cell is determined. The direction is expressed as one of 8 general directions from one cell to the adjacent, with a numeric identification:

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http://www.geog.ubc.ca/courses/klink/gis.notes/ncgia/u38.html#SEC38.1.4


Flow-Accumulation: To create a stream network, use the Flow Accumulation tool to calculate the number of upslope cells flowing to a location. The output of the Flow Direction tool from above is used as input.

Stream Definition. This grid contains a value of ‘1’ for all the cells in the

input flow accumulation grid that have a value grater than the given threshold. All the other cells in the Stream Grid contain no data.

Stream Segmentation: creates a

grid of stream segments that have a unique identification. Either a segment may be a head segment, or it may be defined as a segment between two segment junctions. All the cells in a particular segment have the same grid code that is specific to that segment.

Catchment Polygon Processing:

converts a catchment grid into a catchment polygon feature. It is a recogniziation of sub-catchments areas.

Drainage Line Processing: define

the reticulum network of the catchment. Drainage Point Processing: allows generating the drainage points

associated to the catchment.

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(From: ESRI ArcHydro, 2009).

Information about these products derived from a DEM can be transferred to a GIS for subsequent analysis (Jenson & Domingue, 1988).

Slope Map The ‘land slope estimate’ is a simple process from DEM, although there are

different procedures that lead to different results and therefore, when working with a program is important to know what is the algorithm used to calculate slopes, among them are: the maximum slope central cell with respect to the neighbours (suitable for the assessment of erosion), the average slope of the central cell, the gradient in the direction of downward flow (for hydrological applications) (Alonso Sarría, 2006).

Hillshaded maps

The shaded relief maps are automatically and accurately produced from DEMs, based on a model of how the terrain might look like if it would be made of an ideal material and illuminated from a given position. Hillshaded maps can be extremely useful by themselves for presenting a single image of terrain in which the 3D aspects are accurately portrayed making useful in quantitative landform analysis and when used in combination with thematic information they can greatly enhance the realism of the final map (Burrough, 1986).

Figure 5 DEM and its hillshade map derivated

* Energy: oil resource exploration

Oil source rock and reservoir formations are extracted from ASTER data. Strike and dip (orientation) of those formations are measured from DEM, and a simulation of the underground geological structure will reveal their prospective anticlinal structure. In this way, oil and natural gas potentials can be evaluated without conducting a field investigation, even in an area of conflict.

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6- Military applications Planning routes of roads, visible check, engineering projects, and others to

determine optimal position and equipment, optimize displacement, combat simulation.

7- Risk Probability Models

Another important use of DEMs is providing data for simulation models dealing with the probability occurrence of an event and the damage it can cause. DEM data are widely used in many research areas such as hydrological analysis and soil erosion models.

Some examples are: LISFLOOD model (is an inundation model specifically developed to take advantage of high resolution topographic data sets (Bates et al, 2005), MOBIDIC (distributed and continuous hydrological balance model) (Castelli, 2008). The Topographic Wetness Index (TWI), is a topographic index and describes spatial soil moisture patterns. The TWI has been developed by Beven & Kirkby (1979) within the runoff model TOPMODEL (TWI = ln [a/tan where a is the local upslope area draining through a certain point per unit contour length and tan is the local slope). The TWI has been used to study spatial scale effects on hydrological processes and to identify hydrological flow paths for geochemical modelling as well as to characterize biological processes such as annual net primary production, vegetation patterns and forest site quality. The TWI is usually calculated from DEM and its derivated products like the Flow Accumulation Map (Sørensen et al, 2005).

The ability of each one of DEM’s derived models to predict the real patterns depends on the quality of the DEM used.

8- Some other applications:

Also DEM obtained has been used to draw longitudinal profile of the river channel, based on DEM obtained from SRTM. Brandán method allows to obtain

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in a short time a longitudinal profile that does not really differ from one obtained by field topographical report methods (Brandán, 2009).

DEMs have been used also in classifying urban cover, by the estimation of building heights combining databases of total terrain elevation (DTM) and of bald Earth’s topography, using SRTM-DEM and NED/CDED1 (National Elevation Dataset/ Canadian DEM level 1) DEMs, respectively. The algorithm was SRTM minus NED/CDED1, but since the spatial resolution of the first is lower than the second, the SRTM database has to be disaggregated on the grids of NED and DNED1, defining a 15m resolution for both images (Lemonsu et al, 2005).

3. DEM production

A. Sources for DEM generation

A) Contour lines B) Photogrammetry C) Radargrammetry D) SAR interferometry E) Laser scanning

I. Contour lines

A contour line joins points of equal elevation (height) above a given level, such as mean sea level. A contour map is a map illustrated with contour lines (http://en.wikipedia.org/wiki/Contour_line). Its precision depends on the source of data.

Generating DEM from contour lines is now a main method to get grid DEM because of its low cost and the convenience to get contour maps. One of the most important parts of DEM building is interpolation. During generating DEM from contour lines, the vector contour lines generally need to be converted into raster model data before local interpolation methods can be used. Usually there are single-value cells (SVC) with only one contour line through it, multi-value cell (MVC), with more than one contour line through it, and cells without any contour lines through, called no-value cell (NVC), which are interpolated according to nearby SVCs and MVCs.

The major problem of SVC is that the elevation of the contour line, which goes through the SVC, can not characterize the elevation of the corresponding SVC exactly (Daming et al, 2005).

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http://en.wikipedia.org/wiki/Contour_line


Figure 6 Contour map.

Photogrammetry with optical space sensors

The Earth observation started with photographic cameras used for national security reasons. For the DEM generation with optical images we do need two or more images showing the same area (figure) from different directions. The projection centre has to be known in a specified coordinate system in addition to the view direction for the correct determination of the ground point (Jackobsen, 2003). SAR Interferometry

Interferometric Synthetic Aperture Radar (InSAR) is used in many applications including topographic surveys, estimating ocean’s currents, land monitoring, ground deformation, detecting and locating moving targets (Ge et al, 2007; Janssen et al, 2004).

SAR interferometry is a technique for estimating topography using the phase difference between coincident SAR images. The SAR phase is derived from complex SAR images. InSAR exploits the travel time phase information in the complex-valued SAR images, based on the processing of at least two

complex SAR images covering the same area and acquired from slightly different points of view (or “look angle”) or from the same position in different moments.

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Analysis of the differential phase, and therefore change in distance, between the corresponding pixel centres and the observing antennas can provide information on terrain elevation or, with observations by the same antenna at different times, on terrain displacement (Seymour & Cumming, 1997; Yu et al, 2009).

The identification of objects in SAR images liked SRTM, ERS, JERS, RadarSAT or EnviSAT cannot be compared with the information contents of an optical image with the same pixel size. In addition to the general difficulty of object identification, a SAR image is quite depending upon the view direction and there are the geometric problems of foreshortenning, layover and shadows in mountains. By these reasons SAR images are only used for mapping in areas with more or less permanent cloud coverage. But this change with the very high resolution SAR images like from TerraSAR-X and Cosmo SkyMed-X which shall have a pixel size of 1 m. Nevertheless also this pixel size should not be compared with a 1-pixel-size from an optical sensor. The main advantage of SAR is more located in the generation of DEM by InSAR (Jacobsen, 2003).

Estimation of topography using SAR interferometry is usually seen as a

four step process: firstly, produce the interferogram (see bellow); secondly, unwrap interferogram phase; thirdly, calibrate the interferometer, and fourthly, convert unwrapped phase to terrain height.

In a single SAR image, a pixel’s phase information can not be used. However, the relative phase from two independent SAR images of the same ground pixel target contains useful 3D-terrain information. The ground range resolution is defined as the slant range resolution divided by the cosine of the look angle. SAR images are selected for the same area based on the two adjacent-pass orbits, and the phase difference of the two SAR images is used to generate the interferogram. The elevation information is obtained from the interferogram by “unwrapping” the interferometric phases. In order to generate a DEM, the SAR data have to undergo several processing stages, such as: image registration, interferogram calculation and filtering, phase unwrapping, and phase-to-height transformation. Interferometric processing requires optimal short-time delay between image acquisitions in order to ensure maximum coherence between acquisitions and sufficiently small baselines (Yu et al, 2009).

The advantages of RADAR system are that the active sensor can acquire day and night data, with rain and clouds, and gives information from the visible or IR region of the electromagnetic spectrum (Garcia Gonzalez, 2004).

Two different RADAR systems have been used: the X-band (from the German DLR and ASI from Italy) and the C-band (from NASA). The X-band is not covering the whole area, while the C-band has imaged the world from 58º S and 60º N. Especially with the X-band not the height of the bare ground will be determined, but the height of the visible surface, because X-band cannot penetrate the vegetation, while the longer C-band can penetrate al least partially.

A validation method for InSAR is the called Differential InSAR. Changes of

the DEM can be determined very precise by differential interferometric SAR

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(DInSAR). The interferometric overlay of two InSAR data sets is leading to information about changes of the height with an accuracy of few millimetres if all required corrections have been handled in the correct manner. Very good results have been achieved in areas with limited vegetation. Quite more difficult is the application in areas covered by vegetation, because the change of the vegetation from one imaging period to the other is causing a lot of noise (Jacobsen, 2003).

As InSAR is a remote sensing technique, it has various error sources due to the satellite positions and altitude, atmosphere, and others, so it is important to validate its accuracy, especially for the DEM derived from SAR images before it can be used for various applications such as disaster prevention, flood mapping, and emergency map. The usual method is with GPS. However, tracking satellites and transmitting a data between reference-rover, under trees are still pending tasks to be overcome in GPS positioning techniques. Additionally, Airborne Laser Scanning (ALS) is expected to be an alternative as an effective tool for the validation of DEMs (InSu et al, 2005).

Laser scanning

The laser scanning is the next frontier for topographic and technical analysis of the territory. Using laser-scanning ground was originally developed for the study of architectural, structural and reverse-engineering, but is currently moving towards the application of the method to the morphological and structural characterization of the territory.

LiDAR (Light Detection and Ranging) and ALTM are examples of laser scanning systems (See bellow).

B. DEM generation techniques

Basically, there are two techniques to generate a DEM from points or contour lines of an area:

a) Point interpolation When point data is available for an area, obtained via ground surveys

using theodolites and/or Global Positioning System (GPS), point interpolation can be used to generate a DEM. For complex terrain, the interpolation techniques are also rather complex (ILWIS 2.1 for Windows, 1997).

b) Interpolation of contour lines

Older methods of generating DEMs often involve interpolating digital contour maps that may have been produced by direct survey of the land surface; this method is still used in mountain areas, where interferometry is not always satisfactory.

The interpolation from contour lines may generate artifacts, from the artificial geoforms that dramatically affect DEM quality (Alfonso Sarría, 2006).

To create a DEM from contour interpolation first need to digitalize the contour lines. Likewise it must be defined a segment map with a specific

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georeference, and the contour lines are converted to raster. The resulting raster map will contain values for those pixels converted by a contour line and, all other pixels in the map remain undefined. Later a linear interpolation is made between the pixels with altitude values, to obtain the elevation of the undefined values in between the rasterized contour lines. The output of the contour interpolation is a raster map in which each pixel in the map has a value.

The interpolation method is based on the Borgefors distance method. The operation calculates, for each undefined pixel between the segments, the shortest distance towards the two nearest isolines. The figure bellow shows an example in which the height value (h) for each pixel, between two contour lines, is calculated using the following formula:

H = H2 + ( d2 / ( d1 + d2 ) * ( H1 – H2 ) In which: H1 and H2 are the height values of the two consecutive contour lines. D1

and D2 are the distances from the pixel to the one and other contour lines (see figure bellow).

When working with stereoscopic aerial photographs or satellite images, a large number of points might be sampled -X, Y and Z values- by means of advanced photogrammetrical equipment. After that, the points are interpolated into a regular grid (raster). After that and once both stereo pairs are georeferenced the next step is the union or “matching” between them.

The manual matching of a DEM by a human operator is rather time consuming (each sampling point has to be measure by an expert through the lowering of a floating mark on the stereo model) and requires photogrammetrical experts, and a set of very detailed control points.

Because of this, it will be done usually by automatic image matching (Jackobsen, 2003). Although the hardware and software for generation DEMs by interpolation of stereopairs is still rather expensive, lately software packages that run on a PC have became available (ILWIS 2.1 for Windows, 1997; García Gonzalez, 2004).

Stereoscopic pairs had been long time employed using the digital image correlation method, also called digital image matching, where two optical images are acquired with different angles taken from the same or different pass of an airplane or an Earth observation satellite (such as the HRS sensor of SPOT-5 or the VNIR band of ASTER) (Nikolakopoulos et al, 2006).

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The corresponding image positions of ground points have to be determined. The used methods of matching can be differentiated by the way in generating the approximate positions of corresponding points and the type of elements choose to match.

There are different automatic correlation techniques to generate a DEM from both stereopair images, based on the area, the features or the relation between objects.

The (1) Area Based Matching is based on the correspondence between two areas of the image by its similar grey levels. For this, there are two correlation methods, (1.a) Cross-correlation, in which the reference window moves pixel by pixel across the ‘searching window’, calculating the correlation coefficient in every position (see figure bellow). The best fit would be defined by the maximum of the correlation function (García Gonzalez, 2004).

Figure 7 Area Based Matching

(1.b) Minimum Square Correlation is a iterative method, in which the calculated parameters in the first pass are utilized in the next pass calculus and so on. This method determines the best geometric and radiometric parameters that best fit to the reference window in the searching window.

(2) Objects Based Matching, is a method based in the similarity of some objects attributes, like shape, length, size, situation, etc. Using mathematical operators, a symbolic description of the objects is generated. From here, the same objects shapes in both images are detected by its geometry and environment.

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(3) Relations Based Matching, this method is based in the topologic of the objects. This type of correlation use the objects shape but also its relation with other objects. This class of correlation is based on that topology is a invariable property of the image. It is used to recognize objects (García Gonzalez, 2004).

There are other methods to facilitate the correlation but they are not discussed here (for more information see García Gonzalez, 2004).

The automatic image matching has reached a high level of accuracy for

the matched ground points similar to the accuracy of a human operator. The problem is the selected points. When measuring a grid of height points, in the manual or automatic mode, the floating mark (the selected point in both images to match) is set down to the ground, even if the floating mark is on top of a building. In this sense, the generation will not generate a DEM but a DTM, with points located on top of the visual objects. A similar problem occurs with laser scanning and InSAR (Jacobsen, 2003).

4. DEM aquisition and missions

As described above DEMs can be generated by different sources like aerial photos, space images, laser scanning and InSAR. The main acquisitions and missions for getting these data sources are the followings:

Aerial photographic images

Still do play an important role because up to now only a limited number of digital stereo pairs exist and are of significant precision.

Some sensors for photographic acquisition are: the Metric Camera (MC), Large Format Camera (LFC), KFA-1000, MK4, KATE-200, TK-350. The image geometry of the photographic cameras KFA-1000, MK4 and KATE-200 is not very stable.

The Sowejt Union and today the Russia is using the very high resolution

camera KVR-1000 together with the TK-350 in the Komet class satellites

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(Jackobsen, 2003). The problem with the TK-350 is that the generated DEM is not useful in forested area because the image contrast of the panchromatic film is very poor for the matching (Büyüksalilh et al, 2003).

The other Russian space photographic cameras like KFA-1000, MK4 and KATE-200 or the old German Metric Camera do not play an important role in DEM generation (Jacobsen, 2003).

The images taken by the former USA spy satellites of the CORONA system have been released and are available just for a handling fee. It has a combination of a forward and a backward looking panoramic film camera. Of course these 30 to 40 year old images do not show the actual topographic features, but usually the ground surface is not changing, so the images can be used for the DEM generation. In areas of strong erosion, the old images can be used as reference for the changes (Jacobsen, 2003).

Table 1 Technical data of Russian photographic space cameras usable for DEM generation

(Jacobsen, 2003).

Specifications of CORONA photos:

Image size: 0.05 x 0.76 m Spatial resolution: 3m Stereo angle: 30º. Date: operating nearly 12 years (from August 1960 until May 1972) Imaging resolution: was originally 8 m but improved to 2 m Image size: each image covered an area approximately 10 miles by 120

miles (NRO, 2010).

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Today only Russia is taking perspective photos in unmanned missions. An

advantage of the space photos is the high information contents. Space images A higher number of digital optical space sensors are available. The main

difference is the ground resolution and the view direction. The systems with the view “across the orbit” do have the disadvantage of a longer time interval between imaging the 2 scenes of a stereo model. The new, very high resolution systems IKONOS (US), QuickBird (US), EROS A (Israel) and TES (India) are equipped with reactions wheels, enabling a very fast change of the satellite orientation. So in the same orbit a stereo coverage is possible in changing the view direction (Jacobsen, 2003).

GTOPO30 model GTOPO30 is a global digital elevation model (DEM) with a horizontal grid

spacing of 30 arc seconds (approximately 1 kilometer). GTOPO30 was derived from several raster and vector sources of topographic information. For easier distribution, GTOPO30 has been divided into tiles which can be selected from

the map shown. GTOPO30, completed in late

1996, was developed over a three year period through a collaborative effort led by staff at the US Geological Survey’s EROS Data Center (EDC). The following organizations participated by

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contributing with the source data: NASA, the United Nations Environment Programme/Global Resource Information Database (UNEP/GRID), the U.S. Agency for International Development (USAID), the Instituto Nacional de Estadistica Geografica e Informatica (INEGI) of Mexico, the Geospatial Information Authority of Japan (GSI) Manaaki Whenua Landcare Research of New Zealand, and the Scientific Committee on Antarctic Research (SCAR).

Figure 8 GTOPO30 source data

• Resolution: 30 arc seconds (approx. 1km) • Generation and distribution: USGS • DEM accuracy: 30m • DEM coverage: global • Area of missing data: None • How to download:

http://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/gtopo30_info You can select and download from a map on a single tile. There is a ‘read_me’ file, giving detailed instructions for downloading via ftp (Freegis, 2010).

• Format: DEM, together with header files (.HDR) statistics file (.STX), the projection description file (.PRJ), shaded relief image (.GIF) finally source map (one raster showing the topographic source for each cell).

The NASA SRTM mission

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The Shuttle Radar Topographic Mission (SRTM) is a joint project between NASA (National Aeronautics and Space Administration) and NGA (National Geospatial-Intelligence Agency of the Department of Defence) which consisted of a specially modified radar system that flew onboard the Space Shuttle Endeavour during an 11-day mission in February of 2000, to collected elevation data over 80% of

http://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/gtopo30_info


the surface of the Earth in three dimensions to a level of detail never achieved before.

The Space Shuttle Endeavour had two antennas with parallel flights

separated by a 60 m distance and a horizontal resolution of 3 arc sec. The SRTM radar contained two types of antenna panels, C-band and X-

band. The near-global DEMs of Earth are made from the C-band radar data. Data from the X-band radar are used to create slightly higher resolution DEMs but without the global coverage of the C-band radar. The SRTM X-band radar data are being processed and distributed by the German Aerospace Center, DLR (JPL-NASA, 2010). Since the SRTM elevation data are unedited, they contain occasional gaps, because of areas of extremely low radar backscatter, such as sea, dams, lakes and virtually any water-covered surface (Nikolakopoulos et al, 2006).

Figure 9 SRTM band C coverage The SRTM-DEM, which is currently distributed by the U.S. Geological

Survey (USGS - United States Geological Survey), is provided with a resolution of 1 arc second (about 30 meters, varying with latitude) for the United States and its territories, while it is a distributed version "degraded" resolution of 3 arc seconds (90 meters, variable) for the entire globe. The vertical error is less than the 16 meters.

The DEM has areas of no-data, which correspond to areas where special conditions during shooting (water or presence of clouds) have led to errors in estimating high profile. These areas are small but in some cases can make DEM data less useful for modeling hydrogeological applications on a local scale. There are some software tools that fix it (see bellow).

Format: GeoTiff and ASCII. Resolution: 90 and 30 m (3 and 1 arc seconds, respectively) Generation and distribution: NASA/USGS DEM accuracy: 10m DEM coverage: 60º N, 56º S (see map bellow) Area of missing data: Topographically steep area (due to radar

characteristics)

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How to download: http://srtm.csi.cgiar.org/ This products can be downloaded from the CGIAR- CSI. The CGIAR-CSI is

a Consortium for Spatial Information of 15 international research centers that work with Geographic Information Systems (GIS) and Remote Sensing (RS) for sustainable agricultural development.

To download DEM from the http://srtm.csi.cgiar.org/, go to SRTM Data Searh and Download -> http://srtm.csi.cgiar.org/SELECTION/inputCoord.asp to select the tile.

A comparison between GTOPO30 and SRTM30 in Java volcanoes by validation with 443 GPS ground control points, exhibits a significant improvement of SRTM30 data (Gerstenecker et al, 2005).

Differences between SRTM3 and SRTM30 Some studies confirm the high accuracy of SRTM3 and SRTM30, even if

the a priori defined 90% confidence level of 16 m for the SRTM3 is not always achieved in this mountainous region. The decrease in DEM accuracy is dependent on altitude and slope inclination (Gerstenecker et al, 2005; Freegis, 2010).

GLOBE project GLOBE (Global Land One-km Base Elevation) is an internationally

designed, developed, and independently reviewed global DEM, at a latitude-longitude grid spacing of 30 arc seconds (approx. 1km).

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http://srtm.csi.cgiar.org/


http://srtm.csi.cgiar.org/SELECTION/inputCoord.asp


With the GLOBE project the NOAA has launched, since the 90's update, a global elevation dataset, built from the TOPO30 model. Compared with the latter this represents an alternative product and updated. • Format: DEM, together with header files for ArcInfo / ArcView, GRASS, IDRISI, GeoVu. •Resolution: 30 arc seconds (approx. 1 km) • How to download: You can select one of 16 tiles for download via http, and access the Data Distribution gather information for ftp downloads http://www.ngdc.noaa.gov/mgg/topo/report/

(Freegis, 2010).

Other SAR missions: ASTER, RADARSAT-1, TerraSAR-X, SPOT

In contrast to the short duration of the SRTM mission, the ongoing Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is continuously collecting elevation information with a horizontal resolution of 15 m (Nikolakopoulos et al, 2006).

ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) is a Japanese sensor which is one of five remote sensory devices on board the Terra satellite launched into Earth orbit by NASA in 1999. The instrument has been collecting superficial data since February 2000.

Terra (EOS AM-1) is a multi-national NASA scientific research satellite in a sun-synchronous orbit around the Earth. It is the flagship of the Earth Observing System (EOS). The Ministry of Economy, Trade and Industry of Japan (METI) and the NASA (Wikipedia, 2010).

ASTER provides high-resolution images of the Earth in 15 different bands of the electromagnetic spectrum, ranging from visible to thermal infrared light. The resolution of images ranges between 15 to 90 meters. ASTER data are used to create detailed maps of surface temperature of land, emissivity, reflectance, and elevation. The time of acquisition is 9” for single image, and 64” for an stereo-pair.

25

http://www.ngdc.noaa.gov/mgg/topo/report/


In general there are no problems with the generation of the DEM based on the ASTER images (Büyüksalih et al, 2003).

InSAR is a powerful technique for generating DEMs, in two passes of a

radar satellite (such as RADARSAT-1 or Terra-SAR-X), or a single pass if the satellite is equipped with two antennas (like the SRTM instrumentation), suffice to generate a DEM.

In 1986, the HRS sensor of SPOT-1 satellite provided the first usable

elevation data for a sizeable portion of the planet's landmass, using the two-passes of stereoscopic pairs across the orbit, and it takes at least few days for imaging the same area from a different orbit.

Later, further data were provided by the European Remote-Sensing

Satellite (ERS) using the same method of the SRTM with single-pass SAR and the ASTER instrumentation on the Terra satellite using double-pass stereo pairs.

Through the application of radar interferometry data acquired by a

Spaceborne Imaging Radar-C (SIR-C) and an X-band Synthetic Aperture Radar (X-SAR) were reconstructed profile by about 80% of the land surface between 60°N latitude and 56ºS latitude.

The MOMS sensor was viewing forward, to the nadir and backward, so a

DEM generation is possible with images taken with just a few seconds time interval. The 3 view directions can be used together for a common intersection, improving not only the accuracy but also the reliability.

The general configuration of

IRS-1C/1D is corresponding to SPOT, including the same problem of time delay in taking corresponding images (see forward ‘DEM errors and corrections’). Because of satellite energy problems, not so many stereo models have been taken. Under optimal conditions a vertical accuracy of 7m is possible (Jacobsen, 2003).

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From IKONOS only a few stereo scenes are taken, the generation of stereo models is a “waste of capacity”, nevertheless stereo models can be ordered.


The flexible viewing of IKONOS enables also a stereo view with a base length of just 90km or 12 seconds time difference (Jacobsen, 2003).

QuickBird imagery is a fine resolution remotely sensed product available to

the public through Digital Globe. QuickBird's ultra fine resolution makes this valuable imagery for validation and land cover assessment. With an overlapping of two QuickBird images taken with 2 weeks time difference and a height to base relation of 1.6, no larger changes at the vegetation are seen and so no problems with the automated image matching appeared. GLCF provides access to some free QuickBird imagery, provided by Digital Globe in response to the December 2004 Tsunami. GLCF also provides restricted access for qualified researchers to QuickBird imagery from the NASA Science Data Purchase.

QuickBird images characteristics: Resolution: 0.6 m (multispectral resolution: 2.4 m) Bands: Visible colour, IR, multispectral, panchromatic Format: Geo TIFF DEM accuracy: 1m Data acquisition period: 2002 and ongoing Area of missing data: Areas with constant cloud cover (supplied by other

DEM) How to download: http://glcf.umiacs.umd.edu/data/quickbird/

LIDAR altimeter (Light Detection and Ranging)

It is an airplane laser system which sends light pulses. It reflects the terrain and the objects on it. The signal that turns back is converted from

27

http://glcf.umiacs.umd.edu/data/quickbird/


fotons to electrical pulses and taken by a high-speed data register. There is also the time between pulses and so it can be obtained the distance to the objects. This distance is then combined with the GPS/IMU information system to know the X, Y and Z terrain variables in detail (García Gonzalez, 2004).

LiDAR transmits and receives electromagnetical radiation of the near IR band. Unlike photogrammetry, LiDAR data collection is not affected by sun angle (Conforti Andreoni et al, 2003).

Figure 10 LiDAR

LIDAR precision depends on the three basic elements of the system: GPS,

IMU and Laser. Its spatial resolution is 1m. The LIDAR penetration capacity in forested areas depends on the

registration of the pulses. A ‘first pulse registration’ allows to survey the top of objects, while ‘Last pulse registration’ is used to survey the forest ground (see figure bellow).

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Sometimes the fisonomy of the forest does not allow the large scan laser, and the laser is trapped in the canopy. In those cases is impossible to generate a DEM of the forest ground because there are no measurements available from ground.

Figure 11 Comparison between first and last pulse recibed

Specifications of LIDAR system: Flight high: 200 – 6000 m Vertical accuracy: 15 cm Horizontal accuracy: 0.2 – 1 m Advantages of LIDAR system: direct acquisition of X, Y and Z variable;

few restrictions of the flight; high automatization, and fast data availability.

Disadvantages: low quality and a few images and just for specialized enterprise.

ALTM (Airbone Laser Terrain Mapper)

The ALTM is used for aerial surveys for some time, integrating GPS and inertial systems (IMU) for determining the position of the airplane. Particularly in mountainous and morphologically complex areas the instruments are preferably mounted on helicopters allowing greater operational flexibility. The accuracy and speed of the laser scanner helitransported make it an indispensable tool for Civil Defence, where it is of prime importance the availability of a quickly and accurate relief and the ability to monitor areas affected by catastrophic events without sending opearators directly to the affected site.

The ALTM survey shows, in fact, many advantages over the traditional technique photogrammetry: not affected by light conditions (you can make measurements at night or in shadows), is relatively quick and less expensive (processing of data is semi-automatic and definitely faster than photogrammetry) and the floor support is minimal or no necessary at all (Conforti Andreoni et al, 2003).

Specific missions and applications SRTM V2 released

NASA has released version 2 of the SRTM digital topographic data (also known as the "finished version”). Version 2 is the result of a substantial editing

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effort by the NGA and exhibits well-defined water bodies and coastlines and the absence of spikes and wells (single pixel errors).

Some areas of missing data ('voids') are still present. The Version 2 directory also contains the vector coastline mask derived by NGA during the editing, called the SRTM Water Body Data (SWBD), in shapefile format.

The data may be obtained through this URL: http://dds.cr.usgs.gov/srtm/ and go to the directory where both version 1 and version 2 directories may be found.

SRTM 90m CGIAR

The Centre for Spatial Information CGIAR-CSI provides a digital terrain model reprocessed from that produced by the NASA SRTM, or a version of the SRTM data without no-date "holes". Data are available for download in 5 by 5 degree tiles, in geographic coordinate system (WGS84 datum).

• Format: Arc Info ASCII and GeoTIFF. • Resolution: 3 arc seconds (approx. 90 m) • How to download: You can select one or more tiles through an interactive map (Multiple Selection function), even after cropping with your mouse (Function Enable Mouse Drag), or customize the area of interest by entering coordinates (function Input Coordinates ) is in decimal degrees and in-minutes-seconds. You must specify the format (ASCII, GeoTIFF) and specify the server from which to operate download. Some tests have shown problems in downloading from the server U.S. CGIAR-CSI, therefore it is possible to select the server of the Joint Reaserch Centre in Ispra (Varese). To download is possible via both http and ftp.

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http://dds.cr.usgs.gov/srtm/


• The 90m DEM of SRTM version 2: http://srtm.csi.cgiar.org/ http://www2.jpl.nasa.gov/srtm/ http://dds.cr.usgs.gov/srtm/version2_1/SRTM30/

• http://www.ambiotek.com/topoview (via download KMZ - Google Earth)

Derivate hydro-products from SRTM There are many products derived from SRTM data. Among the

hydroproducts there are: SRTM Water Body Data (SWBD), HydroSHEDS (Hydrological data and maps based on Shuttle Elevation

Derivatives at multiple Scales) is a mapping product that provides hydrographic information for regional and global-scale applications in a consistent format. It offers a suitable geo.refrerenced data sets (vector and raster) at various scales, including river networks, watershed boundaries, drainage directions, and flow accumulations. HydroSHEDS is based on high-resolution elevation data obtained from a SRTM (Lehner et al, 2008).

HydroSHEDS has been developed by the Conservation Science Program from the World Wildlife Foundation (WWF).

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http://www2.jpl.nasa.gov/srtm/

http://www.google.com/url?sa=D&q=http://dds.cr.usgs.gov/srtm/version2_1/SRTM30/&usg=AFQjCNFrkJ1Afxvmztjr4yntBPEa54URZA

http://www.ambiotek.com/topoview


HYDRO1k The HYDRO1k is a product of the GTOPO30 model: HYDRO1k is a geographic database developed to provide comprehensive

and consistent global coverage of topographically derived data sets, including streams, drainage basins and ancillary layers derived from the USGS' 30 arc-second digital elevation model of the world (GTOPO30). HYDRO1k provides a suite of geo-referenced data sets, both raster and vector, which will be of value for all users who need to organize, evaluate, or process hydrologic information on a continental scale.

From: http://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/HYDRO1K http://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/gtopo30/hydro

ASTER Global DEM (ASTER GDEM)

On 29 June 2009, the Global Digital Elevation Model (GDEM) was released to the public. A joint operation between NASA and Japan's Ministry of Economy, Trade and Industry (METI), the Global Digital Elevation Model is the most complete mapping of the earth ever made, covering 99% of its surface. The previous most comprehensive map, NASA's Shuttle Radar Topography Mission, covered approximately 80% of the Earth's surface, with a global resolution of 90 and 30 meters. The GDEM covers the planet from 83º N to 83º S (surpassing SRTM's coverage of 56 °S to 60 °N), becoming the first earth mapping system that provides comprehensive coverage of the polar region.

Despite the high nominal resolution, however, some reviewers have commented that the true resolution is considerably lower, and not as good as that of SRTM data, and serious artifacts are present.

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http://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/HYDRO1K

http://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/gtopo30/hydro


Like MOMS and SPOT HRS, Terra ASTER is generating the stereo model by two images. ASTER GDEM is generated from a stereo-pair of images acquired with nadir and backward angles over the same area.

The areas of missing data of GDEM are due to constant cloud cover and the solution is to supply with other DEM.

The ASTER GDEM is available for high-latitude and steep mountainous areas not covered by SRTM3.

GDEM-ASTER characteristics: • Resolution: 30 m • Generation and distribution:

USGS • DEM accuracy: 7~14m • DEM coverage: 83ºN, 83ºS • Data acquisition period: 2000

and ongoing • Area of missing data: Areas with constant cloud cover (supplied by other

DEM) • How to download: http://www.ersdac.or.jp/GDEM/E/2.html http://www.echo.nasa.gov/reference/astergdem_tutorial.htm

(From: http://www.ersdac.or.jp/GDEM/E/index.html)

5. DEM errors and corrections

DEM is the basic source of information for developing models and GIS,

therefore, the usefulness and validity of the results obtained are intimately associated with the quality of the DEM, as quality is measured in terms of the kind and magnitude of its errors. However, DEMs are created, distributed and used very frequently without any reference to the magnitude of the error implied or to the methods applied to its correction (Felicísimo, 1994b).

DEM quality would depend on the source errors for its generation as well as on the technique used for DEM generation. The errors from the original data can be of two types: horizontal or positioning errors, which implies a wrong geolocalization of the contour line affecting thus the situation in the XY plane. If the original data are data points taken in field, the errors may be due to the method of taken the data itself (i.e. the GPS). If the original data are contour lines it can be because of the original map errors. But more frequent, errors are due to the digitalization process itself when digitalizing the points. They are difficult to detect a posteriori, as general rules are better to digitalize over scanned maps and avoid long working sessions (Alonso Sarría, 2006).

The other kind of error is the vertical or attributable errors, which involves the inaccurate allocation altitude (Z values) associated with the elevation of the

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http://www.ersdac.or.jp/GDEM/E/2.html

http://www.echo.nasa.gov/reference/astergdem_tutorial.htm

http://www.ersdac.or.jp/GDEM/E/index.html


contour line. In the case of digitized contour lines, the errors will be random in nature and easy detectable in the maps derived from DEMs.

The different DEMs have different spatial resolution and coverage. In general,

the spatial resolution of a DEM is inversely proportional to the areal coverage. For example, SAR DEMs have better than 1m resolution and 16km swath, while the SRTM DEM has 30m resolution and 255km swath width. As high resolution DEMs need more data to cover a large region, if all terrain areas of the image had the same high spatial resolution, the size of the resulting DEM would be huge. However, if only certain areas are of interest, such as Convention on Biological Diversity (CBD) areas, industrial areas or disaster affected areas, it would require high spatial resolution, while the other areas, such as forested or mountainous terrain may only need a low resolution DEM. For these cases, some authors propose a method that exploits the information contained in the area of overlap between different DEMs in order to reduce the total DEM data size and processing time, by removing excess data in areas of reduced importance or interest, while at the same time reducing the vertical systematic errors (Yu et al, 2009).

In relation to its spatial resolution, the optimal grid size for a DEM is a compromise between the accuracy of terrain representation and cost of data acquisition. An appropriate grid size is dependent, amongst other factors, on source data density, terrain complexity and applications (Eckert et al, 2005; Fabris & Pesci, 2005).

AUTOMATIC FILTERING OF DEMs In general the result achieved by automatic image matching as well as InSAR

is not a DEM with the height of the ground, but a digital terrain model (DTM) on top of the visible objects, including buildings and trees. The quality of the unfiltered result requires usually an improvement of outliers as well as the effects of buildings and trees. A simple filter should not be used for this, because it goes to the average height and not to the ground (Jacobsen, 2003). As a solution for this, Jacobsen (2001) proposed to exclude some problematic areas from the image before the automatic filtering. In his work, it has been excluded more than 50% of same difficult areas, and this automatic data reduction has been shown as very efficient and much less time consuming than manual or partial manual improvement of a DSM to a DEM (Jacobsen, 2001).

Some programs can improve a DSM based on a serious of tests (for example RASCOR program) (Jacobsen, 2003).

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Figure 12 Data set from LIDAR. Above: original data set. Bellow: after automatic filtering

(jacobsen, 2001).

In addition, for the identification of buildings, in some images, especially for LIDAR-data, sudden changes of the heights are seen an so is easy to solve. But this function has usually no effect when automatic image matching is applied, because such data usually do not show the buildings with a sudden height change, usually they are shown like small hills (Jacobsen, 2003).

Another general problem with DEM is with sensors viewing across the

orbit. If the weather conditions do not allow the imaging, the time interval may be larger. If the object is changing meanwhile, an image matching may be degraded or even impossible. Some studies in the Hannover area shows the great difference between both stereopairs. The images have been taken in June and August, even a human operator could not get a stereoscopic impression because of the complete change of the gray values in the agriculture area where the wheat was changing the color from green to yellow. In SPOT image, this problem has been solved with the HRS-sensor in the SPOT-5, because this sensor is looking forward and backward, generating a stereo overlap in the orbit direction of 5m (Jacobsen, 2003).

Errors in DEMs are usually classified as either sinks or peaks. A sink is an

area surrounded by higher elevation values and is also referred to as a depression or pit. Some of these may be natural, particularly in glacial or karst areas, although many sinks are imperfections in the DEM generation. Likewise, a spike or peak is an area surrounded by cells of lower value. These are more commonly natural features. Errors such as these, especially sinks, should be removed before attempting to derive any surface information.

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The number of sinks in a given DEM is normally higher for coarser resolution DEMs. Another common cause of sinks results from storing the elevation data as an integer number. This can be particularly troublesome in areas of low vertical relief. It is not uncommon to find 1% of the cells in a 30 meter resolution DEM to be sinks. This can increase as much as 5% for a 3 arc–second DEM. To remove this sinks the Fill_sinks function on ArcMap uses a variety of ArcGIS Spatial Analyst functions, to create a depressionless DEM. When creating a depressionless DEM the identification and removal of sinks is an iterative process. When a sink is filled, the boundaries of the filled area may create new sinks, which then need to be filled. For a large DEM or one with many sinks, this can take minutes to hours (ArcMap Help).

DEMs may also contain noticeable horizontal striping, a result of systematic

sampling errors when creating the DEM. Again, this is most noticeable on integer data in flat areas (ArcMap Help).

6. DEM accuracy and validation

The achieved accuracy of DEMs based on space images is mainly depending upon the image resolution, the height-to-base-relation and the image contrast.

As with any other spatial variable, it is necessary to validate a DEM after interpolated and manufacture, its quality depends on the type and magnitude of errors made whose occurrence is inevitable because it is a model and therefore inherently imprecise.

A first visual analysis of the model will allow an overall assessment of its quality, especially if it is a high resolution DEM. 3D visualization can be compared with a photograph of the model or directly in the field.

Later a statistical analysis of the DEM must be done, with a simple heights histogram to discover abnormalities not detectable with the simple visualization. It is also frequently to estimate an average error by a sampling error, calculating the error at various points whose height was measured in the field and do a statistical analysis of them. In this sense, the error in altitude at a x point, is defined as the difference between real and estimated altitude.

However, these statistical analysis only determine the accuracy of the estimate of Z at points of sampling, but a good DEM must also keep relations between cells made by neighboring operations, therefore should be also an error analysis in the maps derived from DEM. In this case, an analysis of transects will verify the occurrence of artifacts, such as abrupt changes in slope. There is another validation of DEM analysis, called consistency analysis, which extends hydrological transect analysis to two dimensions. The objective is to verify that the DEM has the same hydrological answer as the land modeled. A good example of this type of analysis would determine whether we can reconstruct the drainage system properly (Chapter 7, MDT).

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7. CONCLUSION With the rising number of high and very high resolution imaging

satellites, with improved configurations and also by InSAR, DEMs can be generated in any location with accuracy and with details which was not possible few years ago.

For reaching satisfying results, images taken within the same orbit should be preferred, also the ones with higher space resolution and with freely available.

The clear lower accuracy reached with TK350 and ASTER models in comparison to the SRTM data does not justify the expenses for their use. With SPOT-5 approximately the same accuracy has been reached like with the C-band and the X-band InSAR SRTM data. Similar it is with the SPOT-HRS images having also the advantage of being taken from the same orbit. The very high resolution images like IKONOS and QuickBird can lead to better accuracy and details, but for quite higher expenses. For several cases the SRTM DEMs are sufficient, but the horizontal location should be checked and in forest areas they do present the top of the trees. More details can be reached with SPOT-5 and SPOT-HRS models. A filtering of the generated DSMs to DEMs is required.

The generated DTMs have to be reduced to DEMs. This can be without operator interaction. The highly automated processes are the base for the economic creation of orthoimages and line maps required for the optimal planning of resources, enabling a sustainable development especially in developing countries. But the now possible accuracy and details of the DEMs do optimise also the situation in developed countries.

In relation to its spatial resolution, the optimal grid size for a DEM is a compromise between the accuracy of terrain representation and cost of data acquisition. An appropriate grid size is dependent, amongst other factors, on source data density, terrain complexity and applications. Finally, DEM quality will depend on the sampled method choose and the quality of the elements used in it.

Even if interpolation of digital contour map is an old method to generate DEMs is still used in mountain areas where other sophisticated methods like interferometry are not always satisfactory. Taking into account the diversity of techniques to generate a DEM and the spatial resolutions as well as the spatial variability of the Earth's surface, it is concluded that the best technique to generate a DEM depends on the topographic characteristics of the area under study. In the case of an area with medium complexity, the resources for DEM generation can be extensive, but in steep areas, the InSAR technique could be better. However, when the topography is still more complex (presence of high altitude mountains) may be useful to use two methods such as InSAR along with the interpolation from sampled field points to ensure the reliable of DEM.

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As DEM data are widely used in many thematic areas (such as hydrological analysis and soil erosion predictive models) the generation of an accurate DEM and its validation is of major importance.

Different DEMs has different spatial resolution and coverage. In general, the spatial resolution of DEMs is inversely proportional to the areal coverage. For example, SAR DEMs have better than 1m resolution and 16km swath, while the SRTM DEM has 30m resolution and 255km swath width.

As high resolution DEMs need more data to cover a large region, when only certain areas are of interest, is highly recommended to exploit the information contained only in this area, in order to reduce the total DEM data size and processing time, by removing excess data in areas of reduced importance or interest, while at the same time reducing the vertical systematic errors

As the product of automatic image matching or InSAR techniques is not a DEM (with height of the ground) but a DTM (on top of the visible objects), and a simple filter does not solve this problem, because it goes to the average height and not to the ground, a very efficient solution is to exclude some problematic areas from the image before the automatic filtering.

The identification of buildings can be solved in some images, especially for LIDAR-data, because sudden changes of the heights are seen. But this function has usually no effect when automatic image matching is applied, because such data usually do not show the buildings with a sudden height change, usually they are shown like small hills.

For DEM validation there are statistical analyses to determine the accuracy of the estimated Z at points of sampling, but should be also an error analysis by neighboring operations (analysis of transects or the consistency analysis).

References 1- III Curso de Procesamiento de imágenes de satélite- Aplicaciones

cartográficas de la teledetección a la cartografía topográfica (2004) La Antigua Guatemala

2- Arcila Garrido M (2003) Sistemas de Informacion Geografica y Medio Ambiente: Principios Básicos. Spanish Edition book, ISBN 13: 9788477868279

3- Brandán, EM (2009) Perfiles longitudinales de ríos con datos SRTM. Cuarto Simposio Regional Hidráulica de ríos, Salta. Online in <http://irh-fce.unse.edu.ar/Rios2009/CD/RE/A/RE_A03> (23-08-2010).

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http://irh-fce.unse.edu.ar/Rios2009/CD/RE/A/RE_A03_(Brandan,%202009)_Perfiles_Longitudinales.pdf

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8- Fei L, Huang L & He (2008) An indirect generalization of contour lines based on DEM generalization using the 3D Douglas-Peucker algorithm. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Beijing, XXXVII (B1): 751 -756.

9- Felicísimo, AM (1994a) Modelos digitales del terreno. Introducción y aplicaciones en las ciencias ambientales. Pentalfa, Oviedo. Biblioteca de Historia Natural, 222 pp, ISBN 84-7848-475-2.

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13- Gerstenecker C, Läufer GL, Steineck D, Tiede C & B Wrobel. Validation of Digital Elevation Models around Merapi Volcano, Java, Indonesia. Natural Hazards and Earth System Sciences, 5: 863–876.

14- InSu L, Hsing-Chung Ch & G Linlin (2005) GPS Campaigns for Validation of InSAR Derived DEMs. Journal of Global Positioning Systems, 4 (1-2) 82-87Jacobsen K (2001) New developments in Digital Elevation Modelling. Geoinformatics, pp 18 – 21 (http://cmsv021.rrzn.uni-hannover.de/uploads/tx_tkpublikationen/JAC_dubrov04.pdf)

15- Jacobsen K (2003) DEM generation from satellite data, EARSeL Conference Ghent 2003.

16- Jenson SK & JO Domingue (1988) Extracting topographic structure from Digital Elevation Data for Geographic Information System Analysis. Photogrammetric Engineering and Remote Sensing. 54 (11): 1593 – 1600.

17- JPL-NASA (Jet Propulsion Laboratory – National Aeronautics and Space Administration. Online on: < http://www2.jpl.nasa.gov/srtm/dataprod.htm> 02 August 2010.

18- Lehner B, Verdin K & Jarvis A (2008) New global hydrography derived from spaceborne elevation data. Eos, Transactions, AGU, 89 (10): 93-94. Datos descargables desde http://hydrosheds.cr.usgs.gov.

19- Lemonsu A, Leroux A, Bélair S, Trudel S and Mailhot (submitted on 2005) A general methodology of urban cover classification for atmospheric modelling. Remoted Sensing of Environment.

20- Nikolakopoulos KG, Kamaratakis EK, Chrysoulakis N (2006) SRTM vs ASTER elevation products. Comparison for two regions in Crete, Greece International Journal of Remote Sensing 27 (21): 4819–4838.

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http://www.freegis-italia.org/index.php?option=com_content&task=view&id=396&Itemid=33



http://cmsv021.rrzn.uni-hannover.de/uploads/tx_tkpublikationen/JAC_dubrov04.pdf

http://cmsv021.rrzn.uni-hannover.de/uploads/tx_tkpublikationen/JAC_dubrov04.pdf

http://www2.jpl.nasa.gov/srtm/dataprod.htm

http://hydrosheds.cr.usgs.gov/


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21- NRO (National Reconnaissance office), USA. Online on: <http://www.nro.gov/corona/facts.html>

22- Seymour M & I Cumming (1997) InSAR terrain height estimation using low-quality sparse DEMs. Florence, Italy. 3rd ERS Scientific symposium Proceedings, 421- 426 (Available online in: <http://sar.ece.ubc.ca/papers/LOWRESDEM/lowresdem.html>).

23- Sørensen R, Zinko U & J Seibert (2005) On the calculation of the topographic wetness index: evaluation of different methods based on field observations. Hydrology and Earth System Sciences Discussions Journal, 2: 1807-1834.

24- Wikipedia.org <http://en.wikipedia.org/wiki/Digital_elevation_model>, <http://en.wikipedia.org/wiki/Shuttle_Radar_Topography_Mission>

25- Daming W, Yuan T, Yong G & W Lun (2005) A new method of generating grid DEM from contour lines. IEEE Institute of Remote Sensing and Geographic Information System, pp 657– 660.

26- Yu JH, Ge L & C Rizos (2009) Digital Elevation Model generation from interferometric synthetic aperture radar using multi-scale method. School of Surveying and Spatial Information Systems. University of New South Wales, Australia.

Software manuals and tutorials

- Bates P, Horritt M, Wilson M & N Hunter (2009) LISFLOOD-FP, User manual and technical note. University Road, Bristol, UK.

- ESRI® ArcMap TM Version 9.0. 2004 - ESRI®-ArcHydro Arc Hydro Tools Tutorial, Version 1.3. In ArcHydro

software package. - ILWIS 2.1 for Windows. The integrated land and water information

system - User’s Guide. 1997. Online in <http://www.itc.nl/ilwis/>, 15-08-2010.

- Castelli F (2008) MOBIDIC software– Manuale utente. CIMA, Italia.

http://www.nro.gov/corona/facts.html

http://sar.ece.ubc.ca/papers/LOWRESDEM/lowresdem.html

http://en.wikipedia.org/wiki/Digital_elevation_model

http://en.wikipedia.org/wiki/Shuttle_Radar_Topography_Mission

http://www.itc.nl/ilwis/

state of art of digital elevation...

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